I need help with these 4 statistics problems. For reference, we use the 12th edi

Question

I need help with these 4 statistics problems. For reference, we use the 12th edition Elementary Statistics textbook by Levin and Jack.

Here are the problems.

1. Indicate what's wrong with the following statement in less than 10 words? A researcher claims to have evidence of a strong positive correlation (r 30.88) between a person's blood alcohol content (BAC) and the type of alcoholic drink consumed (beer, wine, or hard liquor) Explain, statistically, why this claim makes no sense. 2. Match the correlations to the scatterplots. r 0.09 r 0.38 r 0.89 r -0.81 (a) (b) (c) (d) 3. Runs and Wins in Baseball The two variables we concern here are the number of wins and the number of runs scored during the season. The dataset consists of values for each variable from all 30 MLB teams. From these data we calculate the regression line: Wins 0.362 +0.114(Runs) (a) Which is the explanatory and which is the response variable in this regression line? (b) The Oakland A's won 81 games while scoring 663 runs. Predict the number of games won by Oakland using the regression line. Calculate the residual. Were the A's efficient at winning games with 663 runs? 4. Describe the relationship between X and Y in less than 10 words. X 15 20 25 30 35 40 45 50 Y 532 466 478 320 303 349 275 221

Explanation / Answer

Question 1

For calculation of correlation coefficient, both variables need to have ratio scale of measurement. For the given scenario, the second variable is categorical variable and that’s why we cannot find out correlation between these two variables.

Question 2

The appropriate pairs of correlations and scatter plots are given as below:

Scatter Plot a è r = -0.38

Scatter Plot b è r = -0.81

Scatter Plot c è r = 0.09

Scatter Plot d è r = 0.89

Question 3

Part a

The explanatory variable is given as runs while the response variable is given as wins in this regression line.

Part b

We are given

Wins = 0.362 + 0.114*runs

Wins = 0.362 + 0.114*663 = 75.944

Predicted value = 75.944

Observed value = 81

Residual = Observed value – Predicted value = 81 – 75.944 = 5.056

Residual = 5.056

Question 4

The correlation coefficient between the two variables X and Y is given as -0.93163, which means there is a strong negative linear relationship exists between X and Y.

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