Question

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/Removeda
Model	Variables Entered	Variables Removed	Method
1	q8_3b	.	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	.303a	.092	.089	.54315
a. Predictors: (Constant), q8_3

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	10.425	1	10.425	35.336	.000b
	Residual	103.254	350	.295
	Total	113.679	351
a. Dependent Variable: q6
b. Predictors: (Constant), q8_3

Coefficientsa
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 =           ; p-value =

What is the Adjusted R­­­­² =

Interpret the Adjusted R­­­­²:

What is the coefficient for HP quick delivery (q8_3) =          ; p-value =

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.

Answer 1

Is the regression significant? Examine the ANOVA table:

F = 35.336      ; p-value = 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         ; p-value = 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 p-value < alpha