The following is a chart of 25 baseball players' salaries and statistics from 2019. Player Name RBI's HR's AVG Salary (in millions) Kyle Seager 63 23 0.237 19.500 Rougned Odor 93 30 0.205 7.833 Adam Eaton 49 15 0.279 8.400 Eric Hosmer 99 22 0.265 21.000 Roberto Perez 63 24 0.239 2.625 Russell Martin 20 6 0.220 20.000 Brian Dozier 50 20 0.238 9.000 Ronald Acuna 101 41 0.280 1.000 Christian Yelich 97 44 0.329 9.750 Andrelton Simmons 40 7 0.264 13.000 Carlos Santana 93 34 0.281 20.333 Ketel Marte 92 32 0.329 2.000 Yadier Molina 57 10 0.270 20.000 Scott Kingery 55 19 0.258 1.500 Yasiel Puig 84 24 0.267 9.700 Ryan Braun 75 22 0.285 19.000 Kolten Wong 59 11 0.285 6.500 Martin Prado 15 2 0.233 15.000 Daniel Descalso 15 2 0.173 1.500 Christian Vazquez 72 23 0.276 2.850 Stephen Piscotty 44 13 0.249 7.333 Josh Reddick 56 14 0.275 13.000 Paul Goldschmidt 97 34 0.260 15.500 Gerardo Parra 48 9 0.234 0.555 Eduardo Escobar 118 35 0.269 6.167 In order to have correlation with 95% confidence (5% significance), what is the critical r-value that we would like to have? (Round to three decimal places for all answers on this assignment.) RBI vs. Salary Complete a correlation analysis, using RBI's as the x-value and salary as the y-value. Correlation coefficient: Regression Equation: y=y= Do you have significant correlation? HR vs. Salary Complete a correlation analysis, using HR's as the x-value and salary as the y-value. Correlation coefficient: Regression Equation: y=y= Do you have significant correlation?
The following is a chart of 25 baseball players' salaries and statistics from 2019.
| Player Name | RBI's | HR's | AVG | Salary (in millions) |
|---|---|---|---|---|
| Kyle Seager | 63 | 23 | 0.237 | 19.500 |
| Rougned Odor | 93 | 30 | 0.205 | 7.833 |
| Adam Eaton | 49 | 15 | 0.279 | 8.400 |
| Eric Hosmer | 99 | 22 | 0.265 | 21.000 |
| Roberto Perez | 63 | 24 | 0.239 | 2.625 |
| Russell Martin | 20 | 6 | 0.220 | 20.000 |
| Brian Dozier | 50 | 20 | 0.238 | 9.000 |
| Ronald Acuna | 101 | 41 | 0.280 | 1.000 |
| Christian Yelich | 97 | 44 | 0.329 | 9.750 |
| Andrelton Simmons | 40 | 7 | 0.264 | 13.000 |
| Carlos Santana | 93 | 34 | 0.281 | 20.333 |
| Ketel Marte | 92 | 32 | 0.329 | 2.000 |
| Yadier Molina | 57 | 10 | 0.270 | 20.000 |
| Scott Kingery | 55 | 19 | 0.258 | 1.500 |
| Yasiel Puig | 84 | 24 | 0.267 | 9.700 |
| Ryan Braun | 75 | 22 | 0.285 | 19.000 |
| Kolten Wong | 59 | 11 | 0.285 | 6.500 |
| Martin Prado | 15 | 2 | 0.233 | 15.000 |
| Daniel Descalso | 15 | 2 | 0.173 | 1.500 |
| Christian Vazquez | 72 | 23 | 0.276 | 2.850 |
| Stephen Piscotty | 44 | 13 | 0.249 | 7.333 |
| Josh Reddick | 56 | 14 | 0.275 | 13.000 |
| Paul Goldschmidt | 97 | 34 | 0.260 | 15.500 |
| Gerardo Parra | 48 | 9 | 0.234 | 0.555 |
| Eduardo Escobar | 118 | 35 | 0.269 | 6.167 |
In order to have
(Round to three decimal places for all answers on this assignment.)
RBI vs. Salary
Complete a correlation analysis, using RBI's as the x-value and salary as the y-value.
Regression Equation: y=y=
Do you have significant correlation?
HR vs. Salary
Complete a correlation analysis, using HR's as the x-value and salary as the y-value.
Correlation coefficient:
Regression Equation: y=y=
Do you have significant correlation?
AVG vs. Salary
Complete a correlation analysis, using AVG as the x-value and salary as the y-value.
Correlation coefficient:
Regression Equation: y=y=
Do you have significant correlation?
Prediction
Based on your analysis, if you had to predict a player's salary, which method would be the best?
Using that method, predict the salary for AJ Pollock. His stats were:
RBI: 47
HR: 15
AVG: 0.266
Based on your analysis, his predicted salary would be: $ million
His actual salary was $4.000 million.
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