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Obs vendor earnings age hours 1 21 2841 29 12 2 53 1876 21 8 3 60 2934 62 10 4 1

ID: 3132068 • Letter: O

Question

Obs

vendor

earnings

age

hours

1

21

2841

29

12

2

53

1876

21

8

3

60

2934

62

10

4

184

1552

18

10

5

263

3065

40

11

6

281

3670

50

11

7

354

2005

65

5

8

401

3215

44

8

9

515

1930

17

8

10

633

2010

70

6

11

677

3111

20

9

12

710

2882

29

9

13

800

1683

15

5

14

914

1817

14

7

15

997

4066

33

12

Earnings of Mexican street vendors. Detailed interviews were conducted with over 1,000 street vendors in the city of Puebla, Mexico, in order to study the factors influencing vendors’ incomes (World Development, February 1998). Vendors were defined as individuals working in the street, and included vendors with carts and stands on wheels and excluded beggars, drug dealers, and prostitutes. The researchers collected data on gender, age, hours worked per day, annual earnings, and education level. A subset of these data (STREETVEN) appears in the accompanying table.

A) Give the least squares prediction equation.

  

Obs

vendor

earnings

age

hours

1

21

2841

29

12

2

53

1876

21

8

3

60

2934

62

10

4

184

1552

18

10

5

263

3065

40

11

6

281

3670

50

11

7

354

2005

65

5

8

401

3215

44

8

9

515

1930

17

8

10

633

2010

70

6

11

677

3111

20

9

12

710

2882

29

9

13

800

1683

15

5

14

914

1817

14

7

15

997

4066

33

12

Explanation / Answer

Earnings of Mexican street vendors. Detailed interviews were conducted with over 1,000 street vendors in the city of Puebla, Mexico, in order to study the factors influencing vendors’ incomes (World Development, February 1998). Vendors were defined as individuals working in the street, and included vendors with carts and stands on wheels and excluded beggars, drug dealers, and prostitutes. The researchers collected data on gender, age, hours worked per day, annual earnings, and education level. A subset of these data (STREETVEN) appears in the accompanying table.

A) Give the least squares prediction equation.

Earnings = -20.352+13.3504*age+243.7145*hours

Regression Analysis

0.582

Adjusted R²

0.513

n

15

R

0.763

k

2

Std. Error

547.737

Dep. Var.

earnings

ANOVA table

Source

SS

df

MS

F

p-value

Regression

5,018,231.5433

2  

2,509,115.7717

8.36

.0053

Residual

3,600,196.1900

12  

300,016.3492

Total

8,618,427.7333

14  

Regression output

confidence interval

variables

coefficients

std. error

t (df=12)

p-value

95% lower

95% upper

Intercept

-20.3520

652.7453

-0.031

.9756

-1,442.5619

1,401.8579

age

13.3504

7.6717

1.740

.1074

-3.3647

30.0656

hours

243.7145

63.5117

3.837

.0024

105.3343

382.0947

Regression Analysis

0.582

Adjusted R²

0.513

n

15

R

0.763

k

2

Std. Error

547.737

Dep. Var.

earnings

ANOVA table

Source

SS

df

MS

F

p-value

Regression

5,018,231.5433

2  

2,509,115.7717

8.36

.0053

Residual

3,600,196.1900

12  

300,016.3492

Total

8,618,427.7333

14  

Regression output

confidence interval

variables

coefficients

std. error

t (df=12)

p-value

95% lower

95% upper

Intercept

-20.3520

652.7453

-0.031

.9756

-1,442.5619

1,401.8579

age

13.3504

7.6717

1.740

.1074

-3.3647

30.0656

hours

243.7145

63.5117

3.837

.0024

105.3343

382.0947