1. In econometric analysis, how can outliers in the dataset affect the results of a regression analysis?

Group of answer choices

A. Outliers can disproportionately influence the estimated regression coefficients, potentially leading to biased or incorrect conclusions about the relationships between variables.

B. They ensure a more uniform distribution of residuals, which is beneficial for meeting the assumptions of ordinary least squares (OLS) regression.

C. Outliers can significantly improve the accuracy of the regression coefficients by providing a broader range of data points.

D. They primarily affect the interpretation of categorical variables, making it difficult to discern the true relationship between these variables and the dependent variable.

2. What is the primary purpose of winsorizing data in econometric analysis?

Group of answer choices

A. To convert all numerical variables into categorical variables for easier interpretation and analysis.

B. To increase the sample size by generating synthetic data points that closely match the characteristics of the existing data.

C. To identify and remove all outliers from a dataset, ensuring a perfectly normal distribution of the data.

D. To reduce the effect of outliers by replacing extreme values on both ends of the data distribution with less extreme values, thereby minimizing their impact.

3. How does winsorizing data differ from trimming data in the context of handling outliers in econometric analysis?

Group of answer choices

A. Both winsorizing and trimming create synthetic data points to replace outliers, but winsorizing uses the median while trimming uses the mean.

B. Winsorizing involves adjusting the extreme values to the nearest specified percentile values, whereas trimming involves completely removing the extreme values from the data set.

C. Winsorizing increases the variance of the dataset by adding extreme values, while trimming reduces variance by removing non-outlier data.

D. Trimming adjusts the extreme values to match the mean of the dataset, while winsorizing removes any values that are not within one standard deviation of the mean.