Problem A) Simple Linear Regression

1. Use each variable (state anxiety, trait anxiety, and curiosity) to predict depression individually. Note: you should run three different regressions.
2. Report the results of each regression in paragraphstyle and write each regression line.

Model Fit Measures
	Overall Model Test
Model	R	R	F	df1	df2	p
1		0.520		0.271		36.3		1		98		.001

Model Coefficients - Depression
Predictor	Estimate	SE	t	p	Stand. Estimate
Intercept		12.866		1.4941		8.61		.001
State_Anxiety		0.239		0.0397		6.03		.001		0.520

Model Fit Measures
	Overall Model Test
Model	R	R	F	df1	df2	p
1		0.498		0.248		32.2		1		98		.001

Model Coefficients - Depression
Predictor	Estimate	SE	t	p	Stand. Estimate
Intercept		11.685		1.7807		6.56		.001
Trait_Anxiety		0.254		0.0447		5.68		.001		0.498

Model Fit Measures
			Overall Model Test
Model	R	R	F		df1		df2		p
1	0.143	0.0204	2.04		1		98		0.156

Model Coefficients - Depression
Predictor		Estimate		SE		t		p		Stand. Estimate
Intercept		27.135		4.005		6.77		.001
Curiosity		-0.202		0.142		-1.43		0.156		-0.143

Problem B) Multiple Linear Regression

1. Conduct a multiple regression using all five variables together (state anxiety, trait anxiety, happiness, anger, curiosity) to predict depression scores.
2. Report the results in a paragraph style and write the regression line.
3. Finally, using multiple regression, make a regression table as was demonstrated in class.

Model Fit Measures
						Overall Model Test
Model		R		R		F		df1		df2		p
1		0.665		0.442		14.9		5		94		.001

Model Coefficients - Depression
Predictor		Estimate		SE		t		p	Stand. Estimate
Intercept		7.4307		4.3722		1.700		0.093
State_Anxiety		0.1254		0.0558		2.246		0.027	0.2728
Trait_Anxiety		0.0703		0.0646		1.089		0.279	0.1377
Happiness		0.0956		0.0427		2.242		0.027	0.2271
Anger		0.2131		0.0841		2.533		0.013	0.2400
Curiosity		-0.0208		0.1208		-0.172		0.863	-0.0147

Problem C) Moderation

1. Test if the relationship between hours worked per week predicting Current GPA is moderated by Interdependent motives.
2. Report the results in paragraphstyle using appropriate tables and figures as needed.

Moderation Estimates
			95% Confidence Interval
	Estimate	SE	Lower	Upper	Z	p
HOURS_WORK	0.04119	0.00151	0.03822	0.04415	27.22	.001
Interdependent_Motives	0.06239	0.01095	0.04094	0.08385	5.70	.001
HOURS_WORK ? Interdependent_Motives	-0.00332	0.00105	-0.00538	-0.00127	-3.17	0.002

Simple Slope Estimates
				95% Confidence Interval
		Estimate	SE	Lower	Upper	Z	p
Average		0.0412	0.00154	0.0382	0.0442	26.8	.001
Low (-1SD)		0.0460	0.00207	0.0420	0.0501	22.3	.001
High (+1SD)		0.0363	0.00228	0.0319	0.0408	16.0	.001
Note.shows the effect of the predictor (HOURS_WORK) on the dependent variable (CurrentGPA) at different levels of the moderator (Interdependent_Motives)

Simple Slope Plot name CurrentGPA Average Low (-1SD) High (+1SD) -1 -10 0 10 20 HOURS WORK\f