Section 3: 33 points eks a worker in a A study provided data on variables that m

Question

Section 3: 33 points eks a worker in a A study provided data on variables that may be related to the number of we manufacturing firm has been jobless. The dependent variable in the study (Weeks) was defined as the number of weeks a worker has been jobless due to a layoff. A sample of 50 worke surveyed and data collected included: age (in years), education otherwise), head (head of household - 1: 0 otherwise), tenure (number of years on the job), sales (sales occupations 1:0 otherwise) and manager (management ob = 1,0 otherwise). The rs was (in years), married (married 1 regression results follow: Regression Statistics Multiple FR R Square Adjusted R Square Standard Error Observations 0.769031 0.591409 0.52331 16.34966 50 ANOVA df Regression Residual MS F Significance F 7 16250.44012 2321.491 8.684595 1.48415E-06 42 11227.07988 267.3114 49 27477.52 Coefficients Standard Error tStat P-value Intercept Age Educ Married Head Tenure Manager 22.8507 18.86681009 1.211159 0.232606 1.509288 0.304041272 4.964088 1.2E-05 -0.61331 0.936183203 -0.65512 0.515964 -10.743 6.01238039 -1.78681 0.081187 19.77955.837235286 -3.3885 0.001538 0.426465 0.46685673 0.913481 0.366204 -26.74248.325661098 3.21204 0.002531 -18.5609 6.280605696 -2.95527 0.005104 Sales

Explanation / Answer

1.

The Intercept is a constant which means that the number of weeks a worker has been jobless is 22.8507 weeks without even considering the other factors.

2.

The coefficient for age represents that dependent variable (number of weeks a worker has been jobless) would go up by 1.51 for every increase of 1 in age, keeping all the other factors constant

3.

H0:B=0 (Manager does not affect the number of weeks a worker has been jobless)

4.

p-value for educ is 0.515964 and hence null hypothesis cannot be rejected. This variable is not significant at a=0.05

5.

Adjusted r^2 measures how much variation is brought about in the dependent variable for addition of independent variables. In this case it is 0.52331 which means 52.33% variation.

6.

F-STAT measures the overall significance of the model.

7.

H0: 1 = 2 = … = k = 0 (no linear relationship)

H1: at least one i 0   (at least one independent variable affects Y)

8.

Rejected in this example since F>FCRIT

9.

Fewer

10.

y=22.8507+1.51*Age-0.61331*Educ-10.743*Married-19.7795*Head+0.426465*Tenure-26.7424*Manager-18.5609*Sales

y=22.8507+1.51*44-0.61331*12-10.743*1-19.7795*1+0.426465*8-26.7424*0-18.5609*0

y=54.8202

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