Statistical Applications for the Behavioral and Social
Module Five Activity: Regression Quiz
On this page, you’ll demonstrate your fluency with statistical analysis using linear
5 Linear Correlation and Regression / Page 5.23 Activity: Module Five Activity:
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Now that you’ve learned about linear regression, it’s time to practice some of the critical
interpretation skills that you need in order to successfully complete a statistical analysis
that uses linear regression. In this activity, you’ll practice analyzing data using
regression as well as interpreting the output you obtain from such analyses. This will
help prepare you for upcoming projects.
Download the data set for this activity below.
Module Five Data Set (XLSX) Southern New Hampshire University
The Module Five Data Set spreadsheet contains 100 randomly selected individuals from
the 2016 cohort of the General Social Survey. The values represent how many hours
each individual reported working in the past week and their level of depression as
measured by a composite of five items from the Center for Epidemiological Studies
Depression Scale (CES-D). The items ask how much time the individual experienced the
following over the past week:
1. Feeling depressed
2. Getting restless sleep
3. Feeling happy
4. Feeling lonely
5. Feeling sad
Item three (feeling happy) is reverse coded so that higher scores indicate more time
experiencing symptoms of depression in the past week. The following scale is used:
0 = None or almost none of the time
1 = Some of the time
2 = Most of the time
3 = All or almost all of the time
Thus, a minimum score is 0 and a maximum score is 15. Use a α = .05 to determine
whether there is a significant linear relationship between hours worked and symptoms
of depression in the past week.
You will use your Analysis ToolPak to complete this activity. Start by copying and
pasting the data provided into Excel.
For help performing your linear regression using Excel and the Analysis ToolPak, refer
to this Regression Tips and Tricks document.
Based on your analysis, would you reject or retain the null hypothesis that hours worked is not
related to depressive symptoms in the last week?
What is the average number of hours worked per week in the sample?
What is the average CES-D composite score?
Indicate which values accurately complete the regression equation in the form Y = a + bX.
What does Y represent in the above equation?
Which item in the equation tells you about the relationship between hours worked last week and
symptoms of depression?
Y = 2.52 + (0.16)X
Y = 4.33 + (−0.03)X
Y = −0.03 + 4.33X
Y = 6.08 + (−1.16)X
CES-D composite score
number of hours worked last week
What is the nature of the relationship between hours worked last week and depressive
symptoms based on the slope alone?
Which value represents the coefficient of determination (shared variance between CES-D by
number of hours worked last week)?
What is the standard error of the estimate (SEE)?
Working more hours last week predicts greater depressive symptoms.
There is a positive relationship between depressive symptoms and number of hours
worked last week.
Working more hours last week predicts less depressive symptoms.
More depressive symptoms predict working fewer hours in the past week.
Is there a significant linear relationship between hours worked and the CES-D composite score?
What value did you use to make this determination?
Which statement below summarizes the result from the t test for the slope in APA Style?
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p < .001 p = 2.31E-.08 p = .11 t = 6.08 t(98) = 6.08, p < .001, r 2 = .16 t(99) = −1.61, p = .110, r 2 = .03 t(98) = −1.61, p = .110, r 2 = .03 t(98) = 6.08, p = .110, r 2 = .03