SCENARIO 13-6
One of the Most Common Questions of Prospective

question 45

Short Answer

SCENARIO 13-6
One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X1 ) and the amount of insulation in inches ( X 2 ).Given below is EXCEL output of the regression model. $$\begin{array}{l}\begin{array} { l r } \hline { \text { Regression Statistics } } \\\hline \text { Multiple R } & 0.5270 \\\text { R Square } & 0.2778 \\\text { Adjusted R Square } & 0.1928 \\\text { Standard Error } & 40.9107 \\\text { Observations } & 20 \\\hline\end{array}\\\end{array}$$
$\begin{array}{lrrrrr}\text { ANOVA }\\\hline&\text { df } & \text { SS } & \text { MS } & F & \text { Significance F }\\\hline\text { Regression } & 2 & 10943.0190 & 5471.5095 & 3.2691 & 0.0629 \\\text { Residual } & 17 & 28452.6027 & 1673.6825 & & \\\text { Total } & 19 & 39395.6218 & & &\end{array}$ 13-22 Multiple Regression $$\begin{array}{l}\begin{array} { l r r r r r r } \hline & \text { Coefficients } &{ \text { Standard Error } } & { \text { t Stat } } & \text { P-volue } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & 448.2925 & 90.7853 & 4.9379 & 0.0001 & 256.7522 & 639.8328 \\\text { Temperature } & - 2.7621 & 1.2371 & - 2.2327 & 0.0393 & - 5.3721 & - 0.1520 \\\text { Insulation } & - 15.9408 & 10.0638 & - 1.5840 & 0.1316 & - 37.1736 & 5.2919 \\\hline\end{array}\\\text { Also } \operatorname { SSR } \left( X _ { 1 } \mid X _ { 2 } \right) = 8343.3572 \text { and } \operatorname { SSR } \left( X _ { 2 } \mid X _ { 1 } \right) = 4199.2672\end{array}$$
-Referring to SCENARIO 13-6, the partial F test forH0: Variable X2 does not significantly improve the model after variable X1 has been includedH1: Variable X2 significantly improves the model after variable X1 has been included has and degrees of freedom.

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Definitions:

Standard Deviation

A metric that quantifies the spread or variability among a group of numbers, signaling the extent to which these numbers deviate from their average.

Parameter Estimate

A value used to infer the characteristics of a population from a sample.

Bootstrap

A resampling technique that involves repeatedly drawing samples from a dataset to estimate a population parameter.

Bias

A systematic error or deviation from true values or processes in data collection, analysis, interpretation, and review that can lead to incorrect conclusions.