Question

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.299a	.089	.088	11.80775
a. Predictors: (Constant), FIRSTT, LASTT, INCOME, AVGGIFT

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	31353.012	4	7838.253	56.219	.000b
	Residual	319139.342	2289	139.423
	Total	350492.354	2293
a. Dependent Variable: TARGET_D
b. Predictors: (Constant), FIRSTT, LASTT, INCOME, AVGGIFT

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	.165	1.351		.122	.903
	INCOME	.541	.134	.081	4.040	.000
	AVGGIFT	.413	.030	.289	13.832	.000
	LASTT	-.004	.002	-.042	-2.080	.038
	FIRSTT	.001	.000	.110	5.307	.000
a. Dependent Variable: TARGET_D

Q1: Interpret the model’s R-square. Q2: Interpret the four slope coefficients. Q3: Are the four variables significant predictors of TARGET_D?

Answer 1

Solution:

Here Rsquare = 0.089

Which tells that total variation explained by this model is 8.9% in Target_D

Solution(b)

Slope for coefficient income is 0.541 which means as we increase income by 1 unit than target_d will increase by 0.541 units

Slope for coefficient AVGGIFT is 0.413, which means as we increase AVGGift by one unit than target_D will increase by 0.413 units.

Slope for coefficient Lastt is -0.004, which means as we increase lastt by 1 unit than target_d will decrease by 0.004 units

Slope for coefficient FIRSTT is 0.001, which means as we increase FIRSTT by one unit than target_d will increase by 0.01 unit.

Solution(c)

Here we can see that all four variables are significant predictors of TARGET_D, as all predictors p-value is less than 0.05.