Hello, I am looking for someone to write an essay on Data Interpretation Practicum. It needs to be at least 500 words.A regression procedure would further in predicting the injury rate based on w

Question
Hello, I am looking for someone to write an essay on Data Interpretation Practicum. It needs to be at least 500 words. A regression procedure would further in predicting the injury rate based on working 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 true population mean for average working hours in the three states is bound between 45575.96 and 54345.61 while true injury rate mean for average working hours in the three states is bound between 10.26 and 20.09. From this output, the 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 reject the null hypothesis and conclude that the two variables are correlated. This value is consistent with the observation from a scatterplot of the two variables shown above. A possible explanation for the observation made is that only a few injuries are normally witnessed, hence, increasing 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

Question

Hello, I am looking for someone to write an essay on Data Interpretation Practicum. It needs to be at least 500 words. A regression procedure would further in predicting the injury rate based on working 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 true population mean for average working hours in the three states is bound between 45575.96 and 54345.61 while true injury rate mean for average working hours in the three states is bound between 10.26 and 20.09. From this output, the 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 reject the null hypothesis and conclude that the two variables are correlated. This value is consistent with the observation from a scatterplot of the two variables shown above. A possible explanation for the observation made is that only a few injuries are normally witnessed, hence, increasing 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

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