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Assignment 6 Assignment 6, Study Guides, Projects, Research of Engineering

University of Maine (UMaine)Engineering

Assignment 6 Assignment 6 Assignment 6 Assignment 6 Assignment 6 Assignment 6 Assignment 6 Assignment 6 Assignment 6 Assignment 6

Typology: Study Guides, Projects, Research

2022/2023

Uploaded on 10/30/2023

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elijah-verhoff 🇺🇸

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Name_______________ Assignment of Chapter 6
Part One: Consider the data in Exercise 6-46, use Excel to assist you in multiple linear
regression, and answer the following questions. They account for 50 points of this assignment.
1. Which of these six regressors will be kept in your final regression equation? Why? P-
value = 0.10
2. Find the regression coefficients for each regressor in your final equation.
3. Give the overall multiple R for your final equation.
4. Give the adjusted R square
5. Out print your residual plots for your final regression equation and comment about your
plots.
Part Two: Multiple choice questions (2 points each)
1. Which one of the following is not appropriate for studying the relationship between two quantitative variables?
A. Scatterplot
B. Bar chart
C. Correlation
D. Regression
2. A scatterplot is a
A. one-dimensional graph of randomly scattered data.
B. two-dimensional graph of a straight line.
C. two-dimensional graph of a curved line.
D. two-dimensional graph of data values.
3. Two variables have a positive association when
A. the values of one variable tend to increase as the values of the other variable increase.
B. the values of one variable tend to decrease as the values of the other variable increase.
4. A scatterplot of geographic latitude (x axis) and average January temperature (y axis) for 20 cities in the United
States is given below. Is there a positive association or a negative association? Explain what such an
association means in the context of this situation.
5. Which of the following can not be answered from a regression equation?
A. Predict the value of y at a particular value of x.
B. Estimate the slope between y and x.
C. Estimate whether the linear association is positive or negative.
D. Estimate whether the association is linear or non-linear
Questions 6 to 7: The simple linear regression equation can be written as
6. In the simple linear regression equation, the symbol represents the
A. predicted response.
B. estimated intercept.
C. estimated slope.
D. explanatory variable.
1
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Name_______________ Assignment of Chapter 6

Part One: Consider the data in Exercise 6-46, use Excel to assist you in multiple linear

regression, and answer the following questions. They account for 50 points of this assignment.

1. Which of these six regressors will be kept in your final regression equation? Why? P -

value = 0. 10

2. Find the regression coefficients for each regressor in your final equation.

3. Give the overall multiple R for your final equation.

4. Give the adjusted R square

5. Out print your residual plots for your final regression equation and comment about your

plots.

Part Two: Multiple choice questions (2 points each)

  1. Which one of the following is not appropriate for studying the relationship between two quantitative variables? A. Scatterplot B. Bar chart C. Correlation D. Regression
  2. A scatterplot is a A. one-dimensional graph of randomly scattered data. B. two-dimensional graph of a straight line. C. two-dimensional graph of a curved line. D. two-dimensional graph of data values.
  3. Two variables have a positive association when A. the values of one variable tend to increase as the values of the other variable increase. B. the values of one variable tend to decrease as the values of the other variable increase.
  4. A scatterplot of geographic latitude ( x axis) and average January temperature ( y axis) for 20 cities in the United States is given below. Is there a positive association or a negative association? Explain what such an association means in the context of this situation.
  5. Which of the following can not be answered from a regression equation? A. Predict the value of y at a particular value of x. B. Estimate the slope between y and x. C. Estimate whether the linear association is positive or negative. D. Estimate whether the association is linear or non-linear Questions 6 to 7: The simple linear regression equation can be written as
  6. In the simple linear regression equation, the symbol represents the A. predicted response. B. estimated intercept. C. estimated slope. D. explanatory variable.
  1. In the simple linear regression equation, the symbol x represents the A. estimated or predicted response. B. estimated intercept. C. estimated slope. D. explanatory variable. Questions 8 to 9: A regression between foot length (response variable in cm) and height (explanatory variable in inches) for 33 students resulted in the following regression equation: = 10.9 + 0.23 x
  2. One student in the sample was 73 inches tall with a foot length of 29 cm. What is the predicted foot length for this student? A. 27.69 cm B. 29 cm
  3. One student in the sample was 73 inches tall with a foot length of 29 cm. What is the residual for this student? A. 29 cm B. 1.31 cm C. –1.31 cm Questions 10 to 12 : Past data has shown that the regression line relating the final exam score and the midterm exam score for students who take statistics from a certain professor is: final exam = 50 + 0.5midterm
  4. For a student with a midterm score of 50, the predicted final exam score is A. 50. B. 75. C. 100.
  5. One interpretation of the intercept is A. a student who scored 0 on the midterm would be predicted to score 50 on the final exam. B. a student who scored 2 points higher than another student on the midterm would be predicted to score 1 point higher than the other student on the final exam.
  6. One interpretation of the slope is A. a student who scored 0 on the midterm would be predicted to score 50 on the final exam. B. a student who scored 2 points higher than another student on the midterm would be predicted to score 1 point higher than the other student on the final exam. C. none of the above are an interpretation of the slope.
  7. For which one of these relationships could we use a regression analysis? A. Relationship between weight and height. B. Relationship between political party membership and opinion about abortion. C. Relationship between gender and whether person has a tattoo.
  8. A scatter plot and regression line can be used for all of the following except A. to determine if any ( x , y) pairs are outliers. B. to predict y at a specific value of x. C. to estimate the average y at a specific value of x. D. to determine if a change in x causes a change in y.
  9. A group of adults aged 20 to 80 were tested to see how far away they could first hear an ambulance coming towards them. An equation describing the relationship between distance (in feet) and age was found to be: Distance = 600  3  Age Based on the equation, what is the strength of the relationship between distance and age? A. There is a strong relationship. B. There is a weak relationship. C. There is no relationship. D. Strength can’t be determined from the equation.
  10. Which of the following is a possible value of r^2 , and indicates the strongest linear relationship between two quantitative variables? A. 90% B. 80% C. 120%
  11. Which of the following correlation values indicates the strongest linear relationship between two quantitative variables? A. r = 0.65B. r = 0.30 C. r = 0.
  12. Which of the following correlation values indicates the strongest linear relationship between two quantitative variables? A. r = 0.75B. r = 0.80 C. r = 0.