Homework Answers
y1<-salaries$SALARY
x1<-salaries$EDUC
x2<-salaries$EXPER
x3<-salaries$TIME
fit <- lm(y1 ~ x1 + x2 + x3)
summary(fit)
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Residuals:
Min 1Q Median 3Q Max
-1240.2 -421.5 0.0 349.6 1924.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3179.4798 383.4248 8.292 1.09e-12 ***
x1 139.6093 27.7125 5.038 2.45e-06 ***
x2 1.4840 0.6971 2.129 0.03601 *
x3 20.6291 6.1544 3.352 0.00118 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 602.7 on 89 degrees of freedom
Multiple R-squared: 0.302, Adjusted R-squared:
0.2785
F-statistic: 12.84 on 3 and 89 DF, p-value: 4.803e-07
A) Y1=3179.4798+139.6093X1+1.484X2+20.6291X3
B) F-statistic: 12.84 on 3 and 89 DF, p-value: 4.803e-07
SINCE PVALUE IS LESS THAN 0.05, the equation is good fit
D) Multiple R-squared: 0.302 30.2% of variation in salary is explained by regression
Add Answer to:
"SALARY","EDUC","EXPER","TIME"
3900,12,0,1
4020,10,44,7
4290,12,5,30
4380,8,6,7
4380,8,8,6
4380,12,0,7
4380,12,0,10
4380,12,5,6
4440,15,75,2
4500,8,52,3
4500,12,8,19
4620,12,52,3
4800,8,70,20
4800,12,6,23
4800,12,11,12...
please be detailed in your response :) thank you! 0 pts) You are given the following estimated equation: In(wage) 0.1279+0.0904educ + 0.041 exper-0 (0.1059) (0.0075) (0.0052) (0.00012) R 0.3003 5...
please be detailed in your response :) thank you!
0 pts) You are given the following estimated equation: In(wage) 0.1279+0.0904educ + 0.041 exper-0 (0.1059) (0.0075) (0.0052) (0.00012) R 0.3003 526 in which: log(wage) log of average hourly wage - educ is the number of years of schooling: - exper is the number of years of experience -exper'=experience"experience The plot of the residuals against the fitted values from the regression above, is provided below: .5 2.5 1.5 Fitted values a. With...
To examine the differences between salaries of male and female middle managers of a large bank,...
To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered Salary the monthly salary (excluding fringe benefits and bonuses) Educ the number of years of education Exper the number of months of experience Train the number of weeks of training Gender- the gender of an individual; 1 for males, and O for females Excel partial outputs corresponding to these...
3. The table below shows the regression output of a multiple regression model relating the beginn...
3. The table below shows the regression output of a multiple regression model relating the beginning salaries of employees in a given company to the following independent variables: Sex : an indicator variable (1=man and 0-woman) ducation years of schooling at the time of hire Experience number of months of previous work experience Source Regression Residual Total Df 4 8822,387,82 254,407 92 MS F-value 23.763,297 5,940,82423.35 46,151,118 Coefficient table Variable Constant Sex Education Experience Months t-value 10.94 6.02 3.22 2.16...
An over-the-counter drug manufacturer wants to examine the effectiveness of a new drug in curing an illness most commonly found in older patients. Thirteen patients are given the new drug and 13 patie...
An over-the-counter drug manufacturer wants to examine the effectiveness of a new drug in curing an illness most commonly found in older patients. Thirteen patients are given the new drug and 13 patients are given the old drug. To avoid bias in the experiment, they are not told which drug is given to them. To check how the effectiveness depends on the age of patients, the following data have been collected. To examine the differences between salaries of male and...
An expert witness statistician was analyzing data from a workers compensation discrimination lawsuit filed by female...
An expert witness statistician was analyzing data from a workers compensation discrimination lawsuit filed by female workers at a bank. The data provided to the expert contain the following information: SALARY in dollars), EDUCAT (number of years of schooling), EXPER (# of months of work experience prior to joining the bank), MONTHS (# of months since joining the bank), MALES (an indicator for a worker's gender: 0 for a female, 1 for a male). As part of the investigation, the...
To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables consider...
To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered: Salary = the monthly salary (excluding fringe benefits and bonuses), Educ = the number of years of education, Exper = the number of months of experience, Train = the number of weeks of training, Gender = the gender of an individual; 1 for males, and 0 for females. Excel...
per Help Save & To examine the differences between salaries of male and female middle managers...
per Help Save & To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered: Salary the monthly salary (excluding fringe benefits and bonuses). Educ= the number of years of education, Experthe number of months of experience, Train = the number of weeks of training, Gender the gender of an individual: 1 for males, and 0 for females. Excel portial...
To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables cons...
To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered Salary- the monthly salary (excluding fringe benefits and bonuses), Educ the number of years of education, Exper the number of months of experience, Train the number of weeks of training, Gender- the gender of an individual; 1 for males, and O for females. Excel partial outputs corresponding to these...
Wage EDUC EXPER AGE Male 40 39 38 53 59 36 45 37 37 43 32 40 49 43 31 45 31 37.85 21.72 14.34 21....
NEED TO DO IN PROGRAM R
Wage EDUC EXPER AGE Male 40 39 38 53 59 36 45 37 37 43 32 40 49 43 31 45 31 37.85 21.72 14.34 21.26 24.65 71 25.65 815.45 9 20.39 10 29.13 11 27.33 12 18.02 1320.39 15 12 1 0 12 14 18 1424.18 1517.29 16 15.61 1 10 17 35.07 18 1920.39 20 21 40.33 14 16.61 16.33 30 28 Ch17 009 Data File ype here to search 1 0...
For each part of the question what steps do I need to take in minitab to...
For each part of the question what steps do I need to take in
minitab to find this answer?
Months Since Type of Repair Time (hours) Last ServiceRepair Repairperson electrical Dave Newton mechanical Dave Newtorn electricalBob Jones mechanical Dave Newton electrical Dave Newton 4.8 4.9 7 electrical Bob Jones d Minitab output if you used mechanical Bob Jonesi mechanical Bob Jones electrical Bob Jones 4.2 4.8 4.4 ltration, Inc. provides maintenance service for water-filtration systems ohnson Filtration. throughout southern Florida....
please be detailed in your response :) thank you!
0 pts) You are given the following estimated equation: In(wage) 0.1279+0.0904educ + 0.041 exper-0 (0.1059) (0.0075) (0.0052) (0.00012) R 0.3003 526 in which: log(wage) log of average hourly wage - educ is the number of years of schooling: - exper is the number of years of experience -exper'=experience"experience The plot of the residuals against the fitted values from the regression above, is provided below: .5 2.5 1.5 Fitted values a. With...
To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered Salary the monthly salary (excluding fringe benefits and bonuses) Educ the number of years of education Exper the number of months of experience Train the number of weeks of training Gender- the gender of an individual; 1 for males, and O for females Excel partial outputs corresponding to these...
3. The table below shows the regression output of a multiple regression model relating the beginning salaries of employees in a given company to the following independent variables: Sex : an indicator variable (1=man and 0-woman) ducation years of schooling at the time of hire Experience number of months of previous work experience Source Regression Residual Total Df 4 8822,387,82 254,407 92 MS F-value 23.763,297 5,940,82423.35 46,151,118 Coefficient table Variable Constant Sex Education Experience Months t-value 10.94 6.02 3.22 2.16...
An expert witness statistician was analyzing data from a workers compensation discrimination lawsuit filed by female workers at a bank. The data provided to the expert contain the following information: SALARY in dollars), EDUCAT (number of years of schooling), EXPER (# of months of work experience prior to joining the bank), MONTHS (# of months since joining the bank), MALES (an indicator for a worker's gender: 0 for a female, 1 for a male). As part of the investigation, the...
per Help Save & To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered: Salary the monthly salary (excluding fringe benefits and bonuses). Educ= the number of years of education, Experthe number of months of experience, Train = the number of weeks of training, Gender the gender of an individual: 1 for males, and 0 for females. Excel portial...
To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered Salary- the monthly salary (excluding fringe benefits and bonuses), Educ the number of years of education, Exper the number of months of experience, Train the number of weeks of training, Gender- the gender of an individual; 1 for males, and O for females. Excel partial outputs corresponding to these...
NEED TO DO IN PROGRAM R
Wage EDUC EXPER AGE Male 40 39 38 53 59 36 45 37 37 43 32 40 49 43 31 45 31 37.85 21.72 14.34 21.26 24.65 71 25.65 815.45 9 20.39 10 29.13 11 27.33 12 18.02 1320.39 15 12 1 0 12 14 18 1424.18 1517.29 16 15.61 1 10 17 35.07 18 1920.39 20 21 40.33 14 16.61 16.33 30 28 Ch17 009 Data File ype here to search 1 0...
For each part of the question what steps do I need to take in
minitab to find this answer?
Months Since Type of Repair Time (hours) Last ServiceRepair Repairperson electrical Dave Newton mechanical Dave Newtorn electricalBob Jones mechanical Dave Newton electrical Dave Newton 4.8 4.9 7 electrical Bob Jones d Minitab output if you used mechanical Bob Jonesi mechanical Bob Jones electrical Bob Jones 4.2 4.8 4.4 ltration, Inc. provides maintenance service for water-filtration systems ohnson Filtration. throughout southern Florida....
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