To Fit: The model to the data. To Detect: Any multicollinearity in the data.

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Answer 1

Question

Chapter 12, Problem 12.170ACI

a.

To determine

To Fit: The model to the data.

To Detect: Any multicollinearity in the data.

b.

To determine

To Explain: That a second-order model may be more appropriate for predicting household food consumption.

c.

To determine

To comment: The assumption of constant error variance.

d.

To determine

To Identify: The outliers in the data.

e.

To determine

To Explain: The assumption of normal errors appears to be reasonably satisfied.

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The least-squares regression equation is y = 602.1x + 17,023 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.6748. Complete parts (a) through (d). C Median Income 55000- 25000 ● (c) Interpret the slope. Select the correct choice below and fill in the answer box to complete your choice. (Type an integer or decimal. Do not round.) A. For 0% of adults having a bachelor's degree, the median income is predicted to be $ B. For a median income of $0, the percent of adults with a bachelor's degree is %. O C. For every dollar increase in median income, the percent of adults having at least a bachelor's degree is %, on average. OD. For every percent increase in adults having at least a bachelor's degree, the median income increases by $, on average. 15 20 25 30 35 40 45 50 55 60 Bachelor's % U (a) Predict the…

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Is there a relation between the age difference between husband/wives and the percent of a country that is literate? Researchers found the least-squares regression between age difference (husband age minus wife age), y, and literacy rate (percent of the population that is literate), x, is y = - 0.0432x+ 8.3. The model applied for 16

Answer 2

Question

Chapter 12, Problem 12.170ACI

a.

To determine

To Fit: The model to the data.

To Detect: Any multicollinearity in the data.

b.

To determine

To Explain: That a second-order model may be more appropriate for predicting household food consumption.

c.

To determine

To comment: The assumption of constant error variance.

d.

To determine

To Identify: The outliers in the data.

e.

To determine

To Explain: The assumption of normal errors appears to be reasonably satisfied.

Expert Solution & Answer

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Check out a sample textbook solution

See solution

Answer 3

Question

Chapter 12, Problem 12.170ACI

a.

To determine

To Fit: The model to the data.

To Detect: Any multicollinearity in the data.

b.

To determine

To Explain: That a second-order model may be more appropriate for predicting household food consumption.

c.

To determine

To comment: The assumption of constant error variance.

d.

To determine

To Identify: The outliers in the data.

e.

To determine

To Explain: The assumption of normal errors appears to be reasonably satisfied.

Expert Solution & Answer

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Check out a sample textbook solution

See solution

Answer 4

Question

Chapter 12, Problem 12.170ACI

a.

To determine

To Fit: The model to the data.

To Detect: Any multicollinearity in the data.

b.

To determine

To Explain: That a second-order model may be more appropriate for predicting household food consumption.

c.

To determine

To comment: The assumption of constant error variance.

d.

To determine

To Identify: The outliers in the data.

e.

To determine

To Explain: The assumption of normal errors appears to be reasonably satisfied.

Expert Solution & Answer

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Check out a sample textbook solution

See solution

Answer 5

Chapter 12, Problem 12.170ACI

a.

To determine

To Fit: The model to the data.

To Detect: Any multicollinearity in the data.

b.

To determine

To Explain: That a second-order model may be more appropriate for predicting household food consumption.

c.

To determine

To comment: The assumption of constant error variance.

d.

To determine

To Identify: The outliers in the data.

e.

To determine

To Explain: The assumption of normal errors appears to be reasonably satisfied.

Answer 6

Chapter 12, Problem 12.170ACI

a.

To determine

To Fit: The model to the data.

To Detect: Any multicollinearity in the data.

Answer 7

Chapter 12, Problem 12.170ACI

a.

To determine

To Fit: The model to the data.

To Detect: Any multicollinearity in the data.

Answer 8

b.

To determine

To Explain: That a second-order model may be more appropriate for predicting household food consumption.

Answer 9

b.

To determine

To Explain: That a second-order model may be more appropriate for predicting household food consumption.

Answer 10

c.

To determine

To comment: The assumption of constant error variance.

Answer 11

c.

To determine

To comment: The assumption of constant error variance.

Answer 12

d.

To determine

To Identify: The outliers in the data.

Answer 13

d.

To determine

To Identify: The outliers in the data.

Answer 14

e.

To determine

To Explain: The assumption of normal errors appears to be reasonably satisfied.

Answer 15

e.

To determine

To Explain: The assumption of normal errors appears to be reasonably satisfied.

Answer 16

Expert Solution & Answer

Answer 17

Expert Solution & Answer

Answer 18

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Answer 19

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To Fit: The model to the data. To Detect: Any multicollinearity in the data.

Solution Summary: The author explains that a second-order model may be more appropriate for predicting household food consumption.

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To Fit: The model to the data.

To Detect: Any multicollinearity in the data.

To Explain: That a second-order model may be more appropriate for predicting household food consumption.

To comment: The assumption of constant error variance.

To Identify: The outliers in the data.

To Explain: The assumption of normal errors appears to be reasonably satisfied.

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Chapter 12 Solutions