
- The following output is from a multiple
regression analysis that was run on the variables FEARDTH (fear of death) IMPORTRE (importance of religion), AVOIDDTH (avoidance of death), LAS (meaning in life), and MATRLSM (materialistic attitudes). In the regression analysis, FEARDTH is the criterion variable (Y) and IMPORTRE,AVOIDDTH, LAS, and MATRLSM are the predictors (Xs). The SPSS output is provided below, followed by a number of questions.
|
|||
|
Mean |
Std. Deviation |
N |
feardth |
27.0798 |
8.08365 |
163 |
importre |
5.8282 |
2.46104 |
163 |
avoiddth |
18.5460 |
6.97633 |
163 |
Las |
70.1288 |
9.89460 |
163 |
matrlsm |
53.5552 |
10.21860 |
163 |
Model |
Variables Entered |
Variables Removed |
Method |
1 |
matrlsm, avoiddth, importre, lasa
|
. |
Enter |
a. All requested variables entered. |
b. Dependent Variable: feardth |
Model Summary
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
1 |
.669 |
.447 |
.433 |
6.08700 |
- Predictors: (Constant), matrlsm, avoiddth, importer, las
ANOVA
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
4731.810 |
4 |
1182.952 |
31.927 |
.000a |
Residual |
5854.153 |
158 |
37.052 |
|
|
|
Total |
10585.963 |
162 |
|
|
|
a. Predictors: (Constant), matrlsm, avoiddth, importre, las |
b. Dependent Variable: feardth |
Coefficients:
Model |
Unstandardized Coefficients |
Standardized Coefficients |
T |
Sig. |
||
B |
Std. Error |
Beta |
||||
1 |
(Constant) |
27.738 |
4.979 |
|
5.571 |
.000 |
importre |
-.167 |
.199 |
-.051 |
-.838 |
.403 |
|
avoiddth |
.697 |
.070 |
.601 |
10.004 |
.000 |
|
Las |
-.213 |
.050 |
-.261 |
-4.247 |
.000 |
|
matrlsm |
.044 |
.049 |
.055 |
.885 |
.378 |
a. Dependent Variable: feardth
1. Is R2 significant? Report the appropriate statistical criteria (including the R2, F, df, and p-value) to support your answer
Which predictor(s), if any, are significant? Which predictor(s), if any, are not significant? Be sure to (1) indicate whether each predictor is significant or not and (2) report the corresponding t values and p-values for each of the predictors (whether significant or not) below.
Write the final equation for the regression model.

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