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Instruction 12  Regression statistics \text { Regression statistics }  ANOVA \text { ANOVA }

question 163

True/False

Instruction 12.26
The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:
 Regression statistics \text { Regression statistics }
 MultipleR 0.9447 R Square 0.8924 Adjusted R  Square 0.8886 Standard Error 0.3342 Observations 30\begin{array}{|l|l|}\hline\text { MultipleR } & 0.9447 \\\hline \text { R Square } & 0.8924 \\\hline \begin{array}{l}\text { Adjusted R } \\\text { Square }\end{array} & 0.8886 \\\hline \text { Standard Error } & 0.3342 \\\hline \text { Observations } & 30 \\\hline\end{array}

 ANOVA \text { ANOVA }
dfS5MSF Significance F Regression 125.943825.9438232.22004.3946E15 Residual 283.12820.1117 Total 2929.072\begin{array}{|l|l|l|l|l|l|}\hline & d f & S 5 & M S & F & \begin{array}{l}\text { Significance } \\F\end{array} \\\hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15 \\\hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\\hline \text { Total } & 29 & 29.072 & & & \\\hline\end{array}

 Coefficients  Standard  Error  tStat  p-value  Lower 95%  Upper 95%  Intercept 0.40240.12363.25590.00300.14920.6555 Applications  Recorded 0.01260.000815.23884.3946E150.01090.0143\begin{array}{|l|l|l|l|l|l|l|}\hline & \text { Coefficients } & \begin{array}{l}\text { Standard } \\\text { Error }\end{array} & \text { tStat } & \text { p-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\\hline \begin{array}{l}\text { Applications } \\\text { Recorded }\end{array} & 0.0126 & 0.0008 & 15.2388 & \begin{array}{l}4.3946 \mathrm{E}- \\15\end{array} & 0.0109 & 0.0143 \\\hline\end{array}



Note: 4.3946E-15 is 4.3946 × 10-15.  Instruction 12.26 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:   \text { Regression statistics }   \begin{array}{|l|l|} \hline\text { MultipleR } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.8886 \\ \hline \text { Standard Error } & 0.3342 \\ \hline \text { Observations } & 30 \\ \hline \end{array}    \text { ANOVA }   \begin{array}{|l|l|l|l|l|l|} \hline & d f & S 5 & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & \\ \hline \end{array}    \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { tStat } & \text { p-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array}{l} \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & \begin{array}{l} 4.3946 \mathrm{E}- \\ 15 \end{array} & 0.0109 & 0.0143 \\ \hline \end{array}      Note: 4.3946E-15 is 4.3946 × 10<sup>-</sup><sup>15</sup>.     -Referring to Instruction 12.26,there is no evidence of positive autocorrelation if the Durbin-Watson test statistic is found to be 1.78.  Instruction 12.26 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:   \text { Regression statistics }   \begin{array}{|l|l|} \hline\text { MultipleR } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.8886 \\ \hline \text { Standard Error } & 0.3342 \\ \hline \text { Observations } & 30 \\ \hline \end{array}    \text { ANOVA }   \begin{array}{|l|l|l|l|l|l|} \hline & d f & S 5 & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & \\ \hline \end{array}    \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { tStat } & \text { p-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array}{l} \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & \begin{array}{l} 4.3946 \mathrm{E}- \\ 15 \end{array} & 0.0109 & 0.0143 \\ \hline \end{array}      Note: 4.3946E-15 is 4.3946 × 10<sup>-</sup><sup>15</sup>.     -Referring to Instruction 12.26,there is no evidence of positive autocorrelation if the Durbin-Watson test statistic is found to be 1.78.
-Referring to Instruction 12.26,there is no evidence of positive autocorrelation if the Durbin-Watson test statistic is found to be 1.78.


Definitions:

Utility Maximizing

The process or behavior of consumers attempting to get the greatest amount of satisfaction from their consumption choices, subject to their income and the prices of goods and services.

Consumption Map

A graphical representation showing different combinations of goods or services that provide equal satisfaction or utility to a consumer.

Budget Line

The budget line represents the combinations of two goods that a consumer can purchase with a given budget, taking into account the prices of the goods.

Consumer Preference

Individuals' or groups' choices and priorities regarding products, services, or other consumption activities.

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