Kelly Whitney
Introduction.
The NBA players of 2017 data set is from data sets shared by stat crunch members. It’s a total of 450 players currently on NBA teams. In this project, I will be examining the number of players age and weight. Furthermore, I will consider age as a dependent or response variable (Y), and weight as the independent variable or predictor variable (X).
Analysis.
I’ve used stat crunch to generate the following regression equation Y=23.7221 + 0.0123x. As showed in my introduction above, the independent variable is weight of the NBA players, and the age dependent variable is weight. The coefficient of determination Rsquare is 0.0053 or 0.53%. All in all, age is not useful for predicting the weight of NBA players because 0.53% of the variation in the observe weight is explained by the regression of weight on age.
The linear correlation coefficient R s 0.078. 0.0787 is a weak positive linear correlation between age and weigh. As NBA players age increase, there is a weak tendency for weight. This data implies that regression equation is not useful for making predictions. Also, the value of R suggest that data points are essentially scattered about a horizontal line which is weak or observed of a linear relationship between the variables (Age and weight)
Conclusion
The data used in this project was about current NBA players age and weight. The number of players in the NBA currently stand at 450 men. The independent variable, or predictor variable is weight, and the response variable, or dependent variable is age. The coefficient of determination R – squared is 0.053 and a linear correlation coefficient R of 0.0787 suggest that a linear regression data. In fact, only 0.53% of the variation in the observed weight is explained by the regression. Furthermore, 0.0787 is a very low positive and there is a very weak positive linear correlation.
Simple linear regression results:
Dependent Variable: Weight Independent Variable: Age Weight = 209.73895 + 0.43130017 Age Sample size: 450 R (correlation coefficient) = 0.0728662 Rsq = 0.0053094831 Estimate of error standard deviation: 25.878414 Parameter estimates:
Analysis of variance table for regression model:

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