Given are five observations for two variables, x and y.

y x

16 5

12 6

7 7

18 8

35 9

1 The value in the numerator of the formula to compute the slope coefficient b is:

a 44

b 42

c 40

d 38

2 The value in the denominator of the formula to compute the slope coefficient b is:

a 10

b 12

c 14

d 16

3 The value of the intercept coefficient b is:

a -12.8

b -13.2

c -13.6

d -14.0

4 The predicted value of y when x = 8 is:

a 23.1

b 22.9

c 22.5

d 22.0

5 The prediction error when x = 8 is,

a -4.0

b -4.5

c -4.9

d -5.1

6 The sum of squared errors (SSE) is,

a 250.0

b 252.8

c 255.6

d 258.4

7 The standard error of estimate, se(e), for the model is:

a 9.230

b 8.584

c 7.983

d 7.425

8 The sum of squares total (SST) is,

a 261.0

b 265.1

c 273.7

d 449.2

9 The sum of squares regression (SSR) is,

a 193.6

b 191.8

c 190.0

d 188.2

10 The fraction of variations in y explained by x is:

a 0.4072

b 0.4310

c 0.4606

d 0.4947

11 The measure of dispersion of the estimated slope coefficient (b) about the population slope parameter () is:

se(b) =

a 1.960

b 2.152

c 2.663

d 2.919

12 The test statistic for the null hypothesis H: = 0 is:

a 2.170

b 1.856

c 1.507

d 0.982

Next FOUR questions are based on the following

How much does education affect wage rate?Use the following data to develop an estimated regression equation that could be used to predict the WAGE for a given number of years of schooling.

Use Excel functions for your calculations.Don't waste time on the calculator.

WAGE EDUC

y x

19.45 15

11.5 12

15.34 17

26.21 13

24.99 12

20.6 12

54.38 16

26 12

29.72 16

16.83 13

15.24 11

43.25 16

19.42 11

14.42 14

8.08 12

58.85 22

21 12

21.25 17

22.66 12

69.44 14

10.71 12

10.18 13

11.4 11

8.58 13

15.16 17

13 The estimated regression equation predicts that the wage rate rises by ______ for each additional year of schooling.

a $2.76

b $2.99

c $3.18

d $3.72

14 The predicted wage for a person with 16 years of schooling is,

a $26.58

b $27.98

c $29.63

d $30.78

15 The sum of squared error (SSE) is,

a 4649.49

b 4552.90

c 4367.51

d 4128.61

16 The sum of squares regression (SSR) is,

a 1240.77

b 1498.40

c 1558.33

d 1679.88

17 The sum of squares total (SST) is,

a 5644.25

b 5879.43

c 6232.78

d 6369.38

18 The observed WAGE (y) deviate from the predicted WAGE (y), on average, by,

a 14.07

b 13.49

c 12.68

d 11.07

19 The fraction of variation in WAGE (y) explained by EDUC (x) is,

a 0.2240

b 0.2695

c 0.3172

d 0.3586

20 The standard error of the slope coefficient is ______.

a 0.968

b 1.092

c 1.858

d 2.090

21 The margin of error for a 95% confidence interval for the population slope parameter is:

a 2.95

b 2.63

c 2.26

d 1.98

22 To test, at a 5% level of significance, the hypothesis H: = 0 versus H: 0, the t test statistic is

|t| = ______.

a 1.982

b 2.445

c 2.913

d 3.459

Questions 23-30 are based on the computer output below relating to the following problem

Pete Zaria would like to study the relationship between pizza sales and advertising.The following is the result of a regression analysis Pete conducted for monthly sales of pizza and advertising (both in thousands of dollars)

The exercise involves filling in the values for the shaded cells bellow.

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.63699

R Square

Adjusted R Square 0.39762

Standard Error

Observations

ANOVA

df SS MS F Significance F

Regression 1

Residual 73 25.361

Total 74 3115.482

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 66.6742 1.6233 41.0721 0.0000 63.4389 69.9095

ADVERT 5.2282

23 The predicted sales when $2 (thousand) is spent on advertising is:

a 77.13

b 75.31

c 73.65

d 71.20

24 The value for SSE is,

a 1798.382117

b 1801.481372

c 1851.352719

d 1895.60482

25 The observed SALES (y) deviate from the predicted SALES (y), on average, by,

a 3.975

b 4.969

c 5.036

d 6.770

26 The fraction of the variations is SALES explained by advertising is,

a 0.479

b 0.406

c 0.369

d 0.346

27 Given (x x) = 46.2466, the value of the standard error of the slope coefficient in (6) is:

a 1.685

b 1.250

c 0.983

d 0.741

28 The value of the t Stat to test the hypothesis that advertising has no impact on sales is,

a 5.788

b 6.682

c 7.060

d 7.946

29 The critical value, at a 5 percent level of significance, for the above hypothesis is:

a 2.369

b 2.246

c 2.114

d 1.993

30 The lower and upper ends of a 95% confidence interval for the population slope parameter are:

a 3.75 6.71

b 3.59 6.87

c 3.25 7.21

d 3.18 7.27