Can the likelihood to choose HP again (q6) be explained by respondents’ perceptions of HP’s quick delivery (q8_3)?
Run a simple linear regression in SPSS and paste the output (4 tables below):
Variables Entered/Removed^{a} 

Model 
Variables Entered 
Variables Removed 
Method 
1 
q8_3^{b} 
. 
Enter 
a. Dependent Variable: q6 

b. All requested variables entered. 
Model Summary 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
1 
.303^{a} 
.092 
.089 
.54315 
a. Predictors: (Constant), q8_3 
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
10.425 
1 
10.425 
35.336 
.000^{b} 
Residual 
103.254 
350 
.295 

Total 
113.679 
351 

a. Dependent Variable: q6 

b. Predictors: (Constant), q8_3 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 

B 
Std. Error 
Beta 

1 
(Constant) 
2.030 
.112 
18.193 
.000 

q8_3 
.093 
.016 
.303 
5.944 
.000 

a. Dependent Variable: q6 
Is the regression significant? Examine the ANOVA table: F = ; pvalue =
What is the Adjusted R^{2} =
Interpret the Adjusted R^{2}:
What is the coefficient for HP quick delivery (q8_3) = ; pvalue =
Write the equation for the linear regression. Y = a + bX. Replace Y with the dependent variable, X with the predictor variable, a with the intercept (or constant in SPSS), and b with the coefficient for HP quick delivery:
Interpret the relationship between the independent/predictor variable and the dependent/outcome variable.
Is the regression significant? Examine the ANOVA table:
F = 35.336 ; pvalue = 0.000
What is the Adjusted R^2 = 0.089
It means 8.9% of variation in y is explained by x aftrer adjusting for number of independent variables
What is the coefficient for HP quick delivery (q8_3) = 0.093 ; pvalue = 0.00
Write the equation for the linear regression. Y = a + bX. Replace Y with the dependent variable, X with the predictor variable, a with the intercept (or constant in SPSS), and b with the coefficient for HP quick delivery:
y^ = 2.030  0.093 HP Quick delivery
Interpret the relationship between the independent/predictor variable and the dependent/outcome variable.
there is negative relationship between variables
the relation is significant as pvalue < alpha
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