Below is a partial multiple regression ANOVA table. \[\begin{array} { l l l } \text { Source } & \text { SS } & \text { df } \\ \mathrm { X } _ { 1 } & 535.9569 & 1 \\ \mathrm { X } _ { 2 } & 1,167.5634 & 1 \\ \mathrm { X } _ { 3 } & 18.9886 & 1 \\ \text { Error } & 3,459 .6803 & 8 \end{array}\] -How many observations were in the sample?

The answer of Below is a partial multiple regression ANOVA...

The graph of the prediction equation obtained from fitting the model y = $\beta$ 0+ $\beta$ 1X + $\beta$ 2X2+ $\varepsilon$ Is a(n):

A) Line B) Plane C) Parabola D) Exponential curve E) Single point A) Line B) Plane C) Parabola D) Exponential curve E) Single point

Below is a partial multiple regression computer output based on a quadratic regression model. \[\begin{array} { l l l } \text { Source } & \text { SS } & \text { df } \\ \text { Model } & 29.44 & 2 \\ \text { Error } & 59.96 & 15 \\ & & \text { Standard Error } \\ \text { Variable } & \text { Coefficient } & \left( \mathrm { s } _ { \mathrm { b } } \right) \\ \text { Intercept } & 8.01 & 1.45 \\ \mathrm { X } & - 1.35 & 0.55 \\ \mathrm { X } ^ { 2 } & 0.46 & 0.43 \end{array}\] -What is number of observations in the sample?

A) 2 B) 15 C) 18 D) 29 E) 30 A) 2 B) 15 C) 18 D) 29 E) 30

In the quadratic regression model y = $\beta$ 0 + $\beta$ 1X + $\beta$ 2X2+ $\varepsilon$ The $\beta$ 1term represents the:

A) Rate of curvature of the parabola B) Value of Y when X is zero C) Value of X when Y is zero D) Y-intercept of the parabola E) Shift parameter of the parabola A) Rate of curvature of the parabola B) Value of Y when X is zero C) Value of X when Y is zero D) Y-intercept of the parabola E) Shift parameter of the parabola

Below is a partial multiple regression ANOVA table. \[\begin{array} { l l l } \text { Source } & \text { SS } & \text { df } \\ \text { Model } & 0.242 & 2 \\ \text { Error } & 0.105 & 3 \end{array}\] -What is the explained variation?

A) .210 B) .105 C) .035 D) .242 E) .347 A) .210 B) .105 C) .035 D) .242 E) .347

Consider the following partial computer output for a multiple regression model. \[\begin{array} { l l l } \text { Predictor } & \text { Coefficient } \left( \mathrm { b } _ { \mathrm { i } } \right) & \text { Standard Dev } \left( \mathrm { s } _ { \mathrm { b } } \right) \\ \text { Constant } & 99.3883 & \\ \mathrm { X } 1 & - 0.007207 & 0.0031 \\ \mathrm { X } 2 & 0.0011336 & 0.00122 \\ \mathrm { X } 3 & 0.9324 & 0.373 \\ & & \\ \text { Analysis of Variance } & & \mathrm { SS } \\ \text { Source } & \underline { \text { df } } & 31.308 \\ \text { Regression } & 3 & 9.378 \\ \text { Error (residual) } & 16 & \end{array}\] -What is the mean square error?

A) .4086 B) .3108 C) .9378 D) .9324 E) .5861 A) .4086 B) .3108 C) .9378 D) .9324 E) .5861

Below is a partial multiple regression ANOVA table. \[\begin{array} { l l l } \text { Source } & \text { SS } & \text { df } \\ \mathrm { X } _ { 1 } & 535.9569 & 1 \\ \mathrm { X } _ { 2 } & 1,167.5634 & 1 \\ \mathrm { X } _ { 3 } & 18.9886 & 1 \\ \text { Error } & 3,459.6803 & 8 \end{array}\] -What is the mean square error?

A) 3459.68 B) 432.46 C) 1898.86 D) 1167.56 E) 535.96 A) 3459.68 B) 432.46 C) 1898.86 D) 1167.56 E) 535.96

Below is a partial multiple regression ANOVA table. \[\begin{array} { l l l } \text { Source } & \text { SS } & \text { df } \\ \text { Model } & 0.242 & 2 \\ \text { Error } & 0.105 & 3 \end{array}\] -What is the value of R2?

A) .697 B) .105 C) .035 D) .242 E) .347 A) .697 B) .105 C) .035 D) .242 E) .347

Exam 12: Multiple Regression and Model Building

Below is a partial multiple regression ANOVA table. Source SS df 535.9569 1 1,167.5634 1 18.9886 1 Error 3,459.6803 8 -How many observations were in the sample?

(Short Answer)

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Question 201

The graph of the prediction equation obtained from fitting the model y = $\beta$ ₀+ $\beta$ ₁X + $\beta$ ₂X²+ $\varepsilon$ Is a(n):

(Multiple Choice)

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Question 202

Below is a partial multiple regression computer output based on a quadratic regression model. Source SS df Model 29.44 2 Error 59.96 15 Standard Error Variable Coefficient Intercept 8.01 1.45 -1.35 0.55 0.46 0.43 -What is number of observations in the sample?

(Multiple Choice)

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Question 203

Below is a partial multiple regression ANOVA table. Source SS df 535.9569 1 1,167.5634 1 18.9886 1 Error 3,459.6803 8 -Calculate the explained variation.

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Question 204

In the quadratic regression model y = $\beta$ ₀ + $\beta$ ₁X + $\beta$ ₂X²+ $\varepsilon$ The $\beta$ 1term represents the:

(Multiple Choice)

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Question 205

Below is a partial multiple regression ANOVA table. Source SS df Model 0.242 2 Error 0.105 3 -What is the explained variation?

(Multiple Choice)

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Question 206

A member of the provincial legislature has expressed concern about the differences in the mathematics test scores of grade 9 high school students across the province.She asks her research assistant to conduct a study to investigate what factors could account for the differences.The research assistant looked at a random sample of school districts across the province and used the factors of percentage of mathematics teachers in each district with a degree in mathematics,the average age of mathematics teachers,and the average salary of mathematics teachers Predictor Coef SE Coef Constant 35.178 7.595 Math Dgr 0.22073 0.07131 Age 0.3353 0.1901 Salary 0.0930 0.1675 s = 7.62090 Analysis of Variance Source DF SS Regression 3 1053.09 Residual Error 32 1858.50 Source DF Seq SS Math Dgr 1 672.10 Age 1 363.09 Salary 1 17.90 -Calculate the adjusted R².

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Question 207

The _____ term describes the effects on y of all factors other than the independent variables in a multiple regression model.

(Short Answer)

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Question 208

Below is a partially completed multiple regression analysis of variance (ANOVA)table.Calculate R².

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Question 209

Consider the following partial computer output for a multiple regression model. Predictor Coefficient Standard Dev Constant 99.3883 1 -0.007207 0.0031 2 0.0011336 0.00122 3 0.9324 0.373 Analysis of Variance Source 31.308 Regression 3 9.378 Error (residual) 16 -What is the mean square error?

(Multiple Choice)

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Question 210

In a regression model,a value of the error term depends upon other values of the error term.

(True/False)

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Question 211

When the quadratic regression model y = ?₀+ ?₁x + ?₂x²+ ? is used,the term _____ shows the rate of curvature of the parabola.

(Short Answer)

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Question 212

Below is a partial multiple regression ANOVA table. Source SS df 535.9569 1 1,167.5634 1 18.9886 1 Error 3,459.6803 8 -What is the mean square error?

(Multiple Choice)

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Question 213

An investigator hired by a client suing for sex discrimination has developed a multiple regression model for predicting employee salaries for the company in question.In this multiple regression model,the salaries are measured in thousands of dollars.For example,a data entry of 35 for the dependent variable indicates a salary of $35,000.The indicator (dummy)variable for sex is coded as X₁ = 0 if male and X₁ = 1 if female.The computer output of this multiple regression model shows that the coefficient of X₁ is - 4.2 and that X₁ is significant at the 1% level of significance.Interpret the coefficient of X₁.

(Multiple Choice)

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Question 214

The multiple coefficient of determination is the _______ divided by the total variation.

(Multiple Choice)

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Question 215

Consider the following partial computer output for a multiple regression model. Predictor Coefficient Standard Deviation Constant 41.225 6.380 1.081 1.353 -18.404 4.547 Analysis of Variance Source Regression 2 2270.11 Error (residual) 26 3585.75 -Calculate R².

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Question 216

The manufacturer of a light fixture believes that the dollars spent on advertising,the price of the fixture,and the number of retail stores selling the fixture in a particular month influence the light fixture sales.The manufacturer randomly selects 10 months and collects the following data: Sales Advertising Price \# of stores 41 20 40 1 42 40 60 3 59 40 20 4 60 50 80 5 81 50 10 6 80 60 40 6 100 70 20 7 82 70 60 8 101 80 30 9 110 90 40 10 The sales are in thousands of units per month,the advertising is given in hundreds of dollars per month,the price is the unit retail price for the particular month.Using this data,the following computer output is obtained. The regression equation is Sales = 31.0 + 0.820 Advertising - 0.325 Price + 1.84 Stores Predictor Coef StDev T P Constant 30.992 7.728 4.01 0.007 Advertising 0.8202 0.5023 1.63 0.154 Price -0.32502 0.08935 -3.64 0.011 Stores 1.841 3.855 0.48 0.650 S = 5.465R-Sq = 96.7%R-Sq(adj)= 95.0% Analysis of Variance Source DF SS MS F P Regression 3 5179.2 1726.4 57.81 0.000 Residual Error 6 179.2 29.9 Total 9 5358.4 -Based on the observed data and the multiple regression model given above,estimate the average monthly light fixture sales and calculate the value of the residual,if the company spends $4000 on advertising,the price of the fixture is $60 and the fixture is being sold at 3 retail stores.

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Question 217

As we increase the number of independent variables in a multiple regression model,the F statistic in the analysis of variance table for the multiple regression model will ________ increase.

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Question 218

Below is a partial multiple regression ANOVA table. Source SS df Model 0.242 2 Error 0.105 3 -What is the value of R²?

(Multiple Choice)

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Question 219

In multiple regression analysis,if the simple correlation coefficient between the dependent variable and one of the independent variables is .95,then this indicates that the problem of multicollinearity exists.

(True/False)

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Question 220

Below is a partial multiple regression ANOVA table. Source SS df 535.9569 1 1,167.5634 1 18.9886 1 Error 3,459.6803 8 -How many observations were in the sample?

The graph of the prediction equation obtained from fitting the model y = $\beta$ ₀+ $\beta$ ₁X + $\beta$ ₂X²+ $\varepsilon$ Is a(n):

Below is a partial multiple regression computer output based on a quadratic regression model. Source SS df Model 29.44 2 Error 59.96 15 Standard Error Variable Coefficient Intercept 8.01 1.45 -1.35 0.55 0.46 0.43 -What is number of observations in the sample?

Below is a partial multiple regression ANOVA table. Source SS df 535.9569 1 1,167.5634 1 18.9886 1 Error 3,459.6803 8 -Calculate the explained variation.

In the quadratic regression model y = $\beta$ ₀ + $\beta$ ₁X + $\beta$ ₂X²+ $\varepsilon$ The $\beta$ 1term represents the:

Below is a partial multiple regression ANOVA table. Source SS df Model 0.242 2 Error 0.105 3 -What is the explained variation?

The _____ term describes the effects on y of all factors other than the independent variables in a multiple regression model.

Below is a partially completed multiple regression analysis of variance (ANOVA)table.Calculate R².

Consider the following partial computer output for a multiple regression model. Predictor Coefficient Standard Dev Constant 99.3883 1 -0.007207 0.0031 2 0.0011336 0.00122 3 0.9324 0.373 Analysis of Variance Source 31.308 Regression 3 9.378 Error (residual) 16 -What is the mean square error?

In a regression model,a value of the error term depends upon other values of the error term.

When the quadratic regression model y = ?₀+ ?₁x + ?₂x²+ ? is used,the term _____ shows the rate of curvature of the parabola.

Below is a partial multiple regression ANOVA table. Source SS df 535.9569 1 1,167.5634 1 18.9886 1 Error 3,459.6803 8 -What is the mean square error?

The multiple coefficient of determination is the _______ divided by the total variation.

Consider the following partial computer output for a multiple regression model. Predictor Coefficient Standard Deviation Constant 41.225 6.380 1.081 1.353 -18.404 4.547 Analysis of Variance Source Regression 2 2270.11 Error (residual) 26 3585.75 -Calculate R².

As we increase the number of independent variables in a multiple regression model,the F statistic in the analysis of variance table for the multiple regression model will ________ increase.

Below is a partial multiple regression ANOVA table. Source SS df Model 0.242 2 Error 0.105 3 -What is the value of R²?

In multiple regression analysis,if the simple correlation coefficient between the dependent variable and one of the independent variables is .95,then this indicates that the problem of multicollinearity exists.

An Introduction to Business Statistics

Descriptive Statistics

Probability

Discrete Random Variables

Continuous Random Variables

Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Correlation Coefficient and Simple Linear Regression Analysis

Nonparametric Methods

Chi-Square Tests

Decision Theory

Time Series Forecasting

Filters

Exam 12: Multiple Regression and Model Building

Below is a partial multiple regression ANOVA table. Source SS df 535.9569 1 1,167.5634 1 18.9886 1 Error 3,459.6803 8 -How many observations were in the sample?

The graph of the prediction equation obtained from fitting the model y = β\betaβ 0+ β\betaβ 1X + β\betaβ 2X2+ ε\varepsilonε Is a(n):

Below is a partial multiple regression computer output based on a quadratic regression model. Source SS df Model 29.44 2 Error 59.96 15 Standard Error Variable Coefficient Intercept 8.01 1.45 -1.35 0.55 0.46 0.43 -What is number of observations in the sample?

Below is a partial multiple regression ANOVA table. Source SS df 535.9569 1 1,167.5634 1 18.9886 1 Error 3,459.6803 8 -Calculate the explained variation.

In the quadratic regression model y = β\betaβ 0 + β\betaβ 1X + β\betaβ 2X2+ ε\varepsilonε The β\betaβ 1term represents the:

Below is a partial multiple regression ANOVA table. Source SS df Model 0.242 2 Error 0.105 3 -What is the explained variation?

The _____ term describes the effects on y of all factors other than the independent variables in a multiple regression model.

Below is a partially completed multiple regression analysis of variance (ANOVA)table.Calculate R2.

Consider the following partial computer output for a multiple regression model. Predictor Coefficient Standard Dev Constant 99.3883 1 -0.007207 0.0031 2 0.0011336 0.00122 3 0.9324 0.373 Analysis of Variance Source 31.308 Regression 3 9.378 Error (residual) 16 -What is the mean square error?

In a regression model,a value of the error term depends upon other values of the error term.

When the quadratic regression model y = ?0+ ?1x + ?2x2+ ? is used,the term _____ shows the rate of curvature of the parabola.

Below is a partial multiple regression ANOVA table. Source SS df 535.9569 1 1,167.5634 1 18.9886 1 Error 3,459.6803 8 -What is the mean square error?

The multiple coefficient of determination is the _______ divided by the total variation.

Consider the following partial computer output for a multiple regression model. Predictor Coefficient Standard Deviation Constant 41.225 6.380 1.081 1.353 -18.404 4.547 Analysis of Variance Source Regression 2 2270.11 Error (residual) 26 3585.75 -Calculate R2.

As we increase the number of independent variables in a multiple regression model,the F statistic in the analysis of variance table for the multiple regression model will ________ increase.

Below is a partial multiple regression ANOVA table. Source SS df Model 0.242 2 Error 0.105 3 -What is the value of R2?

In multiple regression analysis,if the simple correlation coefficient between the dependent variable and one of the independent variables is .95,then this indicates that the problem of multicollinearity exists.

An Introduction to Business Statistics

Descriptive Statistics

Probability

Discrete Random Variables

Continuous Random Variables

Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Correlation Coefficient and Simple Linear Regression Analysis

Nonparametric Methods

Chi-Square Tests

Decision Theory

Time Series Forecasting

Filters

The graph of the prediction equation obtained from fitting the model y = $\beta$ ₀+ $\beta$ ₁X + $\beta$ ₂X²+ $\varepsilon$ Is a(n):

In the quadratic regression model y = $\beta$ ₀ + $\beta$ ₁X + $\beta$ ₂X²+ $\varepsilon$ The $\beta$ 1term represents the:

Below is a partially completed multiple regression analysis of variance (ANOVA)table.Calculate R².

When the quadratic regression model y = ?₀+ ?₁x + ?₂x²+ ? is used,the term _____ shows the rate of curvature of the parabola.

Consider the following partial computer output for a multiple regression model. Predictor Coefficient Standard Deviation Constant 41.225 6.380 1.081 1.353 -18.404 4.547 Analysis of Variance Source Regression 2 2270.11 Error (residual) 26 3585.75 -Calculate R².

Below is a partial multiple regression ANOVA table. Source SS df Model 0.242 2 Error 0.105 3 -What is the value of R²?