回归测试试图侦破ermine one variable's ability to predict another variable. In this analysis, one variable is dependent (the one predicted), and the other is independent (the variable that predicts). Therefore, the dependent variable is the y-variable and the independent variable is the x-variable.

The key here is to utilize

.

"Perfect negative linear correlation" means, while the rest of the problem indicatesand. This enables us to solve for.

Use the formula.

Plug in the given values forandand this becomes an algebra problem.

An observation is an outlier if it falls more thanabove the upper quartile or more thanbelow the lower quartile.

. The minimum value isso there are no outliers in the low end of the distribution.

. The maximum value isso there are no outliers in the high end of the distribution.

Use thecriteria:

This states that anything less thanor greater thanwill be an outlier.

Thus, we want to find

where.

Therefore, any new observation greater than 115 can be considered an outlier.

In order to find the outliers, we can use theandformulas.

Only two numbers are outside of the calculated range and therefore are outliers:and.

Using theandformulas, we can determine that both the minimum and maximum values of the data set are outliers.

This allows us to determine that there isat leastone outlier in the upper side of the data set andat leastone outlier in the lower side of the data set. Without any more information, we are not able to determine the exact number of outliers in the entire data set.

Step 1: Recall the definition of an outlier as any value in a data set that is greater thanor less than.

Step 2: Calculate the IQR, which is the third quartile minus the first quartile, or. To findand, first write the data in ascending order.

. Then, find the median, which is. Next, Find the median of data below, which is. Do the same for the data aboveto get. By finding the medians of the lower and upper halves of the data, you are able to find the value,that is greater than 25% of the data and, the value greater than 75% of the data.

Step 3:. No values less than 64.

. In the data set, 105 > 104, so it is an outlier.

An outlier is any data point that fallsabove the 3rd quartile and below the first quartile. The inter-quartile range isand. The lower bound would beand the upper bound would be. The only possible answer outside of this range is.

A residual plot shows the difference between the actual and expected value, or residual. This goes on the y-axis. The plot shows these residuals in relation to the independent variable.