1. Heteroscedasticity of residuals in regression suggests that there is: Select

Question

1. Heteroscedasticity of residuals in regression suggests that there is:

Select one:

a. nonconstant variation in the errors.

b. multicollinearity among the predictors.

c. non-normality in the errors.

d. lack of independence in successive errors.

2. Which is not a name often given to an independent variable that takes on just two values (0 or 1) according to whether or not a given characteristic is absent or present?

Select one:

a. Absent variable

b. Binary variable

c. Dummy variable

3. A log transformation might be appropriate to alleviate which problem(s)?

Select one:

a. Heteroscedastic residuals

b. Multicollinearity

c. Autocorrelated residuals

Explanation / Answer

1. Heteroscedasticity of residuals in regression suggests that there is:nonconstant variation in the errors. Option a is correct.

2. Which is not a name often given to an independent variable that takes on just two values (0 or 1) according to whether or not a given characteristic is absent or present: These are usually known as Binary or Dummy variable. So the correct answer is a. Absent variable.

3. A log transformation is used to reduce skew in the data and conform to normality. So the correct option is b. Multicollinearity

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