Question 1
27 marks
Rice sales: Poisson fit, chi-squared test and whether advertising paid off
- Statistics and probability
(a)(i)
2 marks
Use the statistics mode on the frequency table. There are days.
(a)(ii)
1 mark
For a Poisson distribution the mean equals the variance. These two are close, which is why Aimmika believes the data are Poisson.
(b)
1 mark
Sales must occur independently of each other and at a constant average rate each day. Customers do not influence each other, and the average daily rate does not change over the period.
(c)
5 marks
Use the Poisson distribution with mean 4.2 and multiply by 90.
(d)(i)
1 mark
There are 8 cells and no parameters are estimated from the data.
(d)(ii)
7 marks
- The observed frequencies for the cells are .
- Use a goodness of fit test with .
- Since , there is insufficient evidence to reject . The data are consistent with a Poisson distribution with mean 4.2.
(e)(i)
6 marks
- Over 60 days the mean under is , so .
- Find the smallest with .
- The critical value is 279, so the critical region is .
- The observed 282 is in the critical region, so reject : there is evidence that the advertising increased the number of bags sold.
(e)(ii)
1 mark
The probability of a Type I error is the probability of landing in the critical region when is true.
(f)
3 marks
- Extra bags sold: . The extra profit is
- The advertising cost for 60 days is
- Since , the advertising did not increase profit overall (an average of 247.50 THB extra per day against 300 THB per day). So Nichakarn is right on money, but Aimmika is right that sales rose. There may be other benefits, such as new customers who buy other things or return later.

