Solutions. Discussion D1. a. The middle 95% of a sampling distribution for a binomial proportion ˆp is cut off by the two points

Size: px
Start display at page:

Download "Solutions. Discussion D1. a. The middle 95% of a sampling distribution for a binomial proportion ˆp is cut off by the two points"

Transcription

1 formulas at the AP Exam. Studets should be able to derive it from the expressio for margi of error E i the formula for the cofidece iterval, amely, E z p(1 p) Modelig Good Aswers Oce studets have completed D8, P9, P19, or E5, show them the model aswer as a example of what is expected o the AP Exam. The aswer to D8 illustrates how to use a chart of reasoably likely sample proportios i Display 8.. Cofidece itervals are foud usig the chart ad the formula ad compared i P9. The aswer to P19 illustrates a well-writte coclusio. The aswer to E5 shows how to hadle checkig of coditios whe there is o radomizatio. A PDF file cotaiig each questio ad its model aswer is available at Solutios Discussio D1. a. The middle 95% of a samplig distributio for a biomial proportio ˆp is cut off by the two poits p 1.96 p(1 p) (1 5) So the two values are 8 ad 9. b. There are several ways to do the secod part of this questio. You could covert 145 out of 5 to the sample proportio 9 ad say that because this proportio is t i the iterval i part a, it is t reasoably likely to get 145 out of 5 whe p 5. Alteratively, usig this formula for reasoably likely umbers of successes, p 1.96 p(1 p) 5(5) (5)(1 5) b. The TI-83 Plus or TI-84 Plus commad radbi(,.6,1) returs the umber of successes i 1 differet samples. I Fathom, create a ew collectio ad add 1 cases (Collectio New Cases). Double-click the collectio ad defie a attribute, Success, by the formula radombiomial(,.6). View the umber of successes i each of the 1 samples with a case table. Re-collect the 1 samples by selectig Reradomize from the Collectio meu. c. To geerate a Miitab dot plot for 1 samples of size, use the meus or eter the commads MTB > radom 1 C1; SUBC > biomial.6. MTB > dotplot C1 (To eter commads, you must eable commad laguage by clickig i the sessio widow ad choosig Eable Commad Laguage from the Editor meu.) gives a iterval from about to Because 145 does t fall withi this iterval, it is ot a reasoably likely evet. D. a. Usig radom digits, let 1 through 6 represet successes ad the rest represet failures. Cout the umber of successes i digits. With a TI-83 Plus or TI-84 Plus calculator, you ca use the commad radbi(,.6). The calculator will retur a sigle umber, such as 3, to idicate 3 successes out of a sample size of with p Sample Total ( ; p.6) Each dot i the plot gives the umber of successes i a radom sample of size take from a populatio with 6% successes. 116 Sectio 8.1 Solutios Statistics i Actio Istructor s Guide, Volume

2 A typical Fathom histogram is created by draggig the success attribute from a case table to a graph. Collectio 1 Frequecy of Success Histogram Success The most importat cocept to poit out here (oce agai!) is that results from radom samples vary. All of these results came from radom samples of size take from a populatio with 6% successes. d. Aswers will vary accordig to the results from the simulatio. For the dot plot i part c, % of the time there were 17 successes or fewer. Also, 1% of the time there were 3 successes or more. This is as close as we ca get to.5% without goig over. So it is reasoably likely to get ay umber of heads from 17 to 3. e. Theoretically, the reasoably likely sample proportios will be betwee p 1.96 p(1 p) or betwee.448 ad.75, or from about 18 successes to 3 successes, which is close to the results from the simulatio i part c. The aswers for D3 7 are from the completed chart (Display 8. i the studet book, reproduced as a blacklie master at the ed of this sectio). D3. Studets should look at the horizotal lie segmet they costructed i Activity 8.1b for p.6. It stretches from proportios.448 to.75, or from about 18 successes to 3 successes. Because 7 successes out of is icluded withi this horizotal lie segmet or iterval, the the aswer is yes. Gettig 7 people of Mexica origi from a radomly chose group of Hispaics is a reasoably likely evet. D4. No. The horizotal lie segmet at p goes from about 58 to.44, so a sample proportio of.6 is t a reasoably likely result for a populatio with oly 3% me. Note o D4: It may iterest your studets to kow that, o the other had, about 51% of people uder age 5 are male. [Source: D5. As ca be see by drawig a vertical lie dowward from 34 out of or upward from a sample proportio of.85, the cofidece iterval is about 7% to 95%. (If you look closely, the vertical lie just barely misses 7%, so studets may say that the cofidece iterval is 75% to 95%.) Number of Successes i the Sample ( ) p =.85 Proportio of Successes i the Populatio Proportio of Successes i the Sample D6. As see from the vertical lie o the chart below, the populatios for which a sample proportio of is reasoably likely are 35% to 65%. This ca be writte as 5% 15%. (If you look closely, the vertical lie just barely misses 35% ad 65%; therefore studets may say the cofidece iterval is % to 6% ad the margi of error is 1%.) Number of Successes i the Sample ( ) p = Proportio of Successes i the Populatio Proportio of Successes i the Sample D7. You do t eed a cofidece iterval for ˆp because you already kow exactly what that is from our sample ad you kow that it probably would have bee differet if you had take Statistics i Actio Istructor s Guide, Volume Sectio 8.1 Solutios 117

3 a differet sample. What you wat is a iterval that has a good chace of capturig the true but ukow proportio of successes p i the populatio from which the sample was take. D8. a. I the chart that follows, the 95% cofidece iterval is 35% to 65%. (If you look closely, the vertical lie just barely misses 35% ad 65%, so studets may say the cofidece iterval is % to 6%.) b. Number of Successes i the Sample ( ) p = Proportio of Successes i the Populatio Proportio of Successes i the Sample Number of Successes i the Sample ( ) p =.4 p = Proportio of Successes i the Populatio Proportio of Successes i the Sample c. The horizotal lie segmet shows all the sample proportios ˆp that are reasoably likely for a populatio that has p 5% successes. Those sample proportios are 1.96 (1 ) 55 The edpoits of the horizotal lie segmet are the about 45 ad.655. d. The vertical lie segmet shows the populatio percetages that are i the cofidece iterval. That is, they are the populatios for which a sample proportio of is a reasoably likely result. Because it is about the same legth as the horizotal lie segmet ad is cetered at about the same poit, the edpoits of the vertical lie segmet also are about 45 ad.655. e. The 95% cofidece iterval is about 45 to.655, or about 34.5% to 65.5%. D9. If you sample from a biomial populatio with proportio of successes p ad create a samplig distributio, the reasoably likely sample proportios are those sample proportios i the middle 95% of the samplig distributio. Plausible populatio proportios refers to reasoig i the opposite directio. Suppose you have take a sample of size ad the sample cotais proportio of successes ˆp. The plausible populatio proportios are those populatio proportios for which your sample proportio, ˆp, is a reasoably likely result. D1. Yes. The sample is a simple radom sample from a biomial (success/failure) populatio. Both ˆp 4 ad (1 ˆp) 16 are at least 1. Fially, i a large city the umber of buses would be greater tha 1 times the size of the sample, which is 1(), or. D11. The quatity ˆp 4 is the umber of buses i the sample that have a safety violatio, ad the quatity (1 ˆp) 16 is the umber of buses i the sample that do ot have a safety violatio. I geeral, ˆp is the umber of successes i the sample, ad (1 ˆp) is the umber of failures. D1. No, differet umbers of eve digits i the sample give differet cofidece itervals. D13. a. If, for example, you keep ˆp fixed but double the sample size, the value of z * will eed to be multiplied by to keep the widths the same. The two itervals ad have the same legth, but the secod has a higher capture rate sice z * 1.96 correspods to a capture rate of 95%, but z *.77 correspods to a capture rate of about 99.4%. b. To have the same capture rate, z * must be the same. If, for example, you keep ˆp ad z * fixed but chage the sample size, the itervals will have differet widths. The two itervals ad have the same 95% capture rate, but the first iterval is loger. D14. Margis of error for samples of size 8 will be smaller because the horizotal lie segmets i a chart like Display 8. will be shorter. From Chapters 6 ad 7, studets should uderstad that the spread of the samplig distributio of the proportio of successes decreases as the sample size icreases. 118 Sectio 8.1 Solutios Statistics i Actio Istructor s Guide, Volume

4 D15. a. The edpoits of the horizotal lie segmets for 99% cofidece would be farther apart tha the edpoits for 95% cofidece. If you wat to cover 99% of the possible values of ˆp, you have to have a loger iterval:.576 p(1 p) rather tha 1.96 p(1 p) for each value of p. b. The margi of error would be larger for 99% cofidece tha for 95% cofidece. D16. The sample size appears i the deomiator of the fractio i the formula for the margi of error. Thus, as gets larger, that fractio, ad so the margi of error, gets smaller. D17. For E 1%, use 1.96 (1 ) 96.4 So you eed a sample size of 97, which costs 5(97), or $485. (It is customary always to roud up whe computig sample size.) For E 1%, use 1.96 (1 ) which costs 5(964), or $48,. For E %, use 1.96 (1 ).1 96, which costs 5(96,), or $4,8,. To cut the margi of error by 1 1 requires multiplyig the sample size, ad the cost, by 1. D18. E , or about 3% 168 D19. The term error attributable to samplig meas the same thig as samplig error or variatio due to samplig. It meas that whe we take radom samples from a give populatio, the values of ˆp do ot tur out to be the same each time ad usually are t equal to p. However, these values do ted to cluster aroud p. D. I additio to the variatio i samplig that results from takig a radom sample from a give populatio, geeral categories of sources of error iclude ot gettig a radom sample i the first place (such as volutary respose surveys where people phoe i to talk shows or write i to ewspapers) errors i codig or recordig the resposes (a poll taker misuderstads what a perso is sayig) gettig a ivalid respose from the people surveyed because they misuderstood the questio or because they did ot tell the truth about a cotroversial issue The formula for the margi of error takes ito accout oly the variatio that results from lookig at a radom sample ad ot at the etire populatio. It is geerally impossible to predict the bias that may result from the three sources of error above. Practice P1. a. The middle 95% of the samplig distributio for p.4 is bouded by the two poits p 1.96 p(1 p) (1.4) or 64 ad 36. b. Gettig 5 out of 5 is a sample proportio of. This is a reasoably likely evet from a populatio with p.4. P. Coditios: Radom samples are take idepedetly from a populatio whose proportio of successes is, ad the populatio is large eough that samplig with replacemet is a appropriate model. Model: Repeatedly take samples of size from a populatio with 3% successes. With radom digits, let 1 through 3 represet successes ad the rest represet failures. Cout the umber of successes i digits. With a TI-83 Plus or TI-84 Plus, use the commad radbi(,.3) ad repeatedly press Õ. Repetitio: Do this at least 1 times. Alteratively, the commad radbi(,.3,1) L1 stores the umbers of successes for 1 samples i list L1. Coclusio: Sort the 1 results i ascedig order (O the TI-83 Plus or TI-84 Plus, use the commad SortA(L1)) ad cut off the bottom.5% ad the top.5%. Those cutoff poits give the eds of the horizotal lie segmet for p. P3. The expected umber of residets without health isurace is 6 3. Reasoably likely umbers are betwee , or about to 4. I a radom sample of residets, you would expect betwee about 158 ad 178 residets with health isurace. Note o P4 9: Because studets are estimatig from the chart, they may ot get these umbers exactly. P4. If the coi is fair, p, so gettig aywhere from 14 to 6 heads is reasoably likely. P5. Look at the horizotal lie segmet costructed for p.65 i Display 8.. It stretches from proportios to.8 or from about to 3 successes. Withi this horizotal lie segmet, 33 successes out of is ot icluded, so the Statistics i Actio Istructor s Guide, Volume Sectio 8.1 Solutios 119

5 aswer is o. Whe p.65, gettig 33 out of still i school would be a rare evet. P6. from about 8 to 39 high school graduates; from about.7 to.975 P7. The vertical lie o the followig chart shows that the cofidece iterval is about 15% to %. Number of Successes i the Sample ( ) p = Proportio of Successes i the Populatio Proportio of Successes i the Sample P8. The populatios for which a sample proportio of.45 is reasoably likely are to.6. This ca be writte as (The vertical lie at ˆp.45 just barely misses 3%, so studets may say the cofidece iterval is 5 to.6.) P9. a. Usig the chart i Display 8., the 95% cofidece iterval is about 5% to 75% whe the sample proportio is 5, or.65. Number of Successes i the Sample ( ) p =.65 Proportio of Successes i the Populatio Proportio of Successes i the Sample b. The cofidece iterval has edpoits at.65( ).65 5 So the 95% cofidece iterval is about.475 to.775, or 47.5% to 77.5%. P1. a. The problem states that the sample was selected radomly from all U.S. tees aged 13 to 17. Both ˆp ad (1 ˆp) are at least 1. There are well over tees aged 13 to 17 i the Uited States. All three coditios are met. b. The 95% cofidece iterval is , 6 or betwee approximately 61.% ad 68.8%. The margi of error is approximately 3.8%. c. The 9% cofidece iterval is , 6 or betwee approximately 61.8% ad 68.%. The margi of error is approximately 3.%. d. I terms of the chart, the horizotal lies must be loger to capture the middle 95% of the sample proportios tha the middle 9%. Therefore the (vertical) 95% cofidece iterval is loger because it will itersect more horizotal lies tha that for a 9% cofidece iterval. Also, the calculatios are idetical except for the value of z*, so the iterval with the larger value of z* is loger. P11. a. The problem states that the sample was selected radomly from all U.S. tees aged 13 to 17. Both ˆp ad (1 ˆp) are at least 1. There are well over tees aged 13 to 17 i the Uited States. All three coditios are met. b , 6 or betwee approximately.4% ad 5.6% c. The iterval is shorter for 4% successes tha for 65% successes eve though both sample sizes are 6. The legth of each iterval is determied by its margi of error, which i this case differs oly i the proportios i the umerator: is greater tha So all else beig equal, sample proportios closer to result i loger cofidece itervals tha proportios farther from. P1. 7. If coditios are met, the method used to costruct 9% cofidece itervals captures the true populatio proportio 9% of the time. Thus, you would expect 9% of the 8 cofidece itervals, or 7 cofidece itervals, to cotai the true populatio proportio of.6. P13. Oe. Assumig that the sample size is at least so that p ad (1 p) are both at least 1, ad that more tha mice are produced i a week so that the sample size is less tha 1% of the populatio size, you would expect 95% of the itervals costructed to capture the populatio proportio of.4, meaig that 5% of the samples, or oe sample, would ot capture this proportio. P14. For samples of size 1, the horizotal lie segmets will be shorter. Thus a cofidece iterval for a 1 Sectio 8.1 Solutios Statistics i Actio Istructor s Guide, Volume

6 sample of size 1 will be shorter because a vertical lie will cross fewer of the shorter horizotal lie segmets. Agai, remid studets that the spread of the samplig distributio of the proportio of successes decreases as the sample size icreases. P15. Use z* 1.8 because z 1.8 cuts off the outer 1% o either ed of a ormal distributio. The margi of error would be smaller for 8% cofidece tha for 95% cofidece because 1.8 ˆp(1 ˆp) is smaller tha 1.96 ˆp(1 ˆp). I terms of the chart, the lie segmet that covers the middle 95% of all possible sample proportios has to be loger tha the lie segmet that covers oly the middle 8%. P16. You should have used a sample size 9 times as big, or 9. The margi of error is give by the formula z* ˆp(1 ˆp) For this to be 1_ 3 as large as it was before, you must solve for the ew sample size, m, i the equatio 1 3 z * ˆp(1 ˆp) z* ˆp(1 ˆp) m which will give the result m 9. P17. Because you have o estimate for p, use p. a. For E % with 95% cofidece, use (z*) p(1 p) E 1.96 (1 ). 1 b. For E 1% with 99% cofidece, use (z*) p(1 p) E.576 (1 ).1 16, ,59 c. For E % with 9% cofidece, use (z*) p(1 p) E (1 ).5 7,65 or 7,61 P18. Gallup should use a sample size of 879. (As usual, roud up.) (z*) p(1 p) E P19. a. There is o idicatio that the sample is radomly selected. The populatio of iterest is studets aged 1 to 17 who have Iteret access, ad 971 of these were surveyed. Both ˆp ad (1 ˆp) are at least 1. The populatio is about millio, much more tha 1 971, so the populatio is more tha 1 times the sample size. The secod two coditios are met, but you do t kow about the first. b. The 95% cofidece iterval is about 73.3% to 78.7%: ˆp z* ˆp(1 ˆp) c. the proportio of all studets aged 1 to 17 with Iteret access who would say they go olie to get ews or iformatio about curret evets d. If you could ask all studets aged 1 to 17 with Iteret access whether they go olie to get ews or iformatio about curret evets, you are 95% cofidet that the proportio who would say yes would be somewhere i the iterval 73.3% to 79.7%. e. Suppose you could take 1 radom samples from this populatio ad costruct the 1 resultig cofidece itervals. You would expect that the true proportio of all studets aged 1 to 17 with Iteret access who would say they go olie to get ews or iformatio about curret evets would be i 95 of these itervals. Exercises E1. a. No, a sample proportio of.9 is t a reasoably likely result for a populatio with 75% successes i the populatio. The horizotal lie segmet for p.75 does ot cotai a sample proportio of.9. b. Because 48% is close to 5%, you ca estimate pretty well by usig the lie segmet for the populatio proportio of 5%. The largest is about.65 ad the smallest about 5. c. about 3% to 55% Statistics i Actio Istructor s Guide, Volume Sectio 8.1 Solutios 11

7 E. a. Yes, the horizotal segmet for p. goes from about 5 to 5, so a sample proportio of is i this iterval ad is a reasoably likely result for a populatio with % successes. b. Usig the horizotal lie segmet for p 5 to estimate p 6, the largest reasoably likely sample proportio is about. c. Drawig a vertical lie dowward from 8 out of or upward from a sample proportio of ˆp 8.7 itersects horizotal lie segmets for populatio proportios of about 5 to.85. The cofidece iterval is approximately 55% to 85%. E3. a. About.5 to 5. (The lie for barely misses the sample proportio 5, so studets might give a iterval from.5 to.) b. Usig the formula gives (1 5) 5 11, or.39 to 61. c. They are similar but the iterval i part b is a bit off. There are several reasos for the slight differece i the cofidece itervals from parts a ad b. The chart would give a reasoably accurate cofidece iterval but it does t iclude all possible values of p. Also, ˆp 5 6 1, so the ormal approximatio to the biomial used i part b is ot reliable. (The cofidece iterval for 5 usig the exact biomial distributio is.5% to 3%.) E4. a. about 65% to 9% b (1.8).8 4, or 67.6% to 9.4% c. They are similar. There are several reasos for the slight differece i the cofidece itervals from parts a ad b. The chart would give a reasoably accurate cofidece iterval but it does t iclude all possible values of p. Also, (1 ˆp) (1.8) 8 1, so the ormal approximatio to the biomial used i part b is ot reliable. E5. a. This is a multistage samplig pla with o apparet radomizatio. The Epilepsy Foudatio selected affiliates (assumig they have more tha that), each of which selected schools, presumably i their local area. The surveys were passed out to studets i these schools. You probably should cosider this a atiowide coveiece sample. The lack of radomizatio makes it impossible to draw reliable coclusios from the sample. Note: The data beig weighted by age ad regio meas that, for example, if % of the tees i their sample were 14-year-olds from the South, but 4% of the tees atiowide are 14-year-olds from the South, they would double-cout the resposes of each 14-year-old from the South i the sample. b. No, there is o idicatio of a radom sample. However, ˆp 19, , ad (1 ˆp) 19, ,56.9 are both greater tha 1, ad there are more tha 19, ,441 tees i the Uited States, so the other two coditios have bee met. c. yes E z* ˆp(1 ˆp) (1 1) 19, d. the proportio of all U.S. tees who kow that epilepsy is t cotagious e. Suppose you could take 1 radom samples from this populatio ad costruct the 1 resultig cofidece itervals. You d expect that the actual proportio of all U.S. tees who kow that epilepsy is t cotagious would be i 95 of these itervals. E6. a. You should woder whether the sample was selected radomly from all tees ad adults i the Uited States to see if coditios for a cofidece iterval are met. b. As stated i part a, the simple radom sample coditio may or may ot have bee met. Several sample proportios are listed. The most extreme (farthest from ) proportio listed for all tees is 9%. Sice ˆp ad (1 ˆp) are both at least 1, ad there are more tha tees i the Uited States, the other two coditios for a cofidece iterval for each of the proportios of all tees are met. For the statemet that 4% of girls picked egieerig as the field that most iterested them, you ca t determie whether ˆp is greater tha 1 because you do t kow how may of the sampled tees were girls. However, the populatio is clearly more tha 1 times the sample size. For adults, ˆp ad (1 ˆp) are both at least 1 because the sample size is bigger tha for tees ad all the proportios are larger tha 9%. Also, there are more tha 1 13 adults i the Uited States. c. The largest sample proportio give for the tees was 33%, so the margi of error for tees would be at most Givig the margi of error as 4% is slightly uderstatig it. For adults, the largest sample proportio give was 45%, the margi of error would be , which is very close to 3%. d. You are 95% cofidet that the proportio of all tees i the Uited States who thik 1 Sectio 8.1 Solutios Statistics i Actio Istructor s Guide, Volume

8 egieerig is a attractive career choice is betwee ad 8. e. Suppose you could take 1 radom samples from this populatio ad costruct the 1 resultig cofidece itervals. You d expect that the actual proportio of all U.S. tees who thik egieerig is a attractive career choice would be i 95 of these itervals. E7. The problem states that the sample was radomly selected. Both ˆp ad (1 ˆp) are both more tha 1. There are more tha ,49 teeagers aged 13 to 17 i the Uited States, assumig this is the populatio from which the sample was take. All three coditios have bee met. The 9% cofidece iterval is , or about.485 to 55. You are 9% cofidet that if all tees aged 13 to 17 were asked, betwee 48.5% ad 55.5% of them would respod that it is appropriate for parets to istall a computer program limitig what tees ca access o the Iteret. E8. There is o idicatio of how the sample was selected, so the simple radom sample coditio may ot have bee met. However, ˆp ad (1 ˆp) are both at least 1 ad there are more tha adults atiowide, so the other two coditios are met. The 9% cofidece iterval is ˆp z * ˆp(1 ˆp) , or about 37.3% to 4.7%. Of course, you ca have cofidece i this iterval oly if the samplig was doe radomly. However, if you kew this, you could say that you are 95% cofidet that if all adults atiowide were asked this questio, betwee 37.3% ad 4.7% of them would respod that they were very worried that popular culture is lowerig the moral stadards i this coutry. E9. No, Display 8. applies oly to samples of size. E1. Yes, you ca use Display 8. to estimate the 95% cofidece iterval whe the sample proportio is.5 ad get a cofidece iterval from to. Although ˆp.5 is ot at least 1, the horizotal lie segmets i Display 8. for the populatio proportios of,.5,, 5, were made usig the biomial probability distributio formula that was leared i Sectio 6., ot by usig a ormal approximatio. E11. You are told that you ca assume the sample was radomly selected. Both ˆp.81(1) 81 ad (1 ˆp) 9(1) 19 are at least 1. Fially, the umber of adults i the Uited States is greater tha 1(1). Coditios are met. You are 95% cofidet that if you were to ask all adults from the geeral public whether they thought TV cotributed to a declie i family values, the proportio would be betwee.786 ad.834. The computatios follow: ˆp z * ˆp(1 ˆp) (1.81) E1. No, because the retur rate was oly about 9.4%, it is ulikely that the group who retured the surveys were a radom sample of Hollywood leaders. I geeral, those who feel strogly about issues ted to retur surveys. E13. a. The sample size appears i the deomiator of the fractio i the formula for the margi of error. Thus, as gets larger, that fractio gets smaller, ad so the width of the cofidece iterval gets smaller. b. The width of the cofidece iterval icreases. To have more cofidece that the iterval captures the true populatio percetage p, the iterval must be loger. This is see i the formula, as z * must be larger to have a larger probability of havig ˆp i the iterval aroud p. E14. The symbol p is used for the proportio of successes i the populatio from which we are drawig a sample. This is the ukow parameter the value that we are tryig to estimate. The symbol ˆp is used for the proportio of successes i a sample draw from the populatio with proportio of successes p. The value of ˆp varies from sample to sample. Whe costructig a cofidece iterval, the value of ˆp from the sample is at the ceter of the cofidece iterval ad so is always i it. The value of p may or may ot be i the cofidece iterval. E15. a. For a 95% cofidece iterval, the margi of error is 1.96 ˆp(1 ˆp) The margi of error is actually closer to 4% tha 5%, but they may have reported 5% to avoid uderestimatig the error. b. For a margi of error of 3%, Gallup would eed a sample size of 168 because (z*) p(1 p) E 1.96 () Statistics i Actio Istructor s Guide, Volume Sectio 8.1 Solutios 13

9 E16. Icrease the sample size by a factor of 16. Because quadruplig the sample size cuts the margi of error i half, quadruplig it agai cuts it by oe-fourth. To see this algebraically, suppose that a sample of size gives a margi of error E. The to get a error of E_, 4 the ew sample size would have to be z p(1p) (E 4) 16 z p(1 p) 16 E17. A, D, F, ad H are the correct iterpretatios. E18. D E19. a. For 95% cofidece, the margi of error would be 1.96 ˆp(1 ˆp) or about 1.3%. b. We must solve for , You would eed at least 1,345 rus to get a margi of error less tha.. Note o E19b: It is difficult for some studets to see the basic simplicity of part b. This problem is similar to problems o the AP Exam. E. a. Coditios: The give proportios of defective items are correct, ad the defects o a lie ad betwee lies occur idepedetly of each other. Model: Choose oe digit to represet each selected item. For the first assembly lie, let represet a defective item ad 1 9 represet a acceptable item. For the secod lie, let or 1 represet a defective item ad 9 represet a acceptable item. Select te digits to represet the first assembly lie, followed by te digits to represet the secod lie. Record the total umber of defective items represeted i the set of digits. This questio does ot ask you to complete the simulatio so the Repetitio ad Coclusio portios are ot eeded. b. From the 1 rus, the estimate of the probability of seeig five or more defectives E is 1. For a margi of error of.1 ad 95% cofidece, the total umber of rus eeded is p(1 p) (z*) E You eed , or 4649, additioal rus. E1. a. Because x(1 x) is i the form p(1 p), which we are tryig to maximize. Usig y ad x allows you to graph the fuctio o a graphig calculator ad makes the algebra seem more familiar. The domai of x is restricted because a probability ca be at most 1 ad must be at least. b. The graph of the quadratic y x x is a parabola that opes dow as show here. y x c. The maximum y-value occurs at the vertex. The vertex for a parabola y ax bx c occurs at x (a) b. Here, a 1 ad b 1, so the vertex 1 occurs at x (1) 1_. The value of y at x 1_ is y 1_ 1 1_ 1_ 4. If you graph this fuctio o a graphig calculator, you ca also locate the coordiates of the vertex usig the maximum fuctio. To use this fuctio, graph the equatio, press ND [CALC], select 4:MAXIMUM, select a left boud, right boud, ad guess, ad press Õ. E. The method used i this sectio is based o the idea that the middle 95% of sample proportios will be withi about 1.96 stadard errors of the populatio proportio. That 1.96 comes from a ormal model, so this idea depeds o the distributio of the sample proportio beig approximately ormal. As you leared i Chapter 7, larger samples give the distributio of the sample proportio a more ormal shape, ad whe the sample is large eough so that p ad (1 p) are both at least 1, the the ormal approximatio to the shape of the samplig distributio gives reasoably accurate results. Suppose, for example, a sample of size 5 has 3 successes, so that ˆp is.6. Usig the formula of this sectio, the 95% cofidece iterval would be (.6, 6). Obviously, these are ot all plausible values for the populatio proportio of successes, as this parameter caot be egative Sectio 8.1 Solutios Statistics i Actio Istructor s Guide, Volume

10 E3. a. The distributio of the values of ˆp has mea p ad ca be approximated by a ormal distributio with mea p provided both p ad (1 p) are at least 1. I a ormal distributio, 95% of all values lie withi 1.96 stadard errors of the mea. b. If 95% of all values lie withi 1.96 stadard errors of p, the there is a 95% chace that the proportio ˆp from a radom sample lies withi 1.96 stadard errors of p. Basically, parts a ad b are sayig the same thig. c. If ˆp is withi 1.96 stadard errors of p, the p is withi 1.96 stadard errors of ˆp. d. This formula says i algebra exactly what part c said i words: There is a 95% chace that p is withi 1.96 stadard errors of ˆp. Symbolically, the argumet may be writte this way. From the Cetral Limit Theorem you kow that whe is large eough, P p 1.96 p(1 p) ˆp p 1.96 p(1 p) Solvig for p i the iequality, this is equivalet to p(1 p) p ˆp 1.96 p(1 p).95 P ˆp 1.96 This is almost the formula for a cofidece iterval, except that you eed to use p i the formula for the stadard error ad you do t kow it. So estimate p with ˆp. E4. Results will vary. For a 98% cofidece iterval, use z *.33. Suppose, for example, that 16 of the studets carry backpacks. The, if you were to check all studets o the campus, you would be 98% cofidet that the proportio carryig backpacks would be i the cofidece iterval (, 8)..95 Statistics i Actio Istructor s Guide, Volume Sectio 8.1 Solutios 15

Chapter 8: Estimating with Confidence

Chapter 8: Estimating with Confidence Chapter 8: Estimatig with Cofidece Sectio 8.2 The Practice of Statistics, 4 th editio For AP* STARNES, YATES, MOORE Chapter 8 Estimatig with Cofidece 8.1 Cofidece Itervals: The Basics 8.2 8.3 Estimatig

More information

Statistics 511 Additional Materials

Statistics 511 Additional Materials Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability

More information

Chapter 8: STATISTICAL INTERVALS FOR A SINGLE SAMPLE. Part 3: Summary of CI for µ Confidence Interval for a Population Proportion p

Chapter 8: STATISTICAL INTERVALS FOR A SINGLE SAMPLE. Part 3: Summary of CI for µ Confidence Interval for a Population Proportion p Chapter 8: STATISTICAL INTERVALS FOR A SINGLE SAMPLE Part 3: Summary of CI for µ Cofidece Iterval for a Populatio Proportio p Sectio 8-4 Summary for creatig a 100(1-α)% CI for µ: Whe σ 2 is kow ad paret

More information

Estimation of a population proportion March 23,

Estimation of a population proportion March 23, 1 Social Studies 201 Notes for March 23, 2005 Estimatio of a populatio proportio Sectio 8.5, p. 521. For the most part, we have dealt with meas ad stadard deviatios this semester. This sectio of the otes

More information

AP Statistics Review Ch. 8

AP Statistics Review Ch. 8 AP Statistics Review Ch. 8 Name 1. Each figure below displays the samplig distributio of a statistic used to estimate a parameter. The true value of the populatio parameter is marked o each samplig distributio.

More information

DS 100: Principles and Techniques of Data Science Date: April 13, Discussion #10

DS 100: Principles and Techniques of Data Science Date: April 13, Discussion #10 DS 00: Priciples ad Techiques of Data Sciece Date: April 3, 208 Name: Hypothesis Testig Discussio #0. Defie these terms below as they relate to hypothesis testig. a) Data Geeratio Model: Solutio: A set

More information

Frequentist Inference

Frequentist Inference Frequetist Iferece The topics of the ext three sectios are useful applicatios of the Cetral Limit Theorem. Without kowig aythig about the uderlyig distributio of a sequece of radom variables {X i }, for

More information

Homework 5 Solutions

Homework 5 Solutions Homework 5 Solutios p329 # 12 No. To estimate the chace you eed the expected value ad stadard error. To do get the expected value you eed the average of the box ad to get the stadard error you eed the

More information

Confidence Intervals for the Population Proportion p

Confidence Intervals for the Population Proportion p Cofidece Itervals for the Populatio Proportio p The cocept of cofidece itervals for the populatio proportio p is the same as the oe for, the samplig distributio of the mea, x. The structure is idetical:

More information

Chapter 18 Summary Sampling Distribution Models

Chapter 18 Summary Sampling Distribution Models Uit 5 Itroductio to Iferece Chapter 18 Summary Samplig Distributio Models What have we leared? Sample proportios ad meas will vary from sample to sample that s samplig error (samplig variability). Samplig

More information

Section 9.2. Tests About a Population Proportion 12/17/2014. Carrying Out a Significance Test H A N T. Parameters & Hypothesis

Section 9.2. Tests About a Population Proportion 12/17/2014. Carrying Out a Significance Test H A N T. Parameters & Hypothesis Sectio 9.2 Tests About a Populatio Proportio P H A N T O M S Parameters Hypothesis Assess Coditios Name the Test Test Statistic (Calculate) Obtai P value Make a decisio State coclusio Sectio 9.2 Tests

More information

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals 7-1 Chapter 4 Part I. Samplig Distributios ad Cofidece Itervals 1 7- Sectio 1. Samplig Distributio 7-3 Usig Statistics Statistical Iferece: Predict ad forecast values of populatio parameters... Test hypotheses

More information

A quick activity - Central Limit Theorem and Proportions. Lecture 21: Testing Proportions. Results from the GSS. Statistics and the General Population

A quick activity - Central Limit Theorem and Proportions. Lecture 21: Testing Proportions. Results from the GSS. Statistics and the General Population A quick activity - Cetral Limit Theorem ad Proportios Lecture 21: Testig Proportios Statistics 10 Coli Rudel Flip a coi 30 times this is goig to get loud! Record the umber of heads you obtaied ad calculate

More information

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample. Statistical Iferece (Chapter 10) Statistical iferece = lear about a populatio based o the iformatio provided by a sample. Populatio: The set of all values of a radom variable X of iterest. Characterized

More information

Understanding Samples

Understanding Samples 1 Will Moroe CS 109 Samplig ad Bootstrappig Lecture Notes #17 August 2, 2017 Based o a hadout by Chris Piech I this chapter we are goig to talk about statistics calculated o samples from a populatio. We

More information

Agreement of CI and HT. Lecture 13 - Tests of Proportions. Example - Waiting Times

Agreement of CI and HT. Lecture 13 - Tests of Proportions. Example - Waiting Times Sigificace level vs. cofidece level Agreemet of CI ad HT Lecture 13 - Tests of Proportios Sta102 / BME102 Coli Rudel October 15, 2014 Cofidece itervals ad hypothesis tests (almost) always agree, as log

More information

CONFIDENCE INTERVALS STUDY GUIDE

CONFIDENCE INTERVALS STUDY GUIDE CONFIDENCE INTERVALS STUDY UIDE Last uit, we discussed how sample statistics vary. Uder the right coditios, sample statistics like meas ad proportios follow a Normal distributio, which allows us to calculate

More information

Math 140 Introductory Statistics

Math 140 Introductory Statistics 8.2 Testig a Proportio Math 1 Itroductory Statistics Professor B. Abrego Lecture 15 Sectios 8.2 People ofte make decisios with data by comparig the results from a sample to some predetermied stadard. These

More information

Lecture 2: Monte Carlo Simulation

Lecture 2: Monte Carlo Simulation STAT/Q SCI 43: Itroductio to Resamplig ethods Sprig 27 Istructor: Ye-Chi Che Lecture 2: ote Carlo Simulatio 2 ote Carlo Itegratio Assume we wat to evaluate the followig itegratio: e x3 dx What ca we do?

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Discrete Mathematics for CS Spring 2008 David Wagner Note 22

Discrete Mathematics for CS Spring 2008 David Wagner Note 22 CS 70 Discrete Mathematics for CS Sprig 2008 David Wager Note 22 I.I.D. Radom Variables Estimatig the bias of a coi Questio: We wat to estimate the proportio p of Democrats i the US populatio, by takig

More information

Introducing Sample Proportions

Introducing Sample Proportions Itroducig Sample Proportios Probability ad statistics Aswers & Notes TI-Nspire Ivestigatio Studet 60 mi 7 8 9 0 Itroductio A 00 survey of attitudes to climate chage, coducted i Australia by the CSIRO,

More information

Big Picture. 5. Data, Estimates, and Models: quantifying the accuracy of estimates.

Big Picture. 5. Data, Estimates, and Models: quantifying the accuracy of estimates. 5. Data, Estimates, ad Models: quatifyig the accuracy of estimates. 5. Estimatig a Normal Mea 5.2 The Distributio of the Normal Sample Mea 5.3 Normal data, cofidece iterval for, kow 5.4 Normal data, cofidece

More information

Chapter 23: Inferences About Means

Chapter 23: Inferences About Means Chapter 23: Ifereces About Meas Eough Proportios! We ve spet the last two uits workig with proportios (or qualitative variables, at least) ow it s time to tur our attetios to quatitative variables. For

More information

24.1 Confidence Intervals and Margins of Error

24.1 Confidence Intervals and Margins of Error 24.1 Cofidece Itervals ad Margis of Error Essetial Questio: How do you calculate a cofidece iterval ad a margi of error for a populatio proportio or populatio mea? Resource Locker Explore Idetifyig Likely

More information

STA Learning Objectives. Population Proportions. Module 10 Comparing Two Proportions. Upon completing this module, you should be able to:

STA Learning Objectives. Population Proportions. Module 10 Comparing Two Proportions. Upon completing this module, you should be able to: STA 2023 Module 10 Comparig Two Proportios Learig Objectives Upo completig this module, you should be able to: 1. Perform large-sample ifereces (hypothesis test ad cofidece itervals) to compare two populatio

More information

Mathacle. PSet Stats, Concepts In Statistics Level Number Name: Date: Confidence Interval Guesswork with Confidence

Mathacle. PSet Stats, Concepts In Statistics Level Number Name: Date: Confidence Interval Guesswork with Confidence PSet ----- Stats, Cocepts I Statistics Cofidece Iterval Guesswork with Cofidece VII. CONFIDENCE INTERVAL 7.1. Sigificace Level ad Cofidece Iterval (CI) The Sigificace Level The sigificace level, ofte deoted

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

24.1. Confidence Intervals and Margins of Error. Engage Confidence Intervals and Margins of Error. Learning Objective

24.1. Confidence Intervals and Margins of Error. Engage Confidence Intervals and Margins of Error. Learning Objective 24.1 Cofidece Itervals ad Margis of Error Essetial Questio: How do you calculate a cofidece iterval ad a margi of error for a populatio proportio or populatio mea? Resource Locker LESSON 24.1 Cofidece

More information

Inferential Statistics. Inference Process. Inferential Statistics and Probability a Holistic Approach. Inference Process.

Inferential Statistics. Inference Process. Inferential Statistics and Probability a Holistic Approach. Inference Process. Iferetial Statistics ad Probability a Holistic Approach Iferece Process Chapter 8 Poit Estimatio ad Cofidece Itervals This Course Material by Maurice Geraghty is licesed uder a Creative Commos Attributio-ShareAlike

More information

Math 10A final exam, December 16, 2016

Math 10A final exam, December 16, 2016 Please put away all books, calculators, cell phoes ad other devices. You may cosult a sigle two-sided sheet of otes. Please write carefully ad clearly, USING WORDS (ot just symbols). Remember that the

More information

S160 #12. Review of Large Sample Result for Sample Proportion

S160 #12. Review of Large Sample Result for Sample Proportion S160 #12 Samplig Distributio of the Proportio, Part 2 JC Wag February 25, 2016 Review of Large Sample Result for Sample Proportio Recall that for large sample (ie, sample size is large, say p > 5 ad (1

More information

S160 #12. Sampling Distribution of the Proportion, Part 2. JC Wang. February 25, 2016

S160 #12. Sampling Distribution of the Proportion, Part 2. JC Wang. February 25, 2016 S160 #12 Samplig Distributio of the Proportio, Part 2 JC Wag February 25, 2016 Outlie 1 Estimatig Proportio Usig Itervals Cofidece Iterval for the Populatio Proportio iclicker Questios 2 JC Wag (WMU) S160

More information

Chapter 6 Sampling Distributions

Chapter 6 Sampling Distributions Chapter 6 Samplig Distributios 1 I most experimets, we have more tha oe measuremet for ay give variable, each measuremet beig associated with oe radomly selected a member of a populatio. Hece we eed to

More information

(7 One- and Two-Sample Estimation Problem )

(7 One- and Two-Sample Estimation Problem ) 34 Stat Lecture Notes (7 Oe- ad Two-Sample Estimatio Problem ) ( Book*: Chapter 8,pg65) Probability& Statistics for Egieers & Scietists By Walpole, Myers, Myers, Ye Estimatio 1 ) ( ˆ S P i i Poit estimate:

More information

Introducing Sample Proportions

Introducing Sample Proportions Itroducig Sample Proportios Probability ad statistics Studet Activity TI-Nspire Ivestigatio Studet 60 mi 7 8 9 10 11 12 Itroductio A 2010 survey of attitudes to climate chage, coducted i Australia by the

More information

MATH/STAT 352: Lecture 15

MATH/STAT 352: Lecture 15 MATH/STAT 352: Lecture 15 Sectios 5.2 ad 5.3. Large sample CI for a proportio ad small sample CI for a mea. 1 5.2: Cofidece Iterval for a Proportio Estimatig proportio of successes i a biomial experimet

More information

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc.

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc. Chapter 22 Comparig Two Proportios Copyright 2010, 2007, 2004 Pearso Educatio, Ic. Comparig Two Proportios Read the first two paragraphs of pg 504. Comparisos betwee two percetages are much more commo

More information

Instructor: Judith Canner Spring 2010 CONFIDENCE INTERVALS How do we make inferences about the population parameters?

Instructor: Judith Canner Spring 2010 CONFIDENCE INTERVALS How do we make inferences about the population parameters? CONFIDENCE INTERVALS How do we make ifereces about the populatio parameters? The samplig distributio allows us to quatify the variability i sample statistics icludig how they differ from the parameter

More information

Example: Find the SD of the set {x j } = {2, 4, 5, 8, 5, 11, 7}.

Example: Find the SD of the set {x j } = {2, 4, 5, 8, 5, 11, 7}. 1 (*) If a lot of the data is far from the mea, the may of the (x j x) 2 terms will be quite large, so the mea of these terms will be large ad the SD of the data will be large. (*) I particular, outliers

More information

Topic 10: Introduction to Estimation

Topic 10: Introduction to Estimation Topic 0: Itroductio to Estimatio Jue, 0 Itroductio I the simplest possible terms, the goal of estimatio theory is to aswer the questio: What is that umber? What is the legth, the reactio rate, the fractio

More information

6.3 Testing Series With Positive Terms

6.3 Testing Series With Positive Terms 6.3. TESTING SERIES WITH POSITIVE TERMS 307 6.3 Testig Series With Positive Terms 6.3. Review of what is kow up to ow I theory, testig a series a i for covergece amouts to fidig the i= sequece of partial

More information

Lecture 1. Statistics: A science of information. Population: The population is the collection of all subjects we re interested in studying.

Lecture 1. Statistics: A science of information. Population: The population is the collection of all subjects we re interested in studying. Lecture Mai Topics: Defiitios: Statistics, Populatio, Sample, Radom Sample, Statistical Iferece Type of Data Scales of Measuremet Describig Data with Numbers Describig Data Graphically. Defiitios. Example

More information

Simulation. Two Rule For Inverting A Distribution Function

Simulation. Two Rule For Inverting A Distribution Function Simulatio Two Rule For Ivertig A Distributio Fuctio Rule 1. If F(x) = u is costat o a iterval [x 1, x 2 ), the the uiform value u is mapped oto x 2 through the iversio process. Rule 2. If there is a jump

More information

µ and π p i.e. Point Estimation x And, more generally, the population proportion is approximately equal to a sample proportion

µ and π p i.e. Point Estimation x And, more generally, the population proportion is approximately equal to a sample proportion Poit Estimatio Poit estimatio is the rather simplistic (ad obvious) process of usig the kow value of a sample statistic as a approximatio to the ukow value of a populatio parameter. So we could for example

More information

Exam II Covers. STA 291 Lecture 19. Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Location CB 234

Exam II Covers. STA 291 Lecture 19. Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Location CB 234 STA 291 Lecture 19 Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Locatio CB 234 STA 291 - Lecture 19 1 Exam II Covers Chapter 9 10.1; 10.2; 10.3; 10.4; 10.6

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

- E < p. ˆ p q ˆ E = q ˆ = 1 - p ˆ = sample proportion of x failures in a sample size of n. where. x n sample proportion. population proportion

- E < p. ˆ p q ˆ E = q ˆ = 1 - p ˆ = sample proportion of x failures in a sample size of n. where. x n sample proportion. population proportion 1 Chapter 7 ad 8 Review for Exam Chapter 7 Estimates ad Sample Sizes 2 Defiitio Cofidece Iterval (or Iterval Estimate) a rage (or a iterval) of values used to estimate the true value of the populatio parameter

More information

Announcements. Unit 5: Inference for Categorical Data Lecture 1: Inference for a single proportion

Announcements. Unit 5: Inference for Categorical Data Lecture 1: Inference for a single proportion Housekeepig Aoucemets Uit 5: Iferece for Categorical Data Lecture 1: Iferece for a sigle proportio Statistics 101 Mie Çetikaya-Rudel PA 4 due Friday at 5pm (exteded) PS 6 due Thursday, Oct 30 October 23,

More information

KLMED8004 Medical statistics. Part I, autumn Estimation. We have previously learned: Population and sample. New questions

KLMED8004 Medical statistics. Part I, autumn Estimation. We have previously learned: Population and sample. New questions We have previously leared: KLMED8004 Medical statistics Part I, autum 00 How kow probability distributios (e.g. biomial distributio, ormal distributio) with kow populatio parameters (mea, variace) ca give

More information

4.3 Growth Rates of Solutions to Recurrences

4.3 Growth Rates of Solutions to Recurrences 4.3. GROWTH RATES OF SOLUTIONS TO RECURRENCES 81 4.3 Growth Rates of Solutios to Recurreces 4.3.1 Divide ad Coquer Algorithms Oe of the most basic ad powerful algorithmic techiques is divide ad coquer.

More information

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc.

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc. Chapter 22 Comparig Two Proportios Copyright 2010 Pearso Educatio, Ic. Comparig Two Proportios Comparisos betwee two percetages are much more commo tha questios about isolated percetages. Ad they are more

More information

Chapter 6. Sampling and Estimation

Chapter 6. Sampling and Estimation Samplig ad Estimatio - 34 Chapter 6. Samplig ad Estimatio 6.. Itroductio Frequetly the egieer is uable to completely characterize the etire populatio. She/he must be satisfied with examiig some subset

More information

Economics Spring 2015

Economics Spring 2015 1 Ecoomics 400 -- Sprig 015 /17/015 pp. 30-38; Ch. 7.1.4-7. New Stata Assigmet ad ew MyStatlab assigmet, both due Feb 4th Midterm Exam Thursday Feb 6th, Chapters 1-7 of Groeber text ad all relevat lectures

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Resampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n.

Resampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n. Jauary 1, 2019 Resamplig Methods Motivatio We have so may estimators with the property θ θ d N 0, σ 2 We ca also write θ a N θ, σ 2 /, where a meas approximately distributed as Oce we have a cosistet estimator

More information

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled 1 Lecture : Area Area ad distace traveled Approximatig area by rectagles Summatio The area uder a parabola 1.1 Area ad distace Suppose we have the followig iformatio about the velocity of a particle, how

More information

STAT 350 Handout 19 Sampling Distribution, Central Limit Theorem (6.6)

STAT 350 Handout 19 Sampling Distribution, Central Limit Theorem (6.6) STAT 350 Hadout 9 Samplig Distributio, Cetral Limit Theorem (6.6) A radom sample is a sequece of radom variables X, X 2,, X that are idepedet ad idetically distributed. o This property is ofte abbreviated

More information

Estimation for Complete Data

Estimation for Complete Data Estimatio for Complete Data complete data: there is o loss of iformatio durig study. complete idividual complete data= grouped data A complete idividual data is the oe i which the complete iformatio of

More information

Confidence intervals summary Conservative and approximate confidence intervals for a binomial p Examples. MATH1005 Statistics. Lecture 24. M.

Confidence intervals summary Conservative and approximate confidence intervals for a binomial p Examples. MATH1005 Statistics. Lecture 24. M. MATH1005 Statistics Lecture 24 M. Stewart School of Mathematics ad Statistics Uiversity of Sydey Outlie Cofidece itervals summary Coservative ad approximate cofidece itervals for a biomial p The aïve iterval

More information

MATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4

MATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4 MATH 30: Probability ad Statistics 9. Estimatio ad Testig of Parameters Estimatio ad Testig of Parameters We have bee dealig situatios i which we have full kowledge of the distributio of a radom variable.

More information

1 Inferential Methods for Correlation and Regression Analysis

1 Inferential Methods for Correlation and Regression Analysis 1 Iferetial Methods for Correlatio ad Regressio Aalysis I the chapter o Correlatio ad Regressio Aalysis tools for describig bivariate cotiuous data were itroduced. The sample Pearso Correlatio Coefficiet

More information

Problems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman:

Problems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman: Math 224 Fall 2017 Homework 4 Drew Armstrog Problems from 9th editio of Probability ad Statistical Iferece by Hogg, Tais ad Zimmerma: Sectio 2.3, Exercises 16(a,d),18. Sectio 2.4, Exercises 13, 14. Sectio

More information

Understanding Dissimilarity Among Samples

Understanding Dissimilarity Among Samples Aoucemets: Midterm is Wed. Review sheet is o class webpage (i the list of lectures) ad will be covered i discussio o Moday. Two sheets of otes are allowed, same rules as for the oe sheet last time. Office

More information

Recall the study where we estimated the difference between mean systolic blood pressure levels of users of oral contraceptives and non-users, x - y.

Recall the study where we estimated the difference between mean systolic blood pressure levels of users of oral contraceptives and non-users, x - y. Testig Statistical Hypotheses Recall the study where we estimated the differece betwee mea systolic blood pressure levels of users of oral cotraceptives ad o-users, x - y. Such studies are sometimes viewed

More information

ENGI 4421 Confidence Intervals (Two Samples) Page 12-01

ENGI 4421 Confidence Intervals (Two Samples) Page 12-01 ENGI 44 Cofidece Itervals (Two Samples) Page -0 Two Sample Cofidece Iterval for a Differece i Populatio Meas [Navidi sectios 5.4-5.7; Devore chapter 9] From the cetral limit theorem, we kow that, for sufficietly

More information

PH 425 Quantum Measurement and Spin Winter SPINS Lab 1

PH 425 Quantum Measurement and Spin Winter SPINS Lab 1 PH 425 Quatum Measuremet ad Spi Witer 23 SPIS Lab Measure the spi projectio S z alog the z-axis This is the experimet that is ready to go whe you start the program, as show below Each atom is measured

More information

CH19 Confidence Intervals for Proportions. Confidence intervals Construct confidence intervals for population proportions

CH19 Confidence Intervals for Proportions. Confidence intervals Construct confidence intervals for population proportions CH19 Cofidece Itervals for Proportios Cofidece itervals Costruct cofidece itervals for populatio proportios Motivatio Motivatio We are iterested i the populatio proportio who support Mr. Obama. This sample

More information

MA238 Assignment 4 Solutions (part a)

MA238 Assignment 4 Solutions (part a) (i) Sigle sample tests. Questio. MA38 Assigmet 4 Solutios (part a) (a) (b) (c) H 0 : = 50 sq. ft H A : < 50 sq. ft H 0 : = 3 mpg H A : > 3 mpg H 0 : = 5 mm H A : 5mm Questio. (i) What are the ull ad alterative

More information

Eco411 Lab: Central Limit Theorem, Normal Distribution, and Journey to Girl State

Eco411 Lab: Central Limit Theorem, Normal Distribution, and Journey to Girl State Eco411 Lab: Cetral Limit Theorem, Normal Distributio, ad Jourey to Girl State 1. Some studets may woder why the magic umber 1.96 or 2 (called critical values) is so importat i statistics. Where do they

More information

Properties and Hypothesis Testing

Properties and Hypothesis Testing Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.

More information

Discrete Mathematics for CS Spring 2007 Luca Trevisan Lecture 22

Discrete Mathematics for CS Spring 2007 Luca Trevisan Lecture 22 CS 70 Discrete Mathematics for CS Sprig 2007 Luca Trevisa Lecture 22 Aother Importat Distributio The Geometric Distributio Questio: A biased coi with Heads probability p is tossed repeatedly util the first

More information

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 5 STATISTICS II. Mea ad stadard error of sample data. Biomial distributio. Normal distributio 4. Samplig 5. Cofidece itervals

More information

Final Examination Solutions 17/6/2010

Final Examination Solutions 17/6/2010 The Islamic Uiversity of Gaza Faculty of Commerce epartmet of Ecoomics ad Political Scieces A Itroductio to Statistics Course (ECOE 30) Sprig Semester 009-00 Fial Eamiatio Solutios 7/6/00 Name: I: Istructor:

More information

NUMERICAL METHODS FOR SOLVING EQUATIONS

NUMERICAL METHODS FOR SOLVING EQUATIONS Mathematics Revisio Guides Numerical Methods for Solvig Equatios Page 1 of 11 M.K. HOME TUITION Mathematics Revisio Guides Level: GCSE Higher Tier NUMERICAL METHODS FOR SOLVING EQUATIONS Versio:. Date:

More information

Data Analysis and Statistical Methods Statistics 651

Data Analysis and Statistical Methods Statistics 651 Data Aalysis ad Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasii/teachig.html Suhasii Subba Rao Review of testig: Example The admistrator of a ursig home wats to do a time ad motio

More information

t distribution [34] : used to test a mean against an hypothesized value (H 0 : µ = µ 0 ) or the difference

t distribution [34] : used to test a mean against an hypothesized value (H 0 : µ = µ 0 ) or the difference EXST30 Backgroud material Page From the textbook The Statistical Sleuth Mea [0]: I your text the word mea deotes a populatio mea (µ) while the work average deotes a sample average ( ). Variace [0]: The

More information

Continuous Data that can take on any real number (time/length) based on sample data. Categorical data can only be named or categorised

Continuous Data that can take on any real number (time/length) based on sample data. Categorical data can only be named or categorised Questio 1. (Topics 1-3) A populatio cosists of all the members of a group about which you wat to draw a coclusio (Greek letters (μ, σ, Ν) are used) A sample is the portio of the populatio selected for

More information

Chapter 6 Part 5. Confidence Intervals t distribution chi square distribution. October 23, 2008

Chapter 6 Part 5. Confidence Intervals t distribution chi square distribution. October 23, 2008 Chapter 6 Part 5 Cofidece Itervals t distributio chi square distributio October 23, 2008 The will be o help sessio o Moday, October 27. Goal: To clearly uderstad the lik betwee probability ad cofidece

More information

Mathematical Notation Math Introduction to Applied Statistics

Mathematical Notation Math Introduction to Applied Statistics Mathematical Notatio Math 113 - Itroductio to Applied Statistics Name : Use Word or WordPerfect to recreate the followig documets. Each article is worth 10 poits ad ca be prited ad give to the istructor

More information

Tests of Hypotheses Based on a Single Sample (Devore Chapter Eight)

Tests of Hypotheses Based on a Single Sample (Devore Chapter Eight) Tests of Hypotheses Based o a Sigle Sample Devore Chapter Eight MATH-252-01: Probability ad Statistics II Sprig 2018 Cotets 1 Hypothesis Tests illustrated with z-tests 1 1.1 Overview of Hypothesis Testig..........

More information

Discrete probability distributions

Discrete probability distributions Discrete probability distributios I the chapter o probability we used the classical method to calculate the probability of various values of a radom variable. I some cases, however, we may be able to develop

More information

Goodness-of-Fit Tests and Categorical Data Analysis (Devore Chapter Fourteen)

Goodness-of-Fit Tests and Categorical Data Analysis (Devore Chapter Fourteen) Goodess-of-Fit Tests ad Categorical Data Aalysis (Devore Chapter Fourtee) MATH-252-01: Probability ad Statistics II Sprig 2019 Cotets 1 Chi-Squared Tests with Kow Probabilities 1 1.1 Chi-Squared Testig................

More information

April 18, 2017 CONFIDENCE INTERVALS AND HYPOTHESIS TESTING, UNDERGRADUATE MATH 526 STYLE

April 18, 2017 CONFIDENCE INTERVALS AND HYPOTHESIS TESTING, UNDERGRADUATE MATH 526 STYLE April 18, 2017 CONFIDENCE INTERVALS AND HYPOTHESIS TESTING, UNDERGRADUATE MATH 526 STYLE TERRY SOO Abstract These otes are adapted from whe I taught Math 526 ad meat to give a quick itroductio to cofidece

More information

Simple Random Sampling!

Simple Random Sampling! Simple Radom Samplig! Professor Ro Fricker! Naval Postgraduate School! Moterey, Califoria! Readig:! 3/26/13 Scheaffer et al. chapter 4! 1 Goals for this Lecture! Defie simple radom samplig (SRS) ad discuss

More information

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Solutions Descriptive Statistics. None at all!

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Solutions Descriptive Statistics. None at all! ENGI 44 Probability ad Statistics Faculty of Egieerig ad Applied Sciece Problem Set Solutios Descriptive Statistics. If, i the set of values {,, 3, 4, 5, 6, 7 } a error causes the value 5 to be replaced

More information

4.1 Sigma Notation and Riemann Sums

4.1 Sigma Notation and Riemann Sums 0 the itegral. Sigma Notatio ad Riema Sums Oe strategy for calculatig the area of a regio is to cut the regio ito simple shapes, calculate the area of each simple shape, ad the add these smaller areas

More information

HOMEWORK 2 SOLUTIONS

HOMEWORK 2 SOLUTIONS HOMEWORK SOLUTIONS CSE 55 RANDOMIZED AND APPROXIMATION ALGORITHMS 1. Questio 1. a) The larger the value of k is, the smaller the expected umber of days util we get all the coupos we eed. I fact if = k

More information

STAT 515 fa 2016 Lec Sampling distribution of the mean, part 2 (central limit theorem)

STAT 515 fa 2016 Lec Sampling distribution of the mean, part 2 (central limit theorem) STAT 515 fa 2016 Lec 15-16 Samplig distributio of the mea, part 2 cetral limit theorem Karl B. Gregory Moday, Sep 26th Cotets 1 The cetral limit theorem 1 1.1 The most importat theorem i statistics.............

More information

9.2 Confidence Intervals for Means

9.2 Confidence Intervals for Means 202 CHAPTER 9. ESTIMATION 9.2 Cofidece Itervals for Meas We are give X 1, X 2,..., X that are a S RS ( from a orm(mea = µ, sd = σ distributio, where µ is ukow. We kow that we may estimate µ with X, ad

More information

Stat 421-SP2012 Interval Estimation Section

Stat 421-SP2012 Interval Estimation Section Stat 41-SP01 Iterval Estimatio Sectio 11.1-11. We ow uderstad (Chapter 10) how to fid poit estimators of a ukow parameter. o However, a poit estimate does ot provide ay iformatio about the ucertaity (possible

More information

The Random Walk For Dummies

The Random Walk For Dummies The Radom Walk For Dummies Richard A Mote Abstract We look at the priciples goverig the oe-dimesioal discrete radom walk First we review five basic cocepts of probability theory The we cosider the Beroulli

More information

Activity 3: Length Measurements with the Four-Sided Meter Stick

Activity 3: Length Measurements with the Four-Sided Meter Stick Activity 3: Legth Measuremets with the Four-Sided Meter Stick OBJECTIVE: The purpose of this experimet is to study errors ad the propagatio of errors whe experimetal data derived usig a four-sided meter

More information

Hypothesis Testing. Evaluation of Performance of Learned h. Issues. Trade-off Between Bias and Variance

Hypothesis Testing. Evaluation of Performance of Learned h. Issues. Trade-off Between Bias and Variance Hypothesis Testig Empirically evaluatig accuracy of hypotheses: importat activity i ML. Three questios: Give observed accuracy over a sample set, how well does this estimate apply over additioal samples?

More information

Statistical Intervals for a Single Sample

Statistical Intervals for a Single Sample 3/5/06 Applied Statistics ad Probability for Egieers Sixth Editio Douglas C. Motgomery George C. Ruger Chapter 8 Statistical Itervals for a Sigle Sample 8 CHAPTER OUTLINE 8- Cofidece Iterval o the Mea

More information

Confidence intervals for proportions

Confidence intervals for proportions Cofidece itervals for roortios Studet Activity 7 8 9 0 2 TI-Nsire Ivestigatio Studet 60 mi Itroductio From revious activity This activity assumes kowledge of the material covered i the activity Distributio

More information

A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence

A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece,, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet as

More information

Algebra II Notes Unit Seven: Powers, Roots, and Radicals

Algebra II Notes Unit Seven: Powers, Roots, and Radicals Syllabus Objectives: 7. The studets will use properties of ratioal epoets to simplify ad evaluate epressios. 7.8 The studet will solve equatios cotaiig radicals or ratioal epoets. b a, the b is the radical.

More information

Module 1 Fundamentals in statistics

Module 1 Fundamentals in statistics Normal Distributio Repeated observatios that differ because of experimetal error ofte vary about some cetral value i a roughly symmetrical distributio i which small deviatios occur much more frequetly

More information

Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples Parameter, Statistic ad Radom Samples A parameter is a umber that describes the populatio. It is a fixed umber, but i practice we do ot kow its value. A statistic is a fuctio of the sample data, i.e.,

More information