Data Interpretation Practicum Statistics Project

Data Interpretation Practicum - Statistics Project ExampleA regression procedure would further athletic supporter in predicting the injury rate based on working(a) hours. However, discriminant analysis cannot be used.The average working hours in the three states is 2183.07 hour while the average injury rate in the three states is 2.4446. The unbowed population mean for average working hours in the three states is bound between 45575.96 and 54345.61 while received injury rate mean for average working hours in the three states is bound between 10.26 and 20.09.From this output, the coefficient of correlation coefficient between hours worked and injury rate is -0.636. This implies that as work hours increases, injury rate reduces (p-value 0.000). The test is significant, hence we protest the null hypothesis and conclude that the both variables are correlated. This value is consistent with the observation from a scatterplot of the two variables shown above.A possible explanation f or the observation made is that only a few injuries are ordinarily witnessed, hence, change magnitude the hours worked does not necessarily lead to an increase in the number of injuries. Since injury rate is obtained by dividing the number of hours worked by the number of injuries, the values reduces as hours worked increases. The value of the correlation coefficient does not imply that increasing the number of working hours results into less