Academic Integrity: tutoring, explanations, and feedback — we don’t complete graded work or submit on a student’s behalf.

The variables are per capita cigarette consumption in 1930 (the independent vari

ID: 3042459 • Letter: T

Question

The variables are per capita cigarette consumption in 1930 (the independent variable, “X”) and the death rate from lung cancer in 1950 (the dependent variable, “Y”). The cancer rates are shown for a later time period because it takes time for lung cancer to develop and be diagnosed.

Observation #

Country

Cigarettes consumed
per capita in 1930 (X)

Lung cancer deaths per
million people in 1950 (Y)

1. The standard deviations of X and Y.

2. The correlation coefficient, r, between X and Y.

3. b1, the OLS estimated slope coefficient from the regression Yi=0+1Xi+ui.Yi=0+1Xi+ui.

4. b0, the OLS estimated intercept term from the same regression.

5. Y i,i=i,...,nY^i,i=i,...,n, the predicted values for each country from the regression.

6. u iu^i, the OLS residual for each country.

7. The R2.

8. The SER.

Observation #

Country

Cigarettes consumed
per capita in 1930 (X)

Lung cancer deaths per
million people in 1950 (Y)

1 Switzerland 530 250 2 Finland 1115 350 3 Great Britain 1145 465 4 Canada 510 150 5 Denmark 380 165

Explanation / Answer

Answer:

1. The standard deviations of X and Y.

Descriptive statistics

x

y

n

5

5

mean

736.00

276.00

sample standard deviation

364.41

132.35

sample variance

132,792.50

17,517.50

minimum

380

150

maximum

1145

465

range

765

315

2. The correlation coefficient, r, between X and Y.
r=0.926

3. b1, the OLS estimated slope coefficient from the regression Yi=0+1Xi+ui.Yi=0+1Xi+ui.
b1=0.3364

4. b0, the OLS estimated intercept term from the same regression.
bo=28.3966

5. Y i,i=i,...,nY^i,i=i,...,n, the predicted values for each country from the regression.

Observation

y

Predicted

1

250.0

206.7

2

350.0

403.5

3

465.0

413.6

4

150.0

200.0

5

165.0

156.2

6. u iu^i, the OLS residual for each country.

Observation

y

Predicted

Residual

1

250.0

206.7

43.3

2

350.0

403.5

-53.5

3

465.0

413.6

51.4

4

150.0

200.0

-50.0

5

165.0

156.2

8.8

7. The R2.
R2 = 0.858

8. The SER.

SER=57.602

Regression Analysis

0.858

n

5

r

0.926

k

1

Std. Error

57.602

Dep. Var.

y

ANOVA table

Source

SS

df

MS

F

p-value

Regression

60,116.1644

1  

60,116.1644

18.12

.0238

Residual

9,953.8356

3  

3,317.9452

Total

70,070.0000

4  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=3)

p-value

95% lower

95% upper

Intercept

28.3966

63.6183

0.446

.6856

-174.0652

230.8583

x

0.3364

0.0790

4.257

.0238

0.0849

0.5879

Observation

y

Predicted

Residual

1

250.0

206.7

43.3

2

350.0

403.5

-53.5

3

465.0

413.6

51.4

4

150.0

200.0

-50.0

5

165.0

156.2

8.8

Descriptive statistics

x

y

n

5

5

mean

736.00

276.00

sample standard deviation

364.41

132.35

sample variance

132,792.50

17,517.50

minimum

380

150

maximum

1145

465

range

765

315