An experiment was performed on a certain metal to determine if the strength is a function of heating time. Results based on 10 metal sheets are given below. ∑X = 30 ∑X2 = 104 ∑Y = 40 ∑Y2 = 178 ∑XY = 134 Using the simple linear regression model, find the estimated y-intercept and slope and write the equation of the least squares regression line.

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Exam 14: Simple Linear Regression Analysis

An experiment was performed on a certain metal to determine if the strength is a function of heating time. Partial results based on a sample of 10 metal sheets are given below. The simple linear regression equation is ŷ = 1 + 1X. The time is in minutes, the strength is measured in pounds per square inch, MSE = .5, Σx = 30, and Σx²= 104. Determine the 95 percent confidence interval for the mean value of metal strength when the average heating time is 4 minutes.

(Short Answer)

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Question 41

The following results were obtained from a simple regression analysis. Ŷ = 37.2895 − 1.2024X r²= .6744 s_b = .2934 For each unit change in X (independent variable), what is the estimated change in Y (dependent variable)?

(Short Answer)

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Question 42

Consider the following partial computer output from a simple linear regression analysis. R².9722 Write the equation of the least squares line.

(Short Answer)

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Question 43

Consider the following partial computer output from a simple linear regression analysis. R².9722 What is the estimated slope?

(Short Answer)

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Question 44

Based on 25 time-ordered observations from a simple regression model, we have determined the Durbin-Watson statistic, d = 1.39. At α = .05, test to determine if there is any evidence of positive autocorrelation. State your conclusions.

(Short Answer)

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Question 45

The strength of the relationship between two quantitative variables can be measured by

(Multiple Choice)

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Question 46

An experiment was performed on a certain metal to determine if the strength is a function of heating time. Results based on 10 metal sheets are given below. ∑X = 30 ∑X² = 104 ∑Y = 40 ∑Y² = 178 ∑XY = 134 Using the simple linear regression model, find the estimated y-intercept and slope and write the equation of the least squares regression line.

(Short Answer)

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Question 47

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance What is the explained variance?

(Short Answer)

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Question 48

The ________ distribution is used for testing the significance of the slope term.

(Multiple Choice)

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Question 49

Which of the following is a violation of one of the major assumptions of the simple regression model?

(Multiple Choice)

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Question 50

A data set with 7 observations yielded the following. Use the simple linear regression model. ∑X = 21.57 ∑X² = 68.31 ∑Y = 188.9 ∑Y² = 5,140.23 ∑XY = 590.83 SSE = 1.117 Calculate the standard error.

(Short Answer)

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Question 51

The slope of the simple linear regression equation represents the average change in the value of the dependent variable per unit change in the independent variable (X).

(True/False)

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Question 52

The ________ measures the strength of the linear relationship between the dependent variable and the independent variable.

(Multiple Choice)

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Question 53

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance Determine the 95 percent confidence interval for the mean value of y when x = 9.00. Givens: ∑x = 129.03 and ∑x² = 1178.547

(Short Answer)

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Question 54

Consider the following partial computer output from a simple linear regression analysis with a sample size of 16 observations. Find the t test to test the significance of the model.

(Short Answer)

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Question 55

The following results were obtained from a simple regression analysis. Ŷ = 37.2895 − 1.2024X r² = .6744s_b = .2934 When X (independent variable) is equal to zero, what is the estimated value of Y (dependent variable)?

(Short Answer)

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Question 56

A local tire dealer wants to predict the number of tires sold each month. He believes that the number of tires sold is a linear function of the amount of money invested in advertising. He randomly selects 6 months of data consisting of monthly tire sales (in thousands of tires) and monthly advertising expenditures (in thousands of dollars). The simple linear regression equation is ŷ = 3 + 1x. The dealer randomly selects one of the six observations, with a monthly sales value of 8,000 tires and monthly advertising expenditures of $7,000. Calculate the value of the residual for this observation.

(Short Answer)

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Question 57

A local tire dealer wants to predict the number of tires sold each month. He believes that the number of tires sold is a linear function of the amount of money invested in advertising. He randomly selects 6 months of data consisting of tire sales (in thousands of tires) and advertising expenditures (in thousands of dollars). Based on the data set with 6 observations, the simple linear regression model yielded the following results. ∑X = 24 ∑X² = 124 ∑Y = 42 ∑Y² = 338 ∑XY = 196 Find the rejection point for the t statistic at α = .05 and test H₀: β₁ = 0 vs. H_a: β₁ ≠ 0.

(Short Answer)

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Question 58

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance What is the estimated slope?

(Short Answer)

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Question 59

The ________ is the range of the previously observed values of x.

(Multiple Choice)

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Question 60

Showing 41 - 60 of 147

The following results were obtained from a simple regression analysis. Ŷ = 37.2895 − 1.2024X r²= .6744 s_b = .2934 For each unit change in X (independent variable), what is the estimated change in Y (dependent variable)?

Consider the following partial computer output from a simple linear regression analysis. R².9722 Write the equation of the least squares line.

Consider the following partial computer output from a simple linear regression analysis. R².9722 What is the estimated slope?

Based on 25 time-ordered observations from a simple regression model, we have determined the Durbin-Watson statistic, d = 1.39. At α = .05, test to determine if there is any evidence of positive autocorrelation. State your conclusions.

The strength of the relationship between two quantitative variables can be measured by

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance What is the explained variance?

The ________ distribution is used for testing the significance of the slope term.

Which of the following is a violation of one of the major assumptions of the simple regression model?

A data set with 7 observations yielded the following. Use the simple linear regression model. ∑X = 21.57 ∑X² = 68.31 ∑Y = 188.9 ∑Y² = 5,140.23 ∑XY = 590.83 SSE = 1.117 Calculate the standard error.

The slope of the simple linear regression equation represents the average change in the value of the dependent variable per unit change in the independent variable (X).

The ________ measures the strength of the linear relationship between the dependent variable and the independent variable.

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance Determine the 95 percent confidence interval for the mean value of y when x = 9.00. Givens: ∑x = 129.03 and ∑x² = 1178.547

Consider the following partial computer output from a simple linear regression analysis with a sample size of 16 observations. Find the t test to test the significance of the model.

The following results were obtained from a simple regression analysis. Ŷ = 37.2895 − 1.2024X r² = .6744s_b = .2934 When X (independent variable) is equal to zero, what is the estimated value of Y (dependent variable)?

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance What is the estimated slope?

The ________ is the range of the previously observed values of x.

An Introduction to Business Statistics and Analytics

Descriptive Statistics and Analytics: Tabular and Graphical Methods

Descriptive Statistics and Analytics: Numerical Methods

Probability and Probability Models

Predictive Analytics I: Trees, K-Nearest Neighbors, Naive Bayes,

Discrete Random Variables

Continuous Random Variables

Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Chi-Square Tests

Multiple Regression and Model Building

Predictive Analytics Ii: Logistic Regression, Discriminate Analysis,

Time Series Forecasting and Index Numbers

Nonparametric Methods

Decision Theory

Filters

Exam 14: Simple Linear Regression Analysis

The following results were obtained from a simple regression analysis. Ŷ = 37.2895 − 1.2024X r2 = .6744 sb = .2934 For each unit change in X (independent variable), what is the estimated change in Y (dependent variable)?

Consider the following partial computer output from a simple linear regression analysis. R2.9722 Write the equation of the least squares line.

Consider the following partial computer output from a simple linear regression analysis. R2.9722 What is the estimated slope?

Based on 25 time-ordered observations from a simple regression model, we have determined the Durbin-Watson statistic, d = 1.39. At α = .05, test to determine if there is any evidence of positive autocorrelation. State your conclusions.

The strength of the relationship between two quantitative variables can be measured by

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance What is the explained variance?

The ________ distribution is used for testing the significance of the slope term.

Which of the following is a violation of one of the major assumptions of the simple regression model?

A data set with 7 observations yielded the following. Use the simple linear regression model. ∑X = 21.57 ∑X2 = 68.31 ∑Y = 188.9 ∑Y2 = 5,140.23 ∑XY = 590.83 SSE = 1.117 Calculate the standard error.

The slope of the simple linear regression equation represents the average change in the value of the dependent variable per unit change in the independent variable (X).

The ________ measures the strength of the linear relationship between the dependent variable and the independent variable.

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance Determine the 95 percent confidence interval for the mean value of y when x = 9.00. Givens: ∑x = 129.03 and ∑x2 = 1178.547

Consider the following partial computer output from a simple linear regression analysis with a sample size of 16 observations. Find the t test to test the significance of the model.

The following results were obtained from a simple regression analysis. Ŷ = 37.2895 − 1.2024X r2 = .6744sb = .2934 When X (independent variable) is equal to zero, what is the estimated value of Y (dependent variable)?

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance What is the estimated slope?

The ________ is the range of the previously observed values of x.

An Introduction to Business Statistics and Analytics

Descriptive Statistics and Analytics: Tabular and Graphical Methods

Descriptive Statistics and Analytics: Numerical Methods

Probability and Probability Models

Predictive Analytics I: Trees, K-Nearest Neighbors, Naive Bayes,

Discrete Random Variables

Continuous Random Variables

Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Chi-Square Tests

Multiple Regression and Model Building

Predictive Analytics Ii: Logistic Regression, Discriminate Analysis,

Time Series Forecasting and Index Numbers

Nonparametric Methods

Decision Theory

Filters

The following results were obtained from a simple regression analysis. Ŷ = 37.2895 − 1.2024X r²= .6744 s_b = .2934 For each unit change in X (independent variable), what is the estimated change in Y (dependent variable)?

Consider the following partial computer output from a simple linear regression analysis. R².9722 Write the equation of the least squares line.

Consider the following partial computer output from a simple linear regression analysis. R².9722 What is the estimated slope?

A data set with 7 observations yielded the following. Use the simple linear regression model. ∑X = 21.57 ∑X² = 68.31 ∑Y = 188.9 ∑Y² = 5,140.23 ∑XY = 590.83 SSE = 1.117 Calculate the standard error.

Consider the following partial computer output from a simple linear regression analysis. S = .4862R-Sq = ________ Analysis of Variance Determine the 95 percent confidence interval for the mean value of y when x = 9.00. Givens: ∑x = 129.03 and ∑x² = 1178.547

The following results were obtained from a simple regression analysis. Ŷ = 37.2895 − 1.2024X r² = .6744s_b = .2934 When X (independent variable) is equal to zero, what is the estimated value of Y (dependent variable)?