Question 1

Following is a portion of the Excel output for a regression analysis relating maintenance expense (dollars per month) to usage (hours per week) for a particular brand of computer terminal.

ANOVA

df SS

Regression 1 1575.76

Residual8 349.14

Total 9 1924.90

Coefficients Standard Error

Intercept 6.1092 .9361

Usage 0.8951 .149

The estimated regression equation is

	a.	y = 6.1092 + 0.8951 x
	b.	y = 6.1092 - 0.8951x
	c.	y = .9391 + .149 x
	d.	y = 0.8951 + 6.1092 x

Question 2

Using a t-test to determine whether monthly maintenance expense is related to usage at the .05 level of significance.

What is the value of t-test calculated?

	a.	36.11
	b.	6.00
	c.	6.53
	d.	.8951

Question 3

Based on the value of the t-test and on this regression results, is monthly maintenance expense related to usage?

	a.	There is no evidence to suggest that usage and monthly maintenance are related.
	b.	Yes, usage is significant to explain the changes on monthly maintenance
	c.	No, usage does not help explain changes on monthly maintenance
	d.	Yes, monthly maintenance is significant to explain the changes on usage

Question 4

What is the F calculated?

	a.	6.00
	b.	5.32
	c.	36.11
	d.	2.365

Question 5

Is the model significant?

	a.	Yes, the model as a whole is significant, we reject the null hypothesis
	b.	Yes, the model as a whole is significant, we fail to reject the null
	c.	No, the model is not significant, there is not evidence to suggest that the model is explaining the changes in y
	d.	Not enough information is provided to make conclusions about the model as a whole

Question 6

What is the value of R-squared?

	a.	0.82
	b.	0.89
	c.	0.05
	d.	0.18

Question 7

Did the estimated regression equation provide a good fit?

	a.	No, it does not provide good fit because the size of the sample is smaller than 30
	b.	There is no evidence to make conclusions about the fitness of the model
	c.	Yes, it provides a good fit because both coefficients are statistically significant
	d.	Yes, it provides a good fit as 82% of the variation in y is explained by the model