Variables Fil Filter variables here Name Label sex Sex age Age mrd Marital Status educ Education of Individual cworker Class of Worker region Region race race/ethnicity earnings height weight 1. Complete the following Stata operation with data Earnings_and_Height.dta: 1. Describe the variable earnings at height less than or equal to 67 inches and at height greater than 67 inches. Draw the frequency distribution of the two cases. 2. Calculate the difference of earnings in the above two cases, save as a scalar difference. 3. Use the scatter command to draw with earnings as the Y axis, height as the X axis, and "income" as the Y axis name, "height" as the X axis name. 4. Using hours as the dependent variable and height as the independent variable, we estimated the wages of 67, 70, and 65 inches, respectively. Properties < > Label Type Format Value label Notes Data E 5. The variable height height in centimeters is generated and a monadic linear regression is performed with earnings as the dependent variable and height cm as the independent variable. The heightcm variable was excluded at the end of the regression. 6. Only female workers were used for univariate linear regression with earnings as dependent variable and height as independent variable. 7. Repeat question 6, using only male workers. Filter variables here Name Label age Age mrd educ Marital Status Education of Individual cworker Class of Worker region Region race race/ethnicity earnings height weight Occupation Properties < > Label Type Format Value label Notes X Data Frame ▷ Filename default Earnings and Height.dta Label Notes Variables Observations Size 120 Memory Sorted by 11 17.870 453.73K 64M
Variables Fil Filter variables here Name Label sex Sex age Age mrd Marital Status educ Education of Individual cworker Class of Worker region Region race race/ethnicity earnings height weight 1. Complete the following Stata operation with data Earnings_and_Height.dta: 1. Describe the variable earnings at height less than or equal to 67 inches and at height greater than 67 inches. Draw the frequency distribution of the two cases. 2. Calculate the difference of earnings in the above two cases, save as a scalar difference. 3. Use the scatter command to draw with earnings as the Y axis, height as the X axis, and "income" as the Y axis name, "height" as the X axis name. 4. Using hours as the dependent variable and height as the independent variable, we estimated the wages of 67, 70, and 65 inches, respectively. Properties < > Label Type Format Value label Notes Data E 5. The variable height height in centimeters is generated and a monadic linear regression is performed with earnings as the dependent variable and height cm as the independent variable. The heightcm variable was excluded at the end of the regression. 6. Only female workers were used for univariate linear regression with earnings as dependent variable and height as independent variable. 7. Repeat question 6, using only male workers. Filter variables here Name Label age Age mrd educ Marital Status Education of Individual cworker Class of Worker region Region race race/ethnicity earnings height weight Occupation Properties < > Label Type Format Value label Notes X Data Frame ▷ Filename default Earnings and Height.dta Label Notes Variables Observations Size 120 Memory Sorted by 11 17.870 453.73K 64M
Chapter1: Taking Risks And Making Profits Within The Dynamic Business Environment
Section: Chapter Questions
Problem 1CE
Related questions
Question
Transcribed Image Text:Variables
Fil
Filter variables here
Name
Label
sex
Sex
age
Age
mrd
Marital Status
educ
Education of Individual
cworker
Class of Worker
region
Region
race
race/ethnicity
earnings
height
weight
1. Complete the following Stata operation with data Earnings_and_Height.dta:
1. Describe the variable earnings at height less than or equal to 67 inches and at height
greater than 67 inches. Draw the frequency distribution of the two cases.
2. Calculate the difference of earnings in the above two cases, save as a scalar difference.
3. Use the scatter command to draw with earnings as the Y axis, height as the X axis,
and "income" as the Y axis name, "height" as the X axis name.
4. Using hours as the dependent variable and height as the independent
variable, we estimated the wages of 67, 70, and 65 inches, respectively.
Properties
< >
Label
Type
Format
Value label
Notes
Data
E
5. The variable height height in centimeters is generated and a monadic linear regression is
performed with earnings as the dependent variable and height cm as the independent variable.
The heightcm variable was excluded at the end of the regression.
6. Only female workers were used for univariate linear regression with earnings as dependent variable and height as independent variable.
7. Repeat question 6, using only male workers.
Transcribed Image Text:Filter variables here
Name
Label
age
Age
mrd
educ
Marital Status
Education of Individual
cworker
Class of Worker
region
Region
race
race/ethnicity
earnings
height
weight
Occupation
Properties
< >
Label
Type
Format
Value label
Notes
X
Data
Frame
▷ Filename
default
Earnings and Height.dta
Label
Notes
Variables
Observations
Size
120
Memory
Sorted by
11
17.870
453.73K
64M
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