Create a scatterplot of the two variables.

Compute the value of the correlation coefficient,r.

Describe the linear association between the two variables. How did you determine the linear association?

Compute the equation of the regression line and add the line to the scatterplot. 10. (2 pts) Interpret the meaning of the slope of the regression line in this context.

Compute the means for each of the variables and verify that that point is on the regression line by plugging the mean value of the explanatory variable into the equation for the regression line. 12. (2 pts) For Country 19, compute the predicted value of the response variable.

For Country 19, compute the residual, and explain what this value tells you about your data and the regression line.

Compute R²and explain both what this tells you in terms of how well the line fits the data and in terms of explained variability.

How good do you feel about the regression line's ability to predict values. Explain how you made that decision.

Identify any outliers in the dataset.

Compute the value ofrwith the outlier removed and the equation of the regression line without the suspected outlier.

Based on your outlier analysis was the outlier influential? Explain.

1 Dateset 1 2 Country Years of Schooling Life Expectancy 3 1 10.1 65 4 2 14.2 77.8 5 3 14.4 75.6 6 4 11.4 52.4 7 5 13.9 76.4 8 6 17.3 76.3 9 7 12.7 74.8 8 12.7 72.7 9 12.6 76.1 10 6 76.9 11 10.2 71.8 12 15.3 75.5 13 15.6 72.3 14 12.8 71 15 10.7 60 16 12.5 69.8 17 13.8 77 18 14.2 77.4 19 12.6 65.7 20 15.2 75 21 14.9 77.7 22 7.7 59.9 23 10.6 59.6 24 13.5 73.3 25 10.9 68.7 26 10.4 57.3 27 16.3 82.2 28 7.1 52.5 29 7.3 53.1 30 16.3 85 12.44 70.29333333 Mean 16.3 73 Median 2.901319557 8.937095622 standard deviation Boxplot of Years Of Schooling Years of Schooling Boxplot of Life of Expectancy CO 8 Life of Expectancy