The linear regression equation, Y = a + bX, was estimated. The following computer printout was obtained: $\begin{array}{rllll} \text { DEPENDENT VARIABLE: } & \text { Y } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ON F } \\ \text { OBSERVATIONS: } &18 & 0.3066 & 7.076 & 0.0171 \end{array}$ $\begin{array}{lllll} \text { VARIABLE } & \begin{array}{llll} \text { PARAMETER } \\ \text { ESTIMATE } \end{array} & \begin{array}{l} \text { STANDARD } \\ \text { ERROR } \end{array} & \text { T-RATIO } & \text { P-VALUE } \\ \text { INTERCEPT } & 15.48 & 5.09 & 3.04 & 0.0008\\X&-21.36&8.03&-2.66&0.0171 \end{array}$ -Which of the following statements is correct at the 1% level of significance?

A) Both $\hat { a }$ and $\hat { b }$ are statistically significant. B) Neither $\hat { a }$ nor $\hat { b }$ is statistically significant. C) $\hat { a }$ is statistically significant, but $\hat { b }$ is not. D) $\hat { b }$ is statistically significant, but $\hat { a }$ is not. A) Both $\hat { a }$ and $\hat { b }$ are statistically significant. B) Neither $\hat { a }$ nor $\hat { b }$ is statistically significant. C) $\hat { a }$ is statistically significant, but $\hat { b }$ is not. D) $\hat { b }$ is statistically significant, but $\hat { a }$ is not.

The linear regression equation, Y = a + bX, was estimated. The following computer printout was obtained: $\begin{array}{rllll} \text { DEPENDENT VARIABLE: } & \text { Y } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ON F } \\ \text { OBSERVATIONS: } &18 & 0.3066 & 7.076 & 0.0171 \end{array}$ $\begin{array}{lllll} \text { VARIABLE } & \begin{array}{llll} \text { PARAMETER } \\ \text { ESTIMATE } \end{array} & \begin{array}{l} \text { STANDARD } \\ \text { ERROR } \end{array} & \text { T-RATIO } & \text { P-VALUE } \\ \text { INTERCEPT } & 15.48 & 5.09 & 3.04 & 0.0008\\X&-21.36&8.03&-2.66&0.0171 \end{array}$ -What is the critical value of t at the 1% level of significance?

A) 1.746 B) 2.120 C) 2.878 D) 2.921 A) 1.746 B) 2.120 C) 2.878 D) 2.921

A firm is experiencing theft problems at its warehouse. A consultant to the firm believes that the dollar loss from theft each week (T) depends on the number of security guards (G) and on the unemployment rate in the county where the warehouse is located (U measured as a percent). In order to test this hypothesis, the consultant estimated the regression equation T = a + bG + cU and obtained the following results: \[\begin{array} { c l l l l l } \text { DEPENDENT VARIAELE: } & \text { T } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ONF } \\ \text { OESERVATIONS: } & 27 & \mathbf { 0 . 7 7 9 3 } & 42.38 & \mathbf { 0 . 0 0 0 1 } & \\ & & \text { PARAMETER } & \text { STANDARD } & & \\ \text { VARIAELE } & & \text { ESTMATE } & \text { ERROR } & \text { T-RATIO } & \text { P-VALUE } \\ \text { INTERCEPT } & & 5150.43 & 1740.72 & \mathbf { 2 . 9 6 } & \mathbf { 0 . 0 0 6 8 } \\ \text { G } & & - 480.92 & 130.65 & - \mathbf { 3 . 6 8 } & \mathbf { 0 . 0 0 1 2 } \\ \text { U } & & 211.0 & 75.0 & \mathbf { 2 . 8 1 } & \mathbf { 0 . 0 0 9 5 } \end{array}\] -If the firm hires 6 guards and the unemployment rate in the county is 10% (U = 10), what is the predicted dollar loss to theft per week?

A) $4,375 per week B) $5,150 per week C) $8,300 per week D) $9,955 per week A) $4,375 per week B) $5,150 per week C) $8,300 per week D) $9,955 per week

The linear regression equation, Y = a + bX, was estimated. The following computer printout was obtained: $\begin{array}{rllll} \text { DEPENDENT VARIABLE: } & \text { Y } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ON F } \\ \text { OBSERVATIONS: } &18 & 0.3066 & 7.076 & 0.0171 \end{array}$ $\begin{array}{lllll} \text { VARIABLE } & \begin{array}{llll} \text { PARAMETER } \\ \text { ESTIMATE } \end{array} & \begin{array}{l} \text { STANDARD } \\ \text { ERROR } \end{array} & \text { T-RATIO } & \text { P-VALUE } \\ \text { INTERCEPT } & 15.48 & 5.09 & 3.04 & 0.0008\\X&-21.36&8.03&-2.66&0.0171 \end{array}$ -If X equals 20, what is the predicted value of Y?

A) 186.42 B) 165.69 C) -186.42 D) -411.72 A) 186.42 B) 165.69 C) -186.42 D) -411.72

The linear regression equation, Y = a + bX, was estimated. The following computer printout was obtained: $\begin{array}{rllll} \text { DEPENDENT VARIABLE: } & \text { Y } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ON F } \\ \text { OBSERVATIONS: } &18 & 0.3066 & 7.076 & 0.0171 \end{array}$ $\begin{array}{lllll} \text { VARIABLE } & \begin{array}{llll} \text { PARAMETER } \\ \text { ESTIMATE } \end{array} & \begin{array}{l} \text { STANDARD } \\ \text { ERROR } \end{array} & \text { T-RATIO } & \text { P-VALUE } \\ \text { INTERCEPT } & 15.48 & 5.09 & 3.04 & 0.0008\\X&-21.36&8.03&-2.66&0.0171 \end{array}$ -The parameter estimate of a indicates

A) when X is zero, Y is 5.09. B) when X is zero, Y is 15.48. C) when Y is zero, X is -21.36. D) when Y is zero, X is 8.03. A) when X is zero, Y is 5.09. B) when X is zero, Y is 15.48. C) when Y is zero, X is -21.36. D) when Y is zero, X is 8.03.

Refer to the following nonlinear model which relates W to P, Q, and R: $W = a P ^ { b } Q ^ { c } R ^ { d }$ The computer output form the regression analysis is: \[\begin{array} { r l l l l l } \text { DEPENDENTVARIAELE: } & \text { LNW } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ONF } \\ \text { OESERVATIONS: } & \text { 19 } & \mathbf { 0 . 9 0 2 3 } & 43.12 & \mathbf { 0 . 0 0 0 1 } & \\\\ & \text { PARAMETER } & \text { STANDARD } & & \\ \text { VARIAELE } & \text { ESTMMATE } & \text { ERROR } & \text { T-RATIO } & \text { P-VALUE } \\ \text { INTERCEPT } & \mathbf { 2 . 5 0 } & \mathbf { 0 . 4 5 } & \mathbf { 5 . 5 6 } & \mathbf { 0 . 0 0 0 1 } \\ \text { LNP } & - 5.10 & 1.75 & - \mathbf { 2 . 9 1 } & \mathbf { 0 . 0 1 1 3 } \\ \text { LNQ } & 12.4 & \mathbf { 3 . 2 } & \mathbf { 3 . 8 3 } & \mathbf { 0 . 0 0 1 7 } \\ \text { LNR } & \mathbf { - 6 . 0 0 } & 1.5 & - 400 & \mathbf { 0 . 0 0 1 0 } \end{array}\] -Which of the parameter estimates are statistically significant at the 5% level of significance?

A) All the parameter estimates are statistically significant. B) All parameter estimates except $\hat { a }$ and $\hat { b }$ are statistically significant. C) $\hat { a }$ is not statistically significant, but all the rest of the parameter estimates are significant. D) $\hat {c}$ is not statistically significant, but all the rest of the parameter estimates are significant. A) All the parameter estimates are statistically significant. B) All parameter estimates except $\hat { a }$ and $\hat { b }$ are statistically significant. C) $\hat { a }$ is not statistically significant, but all the rest of the parameter estimates are significant. D) $\hat {c}$ is not statistically significant, but all the rest of the parameter estimates are significant.

Refer to the following nonlinear model which relates W to P, Q, and R: $W = a P ^ { b } Q ^ { c } R ^ { d }$ The computer output form the regression analysis is: \[\begin{array} { r l l l l l } \text { DEPENDENTVARIAELE: } & \text { LNW } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ONF } \\ \text { OESERVATIONS: } & \text { 19 } & \mathbf { 0 . 9 0 2 3 } & 43.12 & \mathbf { 0 . 0 0 0 1 } & \\\\ & \text { PARAMETER } & \text { STANDARD } & & \\ \text { VARIAELE } & \text { ESTMMATE } & \text { ERROR } & \text { T-RATIO } & \text { P-VALUE } \\ \text { INTERCEPT } & \mathbf { 2 . 5 0 } & \mathbf { 0 . 4 5 } & \mathbf { 5 . 5 6 } & \mathbf { 0 . 0 0 0 1 } \\ \text { LNP } & - 5.10 & 1.75 & - \mathbf { 2 . 9 1 } & \mathbf { 0 . 0 1 1 3 } \\ \text { LNQ } & 12.4 & \mathbf { 3 . 2 } & \mathbf { 3 . 8 3 } & \mathbf { 0 . 0 0 1 7 } \\ \text { LNR } & \mathbf { - 6 . 0 0 } & 1.5 & - 400 & \mathbf { 0 . 0 0 1 0 } \end{array}\] -The value of R2 tells us that

A) 0.9023% of the total variation in ln W is explained by the regression equation. B) 90.23% of the total variation in ln W is explained by the regression equation. C) 0.9023% of the total variation in P, W, and R is explained by the regression equation. D) 0.9023% of the total variation in ln P, ln Q, and ln R is explained by the regression equation. A) 0.9023% of the total variation in ln W is explained by the regression equation. B) 90.23% of the total variation in ln W is explained by the regression equation. C) 0.9023% of the total variation in P, W, and R is explained by the regression equation. D) 0.9023% of the total variation in ln P, ln Q, and ln R is explained by the regression equation.

Refer to the following nonlinear model which relates W to P, Q, and R: $W = a P ^ { b } Q ^ { c } R ^ { d }$ The computer output form the regression analysis is: \[\begin{array} { r l l l l l } \text { DEPENDENTVARIAELE: } & \text { LNW } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ONF } \\ \text { OESERVATIONS: } & \text { 19 } & \mathbf { 0 . 9 0 2 3 } & 43.12 & \mathbf { 0 . 0 0 0 1 } & \\\\ & \text { PARAMETER } & \text { STANDARD } & & \\ \text { VARIAELE } & \text { ESTMMATE } & \text { ERROR } & \text { T-RATIO } & \text { P-VALUE } \\ \text { INTERCEPT } & \mathbf { 2 . 5 0 } & \mathbf { 0 . 4 5 } & \mathbf { 5 . 5 6 } & \mathbf { 0 . 0 0 0 1 } \\ \text { LNP } & - 5.10 & 1.75 & - \mathbf { 2 . 9 1 } & \mathbf { 0 . 0 1 1 3 } \\ \text { LNQ } & 12.4 & \mathbf { 3 . 2 } & \mathbf { 3 . 8 3 } & \mathbf { 0 . 0 0 1 7 } \\ \text { LNR } & \mathbf { - 6 . 0 0 } & 1.5 & - 400 & \mathbf { 0 . 0 0 1 0 } \end{array}\] -If Q increases by 8% (all other things constant), W will

A) decrease by 99.2%. B) decrease by 12.5%. C) increase by 0.99%. D) increase by 99.2%. A) decrease by 99.2%. B) decrease by 12.5%. C) increase by 0.99%. D) increase by 99.2%.