To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

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Answer 1

Question

Chapter 29, Problem 29.47E

a.

To determine

To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

b.

To determine

To fit: A multiple regression model using the listed variables for predicting calories burnt.

c.

To check: Whether the model provides a better fit for the given data.

Answer:

The model provides a better fit for the given data.

Explanation:

Justification:

R-square:

The R-square is a multiple correlation coefficient and it is the square of correlation between the observed response variable and predicted response variable.

R-square tells about the fit of the model. If the R-square value is high then the model fits better.

From the MINITAB output it can be seen that the R-square value is 98.62%. This tells that the explanatory variables could explain 98.62% of variation in predicting the calories burnt.

d.

To determine

To find: Whether there is any significant differences in the relationship between the calories burnt and speed for two treadmills.

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Suppose you are gambling on a roulette wheel. Each time the wheel is spun, the result is one of the outcomes 0, 1, and so on through 36. Of these outcomes, 18 are red, 18 are black, and 1 is green. On each spin you bet $5 that a red outcome will occur and $1 that the green outcome will occur. If red occurs, you win a net $4. (You win $10 from red and nothing from green.) If green occurs, you win a net $24. (You win $30 from green and nothing from red.) If black occurs, you lose everything you bet for a loss of $6. a. Use simulation to generate 1,000 plays from this strategy. Each play should indicate the net amount won or lost. Then, based on these outcomes, calculate a 95% confidence interval for the total net amount won or lost from 1,000 plays of the game. (Round your answers to two decimal places and if your answer is negative value, enter "minus" sign.) I worked out the Upper Limit, but I can't seem to arrive at the correct answer for the Lower Limit. What is the Lower Limit?…

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Consider the hypothesis test Ho: 0 s² = = 4.5; s² = 2.3. Use a = 0.01. = σ against H₁: 6 > σ2. Suppose that the sample sizes are n₁ = 20 and 2 = 8, and that (a) Test the hypothesis. Round your answers to two decimal places (e.g. 98.76). The test statistic is fo = 1.96 The critical value is f = 6.18 Conclusion: fail to reject the null hypothesis at a = 0.01. (b) Construct the confidence interval on 02/2/622 which can be used to test the hypothesis: (Round your answer to two decimal places (e.g. 98.76).) 035

Answer 2

Question

Chapter 29, Problem 29.47E

a.

To determine

To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

b.

To determine

To fit: A multiple regression model using the listed variables for predicting calories burnt.

c.

To check: Whether the model provides a better fit for the given data.

Answer:

The model provides a better fit for the given data.

Explanation:

Justification:

R-square:

The R-square is a multiple correlation coefficient and it is the square of correlation between the observed response variable and predicted response variable.

R-square tells about the fit of the model. If the R-square value is high then the model fits better.

From the MINITAB output it can be seen that the R-square value is 98.62%. This tells that the explanatory variables could explain 98.62% of variation in predicting the calories burnt.

d.

To determine

To find: Whether there is any significant differences in the relationship between the calories burnt and speed for two treadmills.

Expert Solution & Answer

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Check out a sample textbook solution

See solution

Answer 3

Question

Chapter 29, Problem 29.47E

a.

To determine

To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

b.

To determine

To fit: A multiple regression model using the listed variables for predicting calories burnt.

c.

To check: Whether the model provides a better fit for the given data.

Answer:

The model provides a better fit for the given data.

Explanation:

Justification:

R-square:

The R-square is a multiple correlation coefficient and it is the square of correlation between the observed response variable and predicted response variable.

R-square tells about the fit of the model. If the R-square value is high then the model fits better.

From the MINITAB output it can be seen that the R-square value is 98.62%. This tells that the explanatory variables could explain 98.62% of variation in predicting the calories burnt.

d.

To determine

To find: Whether there is any significant differences in the relationship between the calories burnt and speed for two treadmills.

Expert Solution & Answer

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Check out a sample textbook solution

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Answer 4

Question

Chapter 29, Problem 29.47E

a.

To determine

To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

b.

To determine

To fit: A multiple regression model using the listed variables for predicting calories burnt.

c.

To check: Whether the model provides a better fit for the given data.

Answer:

The model provides a better fit for the given data.

Explanation:

Justification:

R-square:

The R-square is a multiple correlation coefficient and it is the square of correlation between the observed response variable and predicted response variable.

R-square tells about the fit of the model. If the R-square value is high then the model fits better.

From the MINITAB output it can be seen that the R-square value is 98.62%. This tells that the explanatory variables could explain 98.62% of variation in predicting the calories burnt.

d.

To determine

To find: Whether there is any significant differences in the relationship between the calories burnt and speed for two treadmills.

Expert Solution & Answer

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Check out a sample textbook solution

See solution

Answer 5

Chapter 29, Problem 29.47E

a.

To determine

To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

b.

To determine

To fit: A multiple regression model using the listed variables for predicting calories burnt.

c.

To check: Whether the model provides a better fit for the given data.

Answer:

The model provides a better fit for the given data.

Explanation:

Justification:

R-square:

The R-square is a multiple correlation coefficient and it is the square of correlation between the observed response variable and predicted response variable.

R-square tells about the fit of the model. If the R-square value is high then the model fits better.

From the MINITAB output it can be seen that the R-square value is 98.62%. This tells that the explanatory variables could explain 98.62% of variation in predicting the calories burnt.

d.

To determine

To find: Whether there is any significant differences in the relationship between the calories burnt and speed for two treadmills.

Answer 6

Chapter 29, Problem 29.47E

a.

To determine

To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

Answer 7

Chapter 29, Problem 29.47E

a.

To determine

To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

Answer 8

b.

To determine

To fit: A multiple regression model using the listed variables for predicting calories burnt.

Answer 9

b.

To determine

To fit: A multiple regression model using the listed variables for predicting calories burnt.

Answer 10

c.

To check: Whether the model provides a better fit for the given data.

Answer:

The model provides a better fit for the given data.

Explanation:

Justification:

R-square:

The R-square is a multiple correlation coefficient and it is the square of correlation between the observed response variable and predicted response variable.

R-square tells about the fit of the model. If the R-square value is high then the model fits better.

From the MINITAB output it can be seen that the R-square value is 98.62%. This tells that the explanatory variables could explain 98.62% of variation in predicting the calories burnt.

d.

To determine

To find: Whether there is any significant differences in the relationship between the calories burnt and speed for two treadmills.

Answer 11

c.

To check: Whether the model provides a better fit for the given data.

Answer:

The model provides a better fit for the given data.

Explanation:

Justification:

R-square:

The R-square is a multiple correlation coefficient and it is the square of correlation between the observed response variable and predicted response variable.

R-square tells about the fit of the model. If the R-square value is high then the model fits better.

From the MINITAB output it can be seen that the R-square value is 98.62%. This tells that the explanatory variables could explain 98.62% of variation in predicting the calories burnt.

d.

To determine

To find: Whether there is any significant differences in the relationship between the calories burnt and speed for two treadmills.

Answer 12

Expert Solution & Answer

Answer 13

Expert Solution & Answer

Answer 14

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Answer 15

Ch. 29.1 - Prob. 29.1AYK Ch. 29.1 - Prob. 29.2AYK Ch. 29.2 - Prob. 29.3AYK Ch. 29.2 - Prob. 29.4AYK Ch. 29.2 - Prob. 29.5AYK Ch. 29.3 - Prob. 29.6AYK Ch. 29.3 - Prob. 29.7AYK Ch. 29.4 - Prob. 29.8AYK Ch. 29.4 - Prob. 29.9AYK Ch. 29.4 - Prob. 29.10AYK

To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

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To construct: A scatterplot of calories burnt against MPH for various incline positions obtained from two types of treadmills.

To fit: A multiple regression model using the listed variables for predicting calories burnt.

To find: Whether there is any significant differences in the relationship between the calories burnt and speed for two treadmills.

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Chapter 29 Solutions