Question

1. A public health official in Tennessee wants to know the difference of COVID-19 mortality rate between the States of Tennessee and New York as of April 8, 2020. Of 4,362 confirmed cases in Tennessee, 79 deaths reported with a mortality rate of 1.8% or 0.018 (79/4362). Of 149,401 confirmed cases in New York, 6,268 deaths reported with a mortality rate of 4.2% or 0.042 (6,268 /149,401). Is the COVID-19 mortality rate of Tennessee significantly less than that of New York? (Please perform one-tailed Z test for the proportion difference)

(e)     Reject (or not reject) the null hypothesis in this study

TYPE YOUR ANSWER BELOW:

                  ______

      

         (f)     Draw the appropriate conclusion in this study

TYPE YOUR ANSWER BELOW:

                  ______

                 

     3. Short Answer

         Please describe the usefulness of chi-square test in the field of Public Health

TYPE YOUR ANSWERS BELOW:

               (1)

                  ______

               (2)

                  ______

Answer 1

e. The calculated z value is -11.6166 (approx), which means at 0.05 level of significance, we reject the Null hypothesis.

I have shown the calculation in the sheet below. Please refer the calculation for z statistics.

(f). Since, we have enough evidence to reject the null hypothesis, we conclude that the due to COVID-19 "mortality rate of Tennessee significantly less than that of New York"

3. The Chi-Square ( X2) Statistic

> Categorical data may be displayed in contingency tables

> The chi-square statistic compares the observed count in each table cell to the count which would be expected under the assumption of no association between the row and column classifications

> The chi-square statistic may be used to test the hypothesis of no association between two or more groups, populations, or criteria

> Observed counts are compared to expected counts

Now lets discuss usefulness of the sci square test in public health. The Chi-square statistic is a non-parametric (distribution free) tool designed to analyze group differences when the dependent variable is measured at a nominal level. In public health, we see that variable are often nominal. I have framed one example for the same.

Example 1: Is Disease Associated With Exposure-The relationship between disease and exposure may be displayed in a contingency table and desired chi square statistic cal be calculated.