This is an old question but it was not answered. these are my actual questions:
ID: 3051763 • Letter: T
Question
This is an old question but it was not answered.
these are my actual questions:
A multiple regression analysis was performed and the results printed on the next page.
a. Does income affect absenteeism?
b. Does gender affect absenteeism?
c. Does age affect absenteeism?
d. Does marital status affect absenteeism?
e. Conduct a test of joint significance of the explanatory variables.
In answering the questions use alpha=0.05.
Question: A management consultant was asked to investigate a company's absenteeism problem. The consultant ...
A management consultant was asked to investigate a company's absenteeism problem. The consultant wanted to know what characteristics of workers affect his or her work record. The consultant took a random sample of l00 workers and recorded the following variables.
y = number of days absent last year
= 1 if worker is male
= 0 if worker is female
= 1 if worker is married
= 0 if worker is not married
= age
= annual household income (in $l,000s).
A multiple regression analysis was performed using Excel Data Analysis and the results printed on the next page.
1) Is there a positive linear relationship between incomes and absenteeism?
2) Is the model valid? Conduct an F test of joint significance of the explanatory variables.
3) Can we infer that males are absent more frequently than females?
4) Can we infer that married workers are absent less frequently than unmarried workers?
5) Do older workers miss more work than younger ones?
In answering the questions use alpha=0.05.
Please show me the calculation step by step
A management consultant was asked to investigate a company's absenteeism problem. The consultant wanted to know what characteristics of workers affect his or her work record. The consultant took a random sample of l00 workers and recorded the following variables.
y = number of days absent last year
= 1 if worker is male
= 0 if worker is female
= 1 if worker is married
= 0 if worker is not married
= age
= annual household income (in $l,000s).
A multiple regression analysis was performed using Excel Data Analysis and the results printed on the next page.
1) Is there a positive linear relationship between incomes and absenteeism?
2) Is the model valid? Conduct an F test of joint significance of the explanatory variables.
3) Can we infer that males are absent more frequently than females?
4) Can we infer that married workers are absent less frequently than unmarried workers?
5) Do older workers miss more work than younger ones?
In answering the questions use alpha=0.05.
Please show me the calculation step by step
A management consultant was asked to investigate a
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Expert Answer
Lance Briner
Lance Briner answered this Was this answer helpful?
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How you get the answer of f test and pvalue, thank you.
Great question! :)
Given:
n = 100 workers
p = 4 predictors <-- Gender, Martial status, Age and Income
dfRegression = p = 4
dfError
= n-p-1
= 100-4-1 = 95
-------------------------------------------------------------
To calculate the F-test statistic...
F = MSRegression/MSError
where
MSRegression
= SSRegression/dfRegression
= 112/4
= 27.94
MSError = MSResidual
= SSError/dfError
= 761/95
F = MSRegression/MSError
F = 27.94/8.01
F = 3.49
-------------------------------------------------------------
Finding the p-value by hand...
Using the F-table, go across from dfnumerator = dfRegression = 4 and across from dfdenominator = dfError = 95 --> 60 (round down to be conservative). Since our F-test statistic = 3.49 falls between 3.01 and 3.65, it implies that our p-value is between .01 and .025.
Answer: .01 < p-value < .025
Note: The F-table that I am using can be found on page 17 of the following pdf.
http://www.stat.purdue.edu/~mccabe/ips4tab/bmtables.pdf
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Explanation / Answer
A multiple regression analysis was performed and recorded
a. From the table, p-value of income variable is 0.017 which is less than 0.05. We reject the null hypothesis and concluded that income having a significant affect of absenteeism
b. From the table, p-value of gender variable is 0.008 which is less than 0.05. We reject the null hypothesis and concluded that gender having a strongly significant affect of absenteeism
c. From the table, p-value of age variable is 0.049 which is less than 0.05. We reject the null hypothesis and concluded that age having a weakly significant affect of absenteeism
d. From the table, p-value of marital status variable is 0.146 which is greater than 0.05. We do not reject the null hypothesis and concluded that marital status do not having a significant affect of absenteeism.
e. Conduct a test of joint significance of the explanatory variables by using F-test and see that p-value is 0.0230 which is less than 0.05. We concluded that variables having a significant affect of absenteeism
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