The following information regarding a dependent variable y and an independent variable x is provided: \[\begin{array} { l l } \Sigma x = 90 & \sum \left( y - \frac { y } { ) } \right) ( x - x ) = - 156 \\ \Sigma y = 340 & \Sigma \left( x - \frac { x } { 2 } \right) ^ { 2 } = 234 \\ n = 4 & \sum ( y - y ) ^ { 2 } = 1974 \\ \mathrm { SSR } = 104 & \end{array}\] The mean square error (MSE) is

A) 1870. B) 13. C) 1974. D) 935. A) 1870. B) 13. C) 1974. D) 935.

You are given the following information about y and x. \[\begin{array} { l l } \text { Dependent Variable } ( y ) & \text { Independent Variable } ( x ) \\ 12 & 4 \\ 3 & 6 \\ 7 & 2 \\ 6 & 4 \end{array}\] The least squares estimate of the intercept or b0 equals

A) 1. B) -1. C) -11. D) 11. A) 1. B) -1. C) -11. D) 11.

Below you are given a partial computer output based on a sample of 8 observations relating an independent variable (x) and a dependent variable (y). \[\begin{array}{l} \begin{array} { l c c } & \text { Coefficient } & \text { Standard Error } \\ \text { Intercept } & 13.251 & 10.77 \\ \mathrm { x } & 0.803 & 0.385 \end{array}\\ \mathrm { ANOVA }\\ \begin{array} { l l } & S S \\ \text { Regression } \\ \text { Error (Residual) } & 41.674 \\ \text { Total } & 71.875 \end{array} \end{array}\] a. Develop the estimated regression equation. b. At α = .05, test for the significance of the slope. c. At α = .05, perform an F test. d. Determine the coefficient of determination.

a. @#IMG-DLM& = 13.251 + .803x b. t =

You are given the following information about y and x. \[\begin{array} { l l } \text { Dependent Variable } ( y ) & \text { Independent Variable } ( x ) \\ 5 & 1 \\ 4 & 2 \\ 3 & 3 \\ 2 & 4 \\ 1 & 5 \end{array}\] The least squares estimate of the slope or b1 equals

A) 1. B) -1. C) 6. D) -5. A) 1. B) -1. C) 6. D) -5.

In a regression and correlation analysis, if r2 = 1, then

A) SSE = SST. B) SSE = 1. C) SSR = SSE. D) SSR = SST. A) SSE = SST. B) SSE = 1. C) SSR = SSE. D) SSR = SST.

A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). \[\begin{array} { l l } \Sigma x = 90 & \sum \left( y - \frac { y } { x } \right) \left( x - { } ^ { x } \right) = 466 \\ \Sigma y = 170 & \sum ( x - ) ^ { 2 } = 234 \\ n = 10 & \Sigma \left( y - y ^ { 2 } \right) ^ { 2 } = 1434 \\ \mathrm { SSE } = 505.98 & \end{array}\] The coefficient of determination equals

A) .6472. B) .3528. C) .8045. D) .5490. A) .6472. B) .3528. C) .8045. D) .5490.

The following information regarding a dependent variable y and an independent variable x is provided: \[\begin{array} { l l } \Sigma x = 90 & \sum \left( y - \frac { y } { ) } \right) ( x - x ) = - 156 \\ \Sigma y = 340 & \Sigma \left( x - \frac { x } { 2 } \right) ^ { 2 } = 234 \\ n = 4 & \sum ( y - y ) ^ { 2 } = 1974 \\ \mathrm { SSR } = 104 & \end{array}\] The sum of squares due to error (SSE) is

A) -156. B) 234. C) 1870. D) 1974. A) -156. B) 234. C) 1870. D) 1974.

The following information regarding a dependent variable (y) and an independent variable (x) is provided. \[\begin{array} { l l } y & x \\ 4 & 2 \\ 3 & 1 \\ 4 & 4 \\ 6 & 3 \\ 8 & 5 \end{array}\] SSE = 6 SST = 16 The coefficient of correlation is

A) .7906. B) -.7906. C) .625. D) .375. A) .7906. B) -.7906. C) .625. D) .375.

Regression analysis was applied between sales data (y in $1000s) and advertising data (x in $100s) and the following information was obtained. \[\begin{array} { l } \widehat { y } = 12 + 1.8 x \\ n = 17 \\ \mathrm { SSR } = 225 \\ \mathrm { SSE } = 75 \\ \mathrm {~s} b _ { 1 } = .2683 \end{array}\] Based on the above estimated regression equation, if advertising is $3000, then the point estimate for sales (in dollars) is

A) $66,000. B) $5412. C) $66. D) $17,400. A) $66,000. B) $5412. C) $66. D) $17,400.

Exam 14: Simple Linear Regression

The following information regarding a dependent variable y and an independent variable x is provided: \Sigmax=90 y- (x-x)=-156 \Sigmay=340 \Sigma=234 n=4 (y-y=1974 =104 The mean square error (MSE) is

(Multiple Choice)

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Question 61

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 12 4 3 6 7 2 6 4 The least squares estimate of the intercept or b₀ equals

(Multiple Choice)

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Question 62

Below you are given a partial computer output based on a sample of 8 observations relating an independent variable (x) and a dependent variable (y). Coefficient Standard Error Intercept 13.251 10.77 0.803 0.385 SS Regression Error (Residual) 41.674 Total 71.875 a. Develop the estimated regression equation. b. At α = .05, test for the significance of the slope. c. At α = .05, perform an F test. d. Determine the coefficient of determination.

(Essay)

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Question 63

In regression analysis, the unbiased estimate of the variance is

(Multiple Choice)

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Question 64

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 1 4 2 3 3 2 4 1 5 The least squares estimate of the slope or b₁ equals

(Multiple Choice)

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Question 65

In a regression and correlation analysis, if r² = 1, then

(Multiple Choice)

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Question 66

Regression analysis was applied between sales (in $10,000) and advertising (in $100) and the following regression function was obtained. $\hat { y }$ = 50 + 8x Based on the above estimated regression line, if advertising is $1000, then the point estimate for sales (in dollars) is

(Multiple Choice)

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Question 67

A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). \Sigmax=90 y- x- =466 \Sigmay=170 (x-=234 n=10 \Sigma=1434 =505.98 The coefficient of determination equals

(Multiple Choice)

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Question 68

The following information regarding a dependent variable y and an independent variable x is provided: \Sigmax=90 y- (x-x)=-156 \Sigmay=340 \Sigma=234 n=4 (y-y=1974 =104 The sum of squares due to error (SSE) is

(Multiple Choice)

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Question 69

Given below are five observations collected in a regression study on two variables, x (independent variable) and y (dependent variable). 2 4 3 4 4 3 5 2 6 1 a. Develop the least squares estimated regression equation. b. At the .05 level of significance, perform a t test and determine whether or not the slope is significantly different from zero. c. Perform an F test to determine whether or not the model is significant. Let α = .05. d. Compute the coefficient of determination. e. Compute the coefficient of correlation.

(Essay)

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Question 70

The following information regarding a dependent variable (y) and an independent variable (x) is provided. y x 4 2 3 1 4 4 6 3 8 5 SSE = 6 SST = 16 The coefficient of correlation is

(Multiple Choice)

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Question 71

The proportion of the variation in the dependent variable y that is explained by the estimated regression equation is measured by the

(Multiple Choice)

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Question 72

The following data shows the yearly income (in $1000) and age of a sample of seven individuals. Income (in \1 000) Age 20 18 24 20 24 23 25 34 26 24 27 27 34 27 a. Develop the least squares regression equation. b. Estimate the yearly income of a 30-year-old individual. Give your answer in dollars. c. Compute the coefficient of determination. d. Use a t test to determine whether the slope is significantly different from zero. Let α = .05. e. At the .05 level, perform an F test and determine whether or not the model is significant.

(Essay)

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Question 73

The interval estimate of the mean value of y for a given value of x is the

(Multiple Choice)

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Question 74

Regression analysis was applied between sales data (y in $1000s) and advertising data (x in $100s) and the following information was obtained. =12+1.8x n=17 =225 =75 =.2683 Based on the above estimated regression equation, if advertising is $3000, then the point estimate for sales (in dollars) is

(Multiple Choice)

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Question 75

A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation: $\hat { y }$ = 60 - 8x The above equation implies that an

(Multiple Choice)

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Question 76

In regression analysis, which of the following assumptions is not true about the error term ε?

(Multiple Choice)

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Question 77

The interval estimate of an individual value of y for a given value of x is the

(Multiple Choice)

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Question 78

If only MSE is known, you can compute the

(Multiple Choice)

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Question 79

In a simple linear regression analysis (where y is a dependent and x an independent variable), if the y-intercept is positive, then

(Multiple Choice)

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Question 80

The following information regarding a dependent variable y and an independent variable x is provided: \Sigmax=90 y- (x-x)=-156 \Sigmay=340 \Sigma=234 n=4 (y-y=1974 =104 The mean square error (MSE) is

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 12 4 3 6 7 2 6 4 The least squares estimate of the intercept or b₀ equals

In regression analysis, the unbiased estimate of the variance is

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 1 4 2 3 3 2 4 1 5 The least squares estimate of the slope or b₁ equals

In a regression and correlation analysis, if r² = 1, then

Regression analysis was applied between sales (in $10,000) and advertising (in $100) and the following regression function was obtained. $\hat { y }$ = 50 + 8x Based on the above estimated regression line, if advertising is $1000, then the point estimate for sales (in dollars) is

A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). \Sigmax=90 y- x- =466 \Sigmay=170 (x-=234 n=10 \Sigma=1434 =505.98 The coefficient of determination equals

The following information regarding a dependent variable y and an independent variable x is provided: \Sigmax=90 y- (x-x)=-156 \Sigmay=340 \Sigma=234 n=4 (y-y=1974 =104 The sum of squares due to error (SSE) is

The following information regarding a dependent variable (y) and an independent variable (x) is provided. y x 4 2 3 1 4 4 6 3 8 5 SSE = 6 SST = 16 The coefficient of correlation is

The proportion of the variation in the dependent variable y that is explained by the estimated regression equation is measured by the

The interval estimate of the mean value of y for a given value of x is the

Regression analysis was applied between sales data (y in $1000s) and advertising data (x in $100s) and the following information was obtained. =12+1.8x n=17 =225 =75 =.2683 Based on the above estimated regression equation, if advertising is $3000, then the point estimate for sales (in dollars) is

A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation: $\hat { y }$ = 60 - 8x The above equation implies that an

In regression analysis, which of the following assumptions is not true about the error term ε?

The interval estimate of an individual value of y for a given value of x is the

If only MSE is known, you can compute the

In a simple linear regression analysis (where y is a dependent and x an independent variable), if the y-intercept is positive, then

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Exam 14: Simple Linear Regression

The following information regarding a dependent variable y and an independent variable x is provided: \Sigmax=90 y- (x-x)=-156 \Sigmay=340 \Sigma=234 n=4 (y-y=1974 =104 The mean square error (MSE) is

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 12 4 3 6 7 2 6 4 The least squares estimate of the intercept or b0 equals

In regression analysis, the unbiased estimate of the variance is

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 1 4 2 3 3 2 4 1 5 The least squares estimate of the slope or b1 equals

In a regression and correlation analysis, if r2 = 1, then

Regression analysis was applied between sales (in $10,000) and advertising (in $100) and the following regression function was obtained. y^\hat { y }y^​ = 50 + 8x Based on the above estimated regression line, if advertising is $1000, then the point estimate for sales (in dollars) is

A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). \Sigmax=90 y- x- =466 \Sigmay=170 (x-=234 n=10 \Sigma=1434 =505.98 The coefficient of determination equals

The following information regarding a dependent variable y and an independent variable x is provided: \Sigmax=90 y- (x-x)=-156 \Sigmay=340 \Sigma=234 n=4 (y-y=1974 =104 The sum of squares due to error (SSE) is

The following information regarding a dependent variable (y) and an independent variable (x) is provided. y x 4 2 3 1 4 4 6 3 8 5 SSE = 6 SST = 16 The coefficient of correlation is

The proportion of the variation in the dependent variable y that is explained by the estimated regression equation is measured by the​

The interval estimate of the mean value of y for a given value of x is the

Regression analysis was applied between sales data (y in $1000s) and advertising data (x in $100s) and the following information was obtained. =12+1.8x n=17 =225 =75 =.2683 Based on the above estimated regression equation, if advertising is $3000, then the point estimate for sales (in dollars) is

A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation: y^\hat { y }y^​ = 60 - 8x The above equation implies that an

In regression analysis, which of the following assumptions is not true about the error term ε?

The interval estimate of an individual value of y for a given value of x is the

If only MSE is known, you can compute the

In a simple linear regression analysis (where y is a dependent and x an independent variable), if the y-intercept is positive, then

Data and Statistics

Descriptive Statistics: Tabulargraphical

Descriptive Statistics: Numerical

Introduction to Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling and Sampling Distributions

Interval Estimation

Hypothesis Tests

Inference About Means and Proportions With Two Populations

Inferences About Population Variances

Comparing Multiple Proportions, Test of Independence and Goodness of Fit

Experimental Design and Analysis of Variance

Multiple Regression

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You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 12 4 3 6 7 2 6 4 The least squares estimate of the intercept or b₀ equals

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 1 4 2 3 3 2 4 1 5 The least squares estimate of the slope or b₁ equals

In a regression and correlation analysis, if r² = 1, then

Regression analysis was applied between sales (in $10,000) and advertising (in $100) and the following regression function was obtained. $\hat { y }$ = 50 + 8x Based on the above estimated regression line, if advertising is $1000, then the point estimate for sales (in dollars) is

The proportion of the variation in the dependent variable y that is explained by the estimated regression equation is measured by the

A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation: $\hat { y }$ = 60 - 8x The above equation implies that an