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Instruction 13-16
Given Below Are Results from the Regression Analysis

question 123

Multiple Choice

Instruction 13-16
Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age of the worker (Age) ,the number of years of education received (Edu) ,the number of years at the previous job (Job Yr) ,a dummy variable for marital status (Married: 1 = married,0 = otherwise) ,a dummy variable for head of household (Head: 1 = yes,0 = no) and a dummy variable for management position (Manager: 1 = yes,0 = no) .We shall call this Model 1.
 Regression Statistics  Multiple R 0.7035 R Square 0.4949 Adjusted R 0.4030 Square  Standard 18.4861 Error  Observations 40\begin{array} { l r } \hline { \text { Regression Statistics } } \\\hline \text { Multiple R } & 0.7035 \\\text { R Square } & 0.4949 \\\text { Adjusted R } & 0.4030 \\\text { Square } & \\\text { Standard } & 18.4861 \\\text { Error } \\\text { Observations } & 40 \\\hline\end{array} ANOVA
 df  SS  MS  F  Significance F  Regression 611048.64151841.44025.38850.00057 Residual 3311277.2586341.7351 Total 3922325.9\begin{array}{lrrrrrr} & \text { df } & \text { SS } & {\text { MS }} & \text { F } & \text { Significance F } \\\hline \text { Regression } & 6 & 11048.6415 & 1841.4402 & 5.3885 & 0.00057 \\\text { Residual } & 33 & 11277.2586 & 341.7351 & & \\\text { Total } & 39 & 22325.9 & & &\end{array}

 Coefficients  Standard Error t Stat P-value  Lower 95%  Upper 95%  Intercept 32.659523.183021.40880.168314.50677.8257 Age 1.29150.35993.58830.00110.55922.0238 Edu 1.35371.17661.15040.25823.74761.0402 Job Yr 0.61710.59401.03890.30640.59141.8257 Married 5.21897.60680.68610.497420.695010.2571 Head 14.29787.64791.86950.070429.85751.2618 Manager 24.820311.69322.12260.041448.61021.0303\begin{array}{lrrrrrr} & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & P \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & -14.5067& 7.8257 \\\text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 &2.0238\\\text { Edu } & -1.3537 & 1.1766 & -1.1504 & 0.2582 & -3.7476 & 1.0402\\\text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & -0.5914 & 1.8257 \\\text { Married } & -5.2189 & 7.6068 & -0.6861 & 0.4974 & -20.6950 & 10.2571 \\\text { Head } & -14.2978 & 7.6479 & -1.8695 & 0.0704 & -29.8575 &1.2618\\\text { Manager } & -24.8203 & 11.6932 & -2.1226 & 0.0414 & -48.6102 & -1.0303\end{array} Model 2 is the regression analysis where the dependent variable is Unemploy and the independent variables are Age and Manager.The results of the regression analysis are given below:
 Regression Statistics  Multiple R 0.6391 R Square 0.4085 Adjusted R 0.3765 Square  Standard Error 18.8929 Observations 40\begin{array} { l r } \hline { \text { Regression Statistics } } \\\hline \text { Multiple R } & 0.6391 \\\text { R Square } & 0.4085 \\\text { Adjusted R } & 0.3765 \\\text { Square } & \\\text { Standard Error } & 18.8929 \\\text { Observations } & 40\\\hline\end{array}  ANOVA dfSSMSF Significance F Regression 29119.08974559.544812.77400.0000 Residual 3713206.8103356.9408 Total 3922325.9 Coefficients  Standard Error t Stat P-value  Intercept 0.214311.57960.01850.9853 Age 1.44480.31604.57170.0000 Manager 22.576111.34881.98930.0541\begin{array}{l}\text { ANOVA }\\\begin{array} { l r r r l r } \hline & d f & { S S } & { M S } & F & \text { Significance } F \\\hline \text { Regression } & 2 & 9119.0897 & 4559.5448 & 12.7740 & 0.0000 \\\text { Residual } & 37 & 13206.8103 & 356.9408 & & \\\text { Total } & 39 & 22325.9 & & & \\\hline\end{array}\\\begin{array} { l r r r r } \hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & P \text {-value } \\\hline \text { Intercept } & - 0.2143 & 11.5796 & - 0.0185 & 0.9853 \\\text { Age } & 1.4448 & 0.3160 & 4.5717 & 0.0000 \\\text { Manager } & - 22.5761 & 11.3488 & - 1.9893 & 0.0541 \\\hline\end{array}\end{array}
-Referring to Instruction 13-16 Model 1,which of the following is the correct null hypothesis to test whether being married or not makes a difference in the mean number of weeks a worker is unemployed due to a layoff,while holding constant the effect of all the other independent variables?


Definitions:

Cash Cycle

The duration of time it takes a company to convert its investments in inventory back into cash, taking into account the time needed to sell inventory, collect receivables, and pay bills.

Cash Cycle

The duration between the initial investment in inventory and receiving cash from sales of the inventory.

Accounts Payable Period

The average period it takes for a company to pay off its suppliers, calculated by dividing accounts payable by the average daily purchases.

Operating Cycle

The time period that starts with the purchase of raw materials and ends with the collection of receivables generated from sales, measuring how long it takes for a business to turn expenditures into cash from sales.

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