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Instruction 13 ANOVA Model 2 Is the Regression Analysis Where the Dependent Variable

question 69

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Instruction 13.37
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.
Model 1
Regression Statistics
 Multiple R 0.7035 R Square 0.4949 Adj. R Square 0.4030 Std. Error 18.4861 Observations 40\begin{array} { l l } \text { Multiple R } & 0.7035 \\ \text { R Square } & 0.4949 \\ \text { Adj. R Square } & 0.4030 \\ \text { Std. Error } & 18.4861 \\ \text { Observations } & 40 \end{array}

ANOVA
df SS  MS F Signif F  Regression 611048.64151841.44025.38850.00057 Residual 3311277.2586341.7351 Total 39223325.9 Coeff  StdError  tStat p value  Lower 95%  Upper95%  Intercept 32.659523.183021.40880.168314.506779.8257 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} { l l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signif F } & \\ \text { Regression } & 6 & 11048.6415 & 1841.4402 & 5.3885 & 0.00057 & \\ \text { Residual } & 33 & 11277.2586 & 341.7351 & & & \\ \text { Total } & 39 & 223325.9 & & & & \\ & & & & & & \\ & \text { Coeff } & \text { StdError } & \text { tStat } & p \text { value } & \text { Lower 95\% } & \text { Upper95\% } \\ \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \\ \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:
Mode 2
Regression Statistics
 Multiple R 0.6391 R Square 0.4085 Adj. R Square 0.3765 Std. Error 18.8929 Observations 40\begin{array} { l l } \text { Multiple R } & 0.6391 \\ \text { R Square } & 0.4085 \\ \text { Adj. R Square } & 0.3765 \\ \text { Std. Error } & 18.8929 \\ \text { Observations } & 40 \end{array}

ANOVA
df SS  MS F Signif F  Regression 29119.08974559.544812.77400.0000 Residual 3713206.8103356.9408 Total 3922325.9 Coeff  StdError 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 l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signif F } \\ \text { Regression } & 2 & 9119.0897 & 4559.5448 & 12.7740 & 0.0000 \\ \text { Residual } & 37 & 13206.8103 & 356.9408 & & \\ \text { Total } & 39 & 22325.9 & & & \\ & & & & & \\ & \text { Coeff } & \text { StdError } & t \text { Stat } & p \text { value } & \\ \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 & \end{array}
-Referring to Instruction 13.37 Model 1,which of the following is the correct alternative 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:

Sustainable Earnings

Refers to the portion of a company's income considered to be predictable, repeatable, or likely to continue in the future.

Repeat Customers

Individuals or entities that make more than one purchase from a business, indicating customer loyalty and satisfaction.

Debt Retirement

The process of paying off debt before or at its maturity date, effectively removing it from the company's balance sheet.

Transitory Components

Elements of a company's financial records that are expected to be temporary and not indicative of ongoing operational performance.

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