Solutions for Tutorial 3

Slides:

Advertisements

Similar presentations

Qualitative predictor variables

Advertisements

Topic 12: Multiple Linear Regression

Multicollinearity.

More on understanding variance inflation factors (VIFk)

1 Outliers and Influential Observations KNN Ch. 10 (pp )

Chicago Insurance Redlining Example Were insurance companies in Chicago denying insurance in neighborhoods based on race?

1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Simple Linear Regression Estimates for single and mean responses.

1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Summarizing Bivariate Data Introduction to Linear Regression.

Pris mot antal rum. Regression Analysis: Price versus Rooms The regression equation is Price = Rooms Predictor Coef SE Coef T P Constant.

1 Chapter 17: Introduction to Regression. 2 Introduction to Linear Regression The Pearson correlation measures the degree to which a set of data points.

A (second-order) multiple regression model with interaction terms.

Chapter 12: Linear Regression 1. Introduction Regression analysis and Analysis of variance are the two most widely used statistical procedures. Regression.

Completing the ANOVA From the Summary Statistics.

Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 12-1 Correlation and Regression.

An alternative approach to testing for a linear association The Analysis of Variance (ANOVA) Table.

Copyright ©2011 Nelson Education Limited Linear Regression and Correlation CHAPTER 12.

Solutions to Tutorial 5 Problems Source Sum of Squares df Mean Square F-test Regression Residual Total ANOVA Table Variable.

Sequential sums of squares … or … extra sums of squares.

Inference for regression - More details about simple linear regression IPS chapter 10.2 © 2006 W.H. Freeman and Company.

1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Summarizing Bivariate Data Non-linear Regression Example.

Lack of Fit (LOF) Test A formal F test for checking whether a specific type of regression function adequately fits the data.

Multiple regression. Example: Brain and body size predictive of intelligence? Sample of n = 38 college students Response (Y): intelligence based on the.

Statistics and Numerical Method Part I: Statistics Week VI: Empirical Model 1/2555 สมศักดิ์ ศิวดำรงพงศ์ 1.

Inference for regression - More details about simple linear regression IPS chapter 10.2 © 2006 W.H. Freeman and Company.

Regression through the origin

732G21/732G28/732A35 Lecture 4. Variance-covariance matrix for the regression coefficients 2.

Multicollinearity. Multicollinearity (or intercorrelation) exists when at least some of the predictor variables are correlated among themselves. In observational.

Interaction regression models. What is an additive model? A regression model with p-1 predictor variables contains additive effects if the response function.

Agenda 1.Exam 2 Review 2.Regression a.Prediction b.Polynomial Regression.

732G21/732G28/732A35 Lecture 6. Example second-order model with one predictor 2 Electricity consumption (Y)Home size (X)

Analysis of variance approach to regression analysis … an (alternative) approach to testing for a linear association.

David Housman for Math 323 Probability and Statistics Class 05 Ion Sensitive Electrodes.

Scientific Practice Non-linear and Multiple

Design and Analysis of Experiments

General Full Factorial Design

Chapter 20 Linear and Multiple Regression

Introduction to Regression Lecture 6.2

Least Square Regression

Chapter 13 Created by Bethany Stubbe and Stephan Kogitz.

Least Square Regression

Simple Linear Regression

Chapter 13 Simple Linear Regression

Lecture 12 More Examples for SLR More Examples for MLR 9/19/2018

9/19/2018 ST3131, Lecture 6.

Business Statistics, 4e by Ken Black

...Relax... 9/21/2018 ST3131, Lecture 3 ST5213 Semester II, 2000/2001

Non-linear and Multiple Regression

Lecture 18 Outline: 1. Role of Variables in a Regression Equation

Cases of F-test Problems with Examples

Properties of the LS Estimates Inference for Individual Coefficients

Inference for Regression Lines

Solutions of Tutorial 10 SSE df RMS Cp Radjsq SSE1 F Xs c).

Solutions to Tutorial 6 Problems

Lecture 5 732G21/732G28/732A35 Detta är en generell mall för att göra PowerPoint presentationer enligt LiUs grafiska profil. Du skriver in din.

24/02/11 Tutorial 3 Inferential Statistics, Statistical Modelling & Survey Methods (BS2506) Pairach Piboonrungroj (Champ)

Width vs. Area for Sample Squares

Scientific Practice Regression.

Solution 9 1. a) From the matrix plot, 1) The assumption about linearity seems ok; 2).The assumption about measurement errors can not be checked at this.

End of Life Cell Voltage Peak Current Daily Battery Consumption

Multiple Regression Chapter 14.

Business Statistics, 4e by Ken Black

Interpretation of Regression Coefficients

Is there a relationship between the age of an employee and the number of sick days they take each year? Find the following: Predictor Coef SE Coef.

Lecture 20 Last Lecture: Effect of adding or deleting a variable

Solutions of Tutorial 9 SSE df RMS Cp Radjsq SSE1 F Xs c).

Chapter Fourteen McGraw-Hill/Irwin

Chapter 11 Variable Selection Procedures

Essentials of Statistics for Business and Economics (8e)

Business Statistics, 4e by Ken Black

Presentation transcript:

Solutions for Tutorial 3 1. (a). Results for: P027.txt Correlations: Minutes, Units Pearson correlation of Minutes and Units = 0.994, P-Value = 0.000 Correlations: Minutes, FITS1 Pearson correlation of Minutes and FITS1 = 0.994, P-Value = 0.000 Analysis of Variance Source DF SS MS F P Regression 1 27420 27420 943.20 0.000 Residual Error 12 349 29 Total 13 27768 So (b) SST=27768, (c ) SSE=349. 11/21/2018 ST3131, Solution 3

Pearson correlation of Y1 and X1 = 0.816, P-Value = 0.002 2. Predictor Coef SE Coef T P Constant 3.000 1.125 2.67 0.026 X1 0.5001 0.1179 4.24 0.002 S = 1.237 R-Sq = 66.7% R-Sq(adj) = 62.9% Predictor Coef SE Coef T P Constant 3.001 1.125 2.67 0.026 X2 0.5000 0.1180 4.24 0.002 S = 1.237 R-Sq = 66.6% R-Sq(adj) = 62.9% Predictor Coef SE Coef T P Constant 3.002 1.124 2.67 0.026 X3 0.4997 0.1179 4.24 0.002 S = 1.236 R-Sq = 66.6% R-Sq(adj) = 62.9% Predictor Coef SE Coef T P Constant 3.002 1.124 2.67 0.026 X4 0.4999 0.1178 4.24 0.002 S = 1.236 R-Sq = 66.7% R-Sq(adj) = 63.0% 11/21/2018 ST3131, Solution 3

4. Results for: P027.txt Regression Analysis: Minutes versus Units The regression equation is Minutes = 4.16 + 15.5 Units Predictor Coef SE Coef T P Constant 4.162 3.355 1.24 0.239 Units 15.5088 0.5050 30.71 0.000 11/21/2018 ST3131, Solution 3