Application Of Multiple Regression and the Confounding Variable

Coursework Instructions:

The purpose of this assignment is to apply your understanding of simple and multiple linear regression.
For this assignment, use the "Example Dataset" to complete a simple linear regression using SPSS. Select one dependent variable, one primary independent variable, and one potential confounding variable. You will need to recode string variables into numeric variables to use them in the regression analysis. Prepare a Word document that includes all SPSS outputs.
Based on the results, create a second Word document and answer the following questions in 500-750 words.
State your research question and hypotheses.
Run the regression again without the potential confounding variable and discuss the impact on the coefficient of the primary independent variable. Did it change? If so, to what extent did it change? Based on this, do you think the added variable confounds the association between the independent and dependent variables?
Export both regression output tables as part of the Word document containing the SPSS outputs.
Provide your regression equation based on the results of the full regression model.
Interpret the results by (a) stating the reason the study or test was done; (b) presenting the main results, including coefficients on the main independent variable and significance levels; (c) explaining what the results mean; and (d) making suggestions for future research.
Submit both Word documents to the instructor.
APA style is not required, but solid academic writing is expected.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

Coursework Sample Content Preview:

Application Of Multiple Regression
Name:
Course
Instructor
Date:
Research question and hypotheses
* Does smoking affect exercise levels and physical endurance, and if so, does age influence the strength of the relationship?
* The hypothesis is that smoking affects the time spent exercising
* Age is a confounding factor in the relationship.
Regression with and without the potential confounding variable
When ignoring age, the strength of relationship between being a smoker and the minutes spent exercising is 0.277, and this is a weak relationship. On the other hand, when including age in the relationship between the minutes spent exercising and being a smoker, the coefficient correlation increases slightly to 0.285. Besides the correlation coefficient, the value of the equation changes since here is more than one explanatory factor. Since the confounding factor is associated with the dependent and independent variable (s), which alters the estimates of association measures if these variables are not distributed homogeneously among the research groups.
Regression output tables of the SPSS outputs.
Variables Entered/Removeda

Model

Variables Entered

Variables Removed

Method

1

Age, Smokerrb

.

Enter

a. Dependent Variable: Minutes_Exercise

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.285a

.081

.013

39.035

a. Predictors: (Constant), Age, Smokerr

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

3632.909

2

1816.455

1.192

.319b

Residual

41141.257

27

1523.750

Total

44774.167

29

a. Dependent Variable: Minutes_Exercise

b. Predictors: (Constant), Age, Smoker

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

42.626

28.764

1.482

.150

Smoker

24.604

16.389

.312

1.501

.145

Age

-.254

.686

-.077

-.371

.714

a. Dependent Variable: Minutes_Exercise

Regression 2 with no age/ confounding factor
Variables Entered/Removeda

Model

Variables Entered

Variables Removed

Method

Updated on January 26, 2024

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Application Of Multiple Regression and the Confounding Variable

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