1.
Facts about correlation.
Answer the following questions about correlation (r).
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 ________ to _______.
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.
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.
Then a linear regression was performed on height and weight. The output looks as follows:
In Minitab:
In SPSS:
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 in Y per
unit change in X 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.
Relationship between eighth grade IQ and ninth grade math score.
For a statistics class project, students examined the relationship between x = 8th grade IQ and y = 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
Open the dataset IQ found in the Datasets folder in ANGEL.
a. Create a scatter plot of the measurements by selecting Math Score for the y-axis
(response) and IQ for the x-axis (predictor). Describe the relationship between math score and
IQ.
Minitab Users: Graph > Scatter Plot > Simple.
SPSS Users: Graphs > Legacy Dialogues > Scatter/Dot > Simple Scatter
b. Perform a linear regression with the Response (dependent variable) math score and the
variable IQ 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 of R-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 a
Residual Plot and test for normality using a Probability Plot (or Q-Q plot).
To get a residual plot, simply create a Scatterplot using the Residuals as the y-variable and the
Fitted(Predicted) Values as the x-variable. (Remember these should have been stored/saved
when you first performed the regression per instructions above. If not, re-run regression and
click store/save and click the boxes for unstandardized residuals and fits(predicted) values.)
Now create a probability plot (Q-Q plot if using SPSS) of the residuals.
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?
Minitab Users: Probability plot go to Graphs > Probability Plot > Single and select Residuals
SPSS Users: Q-Q plot with normal test go to Analyze > Descriptive Statistics > Explore and
enter Unstandardized Residuals in Dependent List click Plots and select box for Normal plots
with tests
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 the R2 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 in R2, the slope and how the line fits each set of data. You may want to repeat
the residual plot and probability plot!…
Previous answers to this question
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Get The Answer Lab Activity 11 – Simple Linear Regression Assignment Solution
1.
Facts about correlation.
Answer the following questions about correlation (r).
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 ________ to _______.
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.
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.
Then a linear regression was performed on height and weight. The output looks as follows:
In Minitab:
In SPSS:
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 in Y per
unit change in X 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.
Relationship between eighth grade IQ and ninth grade math score.
For a statistics class project, students examined the relationship between x = 8th grade IQ and y = 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
Open the dataset IQ found in the Datasets folder in ANGEL.
a. Create a scatter plot of the measurements by selecting Math Score for the y-axis
(response) and IQ for the x-axis (predictor). Describe the relationship between math score and
IQ.
Minitab Users: Graph > Scatter Plot > Simple.
SPSS Users: Graphs > Legacy Dialogues > Scatter/Dot > Simple Scatter
b. Perform a linear regression with the Response (dependent variable) math score and the
variable IQ 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 of R-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 a
Residual Plot and test for normality using a Probability Plot (or Q-Q plot).
To get a residual plot, simply create a Scatterplot using the Residuals as the y-variable and the
Fitted(Predicted) Values as the x-variable. (Remember these should have been stored/saved
when you first performed the regression per instructions above. If not, re-run regression and
click store/save and click the boxes for unstandardized residuals and fits(predicted) values.)
Now create a probability plot (Q-Q plot if using SPSS) of the residuals.
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?
Minitab Users: Probability plot go to Graphs > Probability Plot > Single and select Residuals
SPSS Users: Q-Q plot with normal test go to Analyze > Descriptive Statistics > Explore and
enter Unstandardized Residuals in Dependent List click Plots and select box for Normal plots
with tests
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 the R2 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 in R2, the slope and how the line fits each set of data. You may want to repeat
the residual plot and probability plot!…
Previous answers to this question
This is a preview of an assignment submitted on our website by a student. If you need help with this question or any assignment help, click on the order button below and get started. We guarantee authentic, quality, 100% plagiarism free work or your money back.
Get The Answer