Question

I believe that more patients are seen in a walk-in urgent care center leading up to lunch time, then slows down around late afternoon and then increases again approaching closing time. I recorded the number of patients seen every hour throughout the day. How do I apply the Goodness of Fit to test my hypothesis?

Level of Significance: α= 0.05

Time of day (Hour)	Number of patients
8	6
9	8
10	4
11	8
12	9
1	11
2	7
3	6
4	5
5	6
6	8
7	9

Answer 1

Goodness of fit test is a chi-square test. It used to compare whether the expected values and the observed are significantly same or different. Usually the expected values are given in the form of %. Each value can be found by multilplying % * Total no. of observation. Sometimes the distributions are and the exp values can be found using probabilities. Below is the test

: The model is a good fit for the data. (Expected values and observed values are same)

: The model is not a good fit for the data. (Expected values and observed values are different)

Test Stat:

Critical value:

p -value :

Decision: Reject null hypothesis if

T.S. > C.V. or p-value < (level of significance)

In your given data you have to get the expected number of people visiting at different times and then you can carry out the hypothesis testing.