# Lab Activity 11 – Simple Linear Regression

August 30, 2017

Question
Lab Activity 11 – Simple Linear Regression

1. (5 points)

a.What is the strongest the correlation can ever be?

b.If there is no relationship,r is equal to .

c.The correlation coefficient ranges from .

d.If the points fall in an almost perfect, negative linear pattern,r is close to:

e.If the points fall in an almost perfect, positive linear pattern,r is close to:

2. (6 points)

Relationship between Height and Weight.

Data has been collected on 219 STAT 200 students. Weight is measured in pound and Height in inch. Below are some descriptive statistics of Weight and Height.

a.Write the regression equation based on the output.

b.What is the response variable (dependent variable) and what is the predictor (independent variable)?

c.Based on the equation, what is the slope? Please explain slope as the change inY per unit change inX in the context of the variables used in this problem.

d.Based on the output, what is the test of the slope for this regression equation? That is, provide the null and alternative hypotheses, the test statistic,p-value of the test, and state your decision and conclusion.

e.Assume a student is 65 inch tall. Is it possible to predict his weight based on this analysis? If so, please estimate his weight using the regression equation.

f.What do the Fitted (predicted) values and Residuals represent? For example, there is one record in the data set with height = 54 and weight = 110. Please use these numbers to explain what is the fitted value and what is the residual.

3. (11 points) Parts B, D are worth 2 points. Parts C, E are worth 3 points (2 plots and a conclusion for each)

For a statistics class project, students examined the relationship betweenx = 8th grade IQ andy = 9th grade math scores for 20 students. The data are displayed below.

Student

Math Score

IQ

Abstract Reas

1

33

95

28

2

31

100

24

3

35

100

29

4

38

102

30

5

41

103

33

6

37

105

32

7

37

106

34

8

39

106

36

9

43

106

38

10

40

109

39

11

41

110

40

12

44

110

43

13

40

111

41

14

45

112

42

15

48

112

46

16

45

114

44

17

31

114

41

18

47

115

47

19

43

117

42

20

48

118

49

a.Create a scatter plot of the measurements by selectingMath Score for they-axis (response) andIQ for thex-axis (predictor). Describe the relationship betweenmath score andIQ.

b.Perform a linear regression with the Response (dependent variable)math score and the variableIQ as the Predictor (independent variable). Store/Save the (unstandardized) Residuals and Fitted(Predicted) values. These will be stored in the fourth and fifth columns of the data worksheet.

What is the regression equation?

What is the interpretation ofR-square (just use the latest output) and how to calculate correlation based on it?

c.One of the students with a high IQ (number 17) appears to be an outlier. With a sample size of only 20 this can affect our normality assumption. Also, the constant variance assumption could be compromised. We can visually check for constant variance using aResidual Plotand test for normality using aProbability Plot(or Q-Q plot).

Based on these two graphs and what you have learned about hypothesis testing, what interpretations do you come to regarding the assumptions of constant variance and normality?

d.Although outliers should never be deleted without a reason, there are several reasons why it may be legitimate to conduct an analysis without them. Delete the data point for row 17 (click on the cell with the IQ of 114, enter * and then click on any other cell – this “enters” the asterisk in that previous cell. ) and re-calculate the regression line for the remainder of the data.

What is the regression equation with the rest of the data?

What is theR2 and correlation between Math Score and IQ with the outlier removed?

e.How does the fit of the regression line of the original data (i.e. with outlier) compare (visually and statistically) to the fit of the regression line to the data with the outlier removed? Compare the fit of the regression line between the two sets of data. Pay particular attention to the differences inR2, the slope and how the line fits each set of data. Repeat theresidual plot and probability plot!

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