If multicollinearity is perfect in a regression model the standard errors of the regression coefficients are Select one: Indeterminate small negative values Infinite values Determinate
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
- If multicollinearity is perfect in a regression model the standard errors of the regression coefficients are
Select one:
- Indeterminate
- small negative values
- Infinite values
- Determinate
What is the meaning of the term "heteroscedasticity"?
Select one:
- The variance of the errors is not constant
- The errors are not linearly independent of one another
- The errors have non-zero mean
- the variance of the dependent variable is not constant
The coefficient estimated in the presence of heteroscedaticity are NOT
Select one:
- Linear estimators
- Efficient estimators
- Unbiased estimators
- Consistent estimators
Heteroscedaticity is more likely a problem of
Select one:
- Pooled data
- cross-section data and time series data
- Cross-section data
- Time series data
Near multicollinearity occurs when
Select one:
- Two or more explanatory variables are perfectly
correlated with one another - The explanatory variables are highly correlated with the dependent variable
- The explanatory variables are highly correlated with the error term
- Two or more explanatory variables are highly correlated with one another
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