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 coefficient of determination equals

A) 0. B) -1. C) +1. D) -.5. A) 0. B) -1. C) +1. D) -.5. C

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 intercept or b0 equals

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

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 sum of squares due to regression (SSR) is

A) 1434. B) 505.98. C) 50.598. D) 928.02. A) 1434. B) 505.98. C) 50.598. D) 928.02.

A company has recorded data on the daily demand for its product (y in thousands of units) and the unit price (x in hundreds of dollars).A sample of 15 days demand and associated prices resulted in the following data. \[\begin{array} { l l } \Sigma x = 75 & \Sigma ( y - y ) ( x - x ) = - 59 \\ \Sigma y = 135 & \sum ( x - \bar { x } ) ^ { 2 } = 94 \\ \Sigma ( y - y ) ^ { 2 } = 100 & \\ \mathrm { SSE } = 62.9681 & \end{array}\] a. Using the above information, develop the least squares estimated regression line and write the equation. b. Compute the coefficient of determination. c. Perform an F test and determine whether or not there is a significant relationship between demand and unit price. Let α = .05. d. Would the demand ever reach zero? If yes, at what price would the demand be zero?

a. @#IMG-DLM& = 12.138 - .6277x b. r 2 =

You are given the following information about y and x. \[\begin{array} { l l } \text { Dependent Variable } ( y ) & \text { Independent Variable } ( x ) \\ 5 & 4 \\ 7 & 6 \\ 9 & 2 \\ 11 & 4 \end{array}\] The least squares estimate of the slope or b1 equals

A) -10. B) 10. C) .5. D) -.5. A) -10. B) 10. C) .5. D) -.5.

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 y-intercept is

A) -.667. B) .667. C) 100. D) -100. A) -.667. B) .667. C) 100. D) -100.

For the following data, the value of SSE = 18. \[\begin{array} { l l } \text { Dependent Variable } ( y ) & \text { Independent Variable } ( x ) \\ 15 & 4 \\ 17 & 6 \\ 23 & 2 \\ 17 & 4 \end{array}\] The coefficient of determination (r2) equals

A) .7071. B) -.7071. C) .5. D) -.5. A) .7071. B) -.7071. C) .5. D) -.5.

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 coefficient of determination equals

A) -.4364. B) .4364. C) -.1905. D) .1905. A) -.4364. B) .4364. C) -.1905. D) .1905.

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 MSE is

A) 1. B) 2. C) 3. D) 4. A) 1. B) 2. C) 3. D) 4.

You are given the following information about y and x. \[\begin{array} { l l } \text { Dependent Variable } ( y ) & \text { Independent Variable } ( x ) \\ 5 & 4 \\ 7 & 6 \\ 9 & 2 \\ 11 & 4 \end{array}\] The least squares estimate of the intercept or b0 equals

A) -10. B) 10. C) .5. D) -.5. A) -10. B) 10. C) .5. D) -.5.

Shown below is a portion of a computer output for a regression analysis relating x (independent variable) and y (dependent variable). \[\begin{array}{l} \text { ANOVA }\\ \begin{array} { l c l } & d f & S S \\ \text { Regression } & 1 & 115.064 \\ \text { Residual } & 13 & 82.936 \\ \text { Total } & & \\ & \text { Coefficients } & \text { Standard Error } \\ \text { Intercept } & 15.532 & 1.457 \\ \mathrm { x } & - 1.106 & 0.261 \end{array} \end{array}\] a. Perform a t test using the p-value approach and determine whether or not x and y are related. Let α = .05. b. Using the p-value approach, perform an F test and determine whether or not x and y are related. Let α = .05. c. Compute the coefficient of determination and interpret its meaning. Be very specific.

The answer of Shown below is a portion of a...

Regression analysis was applied between sales data (y in $1000s) and advertising data (x in $100s) and the following information was obtained. \[\begin{array} { l } \widehat { y } = 12 + 1.8 x \\ n = 17 \\ \mathrm { SSR } = 225 \\ \mathrm { SSE } = 75 \\ \mathrm {~s} b _ { 1 } = .2683 \end{array}\] The F statistic computed from the above data is

A) 3. B) 45. C) 48. D) 50. A) 3. B) 45. C) 48. D) 50.

Exam 14: Simple Linear Regression

Regression analysis was applied between demand for a product (y) and the price of the product (x), and the following estimated regression equation was obtained. $\hat { y }$ = 120 - 10x Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to

Free

(Multiple Choice)

4.9/5

(32)

Question 1

Correct Answer:

Verified

D

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 coefficient of determination equals

Free

(Multiple Choice)

4.8/5

(35)

Question 2

Correct Answer:

Verified

C

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 intercept or b₀ equals

Free

(Multiple Choice)

4.9/5

(34)

Question 3

Correct Answer:

Verified

C

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 sum of squares due to regression (SSR) is

(Multiple Choice)

4.8/5

(32)

Question 4

A company has recorded data on the daily demand for its product (y in thousands of units) and the unit price (x in hundreds of dollars).A sample of 15 days demand and associated prices resulted in the following data. \Sigmax=75 \Sigma(y-y)(x-x)=-59 \Sigmay=135 (x-=94 \Sigma(y-y=100 =62.9681 a. Using the above information, develop the least squares estimated regression line and write the equation. b. Compute the coefficient of determination. c. Perform an F test and determine whether or not there is a significant relationship between demand and unit price. Let α = .05. d. Would the demand ever reach zero? If yes, at what price would the demand be zero?

(Essay)

4.8/5

(32)

Question 5

In a regression analysis, the coefficient of determination is .4225.The coefficient of correlation in this situation is

(Multiple Choice)

4.9/5

(37)

Question 6

In a residual plot against x that does not suggest we should challenge the assumptions of our regression model, we would expect to see a

(Multiple Choice)

4.9/5

(43)

Question 7

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 4 7 6 9 2 11 4 The least squares estimate of the slope or b₁ equals

(Multiple Choice)

4.8/5

(30)

Question 8

The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y) = β₀ + β₁x, is known as the

(Multiple Choice)

4.8/5

(32)

Question 9

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 y-intercept is

(Multiple Choice)

4.9/5

(36)

Question 10

If two variables, x and y, have a strong linear relationship, then

(Multiple Choice)

4.8/5

(30)

Question 11

The coefficient of correlation

(Multiple Choice)

4.8/5

(34)

Question 12

For the following data, the value of SSE = 18. Dependent Variable (y) Independent Variable (x) 15 4 17 6 23 2 17 4 The coefficient of determination (r²) equals

(Multiple Choice)

4.8/5

(34)

Question 13

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 coefficient of determination equals

(Multiple Choice)

4.9/5

(39)

Question 14

If a data set produces SSR = 400 and SSE = 100, then the coefficient of determination is

(Multiple Choice)

4.7/5

(37)

Question 15

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 MSE is

(Multiple Choice)

4.8/5

(31)

Question 16

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 4 7 6 9 2 11 4 The least squares estimate of the intercept or b₀ equals

(Multiple Choice)

4.8/5

(38)

Question 17

Given below are four observations collected in a regression study on two variables, x (independent variable) and y (dependent variable). 2 4 6 7 9 8 9 9 a. Develop the least squares estimated regression equation. b. At the 5% 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.

(Essay)

4.8/5

(34)

Question 18

Shown below is a portion of a computer output for a regression analysis relating x (independent variable) and y (dependent variable). ANOVA df SS Regression 1 115.064 Residual 13 82.936 Total Coefficients Standard Error Intercept 15.532 1.457 -1.106 0.261 a. Perform a t test using the p-value approach and determine whether or not x and y are related. Let α = .05. b. Using the p-value approach, perform an F test and determine whether or not x and y are related. Let α = .05. c. Compute the coefficient of determination and interpret its meaning. Be very specific.

(Essay)

4.8/5

(24)

Question 19

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 The F statistic computed from the above data is

(Multiple Choice)

4.9/5

(33)

Question 20

Regression analysis was applied between demand for a product (y) and the price of the product (x), and the following estimated regression equation was obtained. $\hat { y }$ = 120 - 10x Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to

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 coefficient of determination 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 intercept or b₀ equals

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 sum of squares due to regression (SSR) is

In a regression analysis, the coefficient of determination is .4225.The coefficient of correlation in this situation is

In a residual plot against x that does not suggest we should challenge the assumptions of our regression model, we would expect to see a

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 4 7 6 9 2 11 4 The least squares estimate of the slope or b₁ equals

The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y) = β₀ + β₁x, is known as the

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 y-intercept is

If two variables, x and y, have a strong linear relationship, then

The coefficient of correlation

For the following data, the value of SSE = 18. Dependent Variable (y) Independent Variable (x) 15 4 17 6 23 2 17 4 The coefficient of determination (r²) equals

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 coefficient of determination equals

If a data set produces SSR = 400 and SSE = 100, then the coefficient of determination 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 MSE is

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 4 7 6 9 2 11 4 The least squares estimate of the intercept or b₀ equals

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 The F statistic computed from the above data is

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

Filters

Exam 14: Simple Linear Regression

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 coefficient of determination 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 intercept or b0 equals

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 sum of squares due to regression (SSR) is

In a regression analysis, the coefficient of determination is .4225.The coefficient of correlation in this situation is

In a residual plot against x that does not suggest we should challenge the assumptions of our regression model, we would expect to see​ a

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 4 7 6 9 2 11 4 The least squares estimate of the slope or b1 equals

The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y) = β0 + β1x, is known as the

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 y-intercept is

If two variables, x and y, have a strong linear relationship, then

The coefficient of correlation

For the following data, the value of SSE = 18. Dependent Variable (y) Independent Variable (x) 15 4 17 6 23 2 17 4 The coefficient of determination (r2) equals

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 coefficient of determination equals

If a data set produces SSR = 400 and SSE = 100, then the coefficient of determination 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 MSE is

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 4 7 6 9 2 11 4 The least squares estimate of the intercept or b0 equals

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 The F statistic computed from the above data is

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

Filters

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 intercept or b₀ equals

In a residual plot against x that does not suggest we should challenge the assumptions of our regression model, we would expect to see a

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 4 7 6 9 2 11 4 The least squares estimate of the slope or b₁ equals

The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y) = β₀ + β₁x, is known as the

For the following data, the value of SSE = 18. Dependent Variable (y) Independent Variable (x) 15 4 17 6 23 2 17 4 The coefficient of determination (r²) equals

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 4 7 6 9 2 11 4 The least squares estimate of the intercept or b₀ equals