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Ch. 9 - Correlation and Regression
Larson - Elementary Statistics: Picturing the World 8th Edition
Larson8th EditionElementary Statistics: Picturing the WorldISBN: 9780137493470당신이 사용하는 게 아니라요?교과서 변경
9장, 문제 9.4.4

"Predicting y-Values In Exercises 3-6, use the multiple regression equation to predict the y-values for the values of the independent variables.
4. Sorghum Yield The equation used to predict the annual sorghum yield (in bushels per
acre) is y = 80.1-20.2x_1 +21.2x_2
where x_1 is the number of acres planted (in millions) and x_2 is the number of acres harvested (in millions). (Adapted from United States Department of Agriculture)
a. x_1 = 5.5, x_2 = 3.9
b. x_1 = 8.3, x_2 = 7.3
c. x_1 = 6.5, x_2 = 5.7
d. x_1 = 9.4, x_2= 7.8"

검증된 단계별 안내
1
Identify the multiple regression equation given: y = 80.1 - 20.2x1 + 21.2x2, where x1 is the number of acres planted (in millions) and x2 is the number of acres harvested (in millions).
For each pair of values of x1 and x2, substitute these values into the regression equation. For example, for part (a), substitute x1 = 5.5 and x2 = 3.9.
Perform the multiplication for each term involving the independent variables: multiply -20.2 by x_1 and 21.2 by x_2.
Add the results of these multiplications to the constant term 80.1, following the signs in the equation (subtract the first product and add the second product). This will give the predicted value of y for the given x_1 and x_2.
Repeat steps 2 to 4 for each set of values given in parts (b), (c), and (d) to find the predicted sorghum yield for each scenario.

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주요 개념

질문에 올바르게 답하기 위해 반드시 이해해야 하는 핵심 개념들은 다음과 같습니다.

Multiple Regression Equation

A multiple regression equation models the relationship between one dependent variable and two or more independent variables. It predicts the dependent variable (y) by combining the independent variables (x₁, x₂, etc.) multiplied by their coefficients, plus a constant term. This allows for understanding how each independent variable influences the outcome while holding others constant.
추천 영상:
가이드 코스
07:01
Intro to Least Squares Regression

Interpretation of Coefficients

In a multiple regression equation, each coefficient represents the expected change in the dependent variable for a one-unit increase in the corresponding independent variable, assuming other variables remain constant. For example, a coefficient of -20.2 for x₁ means that increasing x₁ by one unit decreases y by 20.2 units, holding x₂ fixed.
추천 영상:
가이드 코스
06:14
Coefficient of Determination

Prediction Using Regression Equation

To predict y-values, substitute the given values of independent variables into the regression equation and perform the arithmetic operations. This process yields estimated values of the dependent variable based on the model, enabling practical forecasting or decision-making using the provided data.
추천 영상:
가이드 코스
04:57
Using Regression Lines to Predict Values