Question

In: Statistics and Probability

True or False: Regression analysis is used for prediction, while correlation analysis is used to measure...

True or False: Regression analysis is used for prediction, while correlation analysis is used to measure the strength of the association between two numerical variables.

A. True

B. False

In performing a regression analysis involving two numerical variables, we are assuming

A. the variances of X and Y are equal.

B. the variation around the line of regression is the same for each X value.

C. that X and Y are independent.

D. All of these.

Which of the following assumptions concerning the probability distribution of the random error term is stated incorrectly?

A. The distribution is normal.

B. The errors are independent.

C. The mean of the distribution is 0.

D. The variance of the distribution increases as X increases.

The residuals represent

A. the difference between the actual Y values and the mean of Y.

B. the square root of the slope.

C. the predicted value of Y for the average X value.

D. the difference between the actual Y values and the predicted Y values.

What do we mean when we say that a simple linear regression model is “statistically” useful?

A. All the statistics computed from the sample make sense.

B. The model is “practically” useful for predicting Y.

C. The model is an excellent predictor of Y.

D. The model is a better predictor of Y than the sample mean, .

Expert Solution

True or False: Regression analysis is used for prediction, while correlation analysis is used to measure the strength of the association between two numerical variables.

A. True

In performing a regression analysis involving two numerical variables, we are assuming

B. the variation around the line of regression is the same for each X value.

Which of the following assumptions concerning the probability distribution of the random error term is stated incorrectly?

D. The variance of the distribution increases as X increases.

The residuals represent

D. the difference between the actual Y values and the predicted Y values.

What do we mean when we say that a simple linear regression model is “statistically” useful?

D. The model is a better predictor of Y than the sample mean.

orchestra answered 2 years ago

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