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SSS 590-Online Week 7 - Paired Graded Short Assignment#4 - 2015 Now, please focus on the concept of “regression” for predicting relationship between variables

Statistics Jan 19, 2021

SSS 590-Online Week 7 - Paired Graded Short Assignment#4 - 2015

Now, please focus on the concept of “regression” for predicting relationship between variables. You may want to review understanding MRA tables provided and discussed in the slides!

 

Consider that a researcher wanted to investigate whether variables of age, gender, grades during senior year in high school, and engaging in extra-curricular activity would significantly predict GPA of the first year college students.  Please pay attention to the information in the table and under the table as well!

 

Examine the table below and answer questions below the table:

 

Table X.         Prediction of GPA by Age, Gender, Grade, and Engagement in                                               Extracurricular Activities of Randomly Selected College Students (N = 218)

 

 

 

Unstandardized Coefficients

Standardized Coefficients

 

 

 

Zero-ordera

r

B

St. Error (SE)

 

Beta

t

Sig.

p-value

(Constant)

 

0.353

.11

 

.345

.544

Age

.11

0.012

.13

.121

.435

.322

Gender††

.45*

0.495

.43

.315

3.95

.031

Grade in HS

.75**

1.350

.22

.569

4.22

.001

Extra-Activity

.48**

0.651

.18

.355

4.01

.001

Model Adjusted R-square = 0.55

†† Gender: 1 = Female, 0 = Male

a Simple bivariate Pearson r correlation

* p < 0.05, ** p < 0.001

 

(a) State/write the null hypothesis for this study using non-directional (2-tail) approach:

 

 

 

 

 

(b) Identify the dependent variable in this study:

 

 

(c) Examining the table, which predictor variables significantly predicted the dependent variable? Identify these variables, and explain how you know: 

 

 

 

 

 

 

(d) Examine the result for gender, and explain the direction of this relationship between this variable and the dependent variable:

 

 

 

 

(e) Explain the percent of variance in the dependent variable that is accounted for by this model with the four predictor variables:

 

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