At first a random shop is selected. we collect data of number of customers visiting in three consecutive days for three one-hour intervals. These hours are completely chosen at random and do not have to be consecutive but should not have any overlaps. The three one-hour intervals must remain the same throughout the three consecutive days of study. Select 10 consecutive minutes in each one-hour period to count the number of customers arriving at the grocery store. Since we plan to use the Poisson distribution for the model, we must determine the intensity of the distribution ().

So here are my values from 3 different days in a week **Day -1** Time Period; Time Interval; No. of customers 5pm-6pm; 5:20pm-5:30pm; 20 1 7pm-8pm; 7:30pm-7:40pm; 40 2 9pm-10pm; 9:10pm-9:20pm; 15 3 **Day-2** Time Period; Time Interval; No. of customers 5pm-6pm; 5:20pm-5:30pm; 15 7pm-8pm; 7:30pm-7:40pm; 26 9pm-10pm; 9:10pm-9:20pm; 10 **Day 3** Time Period; Time Interval; No. of customers 5pm-6pm; 5:20pm-5:30pm; 11 7pm-8pm; 7:30pm-7:40pm; 8 9pm-10pm; 9:10pm-9:20pm; 8

Based on the calculated intensity , plot the Poisson distribution function with Minitab for each of the three one-hour long periods. 7. Present your customer number data on Day 1 of validation for each hour in a table format 8. Calculate the probability based on the data you collected on Day 1. 9. Present your customer number data on Day 2 of validation for each hour in a table format 10. Calculate the probability based on the data you collected on Day 2 11. Conclusion. Comment on the effectiveness of this approach of modeling arriving customers.