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Ch. 9 - Correlation and Regression
Larson - Elementary Statistics: Picturing the World 8th Edition
Larson8th EditionElementary Statistics: Picturing the WorldISBN: 9780137493470Not the one you use?Change textbook
Chapter 9, Problem 9.4.1

1. Interpret the meaning of the coefficient -8.2 in the multiple regression equation y=112.1+0.43x_1-8.2x_2+29.5x_3.

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Identify the multiple regression equation given: y = 112.1 + 0.43x1 - 8.2x2 + 29.5x3.
Recall that in a multiple regression equation, each coefficient represents the expected change in the dependent variable y for a one-unit increase in the corresponding independent variable, holding all other variables constant.
Focus on the coefficient of x_2, which is -8.2. This means that for every one-unit increase in x_2, the predicted value of y decreases by 8.2 units, assuming x_1 and x_3 remain unchanged.
Interpret the negative sign of the coefficient as indicating an inverse relationship between x_2 and y: as x_2 increases, y tends to decrease.
Summarize the interpretation: the coefficient -8.2 quantifies the strength and direction of the effect of x_2 on y within the context of the other variables in the model.

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Key Concepts

Here are the essential concepts you must grasp in order to answer the question correctly.

Multiple Regression Equation

A multiple regression equation models the relationship between one dependent variable and two or more independent variables. Each coefficient represents the expected change in the dependent variable for a one-unit change in the corresponding independent variable, holding other variables constant.
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Intro to Least Squares Regression

Interpretation of Regression Coefficients

A regression coefficient quantifies the effect of an independent variable on the dependent variable. A negative coefficient, like -8.2, indicates that as the independent variable increases by one unit, the dependent variable decreases by 8.2 units, assuming other variables remain fixed.
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Holding Other Variables Constant

In multiple regression, the effect of each independent variable is interpreted while keeping other variables constant. This means the coefficient isolates the unique contribution of that variable to changes in the dependent variable, controlling for the influence of other predictors.
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Related Practice
Textbook Question

"Predicting y-Values In Exercises 3-6, use the multiple regression equation to predict the y-values for the values of the independent variables.

3. Cauliflower Yield The equation used to predict the annual cauliflower yield (in pounds

per acre) is y=24,791+4.508x_1-4.723x_2

where x_1 is the number of acres planted and x_2 is the number of acres harvested.(Adapted from United States Department of Agriculture)

a. x_1 = 36,500, x_2 = 36,100

b. x_1 = 38,100, x_2 = 37,800

c. x_1 = 39,000, x_2 = 38,800

d. x_1 = 42,200, x_2 = 42,100"

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Textbook Question

4. For a set of data and a corresponding regression line, describe all values of x that provide meaningful predictions for y.

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Textbook Question

2. Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative?

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Textbook Question

"Constructing and Interpreting a Prediction Interval In Exercises 21-30, construct the indicated prediction interval and interpret the results.

25. Mean Wage Construct a 99% prediction interval for the mean annual wage in Exercise 15 when the percentage of employment in STEM occupations is 13% in the industry."

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Textbook Question

1. Two variables have a positive linear correlation. Does the dependent variable increase or decrease as the independent variable increases? What if the variables have a negative linear correlation?

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Textbook Question

"Constructing and Interpreting a Prediction Interval In Exercises 21-30, construct the indicated prediction interval and interpret the results.

23. Points Earned Construct a 90% prediction interval for total points earned in Exercise 13 when the number of goals allowed by the team is 140."

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