Section 11.2 Day 4.

Slides:



Advertisements
Similar presentations
LINEAR REGRESSION T-PROCEDURES JUST A QUICK OVERVIEW.
Advertisements

© 2010 Pearson Prentice Hall. All rights reserved Least Squares Regression Models.
Stat 112 – Notes 3 Homework 1 is due at the beginning of class next Thursday.
8-5 Testing a Claim About a Standard Deviation or Variance This section introduces methods for testing a claim made about a population standard deviation.
Chapter 12 Section 1 Inference for Linear Regression.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 12 Analyzing the Association Between Quantitative Variables: Regression Analysis Section.
Formulas: Hypothesis test: We would like to know if there is . The data on six-year graduation rate (%), student-related expenditure per full-time.
Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Inference on the Least-Squares Regression Model and Multiple Regression 14.
Inference for Regression Find your notes from last week, Put # of beers in L1 and BAC in L2, then find LinReg(ax+b)
Lecture 14 Multiple Regression Model
Regression with Inference Notes: Page 231. Height Weight Suppose you took many samples of the same size from this population & calculated the LSRL for.
AP Statistics Chapter 27 Notes “Inference for Regression”
Inference for Regression Chapter 14. Linear Regression We can use least squares regression to estimate the linear relationship between two quantitative.
12.1 WS Solutions. (b) The y-intercept says that if there no time spent at the table, we would predict the average number of calories consumed to be
1 Chapter 10: Introduction to Inference. 2 Inference Inference is the statistical process by which we use information collected from a sample to infer.
S-012 Testing statistical hypotheses The CI approach The NHST approach.
Slide Slide 1 Section 8-4 Testing a Claim About a Mean:  Known.
© 2000 Prentice-Hall, Inc. Chap Chapter 10 Multiple Regression Models Business Statistics A First Course (2nd Edition)
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.
Chapter 13 Lesson 13.2a Simple Linear Regression and Correlation: Inferential Methods 13.2: Inferences About the Slope of the Population Regression Line.
Sections 9.1 – 9.3.
Chapter 14: More About Regression
Significance Test for a Mean
23. Inference for regression
Section 11.1 Day 3.
Significance Test for a Mean
Section 9.4 Day 3.
Section 11.2 Day 3.
Chapter 14 Inference on the Least-Squares Regression Model and Multiple Regression.
5-essentials
Inference for Regression (Chapter 14) A.P. Stats Review Topic #3
Regression.
Chapter 24 Comparing Means.
Significance Test for the Difference of Two Proportions
Section 9.5 Day 3.
AP Statistics Chapter 14 Section 1.
CHAPTER 10 Comparing Two Populations or Groups
Two-Sample Inference Procedures with Means
Inferential Statistics Inferences from Two Samples
Regression Inferential Methods
Section 8.4 Day 2.
Section 11.2 Day 5.
Section 11.1 Day 2.
Inferences for Regression
Inference for Regression
Section 8.2 Day 4.
The Practice of Statistics in the Life Sciences Fourth Edition
Regression Inference.
Stat 217 – Day 28 Review Stat 217.
Section 11.2 Day 2.
Unit 3 – Linear regression
Section 9.5 Day 2.
CHAPTER 10 Comparing Two Populations or Groups
WARM – UP.
Pemeriksaan Sisa dan Data Berpengaruh Pertemuan 17
Regression.
Homework: pg. 693 #4,6 and pg. 698#11,12 4.) A. µ=the mean gas mileage for Larry’s car on the highway Ho: µ=26 mpg Ha: µ>26 mpg B.
CHAPTER 10 Comparing Two Populations or Groups
Correlation A measure of the strength of the linear association between two numerical variables.
Making Inferences about Slopes
Inferences for Regression
CHAPTER 10 Comparing Two Populations or Groups
CHAPTER 10 Comparing Two Populations or Groups
Homework: pg. 709 #27, ) µ=the mean dissolved oxygen content in the stream. Ho:µ=5 Ha:µ
CHAPTER 10 Comparing Two Populations or Groups
Significance Test for a Mean
CHAPTER 10 Comparing Two Populations or Groups
Section 8.2 Day 2.
Inferences from Matched Pairs
Chapter 8 Inferences from Two Samples
Presentation transcript:

Section 11.2 Day 4

Tomorrow: Quiz 11.1 – 11.2 -- May use all your notes -- May use your book -- Must work in a group of at least 4 -- all must be engaged in working & discussing the problems

Wednesday – 11.1 – 11.2 Homework Quiz Thursday – Extra credit Fathom lab due Test 11.1 – 11.2 -- both sides of 1 note card

Friday – not free time to waste - work on semester final review packet

Page 769, E14

Page 769, E14 Name:

Page 769, E14 Name: Two-sided significance test for a slope

Conditions

Page 769, E14 Randomness:

Page 769, E14 Randomness: The months were not randomly selected. These are the available monthly records in Boston for a gas-heated single-family residence with no air conditioning.

Page 769, E14 Linearity:

Page 769, E14 Linearity: The scatterplot shows a fairly symmetric linear trend.

Page 769, E14 Uniform residuals:

Page 769, E14 Uniform residuals: The residual plot shows that the variation in mean electricity usage tends to get smaller as the mean temperature increases.

Page 769, E14 Normality:

Page 769, E14 Normality: The boxplot of the residuals is fairly symmetric so the residuals look as if the residuals reasonably could have come from a normally distributed population although there is an outlier.

Page 769, E14 Based on the conditions, what should we do?

Page 769, E14 Based on the conditions, what should we do? Continue with the test, but be cautious in drawing a conclusion. Include caveat about not being a random sample.

Hypotheses

Hypotheses Ho: β1 = 0, where β1 is the slope of the true linear relationship between mean monthly temperature and mean monthly electricity usage.

Hypotheses Ho: β1 = 0, where β1 is the slope of the true linear relationship between mean monthly temperature and mean monthly electricity usage. Ha: β1 ≠ 0

Computations

Page 769, E14 t = ± 2.708 P-value = 0.0144

Conclusion

Page 769, E14 I reject the null hypothesis because the P-value of 0.0144 is less than the significance level of 0.05.

Page 769, E14 I reject the null hypothesis because the P-value of 0.0144 is less than the Significance level of 0.05. If this were a random sample, there would be sufficient evidence to support the claim that the slope of the true linear relationship between mean monthly temperature and mean monthly electricity usage is not 0.

Page 769, E14 b) Strong evidence of a linear relationship or evidence of a strong linear relationship? Why?

Page 769, E14 b) Strong evidence of a linear relationship or evidence of a strong linear relationship? Why?

Page 769, E14 b) Strong evidence of a linear relationship or evidence of a strong linear relationship? Why? This is strong evidence of a linear relationship, but because r is not very close to -1 or 1 (it is 0.538), the relationship is not strong.

Page 769, E15

Page 769, E15

Page 769, E15 Do we use this printout for predicting the: (i) temperature from chirp rate or (ii) chirp rate from temperature?

Page 769, E15 Do we use this printout for predicting the: (i) temperature from chirp rate or (ii) chirp rate from temperature?

Construct a 95% CI for the slope of the true regression line.

Page 769, E15

Page 769, E15 A 95% confidence interval is 1.9925 to 4.5897. Interpretation: I’m 95% confident that the slope of the true regression line for predicting the temperature from the chirp rate is in the interval 1.9925 to 4.5897

Page 769, E15 ii. 95% CI for predicting chirp rate from temperature

Page 769, E15

Page 769, E15 A 95% confidence interval is 0.12831 to 0.29553. I’m 95% confident that the slope of the true regression line for predicting chirp rate from temperature is in the interval 0.12831 to 0.29553.

Page 769, E15

Questions?