Solution 8 12/4/2018 F P1 P2 RESI1 SRES1 TRES1 HI1 FITS1

Slides:



Advertisements
Similar presentations
Experimental design and analysis Multiple linear regression  Gerry Quinn & Mick Keough, 1998 Do not copy or distribute without permission of authors.
Advertisements

Lecture 24: Thurs., April 8th
Regression Diagnostics Checking Assumptions and Data.
Quantitative Business Analysis for Decision Making Simple Linear Regression.
Basics of regression analysis
Taking a Square Root to Solve an Equation. Solve: In order to solve for x, you have to UNDO the squared first (i.e. square root) What are the number(s)
Correlation & Regression
Multiple regression models Experimental design and data analysis for biologists (Quinn & Keough, 2002) Environmental sampling and analysis.
Multiple Linear Regression. Purpose To analyze the relationship between a single dependent variable and several independent variables.
Canonical Correlation Psy 524 Andrew Ainsworth. Matrices Summaries and reconfiguration.
1 Regression Analysis The contents in this chapter are from Chapters of the textbook. The cntry15.sav data will be used. The data collected 15 countries’
5.4 Line of Best Fit Given the following scatter plots, draw in your line of best fit and classify the type of relationship: Strong Positive Linear Strong.
Outliers and influential data points. No outliers?
Class 24: Question 1 Which of the following set of vectors is not an orthogonal set?
Statistical Data Analysis 2010/2011 M. de Gunst Lecture 10.
Statistical Data Analysis 2010/2011 M. de Gunst Lecture 9.
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 15-1 Chapter 15 Multiple Regression Model Building Basic Business Statistics 10 th Edition.
Slide 1 Regression Assumptions and Diagnostic Statistics The purpose of this document is to demonstrate the impact of violations of regression assumptions.
More on regression Petter Mostad More on indicator variables If an independent variable is an indicator variable, cases where it is 1 will.
Lab 4 Multiple Linear Regression. Meaning  An extension of simple linear regression  It models the mean of a response variable as a linear function.
Chapter 13 Lesson 13.2a Simple Linear Regression and Correlation: Inferential Methods 13.2: Inferences About the Slope of the Population Regression Line.
Chapter 12 REGRESSION DIAGNOSTICS AND CANONICAL CORRELATION.
Yandell – Econ 216 Chap 15-1 Chapter 15 Multiple Regression Model Building.
Predicting Energy Consumption in Buildings using Multiple Linear Regression Introduction Linear regression is used to model energy consumption in buildings.
Correlation analysis is undertaken to define the strength an direction of a linear relationship between two variables Two measurements are use to assess.
Modeling in R Sanna Härkönen.
Statistical Data Analysis - Lecture /04/03
REGRESSION (R2).
Correlation, Bivariate Regression, and Multiple Regression
AP Statistics Chapter 14 Section 1.
ENM 310 Design of Experiments and Regression Analysis
Correlation and Regression Analysis
Cautions about Correlation and Regression
Chapter 12: Regression Diagnostics
Multiple Regression.
BIVARIATE REGRESSION AND CORRELATION
…Don’t be afraid of others, because they are bigger than you
بحث في التحليل الاحصائي SPSS بعنوان :
Diagnostics and Transformation for SLR
Lecture 14 Review of Lecture 13 What we’ll talk about today?
1. An example for using graphics
Solutions of Tutorial 10 SSE df RMS Cp Radjsq SSE1 F Xs c).
Multiple Regression A curvilinear relationship between one variable and the values of two or more other independent variables. Y = intercept + (slope1.
Linear Regression.
Residuals The residuals are estimate of the error
Solutions to Tutorial 6 Problems
Multiple Linear Regression
Section 3.3 Linear Regression
Linear regression Fitting a straight line to observations.
Tutorial 8 Table 3.10 on Page 76 shows the scores in the final examination F and the scores in two preliminary examinations P1 and P2 for 22 students in.
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.
Regression is the Most Used and Most Abused Technique in Statistics
Residuals and Residual Plots
Chapter 4, Regression Diagnostics Detection of Model Violation
Solution 7 1. a). The scatter plot or the residuals vs fits plot
Three Measures of Influence
Adequacy of Linear Regression Models
Adequacy of Linear Regression Models
Regression Forecasting and Model Building
Checking Assumptions Primary Assumptions Secondary Assumptions
Solutions of Tutorial 9 SSE df RMS Cp Radjsq SSE1 F Xs c).
Chapter 13 Additional Topics in Regression Analysis
Adequacy of Linear Regression Models
Adequacy of Linear Regression Models
Problems of Tutorial 9 (Problem 4.12, Page 120) Download the “Data for Exercise ” from the class website. The data consist of 1 response variable.
Diagnostics and Transformation for SLR
Linear Algebra Lecture 30.
Correlation & Trend Lines
Solution to Problem 2.25 DS-203 Fall 2007.
Forecasting 3 Regression Analysis Ardavan Asef-Vaziri
Presentation transcript:

Solution 8 12/4/2018 F P1 P2 RESI1 SRES1 TRES1 HI1 FITS1 68 78 73 -4.64839 -1.24647 -1.26609 0.109800 72.6484 75 74 76 2.28885 0.61442 0.60407 0.111725 72.7111 85 82 79 6.36605 1.67143 1.76150 0.071444 78.6340 94 90 96 0.03474 0.00941 0.00916 0.128042 93.9653 86 87 90 -2.46803 -0.64693 -0.63672 0.068384 88.4680 90 90 92 -1.27712 -0.33888 -0.33084 0.090883 91.2771 86 83 95 -3.87486 -1.17900 -1.19199 0.308602 89.8749 68 72 69 0.96978 0.26369 0.25713 0.134232 67.0302 55 68 67 -8.73280 -2.47107 -2.91964 0.200563 63.7328 69 69 70 2.76276 0.77703 0.76862 0.190801 66.2372 91 91 89 1.25065 0.34009 0.33203 0.134383 89.7493 75 79 75 0.51920 0.13757 0.13396 0.088219 74.4808 81 89 84 -4.41249 -1.22480 -1.24218 0.169224 85.4125 91 93 97 -5.10231 -1.39159 -1.42926 0.139485 96.1023 80 87 77 0.26843 0.08266 0.08047 0.324901 79.7316 94 91 96 -0.45360 -0.12255 -0.11933 0.123109 94.4536 94 86 94 3.33216 0.91583 0.91175 0.152636 90.6678 97 91 92 5.23455 1.39977 1.43863 0.104862 91.7655 79 81 82 -1.16172 -0.30114 -0.29381 0.047421 80.1617 84 80 83 3.65458 0.95617 0.95390 0.064906 80.3454 65 70 66 0.96256 0.26818 0.26152 0.175399 64.0374 83 79 81 4.48699 1.17150 1.18381 0.060981 78.5130 12/4/2018

(b) The matrix plot shows F and P1, F and P2 have strong linearity relationship. Moreover, there is a collinearity between P1 and P2. 12/4/2018

(c) The matrix plot of the standardized residuals shows that the standardized residuals are linearly uncorrelated with P1 and P2. This is because the linearity effects of P1 and P2 have been removed. The collinearity relationship between P1 and P2 is still there since nothing has been changed between them. 12/4/2018

(d) The residual plots are as above (d) The residual plots are as above. Except one outlier, the normality assumption seems ok, linearity assumption seems ok, homogeneity may be slightly violated, independence assumption may be ok. 12/4/2018

(e) Except one outlier, the SRES1 and TRES1 are almost on the diagonal line, i.e. they are almost equal. 12/4/2018

(f) F is strongly linearly related with FITS1 (f) F is strongly linearly related with FITS1. The correlation between F and FITS1 is the multiple correlation coefficient, i.e., the squared root of R-square. 12/4/2018

(g) The index plot of the leverages shows that there are two high leverage points, whose leverages are more than 2(p+1)/n=6/22=.27. 12/4/2018