We wish to predict the salary for baseball players (yy) using the variables RBI (x1x1) and HR (x2x2), then we use a regression equation of the form ˆy=b0+b1x1+b2x2y^=b0+b1x1+b2x2. HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error. RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error Salary is in millions of dollars. RBI's HR's Salary (in millions) 108 38 28.050 86 31 27.500 59 25 25.000 119 31 25.000 103 39 24.050 44 15 23.125 49 11 23.000 111 30 22.750 87 31 22.125 90 18 21.857 49 7 21.667 70 21 21.571 108 35 21.500 56 9 21.143 84 38 21.119 80 14 20.802 17 7 20.000 79 24 20.000 91 31 20.000 97 29 20.000 57 13 18.500 44 8 18.000 104 32 18.000 86 27 18.000 100 25 17.454 62 20 17.000 58 20 17.000 100 29 16.083 127 38 16.000 83 29 16.000 59 30 16.000 54 15 15.800 66 17 15.800 68 22 6.750 84 12 6.000 48 12 5.950 38 10 12.000 55 13 11.000 76 18 10.500 90 27 5.100 53 11 5.000 24 7 4.550 47 15 4.150 34 14 3.900 31 12 3.667 72 32 3.300 52 20 2.750 64 20 2.600
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
We wish to predict the salary for baseball players (yy) using the variables RBI (x1x1) and HR (x2x2), then we use a regression equation of the form ˆy=b0+b1x1+b2x2y^=b0+b1x1+b2x2.
- HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error.
- RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error
- Salary is in millions of dollars.
RBI's | HR's | Salary (in millions) |
---|---|---|
108 | 38 | 28.050 |
86 | 31 | 27.500 |
59 | 25 | 25.000 |
119 | 31 | 25.000 |
103 | 39 | 24.050 |
44 | 15 | 23.125 |
49 | 11 | 23.000 |
111 | 30 | 22.750 |
87 | 31 | 22.125 |
90 | 18 | 21.857 |
49 | 7 | 21.667 |
70 | 21 | 21.571 |
108 | 35 | 21.500 |
56 | 9 | 21.143 |
84 | 38 | 21.119 |
80 | 14 | 20.802 |
17 | 7 | 20.000 |
79 | 24 | 20.000 |
91 | 31 | 20.000 |
97 | 29 | 20.000 |
57 | 13 | 18.500 |
44 | 8 | 18.000 |
104 | 32 | 18.000 |
86 | 27 | 18.000 |
100 | 25 | 17.454 |
62 | 20 | 17.000 |
58 | 20 | 17.000 |
100 | 29 | 16.083 |
127 | 38 | 16.000 |
83 | 29 | 16.000 |
59 | 30 | 16.000 |
54 | 15 | 15.800 |
66 | 17 | 15.800 |
68 | 22 | 6.750 |
84 | 12 | 6.000 |
48 | 12 | 5.950 |
38 | 10 | 12.000 |
55 | 13 | 11.000 |
76 | 18 | 10.500 |
90 | 27 | 5.100 |
53 | 11 | 5.000 |
24 | 7 | 4.550 |
47 | 15 | 4.150 |
34 | 14 | 3.900 |
31 | 12 | 3.667 |
72 | 32 | 3.300 |
52 | 20 | 2.750 |
64 | 20 | 2.600 |
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