Question

Examples and what’s the use of Resistance to Outliers in statistics

Answer 1

Resistance to outliers in statistics involves identifying and managing data points that are significantly different from the rest of the dataset to avoid undue influence on the analysis. This can be done by visually inspecting scatter plots or calculating residuals and flagging points that are more than two standard deviations away from the predicted values.

Step-by-step explanation:

Resistance to outliers in statistics refers to the practice of identifying and handling data points that are significantly different from the rest of the dataset. One way to determine outliers is by visually inspecting a scatter plot and looking for data points that are located farther than two standard deviations above or below the best-fit line. Another method is to calculate the residuals (errors) and flag any points that are more than two standard deviations away from the predicted values. The goal of resistance to outliers is to ensure that the outliers do not unduly influence the analysis and interpretation of the remaining data.

Answer 2

Such as stratified samples, an outlier is an observations that is numerically distant from the rest of the data.

Statistics derived from data stes that include outliers will ofthen be misleading.

For example, in calculating the average temperature of 10 objects in a room, if most are between 50 and 55°F, but there is an oven a 300°F, the median of the data can be 53, but the average temperature will be 135°F.

In this case, the median refelects the temperature of the random smaple of an object better tahn the mean. Outliers may be indicative of data belonging to a different population from the rest of the established samples.