MATH 1713 Chapter 12: Linear Regression and Correlation
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Multiple Linear Regression (MLR) Handouts
Multiple Linear Regression (MLR) Handouts Yibi Huang • Data and Models • Least Squares Estimate, Fitted Values, Residuals • Sum of Squares • How to Do Regression in R? • Interpretation of Regression Coefficients • t-Tests on Individual Regression Coefficients • F -Tests for Comparing Nested Models MLR - 1 You may skip this lecture if you have taken STAT 224 or 245. However, you are encouraged to at least read through the slides if you skip the video lecture. MLR - 2 Data for Multiple Linear Regression Models Multiple linear regression is a generalized form of simple linear regression, when there are multiple explanatory variables. SLR MLR x y x1 x2 ::: xp y case 1: x1 y1 x11 x12 ::: x1p y1 case 2: x2 y2 x21 x22 ::: x2p y2 . .. case n: xn yn xn1 xn2 ::: xnp yn I For SLR, we observe pairs of variables. For MLR, we observe rows of variables. Each row (or pair) is called a case, a record, or a data point I yi is the response (or dependent variable) of the ith case I There are p explanatory variables (or covariates, predictors, independent variables), and xik is the value of the explanatory variable xk of the ith case MLR - 3 Multiple Linear Regression Models yi = β0 + β1xi1 + ::: + βpxip + "i In the model above, 2 I "i 's (errors, or noise) are i.i.d. N(0; σ ) I Parameters include: β0 = intercept; βk = regression coefficient (slope) for the kth explanatory variable; k = 1;:::; p 2 σ = Var("i ) = the variance of errors I Observed (known): yi ; xi1; xi2;:::; xip 2 Unknown: β0, β1; : : : ; βp, σ , "i 's I Random variables: "i 's, yi 's 2 Constants (nonrandom): βk 's, σ , xik 's MLR - 4 MLR is just like SLR. -
Statistical Modeling Through Regression Part 1. Multiple Regression
ACADEMIC YEAR 2018-19 DATA ANALYSIS Block 4: Statistical Modeling through Regression OF TRANSPORT AND LOGISTICS – Part 1. Multiple Regression MSCTL - UPC Lecturer: Lídia Montero September 2018 – Version 1.1 MASTER’S DEGREE IN SUPPLY CHAIN, TRANSPORT AND LOGISTICS DATA ANALYSIS OF TRANSPORT AND LOGISTICS – MSCTL - UPC CONTENTS 4.1-1. READINGS____________________________________________________________________________________________________________ 3 4.1-2. INTRODUCTION TO LINEAR MODELS _________________________________________________________________________________ 4 4.1-3. LEAST SQUARES ESTIMATION IN MULTIPLE REGRESSION ____________________________________________________________ 12 4.1-3.1 GEOMETRIC PROPERTIES _______________________________________________________________________________________________ 14 4.1-4. LEAST SQUARES ESTIMATION: INFERENCE __________________________________________________________________________ 16 4.1-4.1 BASIC INFERENCE PROPERTIES __________________________________________________________________________________________ 16 4.1-5. HYPOTHESIS TESTS IN MULTIPLE REGRESSION ______________________________________________________________________ 19 4.1-5.1 TESTING IN R ________________________________________________________________________________________________________ 21 4.1-5.2 CONFIDENCE INTERVAL FOR MODEL PARAMETERS __________________________________________________________________________ 22 4.1-6. MULTIPLE CORRELATION COEFFICIENT_____________________________________________________________________________ -
Principal Component Analysis (PCA) As a Statistical Tool for Identifying Key Indicators of Nuclear Power Plant Cable Insulation
Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2017 Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation Chamila Chandima De Silva Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Materials Science and Engineering Commons, Mechanics of Materials Commons, and the Statistics and Probability Commons Recommended Citation De Silva, Chamila Chandima, "Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation" (2017). Graduate Theses and Dissertations. 16120. https://lib.dr.iastate.edu/etd/16120 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation by Chamila C. De Silva A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Materials Science and Engineering Program of Study Committee: Nicola Bowler, Major Professor Richard LeSar Steve Martin The student author and the program of study committee are solely responsible for the content of this thesis. The Graduate College will ensure this thesis is globally accessible and will not permit alterations after a degree is conferred. -
Stat331: Applied Linear Models
Stat331: Applied Linear Models Helena S. Ven 05 Sept. 2019 Instructor: Leilei Zeng ([email protected]) Office: TTh, 4:00 { 5:00, at M3.4223 Topics: Modeling the relationship between a response variable and several explanatory variables via regression models. 1. Parameter estimation and inference 2. Confidence Intervals 3. Prediction 4. Model assumption checking, methods dealing with violations 5. Variable selection 6. Applied Issues: Outliers, Influential points, Multi-collinearity (highly correlated explanatory varia- bles) Grading: 20% Assignments (4), 20% Midterms (2), 50% Final Midterms are on 22 Oct., 12 Nov. Textbook: Abraham B., Ledolter J. Introduction to Regression Modeling Index 1 Simple Linear Regression 2 1.1 Regression Model . .2 1.2 Simple Linear Regression . .2 1.3 Method of Least Squares . .3 1.4 Method of Maximum Likelihood . .5 1.5 Properties of Estimators . .6 2 Confidence Intervals and Hypothesis Testing 9 2.1 Pivotal Quantities . .9 2.2 Estimation of the Mean Response . 10 2.3 Prediction of a Single Response . 11 2.4 Analysis of Variance and F-test . 11 2.5 Co¨efficient of Determination . 14 3 Multiple Linear Regression 15 3.1 Random Vectors . 15 3.1.1 Multilinear Regression . 16 3.1.2 Parameter Estimation . 17 3.2 Estimation of Variance . 19 3.3 Inference and Prediction . 20 3.4 Maximum Likelihood Estimation . 21 3.5 Geometric Interpretation of Least Squares . 21 3.6 ANOVA For Multilinear Regression . 21 3.7 Hypothesis Testing . 23 3.7.1 Testing on Subsets of Co¨efficients . 23 3.8 Testing General Linear Hypothesis . 25 3.9 Interactions in Regression and Categorical Variables . -
And SPECFIT/32 in the Regression of Multiwavelength
Spectrochimica Acta Part A 86 (2012) 305–314 Contents lists available at SciVerse ScienceDirect Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy j ournal homepage: www.elsevier.com/locate/saa Reliability of dissociation constants and resolution capability of SQUAD(84) and SPECFIT/32 in the regression of multiwavelength spectrophotometric pH-titration data a,∗ a b Milan Meloun , Zuzana Ferencíkovᡠ, Milan Javurek˚ a Department of Analytical Chemistry, University of Pardubice, CZ 532 10 Pardubice, Czech Republic b Department of Process Control, University of Pardubice CZ 532 10 Pardubice, Czech Republic a r t i c l e i n f o a b s t r a c t Article history: The resolving power of multicomponent spectral analysis and the computation reliability of the stability Received 1 June 2011 constants and molar absorptivities determined for five variously protonated anions of physostigmine Received in revised form 23 August 2011 salicylate by the SQUAD(84) and SPECFIT/32 programs has been examined with the use of simulated Accepted 17 October 2011 and experimental spectra containing overlapping spectral bands. The reliability of the dissociation con- stants of drug was proven with goodness-of-fit tests and by examining the influence of pre-selected Keywords: noise level sinst(A) in synthetic spectra regarding the precision s(pK) and also accuracy of the estimated Spectrophotometric titration dissociation constants. Precision was examined as the linear regression model s(pK) = ˇ0 + ˇ1 sinst(A). In Dissociation constant ˇ SPECFIT/32 all cases the intercept 0 was statistically insignificant. When an instrumental error sinst(A) is small and − SQUAD(84) less than 0.5 mAU, the parameters’ estimates are nearly the same as the bias pK = pKa,calc pKa,true is Physostigmine salicylate quite negligible. -
MISPECIFICATION BOOTSTRAP TESTS of the CAPITAL ASSET PRICING MODEL a Thesis by NHIEU BO BS, Texas A&M University-Corpus Chri
MISPECIFICATION BOOTSTRAP TESTS OF THE CAPITAL ASSET PRICING MODEL A Thesis by NHIEU BO BS, Texas A&M University-Corpus Christi, 2014 Submitted in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in MATHEMATICS Texas A&M University-Corpus Christi Corpus Christi, Texas December 2017 c NHIEU BO All Rights Reserved December 2017 MISPECIFICATION BOOTSTRAP TESTS OF THE CAPITAL ASSET PRICING MODEL A Thesis by NHIEU BO This thesis meets the standards for scope and quality of Texas A&M University-Corpus Christi and is hereby approved. LEI JIN, PhD H. SWINT FRIDAY, PhD, CFP Chair Committee Member BLAIR STERBA-BOATWRIGHT, PhD Committee Member December 2017 ABSTRACT The development of the Capital Asset Pricing Model (CAPM) marks the birth of asset pricing framework in finance. The CAPM is a simple and powerful tool to describe the linear relationship between risk and expected return. According to the CAPM, all pricing errors should be jointly equal to zero. Many empirical studies were conducted to test the validity of the model in various stock markets. Traditional methods such as Black, Jensen, and Scholes (1972), Fama-MacBeth (1973) and cross-sectional regression have some limitations and encounter difficulties because they often involve estimation of the covariance matrix between all estimated price errors. It becomes even more difficult when the number of assets becomes larger. Our research is motivated by the objective to overcome the limitations of the traditional methods. In this study, we propose to use bootstrap methods which can capture the characteristics of the original data without any covariance estimation. -
Regression Diagnostics
Error assumptions Unusual observations Regression diagnostics Botond Szabo Leiden University Leiden, 30 April 2018 Botond Szabo Diagnostics Error assumptions Unusual observations Outline 1 Error assumptions Introduction Variance Normality 2 Unusual observations Residual vs error Outliers Influential observations Botond Szabo Diagnostics Error assumptions Unusual observations Introduction Errors and residuals 2 Assumption on errors: "i ∼ N(0; σ ); i = 1;:::; n: How to check? Examine the residuals" ^i 's. If the error assumption is okay," ^i will look like a sample generated from the normal distribution. Botond Szabo Diagnostics Error assumptions Unusual observations Variance Mean zero and constant variance Diagnostic plot: fitted values Y^i 's versus residuals" ^i 's. Illustration: savings data on 50 countries from 1960 to 1970. Linear regression; covariates: per capita disposable income, percentage of population under 15 etc. Botond Szabo Diagnostics Error assumptions Unusual observations Variance R code > library(faraway) > data(savings) > g<-lm(sr~pop15+pop75+dpi+ddpi,savings) > plot(fitted(g),residuals(g),xlab="Fitted", + ylab="Residuals") > abline(h=0) Botond Szabo Diagnostics Error assumptions Unusual observations Variance Plot No significant evidence against constant variance. 10 5 0 Residuals −5 6 8 10 12 14 16 Fitted Botond Szabo Diagnostics Error assumptions Unusual observations Variance Constant variance: examples 2 1.5 1 0.5 0 −1 rnorm(50) rnorm(50) −0.5 −2 −1.5 0 10 20 30 40 50 0 10 20 30 40 50 1:50 1:50 2 2 1 1 0 0 rnorm(50) -
Measures of Explained Variation and the Base-Rate Problem for Logistic Regression
American Journal of Biostatistics 2 (1): 11-19, 2011 ISSN 1948-9889 © 2011 Science Publications Measures of Explained Variation and the Base-Rate Problem for Logistic Regression 1Dinesh Sharma, 2Dan McGee and 3B.M. Golam Kibria 1Department of Mathematics and Statistics, James Madison University, Harrisonburg, VA 22801 2Department of Statistics, Florida State University, Tallahassee, FL 32306 3Department of Mathematics and Statistics, Florida International University, Miami, FL 33199 Abstract: Problem statement: Logistic regression, perhaps the most frequently used regression model after the General Linear Model (GLM), is extensively used in the field of medical science to analyze prognostic factors in studies of dichotomous outcomes. Unlike the GLM, many different proposals have been made to measure the explained variation in logistic regression analysis. One of the limitations of these measures is their dependency on the incidence of the event of interest in the population. This has clear disadvantage, especially when one seeks to compare the predictive ability of a set of prognostic factors in two subgroups of a population. Approach: The purpose of this article is to study the base-rate sensitivity of several R2 measures that have been proposed for use in logistic regression. We compared the base-rate sensitivity of thirteen R2 type parametric and nonparametric statistics. Since a theoretical comparison was not possible, a simulation study was conducted for this purpose. We used results from an existing dataset to simulate populations with different base-rates. Logistic models are generated using the covariate values from the dataset. Results: We found nonparametric R2 measures to be less sensitive to the base-rate as compared to their parametric counterpart. -
The Effect of Nonzero Autocorrelation Coefficients on the Distributions of Durbin-Watson Test Estimator: Three Autoregressive Models
Expert Journal of Economics (2014) 2 , 85-99 EJ © 2014 The Author. Published by Sprint Investify. ISSN 2359-7704 Economics.ExpertJournals.com The Effect of Nonzero Autocorrelation Coefficients on the Distributions of Durbin-Watson Test Estimator: Three Autoregressive Models Mei-Yu LEE * Yuanpei University, Taiwan This paper investigates the effect of the nonzero autocorrelation coefficients on the sampling distributions of the Durbin-Watson test estimator in three time-series models that have different variance-covariance matrix assumption, separately. We show that the expected values and variances of the Durbin-Watson test estimator are slightly different, but the skewed and kurtosis coefficients are considerably different among three models. The shapes of four coefficients are similar between the Durbin-Watson model and our benchmark model, but are not the same with the autoregressive model cut by one-lagged period. Second, the large sample case shows that the three models have the same expected values, however, the autoregressive model cut by one-lagged period explores different shapes of variance, skewed and kurtosis coefficients from the other two models. This implies that the large samples lead to the same expected values, 2(1 – ρ0), whatever the variance-covariance matrix of the errors is assumed. Finally, comparing with the two sample cases, the shape of each coefficient is almost the same, moreover, the autocorrelation coefficients are negatively related with expected values, are inverted-U related with variances, are cubic related with skewed coefficients, and are U related with kurtosis coefficients. Keywords : Nonzero autocorrelation coefficient, the d statistic, serial correlation, autoregressive model, time series analysis JEL Classification : C32, C15, C52 1. -
The Ways of Our Errors
The Ways of Our Errors Optimal Data Analysis for Beginners and Experts c Keith Horne Universit y of St.Andrews DRAFT June 5, 2009 Contents I Optimal Data Analysis 1 1 Probability Concepts 2 1.1 Fuzzy Numbers and Dancing Data Points . 2 1.2 Systematic and Statistical Errors { Bias and Variance . 3 1.3 Probability Maps . 3 1.4 Mean, Variance, and Standard Deviation . 5 1.5 Median and Quantiles . 6 1.6 Fuzzy Algebra . 7 1.7 Why Gaussians are Special . 8 1.8 Why χ2 is Special . 8 2 A Menagerie of Probability Maps 10 2.1 Boxcar or Uniform (ranu.for) . 10 2.2 Gaussian or Normal (rang.for) . 11 2.3 Lorentzian or Cauchy (ranl.for) . 13 2.4 Exponential (rane.for) . 14 2.5 Power-Law (ranpl.for) . 15 2.6 Schechter Distribution . 17 2.7 Chi-Square (ranchi2.for) . 18 2.8 Poisson . 18 2.9 2-Dimensional Gaussian . 19 3 Optimal Statistics 20 3.1 Optimal Averaging . 20 3.1.1 weighted average . 22 3.1.2 inverse-variance weights . 22 3.1.3 optavg.for . 22 3.2 Optimal Scaling . 23 3.2.1 The Golden Rule of Data Analysis . 25 3.2.2 optscl.for . 25 3.3 Summary . 26 3.4 Problems . 26 i ii The Ways of Our Errors c Keith Horne DRAFT June 5, 2009 4 Straight Line Fit 28 4.1 1 data point { degenerate parameters . 28 4.2 2 data points { correlated vs orthogonal parameters . 29 4.3 N data points . 30 4.4 fitline.for . -
12 the Analysis of Residuals
B.Sc./Cert./M.Sc. Qualif. - Statistics: Theory and Practice 12 The Analysis of Residuals 12.1 Errors and residuals Recall that in the statistical model for the completely randomized one-way design, Yij = μ + τi + εij i =1,...,a; j =1,...,ni, (1) 2 the errors εij are assumed to be NID(0,σ ). Specifically, we have the following two assumptions: homogeneity of variance — the error variance is the same for each treatment; normality — the errors are normally distributed. The errors εij are themselves unobservable. From the model (1), εij = Yij − μ − τi i =1,...,a; j =1,...,ni, but μ and the τi are unknown. However, we may replace μ + τi in the above expression by μˆ +ˆτi.Thisgivestheresiduals εˆij (as also defined in Section 10.2), εˆij = Yij − μˆ − τˆi = Yij − Y¯.. − (Y¯i. − Y¯..) = Yij − Y¯i. i =1,...,a; j =1,...,ni, whose observed values, eij, can be calculated from the experimental data. The residuals are closely related to the errors but are not the same and do not have the 2 same distributional properties. Specifically, the errors εij are assumed to be NID(0,σ ), but the residualsε ˆij are not even independently distributed of each other. They are in fact linearly dependent, since they satisfy the identitiesε ˆi. =0orei. =0,i =1,...,a. Furthermore, under our assumptions, the residuals are not normally distributed. Nevertheless, the residuals do have approximately the same joint distribution as the errors, and we shall use them as proxies for the errors to investigate the validity of the assumptions about the error distribution. -
Principal Component Analysis and Optimization: a Tutorial Robert Reris Virginia Commonwealth University, [email protected]
CORE Metadata, citation and similar papers at core.ac.uk Provided by VCU Scholars Compass Virginia Commonwealth University VCU Scholars Compass Statistical Sciences and Operations Research Dept. of Statistical Sciences and Operations Publications Research 2015 Principal Component Analysis and Optimization: A Tutorial Robert Reris Virginia Commonwealth University, [email protected] J. Paul Brooks Virginia Commonwealth University, [email protected] Follow this and additional works at: http://scholarscompass.vcu.edu/ssor_pubs Part of the Statistics and Probability Commons Creative Commons Attribution 3.0 Unported (CC BY 3.0) Recommended Citation Principal component analysis (PCA) is one of the most widely used multivariate techniques in statistics. It is commonly used to reduce the dimensionality of data in order to examine its underlying structure and the covariance/correlation structure of a set of variables. While singular value decomposition provides a simple means for identification of the principal components (PCs) for classical PCA, solutions achieved in this manner may not possess certain desirable properties including robustness, smoothness, and sparsity. In this paper, we present several optimization problems related to PCA by considering various geometric perspectives. New techniques for PCA can be developed by altering the optimization problems to which principal component loadings are the optimal solutions. This Conference Proceeding is brought to you for free and open access by the Dept. of Statistical Sciences and Operations Research at VCU Scholars Compass. It has been accepted for inclusion in Statistical Sciences and Operations Research Publications by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. http://dx.doi.org/10.1287/ics.2015.0016 Creative Commons License Computing Society 14th INFORMS Computing Society Conference This work is licensed under a Richmond, Virginia, January 11{13, 2015 Creative Commons Attribution 3.0 License pp.