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Instruction 13  OUTPUT \text { OUTPUT } Note: Adj R Square = Adjusted R Square; Std

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Instruction 13.13
A financial analyst wanted to examine the relationship between salary (in $1,000) and four variables: age (X1 = Age), experience in the field (X2 = Exper), number of degrees (X3 = Degrees) and number of previous jobs in the field (X4 = Prevjobs). He took a sample of 20 employees and obtained the following Microsoft Excel output:
 OUTPUT \text { OUTPUT }
 SUMMARY Regression  Statistics Multiple R 0.992 R Square 0.984 Adj. R Square 0.979 Std. Error 2.26743 Observations 20\begin{array}{ll}\text { SUMMARY}\\\text { Regression }&\text { Statistics}\\\text { Multiple R } & 0.992 \\\text { R Square } & 0.984 \\\text { Adj. R Square } & 0.979 \\\text { Std. Error } & 2.26743 \\\text { Observations } & 20\end{array}

 ANOVA  df  SS  MS F Signif F  Regression 44609.831641152.45791224.1600.0001 Residual 1577.118365.14122 Total 194686.95000\begin{array}{l}\text { ANOVA }\\\begin{array}{llllll} & \text { df } & \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}\end{array}

 Coeff  Std Error t 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}{lllll} & \text { Coeff } & \text { Std Error } & t \text { Stat } & p \text { 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}

Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error
-Referring to Instruction 13.13,the p-value of the F test for the significance of the entire regression is ___________.


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