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SCENARIO 17-3 a Financial Analyst Wanted to Examine the Relationship $1,000\$ 1,000

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SCENARIO 17-3 A financial analyst wanted to examine the relationship between salary (in $1,000\$ 1,000 ) and 4 variables: age (X1=Age)\left( X _ { 1 } = \mathrm { Age } \right) , experience in the field (X2=\left( X _ { 2 } = \right. Exper )) , number of degrees (X3=\left( X _ { 3 } = \right. Degrees )) , and number of previous jobs in the field ( X4=X _ { 4 } = Prevjobs). He took a sample of 20 employees and obtained the following Microsoft Excel output:

SUMMARY OUTPUT

 Regression Statistics  Multiple R 0.992 R Square 0.984 Adjusted R Square 0.979 Standard Error 2.26743 Observations 20\begin{array}{ll}{\text { Regression Statistics }} \\\text { Multiple R } & 0.992 \\\text { R Square } & 0.984 \\\text { Adjusted R Square } & 0.979 \\\text { Standard Error } & 2.26743 \\\text { Observations } & 20\end{array}


df SS  MS F Signif F Regression 44609.831641152.45791224.1600.0001 Residual 1577.118365.14122 Total 194686.95000\begin{array}{lrrrrr} & d f &{\text { SS }} & \text { MS } & F & \text { Signif } F \\\text { Regression } & 4 & 4609.83164 & 1152.45791 & 224.160 & 0.0001 \\\text { Residual } & 15 & 77.11836 & 5.14122 & & \\\text { Total } & 19 & 4686.95000 & & &\end{array}

 Coeff  StdError  Stat  P-value  Intercept 9.6111982.779886383.4570.0035 Age 1.3276950.1149193011.5530.0001 Exper 0.1067050.142655590.7480.4660 Degrees 7.3113320.803241879.1020.0001 Prevjobs 0.5041680.447715731.1260.2778\begin{array}{lrrrr} & {\text { Coeff }} & \text { StdError } & {\text { Stat }} & \text { P-value } \\\text { Intercept } & -9.611198 & 2.77988638 & -3.457 & 0.0035 \\\text { Age } & 1.327695 & 0.11491930 & 11.553 & 0.0001 \\\text { Exper } & -0.106705 & 0.14265559 & -0.748 & 0.4660 \\\text { Degrees } & 7.311332 & 0.80324187 & 9.102 & 0.0001 \\\text { Prevjobs } & -0.504168 & 0.44771573 & -1.126 & 0.2778\end{array}
-Referring to Scenario 17-3, the estimate of the unit change in the mean of Y per unit change in X4X _ { 4 } , taking into account the effects of the other 3 variables, is ________.


Definitions:

Goodness-of-fit Test

A statistical test used to determine how well sample data fit a distribution from a population with a normal distribution.

Normally Distributed

Describes a statistical distribution where the arrangement is balanced around the mean, indicating that observations close to the mean happen more often than those further away.

Regression Line

A line of best fit through a dataset in regression analysis, showing the relationship between the independent and dependent variables.

Sum Of Squares

The total of the squared differences between each observation and the overall mean.

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