Chapter Linear Regression

Similar documents
Chapter 17. Least Square Regression

Describes techniques to fit curves (curve fitting) to discrete data to obtain intermediate estimates.

8. SIMPLE LINEAR REGRESSION. Stupid is forever, ignorance can be fixed.

Numerical Methods for Eng [ENGR 391] [Lyes KADEM 2007] Direct Method; Newton s Divided Difference; Lagrangian Interpolation; Spline Interpolation.

GCE AS/A Level MATHEMATICS GCE AS/A Level FURTHER MATHEMATICS

Chapter Gauss-Seidel Method

CURVE FITTING LEAST SQUARES METHOD

GCE AS and A Level MATHEMATICS FORMULA BOOKLET. From September Issued WJEC CBAC Ltd.

COMPLEX NUMBERS AND DE MOIVRE S THEOREM

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS

SOME REMARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOMIAL ASYMPTOTE

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini

SOLVING SYSTEMS OF EQUATIONS, DIRECT METHODS

= y and Normed Linear Spaces

The formulae in this booklet have been arranged according to the unit in which they are first

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]

Numerical Analysis Topic 4: Least Squares Curve Fitting

Professor Wei Zhu. 1. Sampling from the Normal Population

The formulae in this booklet have been arranged according to the unit in which they are first

ANOTHER INTEGER NUMBER ALGORITHM TO SOLVE LINEAR EQUATIONS (USING CONGRUENCY)

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Multiple Choice Test. Chapter Adequacy of Models for Regression

ME 501A Seminar in Engineering Analysis Page 1

Chapter Direct Method of Interpolation More Examples Mechanical Engineering

Sequences and summations

Fairing of Parametric Quintic Splines

AS and A Level Further Mathematics B (MEI)

The z-transform. LTI System description. Prof. Siripong Potisuk

Chapter Unary Matrix Operations

Minimizing spherical aberrations Exploiting the existence of conjugate points in spherical lenses

Stats & Summary

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

MTH 146 Class 7 Notes

The Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof

Chapter #2 EEE State Space Analysis and Controller Design

Chapter 2 Intro to Math Techniques for Quantum Mechanics

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION

Difference Sets of Null Density Subsets of

Summary: Binomial Expansion...! r. where

Chapter I Vector Analysis

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ]

3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:

SEPTIC B-SPLINE COLLOCATION METHOD FOR SIXTH ORDER BOUNDARY VALUE PROBLEMS

Uniform Circular Motion

5 - Determinants. r r. r r. r r. r s r = + det det det

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is

Lecture Notes 2. The ability to manipulate matrices is critical in economics.

6.6 The Marquardt Algorithm

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006

BINOMIAL THEOREM SOLUTION. 1. (D) n. = (C 0 + C 1 x +C 2 x C n x n ) (1+ x+ x 2 +.)

under the curve in the first quadrant.

Introduction to mathematical Statistics

PROGRESSION AND SERIES

We show that every analytic function can be expanded into a power series, called the Taylor series of the function.

7.5-Determinants in Two Variables

CBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find

Chapter Direct Method of Interpolation More Examples Electrical Engineering

Mark Scheme (Results) January 2008

ˆ SSE SSE q SST R SST R q R R q R R q

Graphing Review Part 3: Polynomials

XII. Addition of many identical spins

Studying the Problems of Multiple Integrals with Maple Chii-Huei Yu

ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS

Week 8. Topic 2 Properties of Logarithms

14.2 Line Integrals. determines a partition P of the curve by points Pi ( xi, y

For use in Edexcel Advanced Subsidiary GCE and Advanced GCE examinations

ICS141: Discrete Mathematics for Computer Science I

MATRIX AND VECTOR NORMS

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

Chapter 2: Descriptive Statistics

Bond Additive Modeling 5. Mathematical Properties of the Variable Sum Exdeg Index

Available online through

10 Statistical Distributions Solutions

i 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01

1 Onto functions and bijections Applications to Counting

Phys 2310 Fri. Oct. 23, 2017 Today s Topics. Begin Chapter 6: More on Geometric Optics Reading for Next Time

Chapter 2 Infinite Series Page 1 of 9

Preliminary Examinations: Upper V Mathematics Paper 1

VECTOR MECHANICS FOR ENGINEERS: Vector Mechanics for Engineers: Dynamics. In the current chapter, you will study the motion of systems of particles.

π,π is the angle FROM a! TO b

6.6 Moments and Centers of Mass

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek

Lecture 5 Single factor design and analysis

Mathematical Statistics

Minimum Hyper-Wiener Index of Molecular Graph and Some Results on Szeged Related Index

Lecture 2: The Simple Regression Model

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1

Analytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases

Lecture 10: Condensed matter systems

2. Elementary Linear Algebra Problems

Physics 11b Lecture #11

1 Using Integration to Find Arc Lengths and Surface Areas

Advanced Higher Maths: Formulae

«A first lesson on Mathematical Induction»

Advanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University

Transcription:

Chpte 6.3 Le Regesso Afte edg ths chpte, ou should be ble to. defe egesso,. use sevel mmzg of esdul cte to choose the ght cteo, 3. deve the costts of le egesso model bsed o lest sques method cteo,. use emples, the deved fomuls fo the costts of le egesso model, d. pove tht the costts of the le egesso model e uque d coespod to mmum. Le egesso s the most popul egesso model. I ths model, we wsh to pedct espose to dt pots (, ),(, ),...,(, ) b egesso model gve b () whee d e the costts of the egesso model. A mesue of goodess of ft, tht s, how well pedcts the espose vble s the mgtude of the esdul ε t ech of the dt pots. E ( ) () Idell, f ll the esduls ε e zeo, oe m hve foud equto whch ll the pots le o the model. Thus, mmzto of the esdul s objectve of obtg egesso coeffcets. The most popul method to mmze the esdul s the lest sques methods, whee the estmtes of the costts of the models e chose such tht the sum of the squed esduls s mmzed, tht s mmze E. Wh mmze the sum of the sque of the esduls? Wh ot, fo stce, mmze the sum of the esdul eos o the sum of the bsolute vlues of the esduls? Altetvel, costts of the model c be chose such tht the vege esdul s zeo wthout mkg dvdul esduls smll. Wll of these cte eld ubsed 6.3.

6.3. Chpte 6.3 pmetes wth the smllest vce? All of these questos wll be sweed below. Look t the dt Tble. Tble Dt pots... 3. 6.. 6. 3.. To epl ths dt b stght le egesso model, (3) d usg mmzg E s cte to fd d, we fd tht fo (Fgue ) () 6 3 Fgue Regesso cuve fo vs. dt. the sum of the esduls, E s show the Tble. Tble The esduls t ech dt pot fo egesso model. pedcted ε pedcted.... 3. 6.. -.. 6... 3.... ε

Le Regesso 6.3.3 o does ths gve us the smllest eo? It does s E. But t does ot gve uque vlues fo the pmetes of the model. A stght-le of the model 6 () lso mkes E s show the Tble 3. Tble 3 The esduls t ech dt pot fo egesso model 6 pedcted ε.. 6. -. 3. 6. 6... 6. 6.. 3.. 6.. pedcted E 6 Fgue Regesso cuve 6 fo vs. dt. ce ths cteo does ot gve uque egesso model, t cot be used fo fdg the egesso coeffcets. Let us see wh we cot use ths cteo fo geel dt. We wt to mmze E ( ) Dffeettg Equto (6) wth espect to d, we get E () 3.. 3 3. (6)

6.3. Chpte 6.3 E _ () Puttg these equtos to zeo, gve but tht s ot possble. Theefoe, uque vlues of d do ot est. You m thk tht the eso the mmzto cteo E egtve esduls ccel wth postve esduls. o s mmzg does ot wok s tht E bette? Let us look t the dt gve the Tble fo equto. It mkes E s show the followg tble. Tble The bsolute esduls t ech dt pot whe emplog. pedcted ε pedcted.... 3. 6.... 6... 3.... ε The vlue of E lso ests fo the stght le model 6. No othe stght le model fo ths dt hs E <. Ag, we fd the egesso coeffcets e ot uque, d hece ths cteo lso cot be used fo fdg the egesso model. Let us use the lest sques cteo whee we mmze ( ) E () s clled the sum of the sque of the esduls. To fd d, we mmze wth espect to d. ( )( ) () ( )( ) () gvg ()

Le Regesso 6.3. (3) Notg tht... () () Fgue 3 Le egesso of vs. dt showg esduls d sque of esdul t tpcl pot,. olvg the bove Equtos () d () gves (6) () Redefg ( ), ( ) 3, 3 ( ), ), ( ( ), E

6.3.6 Chpte 6.3 () _ () _ () _ () we c ewte () (3) Emple The toque T eeded to tu the tosol spg of mousetp though gle, θ s gve below Tble Toque vesus gle fo toso spg. Agle, θ Toque, T Rds N m.63..3.3.36.3.6.6.6.33 Fd the costts k d k of the egesso model T k k θ oluto Tble 6 shows the summtos eeded fo the clculto of the costts of the egesso model. Tble 6 Tbulto of dt fo clculto of eeded summtos. θ T θ T θ ds N m ds N m.63..3.3 3.3.3.6..36.3..66

Le Regesso 6.3..6.6.6 3. 6.6.33 3.6 6. k θ T 6.3...6 θ θ θ (.6) (6.3)(.) (.) (6.3).6 T N - m/d T _ T..3 N-m θ _ θ 6.3.66 ds k T k θ.3 (.6.6 N - m )(.66)

6.3. Chpte 6.3 Fgue Le egesso of toque vs. gle dt Emple To fd the logtudl modulus of composte mtel, the followg dt, s gve Tble, s collected. Tble tess vs. st dt fo composte mtel. t tess (%) ( MP ).3 36.36 6.3. 3.6. 3..3 6.. 6.6 6 Fd the logtudl modulus E usg the egesso model.

Le Regesso 6.3. σ Eε () oluto Rewtg dt fom Tble, stesses vesus st dt Tble Tble tess vs st dt fo composte I sstem of uts t ( m/m ) tess ( P ).. 3.3 3.6 3 3.6 6. 3.3. 3..3 3.6...3...3.6....6.6.6 Applg the lest sque method, the esduls γ t ech dt pot s γ σ Eε The sum of sque of the esduls s γ ( σ Eε ) Ag, to fd the costt E, we eed to mmze b dffeettg wth espect to E d the equtg to zeo d ( σ Eε )( ε ) de Fom thee, we obt E σ ε ε Note, Equto () ol so f hs show tht t coespods to locl mmum o mmum. C ou show tht t coespods to bsolute mmum. The summtos used Equto () e gve the Tble. ()

6.3. Chpte 6.3 E Tble Tbulto fo Emple fo eeded summtos ε σ ε εσ.... 3.3 3.6 6 3.3. 3 3 3.6 6..6.3 3.3..3. 3..3.. 6 3.6..6.36..3.....363.6.3.6.66 3.......6...6.6.336. ε.6 3 σ ε.333 σ ε ε.333.6. GP 3.6 3 6 6 6.333

Le Regesso 6.3. Fgue Le egesso model of stess vs. st fo composte mtel. QUETION: Gve dt ps, (, ),,(, ), do the vlues of the two costts d the lest sques stght-le egesso model coespod to the bsolute mmum of the sum of the sques of the esduls? Ae these costts of egesso uque? ANWER: Gve dt ps ( ),,(, ),, the best ft fo the stght-le egesso model (A.) s foud b the method of lest sques. ttg wth the sum of the sques of the esduls ( ) (A.) d usg gves two smulteous le equtos whose soluto s (A.3) (A.)

6.3. Chpte 6.3 (A.) (A.b) But do these vlues of d gve the bsolute mmum of vlue of (Equto (A.))? The fst devtve lss ol tells us tht these vlues gve locl mm o mm of, d ot whethe the gve bsolute mmum o mmum. o, we stll eed to fgue out f the coespod to bsolute mmum. We eed to fst coduct secod devtve test to fd out whethe the pot ), ( fom Equto (A.) gves locl mmum o locl mmum of. Ol the c we poceed to show f ths locl mmum (o mmum) lso coespods to the bsolute mmum (o mmum). Wht s the secod devtve test fo locl mmum of fucto of two vbles? If ou hve fucto ( ) f, d we foud ctcl pot ( ) b, fom the fst devtve test, the ( ) b, s mmum pot f > f f f, d (A.6) > f OR > f (A.) Fom Equto (A.) ( ) ( ) ) ( (A.) ( ) ( ) ) ( (A.) the (A.)

Le Regesso 6.3.3 (A.) (A.) o, we stsf codto (A.) becuse fom Equto (A.) we see tht s postve umbe. Although ot equed, fom Equto (A.) we see tht s lso postve umbe s ssumg tht ll dt pots e NOT zeo s esoble. Is the othe codto (Equto (A.6)) fo beg mmum met? Yes, we c show (poof ot gve tht the tem s postve) ( ) ( ) j > (A.3) < j o the vlues of d tht we hve Equto (A.) do coespod to locl mmum of. But, s ths locl mmum lso bsolute mmum. Yes, s gve b Equto (A.), the fst devtves of e zeo t ol oe pot. Ths obsevto lso mkes the stght-le egesso model bsed o lest sques to be uque. As sde ote, the deomto Equtos (A.) s ozeo s show b Equto (A.3). Ths shows tht the vlues of d e fte. LINEAR REGREION Topc Le Regesso umm Tetbook otes of Le Regesso Mjo Geel Egeeg Authos Egwu Klu, Aut Kw, Cuog Ngue Dte August 3, Web te http://umeclmethods.eg.usf.edu