AP Statistics Chapter 15 Notes. Inference for a Regression Line Goal: To determine if there is a relationship between two quantitative variables. Goal:

Slides:



Advertisements
Similar presentations
Inference for Regression: Inference About the Model Section 14.1.
Advertisements

Chapter 12 Inference for Linear Regression
Inference for Linear Regression (C27 BVD). * If we believe two variables may have a linear relationship, we may find a linear regression line to model.
BA 275 Quantitative Business Methods
Chapter 27 Inferences for Regression This is just for one sample We want to talk about the relation between waist size and %body fat for the complete population.
Inference for Regression
Objectives (BPS chapter 24)
Statistics. Overview 1. Confidence interval for the mean 2. Comparing means of 2 sampled populations (or treatments): t-test 3. Determining the strength.
Welcome to class today! Chapter 12 summary sheet Jimmy Fallon video
Quantitative Business Analysis for Decision Making Simple Linear Regression.
Chapter 12 Section 1 Inference for Linear Regression.
Simple Linear Regression Analysis
Correlation & Regression
Active Learning Lecture Slides
Introduction to Linear Regression and Correlation Analysis
Regression Analysis Regression analysis is a statistical technique that is very useful for exploring the relationships between two or more variables (one.
STA291 Statistical Methods Lecture 27. Inference for Regression.
BPS - 3rd Ed. Chapter 211 Inference for Regression.
AP Statistics Section 15 A. The Regression Model When a scatterplot shows a linear relationship between a quantitative explanatory variable x and a quantitative.
A.P. STATISTICS LESSON 14 – 2 ( DAY 2) PREDICTIONS AND CONDITIONS.
+ Chapter 12: Inference for Regression Inference for Linear Regression.
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.
Chapter 14 Inference for Regression AP Statistics 14.1 – Inference about the Model 14.2 – Predictions and Conditions.
Lesson Inference for Regression. Knowledge Objectives Identify the conditions necessary to do inference for regression. Explain what is meant by.
AP Statistics Chapter 27 Notes “Inference for Regression”
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.
+ Chapter 12: More About Regression Section 12.1 Inference for Linear Regression.
Lecture 10 Chapter 23. Inference for regression. Objectives (PSLS Chapter 23) Inference for regression (NHST Regression Inference Award)[B level award]
AP STATISTICS LESSON 14 – 1 ( DAY 1 ) INFERENCE ABOUT THE MODEL.
28. Multiple regression The Practice of Statistics in the Life Sciences Second Edition.
Chapter 12 Inference for Linear Regression. Reminder of Linear Regression First thing you should do is examine your data… First thing you should do is.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 12 More About Regression 12.1 Inference for.
Dependent (response) Variable Independent (control) Variable Random Error XY x1x1 y1y1 x2x2 y2y2 …… xnxn ynyn Raw data: Assumption:  i ‘s are independent.
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 + 
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 12 Analyzing the Association Between Quantitative Variables: Regression Analysis Section.
AP Statistics Section 15 A. The Regression Model When a scatterplot shows a linear relationship between a quantitative explanatory variable x and a quantitative.
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.
The Practice of Statistics Third Edition Chapter 15: Inference for Regression Copyright © 2008 by W. H. Freeman & Company.
Chapter 12 Inference for Linear Regression. Reminder of Linear Regression First thing you should do is examine your data… First thing you should do is.
BPS - 5th Ed. Chapter 231 Inference for Regression.
Chapter 12: Correlation and Linear Regression 1.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 12 More About Regression 12.1 Inference for.
Regression Inference. Height Weight How much would an adult male weigh if he were 5 feet tall? He could weigh varying amounts (in other words, there is.
Introductory Statistics. Inference for Bivariate Data Intro to Inference in Regression Requirements for Linear Regression Linear Relationship Constant.
Chapter 15 Inference for Regression. How is this similar to what we have done in the past few chapters?  We have been using statistics to estimate parameters.
CHAPTER 12 More About Regression
CHAPTER 12 More About Regression
Inference for Regression (Chapter 14) A.P. Stats Review Topic #3
Regression.
AP Statistics Chapter 14 Section 1.
Inferences for Regression
Inference for Regression
CHAPTER 12 More About Regression
Hypothesis Tests: One Sample
Inference for Regression Lines
CHAPTER 29: Multiple Regression*
Chapter 12 Inference on the Least-squares Regression Line; ANOVA
Chapter 12 Regression.
Linear Regression.
Chapter 14 Inference for Regression
Regression Chapter 8.
Simple Linear Regression
Basic Practice of Statistics - 3rd Edition Inference for Regression
CHAPTER 12 More About Regression
Regression.
Chapter 14 Inference for Regression
Inference for Regression Slope
CHAPTER 12 More About Regression
Inference for Regression
Presentation transcript:

AP Statistics Chapter 15 Notes

Inference for a Regression Line Goal: To determine if there is a relationship between two quantitative variables. Goal: To determine if there is a relationship between two quantitative variables. i.e. What is the slope of the regression line? i.e. What is the slope of the regression line? From sample…a and b are statistics From sample…a and b are statistics True regression line…α and β are parameters. True regression line…α and β are parameters.

Conditions 1. Observations are independent. 1. Observations are independent. 2. True relationship is linear. 2. True relationship is linear. 3. σ is constant. 3. σ is constant. Check residual plot…make sure there is no pattern and the magnitude of the residuals is stable. Check residual plot…make sure there is no pattern and the magnitude of the residuals is stable. 4. Response varies Normally about the true regression line. 4. Response varies Normally about the true regression line. Make histogram or stemplot of residuals. Make histogram or stemplot of residuals.

Confidence Interval (estimate β) b ± t* SE b (df = n – 2) b ± t* SE b (df = n – 2) Significance Test Significance Test H o : β = 0t = b/SE b H o : β = 0t = b/SE b H a : β ≠ > < 0