BA 240 Yamasaki Solutions to Practice 5

Slides:

Advertisements

Similar presentations

Coefficient of Determination- R²

Advertisements

TESTING THE STRENGTH OF THE MULTIPLE REGRESSION MODEL.

Test of (µ 1 – µ 2 ),  1 =  2, Populations Normal Test Statistic and df = n 1 + n 2 – 2 2– )1– 2 ( 2 1 )1– 1 ( 2 where ] 2 – 1 [–

Topic 12: Multiple Linear Regression

Chapter 12 Simple Linear Regression

1 Simple Linear Regression and Correlation The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES Assessing the model –T-tests –R-square.

Simple Linear Regression and Correlation (Part II) By Asst. Prof. Dr. Min Aung.

Chapter 12 Simple Linear Regression

Lesson #32 Simple Linear Regression. Regression is used to model and/or predict a variable; called the dependent variable, Y; based on one or more independent.

Korelasi dalam Regresi Linear Sederhana Pertemuan 03 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.

Business 205. Review Exam Least Square Regression Simple Linear Regression.

MAT 254 – Probability and Statistics Sections 1,2 & Spring.

Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.

Announcements: Homework 10: –Due next Thursday (4/25) –Assignment will be on the web by tomorrow night.

EQT 272 PROBABILITY AND STATISTICS

Ms. Khatijahhusna Abd Rani School of Electrical System Engineering Sem II 2014/2015.

No Intercept Regression and Analysis of Variance.

Econ 3790: Business and Economics Statistics

AGENDA I.Homework 3 II.Parameter Estimates Equations III.Coefficient of Determination (R 2 ) Formula IV.Overall Model Test (F Test for Regression)

Econ 3790: Business and Economics Statistics Instructor: Yogesh Uppal

1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 15 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.

Time Series Analysis – Chapter 2 Simple Regression Essentially, all models are wrong, but some are useful. - George Box Empirical Model-Building and Response.

1 1 Slide Simple Linear Regression Coefficient of Determination Chapter 14 BA 303 – Spring 2011.

Further Topics in Regression Analysis Objectives: By the end of this section, I will be able to… 1) Explain prediction error, calculate SSE, and.

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

Chap 12-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition Chapter 12 Introduction to Linear.

Applied Quantitative Analysis and Practices LECTURE#22 By Dr. Osman Sadiq Paracha.

Chapter 13 Multiple Regression

Regression Analysis Relationship with one independent variable.

Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 13-1 Introduction to Regression Analysis Regression analysis is used.

Economics 173 Business Statistics Lecture 19 Fall, 2001© Professor J. Petry

Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.

1 Simple Linear Regression and Correlation Least Squares Method The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES.

Chapter 12 Simple Linear Regression n Simple Linear Regression Model n Least Squares Method n Coefficient of Determination n Model Assumptions n Testing.

1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 

Free Powerpoint Templates ROHANA BINTI ABDUL HAMID INSTITUT E FOR ENGINEERING MATHEMATICS (IMK) UNIVERSITI MALAYSIA PERLIS.

Lecture 10 Introduction to Linear Regression and Correlation Analysis.

1 1 Slide © 2011 Cengage Learning Assumptions About the Error Term  1. The error  is a random variable with mean of zero. 2. The variance of , denoted.

AP STATISTICS LESSON 3 – 3 (DAY 2) The role of r 2 in regression.

© 2001 Prentice-Hall, Inc.Chap 13-1 BA 201 Lecture 19 Measure of Variation in the Simple Linear Regression Model (Data)Data.

REGRESSION AND CORRELATION SIMPLE LINEAR REGRESSION 10.2 SCATTER DIAGRAM 10.3 GRAPHICAL METHOD FOR DETERMINING REGRESSION 10.4 LEAST SQUARE METHOD.

Chapter 12 Simple Regression Statistika.  Analisis regresi adalah analisis hubungan linear antar 2 variabel random yang mempunyai hub linear,  Variabel.

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

INTRODUCTION TO MULTIPLE REGRESSION MULTIPLE REGRESSION MODEL 11.2 MULTIPLE COEFFICIENT OF DETERMINATION 11.3 MODEL ASSUMPTIONS 11.4 TEST OF SIGNIFICANCE.

Multiple Regression.

Chapter 13 Simple Linear Regression

Lecture 11: Simple Linear Regression

Simple Linear Regression & Correlation

Decomposition of Sum of Squares

DSCI 346 Yamasaki Practice for Final Exam

REGRESSION (CONTINUED)

REGRESSION (CONTINUED)

Ch12.1 Simple Linear Regression

Simple Linear Regression

Statistics for Business and Economics (13e)

Regression model with multiple predictors

Relationship with one independent variable

Quantitative Methods Simple Regression.

Linear Regression.

Multiple Regression.

Prepared by Lee Revere and John Large

Simple Linear Regression

AP STATISTICS LESSON 3 – 3 (DAY 2)

Review of Chapter 2 Some Basic Concepts: Sample center

Relationship with one independent variable

Business Statistics, 4e by Ken Black

Simple Linear Regression

Multiple Linear Regression

Presentation transcript:

BA 240 Yamasaki Solutions to Practice 5

DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages) Coefficient of determination DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages) f. Partition Sum of Squares SST = Sy2 – (Sy)2/n = Syy SSE = Sy2 – b0Sy – b1Sxy Regression (ANOVA table) Source SS df MS F Regression SSR 1 MSR MSR/MSE Error SSE N-2 MSE Total SST N-1 SSE = 480 - ( 57.913)*(50) –(-.8114)*(2986) = 7.1904 SST = Syy = 122.8571 Regression (ANOVA table) Source SS df MS F Regression 115.6631 1 115.6631 80.43 Error 7.1904 5 1.4381 Total 122.8571 6 DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages) Simple Linear Regression Model: To estimate regression line, use least squares method. DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages) Example: A real estate agent would like to predict selling price of a single family home based on size of house. She takes a random sample of 15 recently sold homes. House size(100sq ft) Selling Price($1000) 20 89.5 14.8 79.9 20.5 83.1 12.5 56.9 18.0 66.6 14.3 82.5 27.5 126.3 16.5 79.3 24.3 119.9 20.2 87.9 22.0 112.6 19.0 120.8 12.3 78.5 14.0 74.3 16.7 74.8 DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages) Preliminary calculations: Sx = 272.6 Sx2 = 5222.24 Sxy = 25257.97 Sy = 1332.6 Sy2 = 124618.42 DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages)

DCSI Solutions to Practice 5 ( 14 pages)