Based on the following regression output, what conclusion can you reach about F1F1F1S1?F1F1F101? \(\begin{array}{|l|r|l|l|l|l|} \hline {\text { Regression Statistics }} & & & & \\ \hline \text { Multiple R } & 0.917214 & & & & \\ \hline \text { R Square } & 0.841282 & & & & \\ \hline \text { Adjusted R Square } & 0.821442 & & & & \\ \hline \text { Standard Error } & 9.385572 & & & & \\ \hline \text { Observations } & 10 & & & & \\ \hline & & & & & \\ \hline\text { ANOVA } & & & & & \\ \hline &\text { df } & \text { SS } & \text { MS } & F & \text { Significance F } \\ \hline \text { Regression } & 1 & 3735.306 & 3735.306 & 42.40379 & 0.000186 \\ \hline \text { Residual } & 8 & 704.7117 & 88.08896 & & \\ \hline \text { Total } & 9 & 4440.017 & & & \\ \hline & & & & & \\ \hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } \\ \hline \text { Intercept } & 31.62378 & 10.44297 & 3.028236 & 0.016353 & 7.542233 \\ \hline \text { X Variable 1 } & 1.131661 & 0.173786 & 6.511819 & 0.000186 & 0.73091\\ \hline \end{array}\)

A) F1F1F1S1?F1F1F101 = 0, with P-value = 0.016353 B) F1F1F1S1?F1F1F101 F1F1F1S1?F1F1F10 0, with P-value = 0.016353 C) F1F1F1S1?F1F1F101 = 0, with P-value = 0.000186 D) F1F1F1S1?F1F1F101 F1F1F1S1?F1F1F10 0, with P-value = 0.000186 A) F1F1F1S1?F1F1F101 = 0, with P-value = 0.016353 B) F1F1F1S1?F1F1F101 F1F1F1S1?F1F1F10 0, with P-value = 0.016353 C) F1F1F1S1?F1F1F101 = 0, with P-value = 0.000186 D) F1F1F1S1?F1F1F101 F1F1F1S1?F1F1F10 0, with P-value = 0.000186

The terms b0 and b1 are

A) estimated population parameters. B) estimated intercept and slope values, respectively. C) random variables. D) all of these. A) estimated population parameters. B) estimated intercept and slope values, respectively. C) random variables. D) all of these.

For a simple linear regression model, a 100(1 F1F1F1S1F1F1F10 F1F1F1S1F1F1F10)% prediction interval for a new value of Y when X = Xh is computed as

A) h F1F1F1S1F1F1F10 t(1F1F1F1S1F1F1F10/2,nF1F1F1S1F1F1F102)Sp. B) h F1F1F1S1F1F1F10 t(1F1F1F1S1F1F1F10,nF1F1F1S1F1F1F102)Sp C) h F1F1F1S1F1F1F10 t(1F1F1F1S1F1F1F10/2,nF1F1F1S1F1F1F102)Sp D) Yh F1F1F1S1F1F1F10 t(1F1F1F1S1F1F1F10/2,nF1F1F1S1F1F1F102)Sp A) h F1F1F1S1F1F1F10 t(1F1F1F1S1F1F1F10/2,nF1F1F1S1F1F1F102)Sp. B) h F1F1F1S1F1F1F10 t(1F1F1F1S1F1F1F10,nF1F1F1S1F1F1F102)Sp C) h F1F1F1S1F1F1F10 t(1F1F1F1S1F1F1F10/2,nF1F1F1S1F1F1F102)Sp D) Yh F1F1F1S1F1F1F10 t(1F1F1F1S1F1F1F10/2,nF1F1F1S1F1F1F102)Sp

Exhibit 9.1 The following questions are based on the problem description and spreadsheet below. A company has built a regression model to predict the number of labor hours (YS1U1B1iS1U1B0) required to process a batch of parts (XS1U1B1iS1U1B0). It has developed the following Excel spreadsheet of the results. -Refer to Exhibit 9.1. What is the estimated regression function for this problem? Explain what the terms in your equation mean.

i = 4.8400 + 1.4836 X 1i . The average labor

The F1F1F1S1F1F1F101 term indicates

A) the average change in Y for a unit change in X. B) the Y value for a given value of X. C) the change in observed X for a given change in Y. D) the Y value when X equals zero. A) the average change in Y for a unit change in X. B) the Y value for a given value of X. C) the change in observed X for a given change in Y. D) the Y value when X equals zero.

Exhibit 9.5 The following questions are based on the description and spreadsheet below. An analyst has identified 3 independent variables (XS1U1B11S1U1B0, XS1U1B12S1U1B0,XS1U1B13S1U1B0) which might be used to predict Y. He has computed the regression equations using all of the variables and the results are summarized in the following table. \(\begin{array}{lcccl} \text { Independent }&&&&\\ \text {Variable in}&&\text {Adjusted}&&\\ \text { the Model } & R^{2} & -R^{2} & S_{0} & \text { Parameter Estimates }\\ \hline \mathrm{X}_{1} & 0.00089 & -0.124 & 23.548 & \mathrm{~b}_{0}=93.7174, \mathrm{~b}_{1}=0.922 \\ \mathrm{X}_{2} & 0.3870 & 0.3104 & 18.448 & \mathrm{~b}_{0}=57.0803, \mathrm{~b}_{2}=1.545 \\ \mathrm{X}_{1} \text { and } \mathrm{X}_{2} & 0.3910 & 0.2170 & 19.654 & \mathrm{~b}_{0}=50.2927, \mathrm{~b}_{1}=1.952, \mathrm{~b}_{2}=1.554 \\ \mathrm{X}_{3} & 0.8413 & 0.8214 & 9.3858 & \mathrm{~b}_{0}=31.6238, \mathrm{~b}_{3}=1.132 \\ \mathrm{X}_{1} \text { and } \mathrm{X}_{3} & 0.8413 & 0.7960 & 10.033 & \mathrm{~b}_{0}=31.133, \mathrm{~b}_{1}=0.148, \mathrm{~b}_{3}=1.132 \\ \mathrm{X}_{2} \text { and } \mathrm{X}_{3} & 0.9863 & 0.9824 & 2.948 & \mathrm{~b}_{0}=14.169, \mathrm{~b}_{2}=0.985, \mathrm{~b}_{3}=0.995 \\ \mathrm{X}_{1} \text { X } \mathrm{X}_{2} \text { and } \mathrm{X}_{3} & 0.9871 & 0.9807 & 3.085 & \mathrm{~b}_{0}=11.113 . \mathrm{b}_{1}=0.899 . \mathrm{b}_{2}=0.990 . \mathrm{b}_{3}=0.993 \end{array}\) -Refer to Exhibit 9.5. Predict the mean value based on (X1, X2, X3) = (3, 32, 50). Use the best predictive model based on data from the table.

i = b 0 + b 2 X 2I + b 3 X 3i @#IMG-DLM& i

The terms b0 and b1 are referred to as

A) population variables. B) population parameters. C) estimated population variables. D) estimated population parameters. A) population variables. B) population parameters. C) estimated population variables. D) estimated population parameters.