GRA 6020 Multivariate Statistics The regression model Ulf H. Olsson Professor of Statistics.

Slides:



Advertisements
Similar presentations
Økonometri The regression model OLS Regression Ulf H. Olsson Professor of Statistics.
Advertisements

BA 275 Quantitative Business Methods
Simple Linear Regression. Start by exploring the data Construct a scatterplot  Does a linear relationship between variables exist?  Is the relationship.
Objectives (BPS chapter 24)
1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Simple Linear Regression Estimates for single and mean responses.
Simple Linear Regression
Psychology 202b Advanced Psychological Statistics, II February 10, 2011.
GRA 6020 Multivariate Statistics Confirmatory Factor Analysis Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics Regression examples Ulf H. Olsson Professor of Statistics.
1-1 Regression Models  Population Deterministic Regression Model Y i =  0 +  1 X i u Y i only depends on the value of X i and no other factor can affect.
CHAPTER 3 ECONOMETRICS x x x x x Chapter 2: Estimating the parameters of a linear regression model. Y i = b 1 + b 2 X i + e i Using OLS Chapter 3: Testing.
GRA 6020 Multivariate Statistics The regression model OLS Regression Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics; The Linear Probability model and The Logit Model (Probit) Ulf H. Olsson Professor of Statistics.
Measurement Models and CFA; Chi-square and RMSEA Ulf H. Olsson Professor of Statistics.
Økonometri The regression model OLS Regression Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics Ulf H. Olsson Professor of Statistics.
1 BA 275 Quantitative Business Methods Residual Analysis Multiple Linear Regression Adjusted R-squared Prediction Dummy Variables Agenda.
GRA 6020 Multivariate Statistics Ulf H. Olsson Professor of Statistics.
Økonometri The regression model OLS Regression (Ch.7) Ulf H. Olsson Professor of Statistics.
Multiple Linear Regression Introduction to Business Statistics, 5e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing.
GRA 6020 Multivariate Statistics; The Linear Probability model and The Logit Model (Probit) Ulf H. Olsson Professor of Statistics.
Met 2212 Multivariate Statistics
Measurement Error in Linear Multiple Regression Models Ulf H Olsson Professor Dep. Of Economics.
Different chi-squares Ulf H. Olsson Professor of Statistics.
Measurement Models Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics Confirmatory Factor Analysis Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics Factor Analysis Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics The regression model OLS Regression Ulf H. Olsson Professor of Statistics.
Met 2212 Multivariate Statistics Path Analysis Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics The Structural Equation Model Ulf H. Olsson Professor of Statistics.
Met 2651 Instrument variabler (sider: 539,543, 544,545,549,550,551,559,560,561,564) Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics Ulf H. Olsson Professor of Statistics.
Measurement Models and CFA Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics Confirmatory Factor Analysis Ulf H. Olsson Professor of Statistics.
Ulf H. Olsson Professor of Statistics
GRA 6020 Multivariate Statistics Confirmatory Factor Analysis Ulf H. Olsson Professor of Statistics.
Simple Linear Regression Analysis
GRA 6020 Multivariate Statistics Factor Analysis Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics Regression examples Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics; The Linear Probability model and The Logit Model (Probit) Ulf H. Olsson Professor of Statistics.
Regression, Factor Analysis and SEM Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics The regression model OLS Regression Ulf H. Olsson Professor of Statistics.
Factor Analysis Ulf H. Olsson Professor of Statistics.
GRA 6020 Multivariate Statistics Probit and Logit Models Ulf H. Olsson Professor of Statistics.
1 Confidence Intervals for Means. 2 When the sample size n< 30 case1-1. the underlying distribution is normal with known variance case1-2. the underlying.
Quiz 6 Confidence intervals z Distribution t Distribution.
Simple Linear Regression Analysis
Regression Analysis Regression analysis is a statistical technique that is very useful for exploring the relationships between two or more variables (one.
MTH 161: Introduction To Statistics
Ordinary Least Squares Estimation: A Primer Projectseminar Migration and the Labour Market, Meeting May 24, 2012 The linear regression model 1. A brief.
AP Statistics Chapter 15 Notes. Inference for a Regression Line Goal: To determine if there is a relationship between two quantitative variables. Goal:
Byron Gangnes Econ 427 lecture 3 slides. Byron Gangnes A scatterplot.
1 Lecture 4 Main Tasks Today 1. Review of Lecture 3 2. Accuracy of the LS estimators 3. Significance Tests of the Parameters 4. Confidence Interval 5.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
AP Statistics Chapter 15 Notes. Inference for a Regression Line Goal: To determine if there is a relationship between two quantitative variables. –i.e.
Normal Distribution.
Warm-up Ch.11 Inference for Linear Regression Day 2 1. Which of the following are true statements? I. By the Law of Large Numbers, the mean of a random.
Lecture 10 Chapter 23. Inference for regression. Objectives (PSLS Chapter 23) Inference for regression (NHST Regression Inference Award)[B level award]
Regression. Height Weight How much would an adult female weigh if she were 5 feet tall? She could weigh varying amounts – in other words, there is a distribution.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.
Dependent (response) Variable Independent (control) Variable Random Error XY x1x1 y1y1 x2x2 y2y2 …… xnxn ynyn Raw data: Assumption:  i ‘s are independent.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Multiple Regression Chapter 14.
Chapter 26: Inference for Slope. Height Weight How much would an adult female weigh if she were 5 feet tall? She could weigh varying amounts – in other.
Introductory Statistics. Inference for Bivariate Data Intro to Inference in Regression Requirements for Linear Regression Linear Relationship Constant.
Lecturer: Ing. Martina Hanová, PhD..  How do we evaluate a model?  How do we know if the model we are using is good?  assumptions relate to the (population)
Chapter 4. The Normality Assumption: CLassical Normal Linear Regression Model (CNLRM)
Chapter 11: Linear Regression E370, Spring From Simple Regression to Multiple Regression.
BIVARIATE REGRESSION AND CORRELATION
BA 275 Quantitative Business Methods
Linear Regression.
Presentation transcript:

GRA 6020 Multivariate Statistics The regression model Ulf H. Olsson Professor of Statistics

Ulf H. Olsson Regression Analysis

Ulf H. Olsson Regression analysis OLS Regression parameter St.error T-value P-value Confidence interval R-sq R-sq.adj F-value The error term

Ulf H. Olsson Regression Analysis The error term has constant variance The error term follows a normal distribution with expectation equal to zero The x-variables are independent of the error term The x-variables are linearly independent