UNCORRECTED PAGE PROOFS

Size: px
Start display at page:

Download "UNCORRECTED PAGE PROOFS"

Transcription

1 TOPIC Non-lnear relatonshps. Overvew.. Introducton Most people use socal meda. If we were to graph the number of hours after a message or rumour had begun aganst the number of people who had seen or read t, what would t look lke? Have ou ever heated a cup of coffee n the mcrowave? You can t drnk t when t comes out of the mcrowave, or ou ll burn ourself. It doesn t take long for a cup of coffee to cool off enough so ou can drnk t, but t wll stll sta warm for a long tme. And t doesn t get cooler than room temperature. What would the graph of the cup of coffee coolng stuaton look lke? These eamples are functons or graphs, but the are not lnear functons or graphs, because the don t follow a straght lne. DISCUSSION How long do ou thnk t takes a rumour on socal meda to reach people? Does t matter who posts the rumour on socal meda? Does t matter what form of socal meda t s posted on? LEARNING SEQUENCE. Overvew. Eponental functons. Quadratc functons. Recprocal functons. Revew Full worked solutons are avalable for ths topc n the Resources secton of our ebookplus. CURRICULUM CONTENT Students: use an eponental model to solve problems AAM construct and analse a quadratc model to solve practcal problems nvolvng quadratc functons or epressons of the form = a + b + c, for eample brakng dstance aganst speed AAM recognse that recprocal functons of the form = k, where k s a constant, represent nverse varaton, dentf the shape of these graphs and ther mportant features AAM Number of vews Tme TOPIC Non-lnear relatonshps

2 . Eponental functons.. Identfng eponental functons An eponent s another name for a power or nde. The term a has an eponent of. An eponental functon s of the form: = a or = a where a > and a. Features of an eponental functon nclude: t s ether alwas ncreasng or alwas decreasng as ncreases t never equals zero the -ntercept s the pont (, ) consecutve -values ncrease b a common multple; for eample, the double, trple, or halve. At one end, the graph approaches the -as but never touches t. Ths lne that the graph approaches s called an asmptote. The equaton of ths lne s =. horzontal asmptote.. Graphs of the form = a, where a > The graph of = a where a > s a contnuous curve that s: alwas postve, meanng t wll be above the -as ncreasng -values as the -values ncrease. The -ntercept s the pont (, ). Methods for graphng functons nclude: completng a table of values and plottng the ponts recognsng the general shape and features usng technolog. GeoGebra, Desmos and Mcrosoft Mathematcs are eamples of free graphng programs. WORKED EXAMPLE Plot the graph of = b: a. completng a table of values for =,,,,,, b. usng technolog. THINK WRITE a.. Draw up a table of values. Jacaranda Maths Quest Mathematcs Standard E for NSW

3 cnonlnearrelatonshps_prnt // : page #. Complete the table of values. When =, = =. = =. = =. = =. When =, = =. When =, = =. When =, = =. When =, When =, When =, E Plot the ponts and jon them to form a curve. = G. FS Fnd the -values b substtutng n the values of. Hnt: Use our scentfc calculator to fnd the -value as a fracton or decmal where necessar. O. PR O (, ) PA EC TE D ) (, ) (, ) (, Enter the equaton n the technolog of our choce. Note: Some programs wll requre ou to enter the full equaton, =, whereas others wll onl requre ou to enter the rght-hand sde,.. The graph of = wll be drawn. R O U N C (, ) (, ) R b.. (, ) Weblnk: Desmos graphcal calculator TOPIC Non-lnear relatonshps

4 .. Graphs of the form = a, where a > The graph of = a where a > s a contnuous curve that s: alwas postve, whch means t wll be above the -as decreasng n -values as the -value ncreases. The -ntercept s the pont (, ). WORKED EXAMPLE Plot the graph of = b: a. completng a table of values for =,,,,,, b. usng technolog. THINK WRITE a.. Draw up a table of values.. Fnd the -values b substtutng n the values of. Hnt: Use our scentfc calculator to fnd the -value as a fracton or decmal where necessar.. Complete the table of values.. Plot the ponts and jon them to form a curve. When =, = ( ) =. When =, = ( ) =. When =, = ( ) =. When =, = =. When =, = =. When =, = =. When =, = =. = (, ) (, ) (, ) (, ) (, ) (, ) (, ) Jacaranda Maths Quest Mathematcs Standard E for NSW

5 cnonlnearrelatonshps_prnt // : page # Enter the equaton = n the technolog of our choce.. The graph of = wll be drawn. b.. = O PR O (, ) FS E Snce the graphs n Worked eamples and both have powers of, the graphs are smpl a reflecton of each other over the -as... Eponental models PA G Weblnk: Mcrosoft Mathematcs R R EC TE D Eponental functons grow b a common factor over equal ntervals. As such, eponental functons are used to model a wde range of real-lfe stuatons such as populaton growth, radoactve deca, compound nterest and deprecaton. Eponental growth s when a quantt grows b a constant factor or percentage n each fed perod of tme, for eample the growth of the bactera causng swne flu. Eponental deca s when a quantt decreases b a constant factor or percentage n each fed perod of tme, for eample the value of the faml car. When eponental functons are models of practcal growth and deca problems, an ntal quantt that s ether growng or decang needs to be gven. Ths gves the general forms: U N C O Eponental growth: = k a, where a > and k s the ntal quantt that s growng. Eponental deca: = k a, where a > and k s the ntal quantt that s decang. Interactvt: Eponental growth and deca (nt-) TOPIC Non-lnear relatonshps

6 cnonlnearrelatonshps_prnt // : page # WORKED EXAMPLE. c. d.... =. C U N PR O E The radus s ntall. m long. R =. (.) The radus wll be. metres after ears. R (m) R =. (.) (,.) 7 n (ears) R (m) R =. (.) (, ) O. =. G b.. R =. (.) EC TE D. R =. (.)n R. Wrte the equaton. The ntal length of the radus s the value of k n = ka. It s the length of the radus when n = ; that s, R =. (.). State the ntal length. After ears, n =. Substtute n = nto the equaton R =. (.)n and smplf. Wrte the answer. Enter the equaton =. (.) nto the technolog of our choce. The graph wll be drawn. Remember: Label the -as: n (ears) Label the -as: R (metres) Start from (,.), the ntal length of the radus. Hnt: You ma need to alter the settngs to change the aes to a sutable scale. Double the ntal radus, R =. Locate the pont on the curve wth a -value of : (, ). The -value s the number of ears. Answer the queston R a.. WRITE PA THINK O FS The radus of a tree ncreases ever ear accordng to the eponental functon R =. (.)n where R s the radus n metres after n ears. a. Fnd the ntal length of the radus. b. Calculate the length of the radus after ears. c. Use technolog to sketch the graph of the functon R aganst n. d. Use the graph to estmate the number of ears for the length of the radus to double. 7 n (ears) It takes ears for the radus of the tree to double. Weblnk: GeoGebra Jacaranda Maths Quest Mathematcs Standard E for NSW

7 In real-lfe problems, the ncrease or decrease n quanttes s sometmes gven as a percentage for the gven tme perod, gvng the value for a n the eponental equaton = a. In practcal growth problems, the quantt ncreases b a fed percent over equal tme ntervals. If the percentage ncrease s r%, the quantt ncreases b a factor of ( + r). The growth factor s ( + r), where r s the percentage epressed as a decmal. The constant, k, represents the ntal value of the quantt. The amount,, after ears s gven b = k ( + r). In practcal deca problems, the quantt decreases b a fed percent over equal tme ntervals. If the percentage decrease s r%, the quantt decreases b a factor of ( r) %. The deca factor s ( r), where r s the percentage epressed as a decmal. The constant, k, represents the ntal value of the quantt. The amount,, after ears s gven b = k ( r). Note: Snce ( r) <, the eponental equaton used to model ths stuaton s = a. WORKED EXAMPLE The student enrolment,, of a hgh school was n and ncreased b % per ear untl 7. a. What s the equaton whch models the eponental growth of the student enrolment? b. How man students were enrolled n the hgh school n? THINK a.. Use the general equaton for eponental growth. Identf the ke components of the equaton.. Substtute these values nto the general equaton. WRITE = k ( + r) k =, r = % =. = k ( + r) = ( +.) = (.). Wrte the answer. The equaton whch models the growth of the student enrolment s = (.), where s the student enrolment after ears. b.. Determne the requred value of. = for the ear. Therefore, corresponds to =.. Substtute ths value nto the equaton found n part a. When = : = (.) =.. Wrte the answer. In the ear, students were enrolled n the hgh school. TOPIC Non-lnear relatonshps 7

8 WORKED EXAMPLE A new car costng $ s deprecatng b 7% each ear. a. What s the equaton that models the eponental deca of the value of the car? b. Calculate the value of the car, to the nearest $, after:. ear. ears. c. Use technolog to estmate how man ears t would take for the car to be valued at less than $. THINK a.. Use the general equaton for eponental deca when deca s gven as a percentage. Identt the ke components of the equaton.. Substtute these values nto the general equaton. WRITE = k ( r) k = r = 7% =.7 ( r) =. = (.). Answer the queston. The equaton that models the eponental deca for the value of the car,, after ears s b... Substtute = nto the equaton and calculate. = (.) = (.) = 7. Answer the queston. The value of the car after one ear s $ 7... Substtute = nto the equaton and calculate. = (.) = (.) = 9.7. Answer the queston. The value of the car after three ears s $ 9 (to the nearest $). c.. Graph = (.).. Locate the pont where =.. Plot the pont (.9, ). Value ($) (, ) = (.) (.9, ) 7 9. Answer the queston. The car wll be valued less than $ after ears. Year Jacaranda Maths Quest Mathematcs Standard E for NSW

9 Eercse. Eponental functons Understandng, fluenc and communcatng. WE Usng technolog of our choce, graph the followng functons on the same set of aes. Descrbe the smlartes and dfferences between these graphs. a. = b. = c. = d. =. WE Usng technolog of our choce, graph the followng functons on the same set of aes. Descrbe the smlartes and dfferences between these graphs. a. = b. = c. = d. =. WE A partcular form of mcroscopc sea lfe gans mass accordng to the eponental functon m = (.) t, where m s the mass n mllonths of a gram and t s tme measured n das. a. Fnd the ntal mass of the mcroscopc sea lfe. b. Calculate the mass after das. c. Usng technolog of our choce, sketch a graph of mass aganst tme for das. d. Use the graph to estmate the number of das t takes the mass to double.. A partcular form of radoactve materal loses mass accordng to the rule m = (.) t where m s the mass and t s tme, measured n das. a. Fnd the ntal mass. b. Calculate the mass of radoactve materal after ears. c. Usng a technolog of our choce, graph the functon m aganst t. d. Use the graph to estmate the number of ears t would take for the mass to halve.. It s thought that the core temperature, T C, of a bun t mnutes after beng taken out of the oven s gven b the eponental functon T = (.) t. a. What s the temperature mmedatel after the bun s taken out of the oven? b. What s the temperature after mnutes?. WE In Januar 99, there were about nternet hosts. Durng the net ears, the number of hosts ncreased b 9% per ear. a. Wrte an eponental equaton whch models the number (n mllons) of hosts t ears after 99. b. Eplan the meanng of the ntal amount. c. Graph the model usng technolog. d. Use the graph to estmate the ear when there were mllon hosts. 7. The populaton of a rare brd n a regon of outback Australa ncreases b % per ear. When frst observed, the populaton was brds. a. Wrte an eponental equaton that models the number of brds,, after observng them for ears. b. Calculate the number of brds n ths regon after 7 ears. Gve our answer to the nearest whole number.. WE Cars deprecate n value each ear. A new car costng $ s to be deprecated at a rate of % each ear. a. What s the equaton that models the eponental deca of the value of the car, where V s the value of the car after t ears? b. Calculate the value of the car, to the nearest $, after:. ear. ears TOPIC Non-lnear relatonshps 9

10 9. Durng a controlled det n preparaton for a major trathlon, an athlete decreases n weght b % per month. He weghed 7 kg at the start of the controlled det. a. Wrte an eponental equaton that models the weght, kg, of the athlete, months nto the controlled det perod. b. Calculate the weght of the athlete after months on the controlled det. Gve our answer correct to decmal place.. A substance dsntegrates accordng to the eponental functon: M = (.) t, t where M s the mass n grams after t ears. a. Fnd the ntal mass. b. Calculate, to the nearest nteger, the mass of the substance after:. ears. ears. ears v. ears c. Sketch the graph of the mass, M, aganst tme, t, b usng ether part b or technolog of our choce. d. Estmate the number of ears t would take for the mass to reduce to a quarter of ts orgnal mass. Problem solvng, reasonng and justfcaton. The number of frut fles, N, n a colon after t das of observaton s modelled b N = A (.) t where A s a constant. The graph of ths equaton s shown. N 7 (, ) t a. What s the value of A? b. What s the dal growth rate of the number of frut fles n the colon? Wrte our answer as a percentage. Comment on the reasonableness of our answer. c. How man of the nsects were present after das? Gve our answer to the nearest nteger. Justf our answer. d. How man das does t take for the number of frut fles n the colon to double? Justf our answer. Jacaranda Maths Quest Mathematcs Standard E for NSW

11 . The value, V, of a certan machne after t ears of V operatng s modelled b the eponental deca equaton V = A (.7) t, where A s a constant. The graph of the value of ths machne s gven below. (, ) a. What s the value of A? b. What s the earl deca rate of ths machne? Gve our answer as a percentage and justf. c. Calculate the value of the machne after:. ears. ears. 7 9 t d. How man ears appromatel would t take for the machne to halve n value?. Harr nvested $ n a fund pang an nterest rate of.% p.a. wth nterest compounded annuall. a. Fnd the equaton whch models the eponental growth of the value of Harr s nvestment. b. Calculate the value of hs nvestment, to the nearest dollar, after:. ear. ears. c. Usng technolog, estmate how long t would take for Harr s nvestment to reach $.. Megan nvested $ n a fund pang an nterest rate of.% p.a. wth nterest compounded monthl. a. State the nterest rate per month. b. Fnd the equaton whch models the eponental growth of the value of Megan s nvestment. c. Calculate the value of her nvestment, to the nearest dollar, after:. ear. ears. d. Usng technolog, estmate how long t would take for Megan s nvestment to reach $. e. Dscuss the dfferences between Megan s nvestment and Harr s nvestment from the prevous queston.. The ntal pulse rate (beats per mnute) of a dancer s 7. The pulse rate decreases b % per mnute. a. Wrte an eponental equaton whch models the pulse rate of the dancer. b. Calculate the pulse rate after mnutes. c. Graph the model usng technolog. d. What practcal range of values would be used to model the pulse rate of the dancer? Eplan.. A populaton of bactera doubles ever hour. At noon the number of bacteral cells s. a. Wrte an eponental functon to model the growth of the bactera. b. What s the bactera populaton at pm? c. What was the populaton at 9 am, three hours before the number of bacteral cells were counted? TOPIC Non-lnear relatonshps

12 . Quadratc functons.. Identfng quadratc functons Quadratcs derve ther name from quadratus, the Latn work for squared. In a quadratc epresson: the varable s rased to the power of two, or squared the hghest power, or nde, s, for eample or + 7. The coeffcent of a partcular varable s the number the varable s multpled b. For eample, n the epresson + 7: the coeffcent of s the coeffcent of s. The general form of a quadratc functon s: = a + b + c where: a s the coeffcent of b s the coeffcent of c s the constant term. The graphs of quadratc functons are called parabolas. Methods to graph a quadratc functon nclude: constructng a table of values usng technolog, such as GeoGebra or Desmos. When graphed, a parabola has the followng shape: WORKED EXAMPLE State the coeffcent of n each of the followng quadratc epressons. a. + b. + c. d. + 7 THINK WRITE a. Fnd the term:. The coeffcent of s. b. Fnd the term:. The coeffcent of s. c. Fnd the term:. The coeffcent of s. d. Fnd the term:. The coeffcent of s. Jacaranda Maths Quest Mathematcs Standard E for NSW

13 WORKED EXAMPLE 7 Identf whether the followng graphs could show a quadratc relatonshp. a. b. c. THINK 7 WRITE a. Not a parabolc shape Not a quadratc relatonshp b. Parabolc shape A quadratc relatonshp c. The graph looks lke two parabolas joned together. A quadratc relatonshp has onl one parabola. Not a quadratc relatonshp.. Graphng the quadratc functon (or parabola) Important features of a parabola are the: the turnng pont, where the curve changes drecton the as of smmetr, where one sde of the parabola s the mrror mage of the other sde the as ntercepts, where the curve crosses the - and -aes. the mamum or mnmum values. The graph of a parabola s ether uprght or nverted. Uprght parabola Turnng pont Inverted parabola Turnng pont If the coeffcent of s negatve, the parabola s nverted. The parabola has eactl one turnng pont, lng on the as of smmetr. The as of smmetr s a vertcal lne through the turnng pont. The turnng pont s called ether a mamum or a mnmum turnng pont, as t gves ether the greatest (mamum) or least (mnmum) value of the quadratc epresson or parabola. TOPIC Non-lnear relatonshps

14 Mamum turnng pont Mnmum turnng pont As of smmetr As of smmetr A parabola wll alwas ntersect the -as at one pont, called the -ntercept. The coordnates of the -ntercept are (, c) for the general quadratc = a + b + c. The ponts at whch a parabola ntersects the -as are called the -ntercepts. A parabola wll ether: ntersect the -as at two ponts ntersect the -as at one pont not ntersect the -as at all. c Two -ntercepts c Two -ntercepts c One -ntercept c One -ntercept c No -ntercepts No -ntercepts c Jacaranda Maths Quest Mathematcs Standard E for NSW

15 WORKED EXAMPLE Consder the graph shown at rght. a. Descrbe the orentaton of the parabola (uprght or nverted). b. Wrte the equaton of the as of smmetr. c. State the coordnates of the turnng pont. d. State the coordnates of an:. -ntercepts. -ntercepts. e. State whether the parabola has a mnmum or mamum turnng pont. THINK a. The arms of the parabola are pontng upwards. b. The as of smmetr s the vertcal lne passng through the turnng pont. c. The turnng pont s at the lowest pont of the parabola. d.. The -ntercepts are the ponts where the parabola ntersects the -as.. The -ntercept s the pont where the parabola ntersects the -as. e. The parabola s uprght, so the turnng pont s a mnmum. WORKED EXAMPLE 9 WRITE The parabola s uprght. The equaton of the as of smmetr s =. The turnng pont s (, ). The -ntercepts are at (, ) and (, ). The -ntercept s at (, ). The parabola has a mnmum turnng pont. a. Complete the table of values for the quadratc functon =. b. Plot the ponts on a graph and jon them wth a smooth curve. c. State the coordnates of:. the turnng pont. the -ntercept. the -ntercepts. d. State the equaton of the as of smmetr. e. State whether the parabola has a mamum or mnmum turnng pont. TOPIC Non-lnear relatonshps

16 THINK a.. Substtute each -value nto the epresson to fnd the -value. WRITE When = : = ( ) ( ) = + = When = : = ( ) ( ) = + = When = : = () () = When = : = () () = = When = : = () () = = When = : = () () = 9 = When = : = () () = = When = : = () () = = When = : = =. Complete the table of values. b.. Plot the ponts.. Jon them wth a smooth curve. = 7 7 Jacaranda Maths Quest Mathematcs Standard E for NSW

17 c.. Locate the turnng pont. The turnng pont s (, ).. Fnd where the curve crosses the -as.. Fnd where the curve crosses the -as. d. The as of smmetr s the vertcal lne through the turnng pont (, ). e. Look at the shape of the graph. It s an upward parabola. The -ntercept s (, ). The -ntercepts are (, ) and (, ). The equaton of the as of smmetr s =. The turnng pont s a mnmum turnng pont. When sketchng a quadratc functon, or parabola, alwas show the coordnates of: the - and -ntercepts the turnng pont. If the -values are restrcted, stop at the end ponts. Show the coordnates of these endponts. WORKED EXAMPLE a. B completng a table of values, sketch the graph of = + for. b. State and label the coordnates of:. the -ntercept. the -ntercepts. the turnng ponts v. the end ponts. THINK a.. Create a table of values for the gven -values.. Substtute the -values nto the epresson to fnd the -values. WRITE a. When =, = ( ) + ( ) = = When =, = ( ) + ( ) = = When =, = () + () = When =, = () + () = + = When =, = () + () = + = TOPIC Non-lnear relatonshps 7

18 . Complete the table of values.. Plot the ponts and jon them wth a smooth curve, startng where = and endng where =. b. From the graph: b.. The -ntercept s where the curve crosses the -as.. The -ntercept s where the curve crosses the -as.. The turnng pont s where the curve changes drecton. v. The endponts are where the curve starts and stops... Quadratc models (, ) (, ) = + (, ) (, ) (, ) The coordnates of the -ntercept are (, ). The coordnates of the -ntercept are (, ). The coordnates of the turnng pont are (, ). The endponts are (, ) and (, ). Quadratc functons are also used to model a wde range of real-lfe stuatons to solve practcal problems. Quadratc functons can model the moton of a fallng object, the flght of a projectle or the shape of objects such as brdges. The are also used n economc models of cost and revenue. The turnng pont s used to fnd a mamum, the greatest value, or mnmum, the least value, of the quadratc model. The -ntercepts show where the -value changes from postve to negatve or negatve to postve. In a busness venture, for eample, t can show when ou start makng a proft. The -ntercept refers to the startng value, or ntal value, of the model. When a parabola s sketched to represent a practcal model, we must consder the -values and -values to ensure t makes sense n a practcal contet. Jacaranda Maths Quest Mathematcs Standard E for NSW

19 WORKED EXAMPLE A ball s thrown vertcall nto the ar. The graph shows the heght h metres, of the ball after t seconds. a. What s the mamum heght the ball reaches above the h ground? (,.) b. The ball was thrown from a platform. How hgh s the platform above the ground? c. How man seconds does t take for the ball to reach ts greatest heght? d. When s the ball metres above the ground? (, ) e. Appromatel how man seconds does t take for the ball to reach the ground? (., ) f. Onl part of the graph of the parabola s drawn. Can ou t suggest wh? THINK WRITE a.. Locate the turnng pont. Turnng pont (,.). The greatest heght wll be the h-value. h-value =.. Answer the queston. The ball s mamum heght above the ground s. metres. b.. Locate the ntal pont, the h-ntercept. The h-ntercept s (, ).. The heght s the h-value. h-value =. Answer the queston. The platform s metres above the ground. c.. Locate the turnng pont. Turnng pont (,.). Tme s the t-coordnate. t-value =. Answer the queston. It takes seconds for the ball to reach ts greatest heght. d.. Locate ponts where h =. Ponts (, ) and (, ). Tme s the t-coordnate. t-value = or. Answer the queston. Intall the ball s metres above the ground. It s agan metres above the ground after seconds when t s fallng. e.. Locate the ponts where h =. Pont (., ). Tme s the t-coordnate. t-value =.. Answer the queston. It takes appromatel seconds for the ball to reach the ground. f.. The t-as s tme, so t, and the h-as s heght above ground, so h.. Answer the queston. Snce both tme and heght above the ground need to be greater than or equal to zero, the parabola would start at (, ) and stop at (., ). TOPIC Non-lnear relatonshps 9

20 WORKED EXAMPLE A landscaper has metres of edgng wth whch to construct a rectangular garden bed. He wshes to construct the garden bed that has the greatest area. a. If one sde of the garden bed s metres, calculate:. the length of the other sde. the area of the garden bed. b. If one sde of the garden bed s metres, fnd an epresson for:. the length of the other sde. the area, A, of the garden bed. c. State a sutable range of values for for ths practcal stuaton. d. B completng a table of values or usng technolog of our choce, sketch the quadratc functon whch models ths problem. e. Fnd the mamum area of the rectangular garden bed and ts dmensons. THINK a... State the permeter formula for a rectangle.. Substtute n the known values: metres for the permeter and metres for the length. WRITE w l l Permeter = l + w w = + w. Fnd the wdth, w. = + w w = w =. Answer the queston. The length of the other sde s metres. Note: Snce the permeter s metres, the length and wdth must add to metres, half the permeter... State the area formula for a rectangle and substtute n the known values. Area = length wdth = =. Answer the queston. The area of the garden bed s m. b... Name the other sde. Let the other sde be w metres.. The two sdes must add to metres. + w =. Answer the queston. The other sde s ( ) metres... State the area formula. Area = length wdth. Substtute for length and ( ) for wdth. A = ( ) A =. Answer the queston. The area s A =. Jacaranda Maths Quest Mathematcs Standard E for NSW

21 c. Fnd a sutable range of values for, rememberng that s a length of a garden bed and halfwa around the garden s metres. The length,, needs to be postve and less than. d.. Draw up a table of values from to. When =, A =. Substtute the -values nto the epresson to fnd the values of A. A = () () A = When =, A = () () A = A = 9 When =, A = () () A = A = When =, A = () () A = 9 A = When =, A = () () A = A = When =, A = () () A = A = When =, A = () () A = A = When = 7, A = (7) (7) A = 7 9 A = When =, A = () () A = A = When = 9, A = (9) (9) A = 9 A = 9 When =, A = () () A = A =. Complete the table of values TOPIC Non-lnear relatonshps

22 . Plot the ponts and jon them wth a smooth curve. Alternatvel, use A (, ) technolog of our choce to show the same graph of the quadratc model. = (, ) (, ) 7 9 e.. Locate the turnng pont. Mamum turnng pont (, ). Area s the -value. -value =. Answer the queston, rememberng that the dmensons are and ( ). WORKED EXAMPLE The mamum area of the garden bed s m, and ts dmensons are m b m. A bungee jumper dves from a platform from a brdge over a rver. The heght, h metres, above the ground after t seconds n the ar s gven b h = t +. a. Usng technolog, sketch the graph of the bungee jump. What restrctons need to be consdered? b. What wll be the bungee jumper s heght after second? c. From what heght dd the bungee jumper ntall jump? d. For how long was the bungee jumper n the ar? e. Is t realstc for the bungee jumper to spend. seconds on the jump? Eplan. THINK a.. Use technolog to sketch the graph of h = t +, where t represents the tme and h represents the heght.. For the practcal stuaton, the doman needs to be restrcted as the tme must be greater than. Tme cannot be a negatve value. The range also needs to be restrcted. If the heght s negatve, ths means that the bungee jumper has ht the surface of the rver and gone below water level. WRITE h t Jacaranda Maths Quest Mathematcs Standard E for NSW

23 b.. Use the data settngs or other functons of the technolog to fnd the value of h when t =.. Answer the queston. After second, the bungee jumper s m above ground. c.. The ntal heght can be calculated b fndng the h-ntercept. When t = : h = () + =. Answer the queston. The bungee jumper was ntall m above the ground. d.. Determne the tme when the bungee jumper s at ground level b fndng the t-ntercept. h (., ). Answer the queston. The bungee jumper was n the ar for. seconds. e.. Use the technolog to fnd the value of h when t =... Answer the queston. If the bungee jumper took. seconds, the bungee jumper would have gone metres below the rver surface. Ths does not seem realstc, as the rver s probabl not metres n depth and the bungee cord would not be ths long. The bungee jumpee would probabl stop just above the rver surface. Eercse. Quadratc functons Understandng, fluenc and communcatng. WE State the coeffcent of n each of the followng quadratc epressons. a. + b. + c. +. State the coeffcent of n each of the followng quadratc epressons. a. + b. + c. + t TOPIC Non-lnear relatonshps

24 . WE 7 Identf whch of the followng graphs s a quadratc relatonshp. a. b. c.. MC Whch of the followng graphs shows a quadratc relatonshp? 7 B C D d. 7 7 A. WE For each of the graphs shown:. descrbe the orentaton of the parabola. state the coordnates of the turnng pont. wrte the equaton of the as of smmetr v. state the coordnates of an -ntercepts and/or -ntercepts v. state whether the parabola has a mamum or mnmum turnng pont. Jacaranda Maths Quest Mathematcs Standard E for NSW

25 a. b.. a. Draw up a table of values from = to = for each of the quadratc functons below and graph them on the same number plane.. =. =. = v. = b. Descrbe the smlartes and dfferences between the curves graphed. 7. a. Draw up a table of values from = to = for each of the quadratc functons below and graph them on the same number plane.. =. = +. = + v. = + b. Descrbe the smlartes and dfferences between the curves graphed. c. Sketch = wthout plottng ponts. Check our answer usng technolog.. WE 9 a. Complete the table of values for the quadratc functon = +. b. Plot the ponts on a graph and jon them wth a smooth curve. c. State the coordnates of:. the turnng pont. the -ntercept. the -ntercepts. d. State the equaton of the as of smmetr. e. State whether the parabola has a mamum or mnmum turnng pont. 9. WE a. B completng a table of values, sketch the graph of =, for. b. State and label the coordnates of:. the -ntercept. the -ntercepts. the turnng ponts v. the end ponts. TOPIC Non-lnear relatonshps

26 cnonlnearrelatonshps_prnt // : page #. WE A mssle s fred vertcall nto the ar from the top of a clff. The graph shows the heght, h metres above the ground, of the mssle after t seconds. h FS (, ) (.9, ) t PR O O (, ) What s the mamum heght the mssle reaches above the ground? b. The mssle was fred from a launchng pad. How hgh s the launchng pad above the ground? c. How man seconds does t take for the mssle to reach ts greatest heght? d. When s the mssle metres above the ground? e. Appromatel how man seconds does t take for the mssle to reach the ground? f. Onl part of the graph of the parabola s drawn. Can ou suggest wh?. The quadratc functon below models the path of a football where h s the heght above the ground n metres and d s the horzontal dstance, n metres, from the plaer s foot. PA G E a. h EC TE D (7.7, 7.) (, ) R (.7, ) d What s the mamum heght reached b the football? Gve our answer to decmal place. b. How hgh above the ground was the football when t was kcked? c. How far wll the football have travelled horzontall when t touches the ground? Gve our answer to decmal place. U N C O R a. Jacaranda Maths Quest Mathematcs Standard E for NSW

27 . WE A farmer has metres of fencng wth whch to create a rectangular feld wth the greatest area. a. If one sde of the feld s metres, calculate:. the length of the other sde. the area of the feld. b. If one sde of the feld s metres, fnd an epresson for:. the length of the other sde. the area, A, of the feld. c. State a sutable range of values for for ths practcal stuaton. d. B completng a table of values or usng technolog of our choce, sketch the quadratc functon whch models ths problem. (Hnt: For a table of values, use =,,,,.) e. Fnd the mamum area of the rectangular feld and ts dmensons.. The sum of two postve numbers s. a. Let one of the numbers be. What s the other number? b. Show that the product of these two numbers can be modelled b the quadratc functon =. c. Usng a method of our choce, sketch a graph of ths quadratc functon. d. Fnd the mamum product of the two numbers.. WE When a to rocket s launched, ts path s n the form of the quadratc functon = +, where metres s the heght of the rocket above the ground and metres s the horzontal dstance from the launch ste. a. Usng a method of our choce, sketch the graph of the quadratc functon modellng the flght path of the to rocket. (Hnt: For a table of values, use =,,,...) b. Fnd the horzontal dstance travelled b the to rocket. c. Fnd the mamum heght reached b the rocket. Problem solvng, reasonng and justfcaton. A suspenson brdge has overhead cables supportng the roadwa. The cables are attached to plons and form parabolas shapes. The heght, metres, of the cables above the roadwa s gven b the quadratc functon = + 7, where s the, n metres, between the plons. The graph of the quadratc functon s shown. (, 7) = + 7 (9, ) (, 7) TOPIC Non-lnear relatonshps 7

28 a. How hgh above the roadwa are the cables when the are attached to the plons? b. What s the dstance between each plon? c. What s the mnmum dstance between the roadwa and the cable? d. Onl part of the parabola s drawn. Can ou suggest a reason for ths?. The monthl proft or loss, p (n thousands of dollars), for a new brand of hamburgers s modelled b the quadratc functon p =, where s the number of months after the ntroducton of the new hamburger nto the market. a. Usng a method of our choce, sketch the graph of the quadratc functon. b. Fnd the frst month for whch a proft was shown. c. In whch month s the proft $? Justf our answer. 7. The graph shows the heght, h metres, of a golf ball as a functon of the tme, t seconds, after t s ht. a. For how man seconds s the golf ball n the ar? b. What s the greatest heght reached b the golf ball? c. For how man seconds s the golf ball above a heght of metres? Justf our answer. h (.,.) (, ) (, ). When ou are drvng a car, the brakng dstance requred to stop and the speed at whch ou are travellng s an eample of a quadratc model. One of these quadratc functons s gven below: t d = s (f) where d s the breakng dstance n metres s s the speed n km/h f s the coeffcent of frcton, a constant that depends on the road surface. a. When the road surface s dr asphalt, the coeffcent of frcton s appromatel... Rewrte the quadratc functon n terms of d and s.. Calculate the breakng dstance requred when travellng on dr asphalt at a speed of km/h. b. When the road surface s c, the coeffcent of frcton s appromatel... Rewrte the quadratc functon n terms of d and s.. Calculate the breakng dstance requred when travellng on dr asphalt at a speed of km/h. c. Comment on the dfference n breakng dstances at km/hour. Jacaranda Maths Quest Mathematcs Standard E for NSW

29 9. The path taken b a netball thrown b an Australan plaer s gven b the quadratc functon = where s the heght of the netball and s the horzontal dstance from the plaer s upstretched hand. a. Complete a table of values for for ths functon. b. Plot the graph of = c. What values of are not reasonable for ths model and wh? d. How far above the floor was the ball when t was thrown? e. Estmate the mamum heght reached b the netball. f. Assumng that nothng hts the netball, estmate how far awa from the plaer the netball wll strke the ground. Justf our answer.. A car engne spark plug produces a spark of electrct. The sze of the spark depends on how far apart the termnals are. The percentage performance, Z%, of a certan brand s thought to be modelled b the quadratc functon Z = g g, where g s the dstance between the termnals. Below s a graph of the quadratc functon =. (, ). = a. From the graph, what values for and would be sutable to model the performance of the spark plug? b. When s the performance greatest? c. From the graph, estmate the dstances between the termnals for whch the percentage performance s greater than %. (, ). TOPIC Non-lnear relatonshps 9

30 . The path of a baseball from the ptcher s hand can be modelled b the equaton = , where s the heght of the baseball above the ground (,.9) and s ts dstance from the ptcher. Both dstances are n metres. To help the team coach, ou have sketched the graph for ths quadratc model. It s shown below. (,.) a. State the range of values for and for whch our graph makes sense n ths practcal contet. (.9, ) (.9, ) b. At what heght does the baseball leave the ptcher s hand? c. How far does the baseball land from the ptcher s hand? Gve our answer to decmal place. d. How hgh wll the baseball be above ground when t s metres from the ptcher s hand? Gve our answer to decmal place. e. The catcher s mtt s. metres above the ground. If the ball hts the catcher s mtt wthout the catcher movng t, estmate how far the catcher s from the ptcher.. Recprocal functons.. Identfng recprocal or hperbolc functons Recall that the recprocal of the number s, or for an number s. The basc recprocal functon s: The general recprocal functon s: Methods to graph the recprocal functon nclude: completng a table of values usng technolog, ncludng a spreadsheet recognsng the general shape of the functon. Consder the recprocal functon =,. =, = k,, where k s a constant. The table of values from = to = s gven below. 7 Jacaranda Maths Quest Mathematcs Standard E for NSW

31 Plottng these ponts and jonng them wth a smooth curve gves the graph, as shown below. Notce that the recprocal of zero s undefned. 7 (, ) = (, ) (, ) (, ) (, ) (, ) (, ) 7 7 (, ) (, (, ) ) (, ) (, ) 7 The graph of the recprocal functon s called a rectangular hperbola. The rectangular hperbola has the followng specal features. The graph has two parts. These are called the branches of the functon. It s alwas undefned for =, as we cannot dvde b zero. As ncreases to nfnt, the graph gets closer to the -as but never touches t. Smlarl, as decreases to negatve nfnt, the graph gets closer to the -as. The -as s called the horzontal asmptote of the graph. The curve approaches the lne = (the -as) but t never touches t. As gets closer to, the graph gets closer to the -as but never touches t. The -as s called the vertcal asmptote of the graph. The curve approaches the lne = (the -as) but t never touches t. These specal features are shown on the graph of = below. Horzontal asmptote 7 Branch = 7 7 Branch Vertcal asmptote 7 TOPIC Non-lnear relatonshps 7

32 cnonlnearrelatonshps_prnt // :7 page 7 # elesson: Sketchng quadratcs n turnng pont form (eles-9) O the table of values for =. PR O a. Complete FS WORKED EXAMPLE the ponts to sketch the graph of the recprocal functon =. c. Comment on the features of the curve. Plot the ponts to sketch the graph of the functon and jon wth a smooth curve. EC TE D b. Complete the table of values. R Undefned R O 7 Comment on the features. C U N c. G a. WRITE PA THINK E b. Plot The curve conssts of two branches. The curve approaches the lnes = and =, but t never actuall touches them. The horzontal asmptote s = and the vertcal asmptote s =. Jacaranda Maths Quest Mathematcs Standard E for NSW

33 .. Inverse varaton Varaton eamnes the effect that changng one varable has on another varable. If two quanttes var nversel, then ncreasng one varable decreases the other. For the two varables and, where > and >, we sa vares nversel wth f decreases as ncreases or vce versa. If vares nversel to, t s wrtten as or = k, where k s the constant of varaton or the proportonalt constant. The smbol means vares wth. Inverse varaton gves the graph of a rectangular hperbola defned for >, the postve branch... Recprocal models Recprocal functons ma also be used to model a wde range of real-lfe stuatons to solve practcal problems. Recprocal functons model practcal relatonshps such as speed and tme, volume and pressure, or current and resstance n an electrcal applance. In practcal stuatons, when two quanttes are nversel proportonal: the are modelled b the rectangular hperbola, = k, > (the postve branch) the product of the two quanttes equals the constant of varaton, k. That s, k =. WORKED EXAMPLE For a gven mass of gas kept at a constant temperature, the volume, V cm, vares nversel as the pressure, P unts. The graph shows ths relatonshp for a partcular tpe of gas. a. Fnd:. the volume when the pressure s unts. the pressure when the volume s cm. b. Fnd the recprocal functon that models ths graph. c. Usng the recprocal functon found n part b, fnd:. the volume when the pressure s unts. the pressure when the volume s cm. THINK WRITE a... Locate on the horzontal as. P = V 9 7. Locate the pont on the curve. Pont on the curve: (, ) 7 9 P. Answer the queston The volume s cm when the pressure s unts... Locate on the vertcal as. V =. Locate the pont on the curve. Pont on the curve: (, ). Answer the queston. The pressure s unts when the volume s cm. TOPIC Non-lnear relatonshps 7

34 b.. Wrte an epresson for V vares nversel wth P. V P. Rewrte wth an equals sgn. V = k P. Choose a pont: Choose a pont: (, ). Substtute to fnd k, k = V P k = k =. Answer the queston. V = s the recprocal functon of P ths model. c... Substtute P = nto the model. V = P = =. Answer the queston. The volume s cm when the pressure s unts... Substtute V = nto the model. V = P = P. To fnd P: P = P P multpl both sdes b P P = smplf P dvde both sdes b = smplf. P =. Answer the queston. The pressure s unts when the volume s cm. WORKED EXAMPLE The table shows how the tme, T hours taken to complete a task vares nversel wth the number of workers, n. Number of workers, n Tme taken (hours), T 7 Jacaranda Maths Quest Mathematcs Standard E for NSW

35 a. Plot the ponts to represent ths data. b. Wrte a mathematcal statement connectng T, n and k, the constant of varaton. c. Calculate:. the tme that t would take 7 people to complete the task.. the number of people requred to complete the task n half an hour. THINK a. The amount of tme taken to complete the task depends on the number of workers; tme s the dependent varable so place t on the vertcal as. The smallest number of hours s and the hghest s so use a scale of on the vertcal as. The smallest number of workers s and the hghest s. Use a scale of on the horzontal as. b.. The graph looks lke a hperbola so assume that the varables are nversel proportonal. Wrte ths mathematcall.. The proporton sgn can be replaced wth = k. Wrte the equaton.. Choose one par of coordnates to help fnd k, sa (, ). Substtute the values T = and n =. Solve for k b multplng both sdes b.. Replace the k wth ts value of n the equaton. Wrte the equaton. c... To fnd the tme taken for 7 workers to complete the task, substtute the value n = 7 nto the equaton T = n and solve for T. WRITE Tme (hours) T T n T = k n = k = k = k T = n n Number of workers T = 7 T =.7 hours. State the result. It would take 7 people.7 hours to complete the task... To determne the number of workers requred to complete the task n half an hour, substtute T = nto the formula. T = n = n TOPIC Non-lnear relatonshps 7

36 . Solve for n b: multplng both sdes b n multplng both sdes b. n = n n n = n = n =. State the answer. people are requred to complete the task n half an hour. WORKED EXAMPLE 7 The table of values shows the fracton of pzza receved when sharng a pzza between an ncreasng number of people. Number of people (n) 7 Fracton of pzza (A) Plot ths nformaton on a number plane. Note: A spreadsheet has been used n ths worked eample as an llustraton of technolog. a. Fnd the nverse varaton rule connectng A and n n ths eample. b. What fracton of pzza would each person receve f the pzza s to be shared equall between people? THINK a.. Usng a spreadsheet, cop the data nto two columns. WRITE 7 A n B A / / / / / /7 / 7 Jacaranda Maths Quest Mathematcs Standard E for NSW

37 . Use the spreadsheet functons to draw a scatterplot of the data. Sharng one pzza. Use the spreadsheet functons to add a trend lne. As a hperbola s requred, select the opton Power or equvalent. b. Use the equaton on the chart to fnd the rule connectng A and n. Fracton of pzza Fracton of pzza Number of people Sharng one pzza = 7 Number of people The equaton gven on the chart s =, whch s the same as =. corresponds to the fracton of pzza (A) and corresponds to the number of people (n). Therefore, the rule connectng A and n s A = n. c.. Substtute n =. A =. Answer the queston. Each person would receve of the pzza. TOPIC Non-lnear relatonshps 77

38 WORKED EXAMPLE Speed and tme for a partcular journe are nversel proportonal. a. If the speed s km/h and the tme s hours, wrte a mathematcal statement connectng these quanttes. b. Rewrte the statement usng the constant of varaton, k. c. It takes hours at an average speed of km/h to complete a certan journe. Fnd the value of k. d. How long would t take to complete the same journe at an average speed of km/h? THINK a. Speed and tme are nversel proportonal. b. Replace the varaton sgn wth an equals sgn and nclude k, the constant of varaton. WRITE c. Substtute = and = to fnd k. = k = k where k s the constant of varaton. = k = k d.. Rewrte the recprocal functon wth = the value of k.. Substtute =. = =. k =. Answer the queston. It would take. hours (or hours and mnutes) to complete the journe. Eercse. Recprocal functons Understandng, fluenc and communcatng. a. WE Complete the table of values for =. b. Plot the ponts to sketch the graph of the recprocal functon =. c. Comment on the features of ths curve.. a. Complete the table of values for =. 7 Jacaranda Maths Quest Mathematcs Standard E for NSW

39 b. Plot the ponts to sketch the graph of the recprocal functon =. c. Comment on the features of ths curve.. WE For a gven mass of gas, the volume, V cm, vares nversel as the pressure, P unts. The graph shows ths relatonshp for a partcular tpe of gas. Volume (cm ) Pressure (unts) a. Fnd:. the volume when the pressure s unts. the pressure when the volume s cm. b. Fnd the recprocal functon that models ths graph. c. Usng the recprocal functon found n part b, fnd:. the volume when the pressure s unts. the pressure when the volume s cm.. The tme, t hours, requred to complete a journe s nversel proportonal to the speed, s km/h. The graph shows ths relatonshp for a partcular journe. a. Fnd:. the tme requred to complete the journe f the average speed s km/h t (h). the average speed f the tme taken s hours. b. Fnd the recprocal functon that models ths graph. c. Usng the recprocal functon found n part b, fnd:. the tme requred to complete the journe f the average speed s km/h. the average speed f the tme taken s hours. s (km/h) TOPIC Non-lnear relatonshps 79

40 . WE The table below shows how the tme, T hours, taken to complete a task vares nversel wth the number of workers, n. Number of workers, n Tme taken, T hours a. Plot the ponts to represent ths data. b. Wrte a mathematcal statement connectng T and n to model ths problem. c. Calculate:. the tme that t would take people to complete the task. the number of people requred to complete the task n seven and a half hours.. A bag contans sweets. Snce the sweets are to be dvded equall between frends, the number of sweets, s, that each frend receves vares nversel wth the number of frends, n. a. Cop and complete the table to show the number of sweets each frend receves for varous numbers of frends. Number of f rends, n Number of sweets, s 7 b. Plot ths nformaton on a number plane. c. Wrte a mathematcal statement connectng s and n to model ths problem. d. Calculate:. the number of sweets each frend would receve f the sweets were dvded equall between 9 frends. the number of frends sharng the bag of sweets f the each receved sweets. 7. WE 7 The table below shows the fracton of a cake receved b each person when a cake s shared equall between an ncreasng number of people. Number of people, n 7 9 Fracton of cake, A a. Cop and complete the table to show the fracton of the cake that each person receves. b. Usng a method of our choce, fnd the nverse varaton rule connectng A and n. c. If the cake s to be shared equall between people, what fracton of the cake would each receve?. The pressure n a bccle tre pump, P, s nversel proportonal to the volume of ar, V. The table below shows measurements of the pressure for dfferent volumes of ar. Volume of ar, V Pressure, P a. Usng a method of our choce, sketch the graph to represent ths nformaton. b. Wrte a mathematcal statement connectng P and V to model ths problem. Jacaranda Maths Quest Mathematcs Standard E for NSW

41 cnonlnearrelatonshps_prnt // :7 page # Usng the recprocal functon found n part b, calculate:. the pressure when the volume s unts. the volume when the pressure s unts. 9. WE The tme requred for a partcular journe s nversel proportonal to the speed. a. Usng the smbols t for tme n hours and s for speed n km/h, together wth the varaton sgn, wrte a mathematcal statement that connects tme and speed. b. Rewrte the statement usng the constant of varaton, k. c. It takes hours at an average speed of 7 km/h to complete a certan journe. Fnd the value of k. d. How long would t take to complete the same journe at an average speed of 9 km/h? Gve our answer to the nearest hour.. For a partcular dstance, tme and speed are nversel proportonal. It takes hours at an average speed of km/h to complete a certan journe. How long would t take f the average speed was km/h? Gve our answer correct to the nearest half hour.. The cost per person to rent a mountan cabn s nversel proportonal to the number of people who share the rent. If the cost s $ per person when people share, what s the cost per person when people share? Problem solvng, reasonng and justfcaton G E PR O O FS c. The ptch of a muscal note vares nversel as ts wavelength. A tone wth a ptch of vbratons per second has a wavelength of.7 m. a. Fnd the ptch of a tone that has a wavelength of. m. b. Fnd the ptch of a tone that has a wavelength of cm.. The current, I amperes, that flows n an electrcal applance vares nversel as the resstance of the applance, R ohms. If the current s amperes when the resstance s ohms, fnd: a. the resstance when the current s amperes b. the current when the resstance s ohms.. A compan makng earplugs fnds that the number sold depends on the prce. If the prce s hgher, fewer are sold. The market research gves the followng epected sales results: Prce of the earplugs $ $ $ $ $ $ $ R EC TE D PA. Number sold (thousands) R Draw a graph of the number sold, n (n thousands), versus the prce, $P. O a. Wrte a mathematcal statement connectng n and P, gven that the number of earplugs sold vares nversel wth the prce of the earplug. c. Usng the recprocal functon found n part b:. predct the number of earplugs sold f the prce was $ each. calculate the prce f earplugs were sold. U N C b. TOPIC Non-lnear relatonshps

42 . A process to sterlse surgcal nstruments s tested and ts success s found to depend on the temperature used. The results are shown n the table. Temperature of sterlser ( C) 9 Mcrobes remanng alve (%) a. Usng a method of our choce, draw a graph of the percentage of mcrobes remanng alve, M, versus the temperature of the sterlser, T C. b. Wrte a mathematcal statement connectng M and T, gven that the percentage of mcrobes remanng alve vares nversel wth the temperature of the sterlser. c. Usng the recprocal functon found n part b:. predct the percentage of mcrobes remanng, to decmal place, f a temperature of 7 C was used. Justf our answer.. calculate the temperature requred to ensure that no more than % of the mcrobes reman alve.. a. Cop and complete the table shown. b. These ponts represent an nverse varaton problem. Fnd:. the constant of varaton. the equaton of the recprocal functon to represent these ponts. 7. The varables and var nversel for the followng graphs. Wrte an equaton relatng and for each graph. a. (, ) b. 7. There are 9 was to arrange square blocks to form a rectangle. Here are two was. Heght = Base = Heght = a. Fnd the other seven was and record our results n the table. b. Plot these 9 ponts on a number plane of heght, h versus the base, b. (, ) 7 Base = 9 Heght, h Base, b Area, A 9 Jacaranda Maths Quest Mathematcs Standard E for NSW

43 cnonlnearrelatonshps_prnt // :7 page # c. Descrbe the relatonshp between the heght and the base.? e. Dscuss wh t s not sutable to use the graph of the hperbola for all values of > to model ths nverse relatonshp. d. Wh do the ponts all le on the rectangular hperbola = FS. Revew.. Summar EC TE D PA G E PR O O In ths topc ou have learnt: to graph and recognse an eponental functon n the form = a or = a (a > ) usng technolog to nterpret the meanng of the ntercepts of an eponental graph n a varet of contets to construct and analse an eponental model to solve a practcal problem to construct and analse a quadratc model to solve practcal problems nvolvng quadratc functons or epressons of the form = a + b + c to recognse the shape of a parabola and that t alwas has a turnng pont and an as of smmetr to nterpret the turnng pont and ntercepts of a parabola n a practcal contet to consder the ranges of values for and for whch the quadratc model makes sense n a practcal contet k to recognse that recprocal functons of the form =, where k s a constant, represent nverse varaton, and to dentf the shape of these graphs and ther mportant features to use a recprocal model to solve practcal nverse varaton problems algebracall and graphcall to graph an eponental, quadratc or nverse functon usng technolog. Dgtal doc: Topc summar a comprehensve summar of ke learnng ponts (doc-7) Eercse. Revew R Understandng, fluenc and communcatng MC Durng a growng spurt, our frend ncreases n heght b % per month. If her orgnal heght was cm, her heght, to the nearest centmetre, after months s: A. cm B. cm C. cm D. cm. MC The populaton of bats n a Northern European countr s decreasng at the rate of % per annum. If the orgnal populaton was bats, the appromate populaton after ears s: A. B. 7 C. D. 7. A collectable football card s bought for $. Each ear the value of the card ncreases b %. Fnd the value of the card after ears.. The value of a machne s deprecatng b % per ear. If the orgnal prce of the machne was $, fnd the value of the machne after ears. U N C O R. TOPIC Non-lnear relatonshps

44 . MC Whch of the followng equatons could the graph represent? A. = B. = C. = D. =. MC Whch of the followng graphs best represents the equaton =? A. B. (, ) (, ) C. (, ) 7. MC Whch statement s correct? A. The turnng pont s (, ). B. The as of smmetr s =. C. The -ntercept s (, ). D. The turnng pont s (, ). D. (, ) (, ) (, ) (, ) (, ) Jacaranda Maths Quest Mathematcs Standard E for NSW

45 . MC Whch statement s ncorrect? A. The mamum turnng pont s (, ). B. The equaton of the as of smmetr s =. C. The -ntercept s (, ). D. The -ntercepts are (, ) and (, ). 9. The populaton of rabbts n a certan area s thought to be modelled b the eponental functon = (.7) where represents the rabbt populaton after ears. a. What s the ntal populaton? b. What s the epected rabbt populaton after ears? Gve our answer to the nearest rabbts.. MC Whch statement about the graph s not true? A. It represents an eponental functon. B. It has two branches. C. The vertcal asmptote s =. D. The horzontal asmptote s =.. For each of the followng quadratc functons, state:. the turnng pont. the -ntercept. the -ntercepts v. the as of smmetr v. whether the functon has a mamum or a mnmum value. a. 9 7 (, ) (, ) (, ) 7 (, ) (, ) (, ) (, ) 9 7 (, ) (, ) b. TOPIC Non-lnear relatonshps

46 . The prce per person to hre a lmousne for a Year formal vares nversel as the number of passengers. It costs $9 each for students. How man students would there be f hrng the lmousne cost each student $.?. MC When an amount of gas s enclosed n a contaner, the pressure s nversel proportonal to the volume. Ths s notceable when usng a bccle pump. As the volume nsde the pump s reduced b pushng the plunger, the pressure ncreases. Usng P for pressure and V for volume, whch of the followng s not true? A. P B. As V ncreases, P decreases. V P C. If V s halved, P s doubled. D. V = k. MC If and = when =, then when =, equals: k A. B.. C. D.. For a rectangle wth a constant area, the wdth, w cm, s nversel proportonal to the length, l cm. a. Wrte a mathematcal statement connectng length, wdth and k, the constant of varaton. b. Fnd k when the length of the rectangle s cm and the wdth s cm. c. Fnd the length of another rectangle wth the same constant area when the wdth s 9 cm.. For a partcular dstance, tme and speed are nversel proportonal. It takes 7 hours at an average speed of km/h to complete a certan journe. How long would t take f ou ncreased our average speed b km/h? Gve our answer to the nearest hour. 7. The table below shows how the tme, T hours, taken to complete a task vares nversel wth the number of workers, n. Number of workers, n Tme taken, T hours a. Plot the ponts to represent ths data. b. Wrte a mathematcal statement connectng T and n to model ths problem. c. Calculate:. the tme t would take workers to complete the task. the number of workers requred to complete the task n hours.. The current, I amperes, that flows n an electrcal applance vares nversel as the resstance of the applance, R ohms. If the current s amperes when the resstance s ohms, fnd: a. the resstance when the current s. amperes b. the current when the resstance s ohms. 9. A partcular nvestment apprecates b % per ear. If an nvestment of $ was made now, how much would t be worth, to the nearest $ after: a. ear b. ears c. ears? Jacaranda Maths Quest Mathematcs Standard E for NSW

47 . The monthl proft or loss, $p (n thousands of dollars), for a new wdget s modelled b the quadratc functon p =, where s the number of months after the ntroducton of the new wdgets nto the market. a. Cop and complete the table of values for ths functon. 7 9 p b. Plot the ponts to graph the functon. c. Comment on wh the table of values started at =. d. Was the compan makng a proft or a loss ntall? e. Fnd the frst month for whch a proft was shown. f. Estmate from our graph n whch month the proft would reach $7. Problem solvng, reasonng and justfcaton. In 9 wnd turbnes n Europe generated about ggawatt-hours of energ. Over the net ears, the amount of energ that was generated ncreased b appromatel % per ear. a. How much wnd energ, to decmal place, was generated n:. 9 (Hnt: t = ) ? b. Usng a method of our choce, estmate n whch ear ggawatt-hours (GWh) of energ was generated.. The populaton of a partcular fsh, f, located n the Great Barrer Reef was observed to be n and was decreasng b % per ear. Gve our answers to the nearest nteger where necessar. a. Form an equaton to model the eponental deca of the fsh populaton, f, over t ears. b. How man of the fsh were estmated to be n the Great Barrer Reef n? c. Assumng the populaton of fsh contnues to decrease at the same rate, how man would ou estmate to be n the Great Barrer Reef n?. The graph shows the path of a hocke ball gven b = (m) ( ) where s the heght, n metres, of the ball above the ground and s the horzontal dstance, n metres, of the ball from the hocke stck. a. Determne where the hocke ball hts the ground. b. What s the mamum heght of the ball above the ground? Justf our answer. (, ) (, ) (m) TOPIC Non-lnear relatonshps 7

Unit 5: Quadratic Equations & Functions

Unit 5: Quadratic Equations & Functions Date Perod Unt 5: Quadratc Equatons & Functons DAY TOPIC 1 Modelng Data wth Quadratc Functons Factorng Quadratc Epressons 3 Solvng Quadratc Equatons 4 Comple Numbers Smplfcaton, Addton/Subtracton & Multplcaton

More information

The Fundamental Theorem of Algebra. Objective To use the Fundamental Theorem of Algebra to solve polynomial equations with complex solutions

The Fundamental Theorem of Algebra. Objective To use the Fundamental Theorem of Algebra to solve polynomial equations with complex solutions 5-6 The Fundamental Theorem of Algebra Content Standards N.CN.7 Solve quadratc equatons wth real coeffcents that have comple solutons. N.CN.8 Etend polnomal denttes to the comple numbers. Also N.CN.9,

More information

Physics 2A Chapter 3 HW Solutions

Physics 2A Chapter 3 HW Solutions Phscs A Chapter 3 HW Solutons Chapter 3 Conceptual Queston: 4, 6, 8, Problems: 5,, 8, 7, 3, 44, 46, 69, 70, 73 Q3.4. Reason: (a) C = A+ B onl A and B are n the same drecton. Sze does not matter. (b) C

More information

Kinematics in 2-Dimensions. Projectile Motion

Kinematics in 2-Dimensions. Projectile Motion Knematcs n -Dmensons Projectle Moton A medeval trebuchet b Kolderer, c1507 http://members.net.net.au/~rmne/ht/ht0.html#5 Readng Assgnment: Chapter 4, Sectons -6 Introducton: In medeval das, people had

More information

Module 14: THE INTEGRAL Exploring Calculus

Module 14: THE INTEGRAL Exploring Calculus Module 14: THE INTEGRAL Explorng Calculus Part I Approxmatons and the Defnte Integral It was known n the 1600s before the calculus was developed that the area of an rregularly shaped regon could be approxmated

More information

Pre-Calculus Summer Assignment

Pre-Calculus Summer Assignment Pre-Calculus Summer Assgnment Dear Future Pre-Calculus Student, Congratulatons on our successful completon of Algebra! Below ou wll fnd the summer assgnment questons. It s assumed that these concepts,

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

Math1110 (Spring 2009) Prelim 3 - Solutions

Math1110 (Spring 2009) Prelim 3 - Solutions Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.

More information

Gravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11)

Gravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11) Gravtatonal Acceleraton: A case of constant acceleraton (approx. hr.) (6/7/11) Introducton The gravtatonal force s one of the fundamental forces of nature. Under the nfluence of ths force all objects havng

More information

Chapter 8. Potential Energy and Conservation of Energy

Chapter 8. Potential Energy and Conservation of Energy Chapter 8 Potental Energy and Conservaton of Energy In ths chapter we wll ntroduce the followng concepts: Potental Energy Conservatve and non-conservatve forces Mechancal Energy Conservaton of Mechancal

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

ACTM State Calculus Competition Saturday April 30, 2011

ACTM State Calculus Competition Saturday April 30, 2011 ACTM State Calculus Competton Saturday Aprl 30, 2011 ACTM State Calculus Competton Sprng 2011 Page 1 Instructons: For questons 1 through 25, mark the best answer choce on the answer sheet provde Afterward

More information

Mathematics Intersection of Lines

Mathematics Intersection of Lines a place of mnd F A C U L T Y O F E D U C A T I O N Department of Currculum and Pedagog Mathematcs Intersecton of Lnes Scence and Mathematcs Educaton Research Group Supported b UBC Teachng and Learnng Enhancement

More information

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis Resource Allocaton and Decson Analss (ECON 800) Sprng 04 Foundatons of Regresson Analss Readng: Regresson Analss (ECON 800 Coursepak, Page 3) Defntons and Concepts: Regresson Analss statstcal technques

More information

SIMPLE LINEAR REGRESSION

SIMPLE LINEAR REGRESSION Smple Lnear Regresson and Correlaton Introducton Prevousl, our attenton has been focused on one varable whch we desgnated b x. Frequentl, t s desrable to learn somethng about the relatonshp between two

More information

GAUTENG DEPARTMENT OF EDUCATION SENIOR SECONDARY INTERVENTION PROGRAMME PHYSICAL SCIENCES GRADE 12 SESSION 1 (LEARNER NOTES)

GAUTENG DEPARTMENT OF EDUCATION SENIOR SECONDARY INTERVENTION PROGRAMME PHYSICAL SCIENCES GRADE 12 SESSION 1 (LEARNER NOTES) PHYSICAL SCIENCES GRADE 1 SESSION 1 (LEARNER NOTES) TOPIC 1: MECHANICS PROJECTILE MOTION Learner Note: Always draw a dagram of the stuaton and enter all the numercal alues onto your dagram. Remember to

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

e i is a random error

e i is a random error Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where + β + β e for,..., and are observable varables e s a random error How can an estmaton rule be constructed for the unknown

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

Solutions to Selected Exercises

Solutions to Selected Exercises 6 Solutons to Selected Eercses Chapter Secton.. a. f ( 0) b. Tons of garbage per week s produced by a cty wth a populaton of,000.. a. In 99 there are 0 ducks n the lake b. In 000 there are 0 ducks n the

More information

x yi In chapter 14, we want to perform inference (i.e. calculate confidence intervals and perform tests of significance) in this setting.

x yi In chapter 14, we want to perform inference (i.e. calculate confidence intervals and perform tests of significance) in this setting. The Practce of Statstcs, nd ed. Chapter 14 Inference for Regresson Introducton In chapter 3 we used a least-squares regresson lne (LSRL) to represent a lnear relatonshp etween two quanttatve explanator

More information

Chapter 14 Simple Linear Regression

Chapter 14 Simple Linear Regression Chapter 4 Smple Lnear Regresson Chapter 4 - Smple Lnear Regresson Manageral decsons often are based on the relatonshp between two or more varables. Regresson analss can be used to develop an equaton showng

More information

Chapter Twelve. Integration. We now turn our attention to the idea of an integral in dimensions higher than one. Consider a real-valued function f : D

Chapter Twelve. Integration. We now turn our attention to the idea of an integral in dimensions higher than one. Consider a real-valued function f : D Chapter Twelve Integraton 12.1 Introducton We now turn our attenton to the dea of an ntegral n dmensons hgher than one. Consder a real-valued functon f : R, where the doman s a nce closed subset of Eucldean

More information

Solutions to Homework 7, Mathematics 1. 1 x. (arccos x) (arccos x) 1

Solutions to Homework 7, Mathematics 1. 1 x. (arccos x) (arccos x) 1 Solutons to Homework 7, Mathematcs 1 Problem 1: a Prove that arccos 1 1 for 1, 1. b* Startng from the defnton of the dervatve, prove that arccos + 1, arccos 1. Hnt: For arccos arccos π + 1, the defnton

More information

Chapter 3. r r. Position, Velocity, and Acceleration Revisited

Chapter 3. r r. Position, Velocity, and Acceleration Revisited Chapter 3 Poston, Velocty, and Acceleraton Revsted The poston vector of a partcle s a vector drawn from the orgn to the locaton of the partcle. In two dmensons: r = x ˆ+ yj ˆ (1) The dsplacement vector

More information

Supplemental Instruction sessions next week

Supplemental Instruction sessions next week Homework #4 Wrtten homework due now Onlne homework due on Tue Mar 3 by 8 am Exam 1 Answer keys and scores wll be posted by end of the week Supplemental Instructon sessons next week Wednesday 8:45 10:00

More information

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise. Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where y + = β + β e for =,..., y and are observable varables e s a random error How can an estmaton rule be constructed for the

More information

DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA. 1. Matrices in Mathematica

DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA. 1. Matrices in Mathematica demo8.nb 1 DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA Obectves: - defne matrces n Mathematca - format the output of matrces - appl lnear algebra to solve a real problem - Use Mathematca to perform

More information

Chapter 3 Describing Data Using Numerical Measures

Chapter 3 Describing Data Using Numerical Measures Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The

More information

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming OPTIMIATION Introducton ngle Varable Unconstraned Optmsaton Multvarable Unconstraned Optmsaton Lnear Programmng Chapter Optmsaton /. Introducton In an engneerng analss, sometmes etremtes, ether mnmum or

More information

CHAPTER 4. Vector Spaces

CHAPTER 4. Vector Spaces man 2007/2/16 page 234 CHAPTER 4 Vector Spaces To crtcze mathematcs for ts abstracton s to mss the pont entrel. Abstracton s what makes mathematcs work. Ian Stewart The man am of ths tet s to stud lnear

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

χ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body

χ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body Secton.. Moton.. The Materal Body and Moton hyscal materals n the real world are modeled usng an abstract mathematcal entty called a body. Ths body conssts of an nfnte number of materal partcles. Shown

More information

Section 8.1 Exercises

Section 8.1 Exercises Secton 8.1 Non-rght Trangles: Law of Snes and Cosnes 519 Secton 8.1 Exercses Solve for the unknown sdes and angles of the trangles shown. 10 70 50 1.. 18 40 110 45 5 6 3. 10 4. 75 15 5 6 90 70 65 5. 6.

More information

AP Physics 1 & 2 Summer Assignment

AP Physics 1 & 2 Summer Assignment AP Physcs 1 & 2 Summer Assgnment AP Physcs 1 requres an exceptonal profcency n algebra, trgonometry, and geometry. It was desgned by a select group of college professors and hgh school scence teachers

More information

Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 212. Chapters 14, 15 & 16. Professor Ahmadi, Ph.D. Department of Management

Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 212. Chapters 14, 15 & 16. Professor Ahmadi, Ph.D. Department of Management Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 1 Chapters 14, 15 & 16 Professor Ahmad, Ph.D. Department of Management Revsed August 005 Chapter 14 Formulas Smple Lnear Regresson Model: y =

More information

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS Unversty of Oulu Student Laboratory n Physcs Laboratory Exercses n Physcs 1 1 APPEDIX FITTIG A STRAIGHT LIE TO OBSERVATIOS In the physcal measurements we often make a seres of measurements of the dependent

More information

/ n ) are compared. The logic is: if the two

/ n ) are compared. The logic is: if the two STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence

More information

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands Content. Inference on Regresson Parameters a. Fndng Mean, s.d and covarance amongst estmates.. Confdence Intervals and Workng Hotellng Bands 3. Cochran s Theorem 4. General Lnear Testng 5. Measures of

More information

Statistics MINITAB - Lab 2

Statistics MINITAB - Lab 2 Statstcs 20080 MINITAB - Lab 2 1. Smple Lnear Regresson In smple lnear regresson we attempt to model a lnear relatonshp between two varables wth a straght lne and make statstcal nferences concernng that

More information

First Law: A body at rest remains at rest, a body in motion continues to move at constant velocity, unless acted upon by an external force.

First Law: A body at rest remains at rest, a body in motion continues to move at constant velocity, unless acted upon by an external force. Secton 1. Dynamcs (Newton s Laws of Moton) Two approaches: 1) Gven all the forces actng on a body, predct the subsequent (changes n) moton. 2) Gven the (changes n) moton of a body, nfer what forces act

More information

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law: CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and

More information

Physics 201, Lecture 4. Vectors and Scalars. Chapters Covered q Chapter 1: Physics and Measurement.

Physics 201, Lecture 4. Vectors and Scalars. Chapters Covered q Chapter 1: Physics and Measurement. Phscs 01, Lecture 4 Toda s Topcs n Vectors chap 3) n Scalars and Vectors n Vector ddton ule n Vector n a Coordnator Sstem n Decomposton of a Vector n Epected from prevew: n Scalars and Vectors, Vector

More information

Statistics for Economics & Business

Statistics for Economics & Business Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

Motion in One Dimension

Motion in One Dimension Moton n One Dmenson Speed ds tan ce traeled Aerage Speed tme of trael Mr. Wolf dres hs car on a long trp to a physcs store. Gen the dstance and tme data for hs trp, plot a graph of hs dstance ersus tme.

More information

Work is the change in energy of a system (neglecting heat transfer). To examine what could

Work is the change in energy of a system (neglecting heat transfer). To examine what could Work Work s the change n energy o a system (neglectng heat transer). To eamne what could cause work, let s look at the dmensons o energy: L ML E M L F L so T T dmensonally energy s equal to a orce tmes

More information

Q1: Calculate the mean, median, sample variance, and standard deviation of 25, 40, 05, 70, 05, 40, 70.

Q1: Calculate the mean, median, sample variance, and standard deviation of 25, 40, 05, 70, 05, 40, 70. Q1: Calculate the mean, medan, sample varance, and standard devaton of 5, 40, 05, 70, 05, 40, 70. Q: The frequenc dstrbuton for a data set s gven below. Measurements 0 1 3 4 Frequenc 3 5 8 3 1 a) What

More information

For all questions, answer choice E) NOTA" means none of the above answers is correct.

For all questions, answer choice E) NOTA means none of the above answers is correct. 0 MA Natonal Conventon For all questons, answer choce " means none of the above answers s correct.. In calculus, one learns of functon representatons that are nfnte seres called power 3 4 5 seres. For

More information

Lecture 6: Introduction to Linear Regression

Lecture 6: Introduction to Linear Regression Lecture 6: Introducton to Lnear Regresson An Manchakul amancha@jhsph.edu 24 Aprl 27 Lnear regresson: man dea Lnear regresson can be used to study an outcome as a lnear functon of a predctor Example: 6

More information

Statistics Chapter 4

Statistics Chapter 4 Statstcs Chapter 4 "There are three knds of les: les, damned les, and statstcs." Benjamn Dsrael, 1895 (Brtsh statesman) Gaussan Dstrbuton, 4-1 If a measurement s repeated many tmes a statstcal treatment

More information

Section 3.6 Complex Zeros

Section 3.6 Complex Zeros 04 Chapter Secton 6 Comple Zeros When fndng the zeros of polynomals, at some pont you're faced wth the problem Whle there are clearly no real numbers that are solutons to ths equaton, leavng thngs there

More information

Physics 40 HW #4 Chapter 4 Key NEATNESS COUNTS! Solve but do not turn in the following problems from Chapter 4 Knight

Physics 40 HW #4 Chapter 4 Key NEATNESS COUNTS! Solve but do not turn in the following problems from Chapter 4 Knight Physcs 40 HW #4 Chapter 4 Key NEATNESS COUNTS! Solve but do not turn n the ollowng problems rom Chapter 4 Knght Conceptual Questons: 8, 0, ; 4.8. Anta s approachng ball and movng away rom where ball was

More information

EN40: Dynamics and Vibrations. Homework 4: Work, Energy and Linear Momentum Due Friday March 1 st

EN40: Dynamics and Vibrations. Homework 4: Work, Energy and Linear Momentum Due Friday March 1 st EN40: Dynamcs and bratons Homework 4: Work, Energy and Lnear Momentum Due Frday March 1 st School of Engneerng Brown Unversty 1. The fgure (from ths publcaton) shows the energy per unt area requred to

More information

Analytical Chemistry Calibration Curve Handout

Analytical Chemistry Calibration Curve Handout I. Quck-and Drty Excel Tutoral Analytcal Chemstry Calbraton Curve Handout For those of you wth lttle experence wth Excel, I ve provded some key technques that should help you use the program both for problem

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear

More information

9. Complex Numbers. 1. Numbers revisited. 2. Imaginary number i: General form of complex numbers. 3. Manipulation of complex numbers

9. Complex Numbers. 1. Numbers revisited. 2. Imaginary number i: General form of complex numbers. 3. Manipulation of complex numbers 9. Comple Numbers. Numbers revsted. Imagnar number : General form of comple numbers 3. Manpulaton of comple numbers 4. The Argand dagram 5. The polar form for comple numbers 9.. Numbers revsted We saw

More information

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan Wnter 2008 CS567 Stochastc Lnear/Integer Programmng Guest Lecturer: Xu, Huan Class 2: More Modelng Examples 1 Capacty Expanson Capacty expanson models optmal choces of the tmng and levels of nvestments

More information

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens THE CHINESE REMAINDER THEOREM KEITH CONRAD We should thank the Chnese for ther wonderful remander theorem. Glenn Stevens 1. Introducton The Chnese remander theorem says we can unquely solve any par of

More information

Physics 207: Lecture 20. Today s Agenda Homework for Monday

Physics 207: Lecture 20. Today s Agenda Homework for Monday Physcs 207: Lecture 20 Today s Agenda Homework for Monday Recap: Systems of Partcles Center of mass Velocty and acceleraton of the center of mass Dynamcs of the center of mass Lnear Momentum Example problems

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems Chapter. Ordnar Dfferental Equaton Boundar Value (BV) Problems In ths chapter we wll learn how to solve ODE boundar value problem. BV ODE s usuall gven wth x beng the ndependent space varable. p( x) q(

More information

1 Matrix representations of canonical matrices

1 Matrix representations of canonical matrices 1 Matrx representatons of canoncal matrces 2-d rotaton around the orgn: ( ) cos θ sn θ R 0 = sn θ cos θ 3-d rotaton around the x-axs: R x = 1 0 0 0 cos θ sn θ 0 sn θ cos θ 3-d rotaton around the y-axs:

More information

Numerical Solution of Ordinary Differential Equations

Numerical Solution of Ordinary Differential Equations Numercal Methods (CENG 00) CHAPTER-VI Numercal Soluton of Ordnar Dfferental Equatons 6 Introducton Dfferental equatons are equatons composed of an unknown functon and ts dervatves The followng are examples

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Recall: man dea of lnear regresson Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 8 Lnear regresson can be used to study an

More information

Advanced Circuits Topics - Part 1 by Dr. Colton (Fall 2017)

Advanced Circuits Topics - Part 1 by Dr. Colton (Fall 2017) Advanced rcuts Topcs - Part by Dr. olton (Fall 07) Part : Some thngs you should already know from Physcs 0 and 45 These are all thngs that you should have learned n Physcs 0 and/or 45. Ths secton s organzed

More information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 008 Recall: man dea of lnear regresson Lnear regresson can be used to study

More information

Bezier curves. Michael S. Floater. August 25, These notes provide an introduction to Bezier curves. i=0

Bezier curves. Michael S. Floater. August 25, These notes provide an introduction to Bezier curves. i=0 Bezer curves Mchael S. Floater August 25, 211 These notes provde an ntroducton to Bezer curves. 1 Bernsten polynomals Recall that a real polynomal of a real varable x R, wth degree n, s a functon of the

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

PROBABILITY PRIMER. Exercise Solutions

PROBABILITY PRIMER. Exercise Solutions PROBABILITY PRIMER Exercse Solutons 1 Probablty Prmer, Exercse Solutons, Prncples of Econometrcs, e EXERCISE P.1 (b) X s a random varable because attendance s not known pror to the outdoor concert. Before

More information

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant

More information

How Differential Equations Arise. Newton s Second Law of Motion

How Differential Equations Arise. Newton s Second Law of Motion page 1 CHAPTER 1 Frst-Order Dfferental Equatons Among all of the mathematcal dscplnes the theory of dfferental equatons s the most mportant. It furnshes the explanaton of all those elementary manfestatons

More information

PHYS 705: Classical Mechanics. Calculus of Variations II

PHYS 705: Classical Mechanics. Calculus of Variations II 1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

1 GSW Iterative Techniques for y = Ax

1 GSW Iterative Techniques for y = Ax 1 for y = A I m gong to cheat here. here are a lot of teratve technques that can be used to solve the general case of a set of smultaneous equatons (wrtten n the matr form as y = A), but ths chapter sn

More information

( ) 1/ 2. ( P SO2 )( P O2 ) 1/ 2.

( ) 1/ 2. ( P SO2 )( P O2 ) 1/ 2. Chemstry 360 Dr. Jean M. Standard Problem Set 9 Solutons. The followng chemcal reacton converts sulfur doxde to sulfur troxde. SO ( g) + O ( g) SO 3 ( l). (a.) Wrte the expresson for K eq for ths reacton.

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

MATH 5630: Discrete Time-Space Model Hung Phan, UMass Lowell March 1, 2018

MATH 5630: Discrete Time-Space Model Hung Phan, UMass Lowell March 1, 2018 MATH 5630: Dscrete Tme-Space Model Hung Phan, UMass Lowell March, 08 Newton s Law of Coolng Consder the coolng of a well strred coffee so that the temperature does not depend on space Newton s law of collng

More information

Temperature. Chapter Heat Engine

Temperature. Chapter Heat Engine Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

PHYS 1101 Practice problem set 12, Chapter 32: 21, 22, 24, 57, 61, 83 Chapter 33: 7, 12, 32, 38, 44, 49, 76

PHYS 1101 Practice problem set 12, Chapter 32: 21, 22, 24, 57, 61, 83 Chapter 33: 7, 12, 32, 38, 44, 49, 76 PHYS 1101 Practce problem set 1, Chapter 3: 1,, 4, 57, 61, 83 Chapter 33: 7, 1, 3, 38, 44, 49, 76 3.1. Vsualze: Please reer to Fgure Ex3.1. Solve: Because B s n the same drecton as the ntegraton path s

More information

Lecture 3 Stat102, Spring 2007

Lecture 3 Stat102, Spring 2007 Lecture 3 Stat0, Sprng 007 Chapter 3. 3.: Introducton to regresson analyss Lnear regresson as a descrptve technque The least-squares equatons Chapter 3.3 Samplng dstrbuton of b 0, b. Contnued n net lecture

More information

Experiment 1 Mass, volume and density

Experiment 1 Mass, volume and density Experment 1 Mass, volume and densty Purpose 1. Famlarze wth basc measurement tools such as verner calper, mcrometer, and laboratory balance. 2. Learn how to use the concepts of sgnfcant fgures, expermental

More information

THE ROYAL STATISTICAL SOCIETY 2006 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE

THE ROYAL STATISTICAL SOCIETY 2006 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE THE ROYAL STATISTICAL SOCIETY 6 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutons to assst canddates preparng for the eamnatons n future years and for

More information

STAT 511 FINAL EXAM NAME Spring 2001

STAT 511 FINAL EXAM NAME Spring 2001 STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte

More information

Linear Feature Engineering 11

Linear Feature Engineering 11 Lnear Feature Engneerng 11 2 Least-Squares 2.1 Smple least-squares Consder the followng dataset. We have a bunch of nputs x and correspondng outputs y. The partcular values n ths dataset are x y 0.23 0.19

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

Exercise 1 The General Linear Model : Answers

Exercise 1 The General Linear Model : Answers Eercse The General Lnear Model Answers. Gven the followng nformaton on 67 pars of values on and -.6 - - - 9 a fnd the OLS coeffcent estmate from a regresson of on. Usng b 9 So. 9 b Suppose that now also

More information

Measuring the Strength of Association

Measuring the Strength of Association Stat 3000 Statstcs for Scentsts and Engneers Measurng the Strength of Assocaton Note that the slope s one measure of the lnear assocaton between two contnuous varables t tells ou how much the average of

More information

Comparison of Regression Lines

Comparison of Regression Lines STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence

More information

CinChE Problem-Solving Strategy Chapter 4 Development of a Mathematical Model. formulation. procedure

CinChE Problem-Solving Strategy Chapter 4 Development of a Mathematical Model. formulation. procedure nhe roblem-solvng Strategy hapter 4 Transformaton rocess onceptual Model formulaton procedure Mathematcal Model The mathematcal model s an abstracton that represents the engneerng phenomena occurrng n

More information

Problem Solving in Math (Math 43900) Fall 2013

Problem Solving in Math (Math 43900) Fall 2013 Problem Solvng n Math (Math 43900) Fall 2013 Week four (September 17) solutons Instructor: Davd Galvn 1. Let a and b be two nteger for whch a b s dvsble by 3. Prove that a 3 b 3 s dvsble by 9. Soluton:

More information

Complex Numbers Alpha, Round 1 Test #123

Complex Numbers Alpha, Round 1 Test #123 Complex Numbers Alpha, Round Test #3. Wrte your 6-dgt ID# n the I.D. NUMBER grd, left-justfed, and bubble. Check that each column has only one number darkened.. In the EXAM NO. grd, wrte the 3-dgt Test

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

The equation of motion of a dynamical system is given by a set of differential equations. That is (1)

The equation of motion of a dynamical system is given by a set of differential equations. That is (1) Dynamcal Systems Many engneerng and natural systems are dynamcal systems. For example a pendulum s a dynamcal system. State l The state of the dynamcal system specfes t condtons. For a pendulum n the absence

More information

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method Soluton of Lnear System of Equatons and Matr Inverson Gauss Sedel Iteraton Method It s another well-known teratve method for solvng a system of lnear equatons of the form a + a22 + + ann = b a2 + a222

More information

Spring Force and Power

Spring Force and Power Lecture 13 Chapter 9 Sprng Force and Power Yeah, energy s better than orces. What s net? Course webste: http://aculty.uml.edu/andry_danylov/teachng/physcsi IN THIS CHAPTER, you wll learn how to solve problems

More information