Which of the following is NOT considered the assumption about the pattern of heteroscedasticity Select one: The error variance is proportional to the square of the mean value of Y The error variance is proportional to XY The error variance is proportional to X The error variance is proportional to Y Which one of the following is NOT a plausible remedy for near multicollinearity? Select one: Drop one of the collinear variables Use a longer run of data use principle components analysis Take logarithems of each of the variables A sure way of removing multicollinearity from the model is to Select one: Transform the variables by first differencing them Obtaining additional sample data Work with panel data Drop variables that cause multicollinearity in the first place The p value is Select one: the power 2 plus power 2 minimum power none of these The confidence interval constructed for B2 will be same irrespective of the sample analysed. This statement is Select one: True False Data on one or variables collected at a given point of time Select one: Cross-section data time series data Panel data Pooled data BLUE is Select one: Best Linear Unconditional Estimator Best Linear Unbiased Estimator Basic Linear Unconditional Estimator Best Linear biased estimator Heteroscedaticity may arise due to various reasons. Which one of these is NOT a reason Select one: Correlation of variables over time Incorrect transformation of variables Extremely low or high values of X and Y coordinates in the dataset Incorrect specification of the functional form of the model
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.
Which of the following is NOT considered the assumption about the pattern of heteroscedasticity
Select one:
- The error variance is proportional to the square of the
mean value of Y - The error variance is proportional to XY
- The error variance is proportional to X
- The error variance is proportional to Y
Which one of the following is NOT a plausible remedy for near multicollinearity?
Select one:
- Drop one of the collinear variables
- Use a longer run of data
- use principle components analysis
- Take logarithems of each of the variables
A sure way of removing multicollinearity from the model is to
Select one:
- Transform the variables by first differencing them
- Obtaining additional sample data
- Work with panel data
- Drop variables that cause multicollinearity in the first place
The p value is
Select one:
- the power
- 2 plus power
- 2 minimum power
- none of these
The confidence interval constructed for B2 will be same irrespective of the sample analysed. This statement is
Select one:
True
False
Data on one or variables collected at a given point of time
Select one:
- Cross-section data
- time series data
- Panel data
- Pooled data
BLUE is
Select one:
- Best Linear Unconditional Estimator
- Best Linear Unbiased Estimator
- Basic Linear Unconditional Estimator
- Best Linear biased estimator
Heteroscedaticity may arise due to various reasons. Which one of these is NOT a reason
Select one:
Correlation of variables over time- Incorrect transformation of variables
- Extremely low or high values of X and Y coordinates in the dataset
- Incorrect specification of the
functional form of the model
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