Last time: One-way Analysis of Variance. Example List of 50 spoken words 3 x 10 Subjects (split among I=3 groups) Group 1: (Fast sound) Person in movie.

Slides:



Advertisements
Similar presentations
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 [–
Advertisements

Chapter 12 Simple Linear Regression
Chap 12-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 12 Simple Regression Statistics for Business and Economics 6.
Forecasting Using the Simple Linear Regression Model and Correlation
BA 275 Quantitative Business Methods
BPS - 5th Ed. Chapter 241 One-Way Analysis of Variance: Comparing Several Means.
Regression Analysis Module 3. Regression Regression is the attempt to explain the variation in a dependent variable using the variation in independent.
Simple Linear Regression. G. Baker, Department of Statistics University of South Carolina; Slide 2 Relationship Between Two Quantitative Variables If.
Simple Linear Regression
Chapter 12 Simple Linear Regression
1 1 Slide © 2014 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Classical Regression III
© 2010 Pearson Prentice Hall. All rights reserved Least Squares Regression Models.
© 2010 Pearson Prentice Hall. All rights reserved Single Factor ANOVA.
Chapter 10 Simple Regression.
Chapter 12 Simple Regression
1 Pertemuan 13 Uji Koefisien Korelasi dan Regresi Matakuliah: A0392 – Statistik Ekonomi Tahun: 2006.
SIMPLE LINEAR REGRESSION
Pengujian Parameter Koefisien Korelasi Pertemuan 04 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Chapter Topics Types of Regression Models
Chapter 11 Multiple Regression.
Simple Linear Regression Analysis
Linear Regression Example Data
Korelasi dalam Regresi Linear Sederhana Pertemuan 03 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
© 2000 Prentice-Hall, Inc. Chap Forecasting Using the Simple Linear Regression Model and Correlation.
BCOR 1020 Business Statistics
Simple Linear Regression and Correlation
Chapter 7 Forecasting with Simple Regression
Introduction to Regression Analysis, Chapter 13,
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS & Updated by SPIROS VELIANITIS.
Introduction to Linear Regression and Correlation Analysis
Chapter 13: Inference in Regression
Hypothesis Testing in Linear Regression Analysis
One-Factor Experiments Andy Wang CIS 5930 Computer Systems Performance Analysis.
Simple Linear Regression Models
Inferences in Regression and Correlation Analysis Ayona Chatterjee Spring 2008 Math 4803/5803.
1 1 Slide © 2005 Thomson/South-Western Slides Prepared by JOHN S. LOUCKS St. Edward’s University Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.
Statistics for Business and Economics 7 th Edition Chapter 11 Simple Regression Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Ch.
© 2003 Prentice-Hall, Inc.Chap 13-1 Basic Business Statistics (9 th Edition) Chapter 13 Simple Linear Regression.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 15 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.
1 1 Slide Simple Linear Regression Coefficient of Determination Chapter 14 BA 303 – Spring 2011.
1 1 Slide © 2014 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
EQT 373 Chapter 3 Simple Linear Regression. EQT 373 Learning Objectives In this chapter, you learn: How to use regression analysis to predict the value.
1 Chapter 12 Simple Linear Regression. 2 Chapter Outline  Simple Linear Regression Model  Least Squares Method  Coefficient of Determination  Model.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.
© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.
Chapter 13 Multiple Regression
STA 286 week 131 Inference for the Regression Coefficient Recall, b 0 and b 1 are the estimates of the slope β 1 and intercept β 0 of population regression.
Lecture 10: Correlation and Regression Model.
Multiple Regression. Simple Regression in detail Y i = β o + β 1 x i + ε i Where Y => Dependent variable X => Independent variable β o => Model parameter.
Applied Quantitative Analysis and Practices LECTURE#25 By Dr. Osman Sadiq Paracha.
Statistics for Managers Using Microsoft® Excel 5th Edition
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 + 
Formula for Linear Regression y = bx + a Y variable plotted on vertical axis. X variable plotted on horizontal axis. Slope or the change in y for every.
INTRODUCTION TO MULTIPLE REGRESSION MULTIPLE REGRESSION MODEL 11.2 MULTIPLE COEFFICIENT OF DETERMINATION 11.3 MODEL ASSUMPTIONS 11.4 TEST OF SIGNIFICANCE.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Bivariate Regression. Bivariate Regression analyzes the relationship between two variables. Bivariate Regression analyzes the relationship between two.
Statistics for Managers using Microsoft Excel 3rd Edition
Statistics for Business and Economics (13e)
Quantitative Methods Simple Regression.
PENGOLAHAN DAN PENYAJIAN
Review of Chapter 2 Some Basic Concepts: Sample center
SIMPLE LINEAR REGRESSION
Simple Linear Regression
Introduction to Regression
St. Edward’s University
Presentation transcript:

Last time: One-way Analysis of Variance

Example List of 50 spoken words 3 x 10 Subjects (split among I=3 groups) Group 1: (Fast sound) Person in movie reads list, but sounds precede lip movement slightly Group 2: (Slow sound) Person in movie reads list, but sounds lag behind lip movement slightly Group 3: (Synchrony) Person in movie reads list with auditory and visual stimuli in synchrony Memory Task: Subjects are asked to recall as many items as possible.

One-way Analysis of Variance Model Assumptions: I many Independent Groups Data … … Popu lation Sample Size … …

One-way Analysis of Variance

Similar recipe as in Linear Regression! Sum Squares Total (SST) Sum Squares Error (SSE) Sum Squares Groups (SSG) Degrees of Freedom DFT = N-1 Degrees of Freedom DFG = I-1 Degrees of Freedom DFE=N-I = + MSG

Let’s grind it out for our example… MSG Large MSG leads to significant F statistic. Reject Null Hypothesis! Conclusion: The population means are not identical across groups

What if I=2? Remember: The Square of a t Random Variable with n-2 degrees of freedom is an F Random Variable with 1 degree of freedom in the numerator and with n-2 degrees of freedom in the denominator. Thus, the one-way analysis of variance is a natural extension of the comparison of two means from independent samples (with equal population variances).

Robustness If the samples sizes are equal, then the assumption of equal variance (equal standard deviation) is not crucial. CLT helps with violations of normality, i.e. as long as sample sizes are large, we do not need normality of the X variables.

Today: Wrap up “Loose Ends” An Illustrating Example on Simple Regression Typo Correction One last quiz…

etc.

(Rent per square foot) (Square-footage)

Is there significant evidence for a linear relationship? Test using the correlation Test using the slope Test using the ANOVA table

Sample correlation R n n-2 t-stat

Sample correlation R t-stat

Sample correlation R t-stat The correlation is significant at 5% significance level. Yes, significant evidence for a linear relationship.

Observed t-statistics for * * p-value = 95% CIs

Observed t-statistic for * * p-value <.001 Yes, significant evidence for linear relationship 95% CI

p-value <.001 Yes, significant evidence for linear relationship

What is the best fitting regression equation?

“I bet the population intercept is more then 900” This would mean that you pay a fixed minimum flat amount of $900, plus whatever rent you need to pay based on square footage.

I bet, for every additional 10 Square Feet, you have to pay more than an extra $4 Rent! That would mean more than $.4 extra rent per extra square foot. That would mean the slope is >.4.

Significant at 2% significance level. Yes, significant evidence that we pay over $4 extra per 10sqft extra.

For every additional 1,000 Square Feet, how much extra Rent do you have to pay? Give a 95% Confidence Interval

This is our 95% CI for the extra Rent per extra Square Foot. Thus: 95% CI for extra Rent per 1,000 Square Feet: [$407, $496]

What is our best guess at the standard deviation of the Error Term? What percentage of the variance are we able to explain with this model?

SSR = SST-SSE

Prediction Region

Slide Typo Correction: 2x2 Contingency Tables

Special Case: 2x2 Tables This typo occurred in several slides due to cut and pasting.

Last (and special) Quiz Counts as 5 Bonus Points in Grand Total Regression