Statistics 350 Lecture 23. Today Today: Exam next day Good Chapter 7 questions: 7.1, 7.2, 7.3, 7.28, 7.29.

Slides:



Advertisements
Similar presentations
Kin 304 Regression Linear Regression Least Sum of Squares
Advertisements

CHAPTER 3: TWO VARIABLE REGRESSION MODEL: THE PROBLEM OF ESTIMATION
Simple Linear Regression
LINEAR REGRESSION: Evaluating Regression Models. Overview Standard Error of the Estimate Goodness of Fit Coefficient of Determination Regression Coefficients.
Regression with a Binary Dependent Variable
Lecture 23: Tues., Dec. 2 Today: Thursday:
Stat 200b. Chapter 8. Linear regression models.. n by 1, n by 2, 2 by 1, n by 1.
Statistics 350 Lecture 15. Today Last Day: More matrix results and Chapter 5 Today: Start Chapter 6.
1 Econometrics 1 Lecture 1 Classical Linear Regression Analysis.
Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Lecture 23: Tues., April 6 Interpretation of regression coefficients (handout) Inference for multiple regression.
Correlation-Regression The correlation coefficient measures how well one can predict X from Y or Y from X.
Chapter 4 Multiple Regression. 4.1 Introduction.
Stat 112: Lecture 8 Notes Homework 2: Due on Thursday Assessing Quality of Prediction (Chapter 3.5.3) Comparing Two Regression Models (Chapter 4.4) Prediction.
Statistics 350 Lecture 11. Today Last Day: Start Chapter 3 Today: Section 3.8 Mid-Term Friday…..Sections ; ; (READ)
BCOR 1020 Business Statistics
Statistics 350 Lecture 17. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Lecture 5: Simple Linear Regression
Forecasting Outside the Range of the Explanatory Variable: Chapter
1 Chapter 17: Introduction to Regression. 2 Introduction to Linear Regression The Pearson correlation measures the degree to which a set of data points.
Simple Linear Regression NFL Point Spreads – 2007.
Multiple Linear Regression Response Variable: Y Explanatory Variables: X 1,...,X k Model (Extension of Simple Regression): E(Y) =  +  1 X 1 +  +  k.
Stats for Engineers Lecture 9. Summary From Last Time Confidence Intervals for the mean t-tables Q Student t-distribution.
Managerial Economics Demand Estimation. Scatter Diagram Regression Analysis.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 15 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.
Multiple Regression I KNNL – Chapter 6. Models with Multiple Predictors Most Practical Problems have more than one potential predictor variable Goal is.
Slide 8- 1 Copyright © 2010 Pearson Education, Inc. Active Learning Lecture Slides For use with Classroom Response Systems Business Statistics First Edition.
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
Simple Linear Regression. The term linear regression implies that  Y|x is linearly related to x by the population regression equation  Y|x =  +  x.
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
Sequential sums of squares … or … extra sums of squares.
Chapter 6 (cont.) Difference Estimation. Recall the Regression Estimation Procedure 2.
Chapter 10: Determining How Costs Behave 1 Horngren 13e.
Lesson 14 - R Chapter 14 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.
Engineering Analysis ENG 3420 Fall 2009 Dan C. Marinescu Office: HEC 439 B Office hours: Tu-Th 11:00-12:00.
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
Feature Selection and Extraction Michael J. Watts
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 + 
Correlation and Regression Chapter 9. § 9.2 Linear Regression.
Statistics 350 Lecture 19. Today Last Day: R 2 and Start Chapter 7 Today: Partial sums of squares and related tests.
Statistics 350 Lecture 13. Today Last Day: Some Chapter 4 and start Chapter 5 Today: Some matrix results Mid-Term Friday…..Sections ; ;
Statistics 350 Lecture 2. Today Last Day: Section Today: Section 1.6 Homework #1: Chapter 1 Problems (page 33-38): 2, 5, 6, 7, 22, 26, 33, 34,
Statistics 350 Review. Today Today: Review Simple Linear Regression Simple linear regression model: Y i =  for i=1,2,…,n Distribution of errors.
4.2 – Linear Regression and the Coefficient of Determination Sometimes we will need an exact equation for the line of best fit. Vocabulary Least-Squares.
Section Copyright © 2015, 2011, 2008 Pearson Education, Inc. Lecture Slides Essentials of Statistics 5 th Edition and the Triola Statistics Series.
REGRESSION G&W p
Reasoning in Psychology Using Statistics
Micro Economics in a Global Economy
Regression Diagnostics
Ch12.1 Simple Linear Regression
G Lecture 10b Example: Recognition Memory
S519: Evaluation of Information Systems
Multiple Regression II
Chapter 8 – Linear Regression
Chapter 12 Inference on the Least-squares Regression Line; ANOVA
Managerial Economics in a Global Economy
Lecture Slides Elementary Statistics Twelfth Edition
Linear Regression.
Linear Regression.
Multiple Regression II
Prediction of new observations
7.4 – The Method of Least-Squares
Section 2: Linear Regression.
Nonlinear Fitting.
Lecture Slides Elementary Statistics Twelfth Edition
Statistics 350 Lecture 18.
Descriptive Statistics Univariate Data
Cases. Simple Regression Linear Multiple Regression.
Business Statistics - QBM117
Presentation transcript:

Statistics 350 Lecture 23

Today Today: Exam next day Good Chapter 7 questions: 7.1, 7.2, 7.3, 7.28, 7.29

Some stuff from office hours Deriving the least squares estimator for  Useful result: Why useful?

Some stuff from office hours Mistakes in book:

Some stuff from office hours Test for lack of fit: Full model and reduced model terminology?

Some stuff from office hours How to estimate variance of mean response for multiple linear regression model? Prediction Variance?

Some stuff from office hours Sums of squares…what will I have available?

Some stuff from office hours How do I compute the extra sums of squares?

Examples from Chapter a Show that SSR(X 1, X 2, X 3, X 4 )=SSR(X 2, X 3 ) + SSR(X 1 | X 2, X 3 ) + SSR(X 4 | X 1, X 2, X 3 )

Examples from Chapter b For the multiple linear regression model with 5 variables, what is the relevant extra sum of squares for testing whether or not  5 =0 ?