Question

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.884a	.782	.775	1134.08895
a. Predictors: (Constant), Tuition2000

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	1133.148	701.455		1.615	.116
	Tuition2000	1.692	.160	.884	10.551	.000
a. Dependent Variable: Tuition2008

1. What is the regression equation?
2. What is the percent of variation in BMI explained by the regression line?
3. Predict 2008 Tuition for Oregon given their 2000 tuition rate.
4. Predict 2008 Tuition if the 2000 tuition rate of a University was $10,000.

2. How far off is the predicted value from the actual 2008 tuition for Oregon (#8)? Calculate the residual.
3. Give the value of the slope and interpret it.
4. Give the value of the intercept and interpret it.
5. Will this line give us good predictions? Are there any concerns with our model? Should we trust the results?

Answer 1

Ans:

1)Regression equation:

Tuition 2008=1133.148+ 1.692*Tuition 2000

2)Coefficient of determination,R^2=0.782 or 78.2%

So, 78.2% of variation in BMI explained by the regression line

3)

Tuition 2008=1133.148+1.692*10000=18053.15

4)slope=1.692

for each unit of increase in Tuition 2000,there will be on average increase of 1.692 in Tuition2008

5)intercept=1133.148

When Tuition2000=0,on average Tuition2008 will be equal to 1133.148

6)Yes,as p-value<0.001,we reject the null hypothesis.so we can conclude that there is significant linear relationship between both variables.