In: Statistics and Probability
| Customers | Morning | Afternoon | Evening | Night |
| 99 | 0 | 0 | 0 | 1 |
| 148 | 0 | 1 | 0 | 0 |
| 130 | 0 | 1 | 0 | 0 |
| 106 | 0 | 0 | 0 | 1 |
| 133 | 0 | 0 | 0 | 1 |
| 119 | 0 | 0 | 0 | 1 |
| 105 | 1 | 0 | 0 | 0 |
| 74 | 1 | 0 | 0 | 0 |
| 106 | 0 | 0 | 0 | 1 |
| 94 | 0 | 1 | 0 | 0 |
| 69 | 0 | 0 | 1 | 0 |
| 86 | 0 | 0 | 1 | 0 |
| 95 | 1 | 0 | 0 | 0 |
| 99 | 0 | 1 | 0 | 0 |
| 71 | 0 | 0 | 1 | 0 |
| 80 | 0 | 0 | 1 | 0 |
| 63 | 0 | 0 | 1 | 0 |
| 93 | 0 | 0 | 0 | 1 |
| 117 | 1 | 0 | 0 | 0 |
| 136 | 0 | 1 | 0 | 0 |
| 91 | 0 | 0 | 1 | 0 |
| 131 | 0 | 0 | 0 | 1 |
| 112 | 0 | 1 | 0 | 0 |
| 88 | 1 | 0 | 0 | 0 |
| 59 | 0 | 0 | 1 | 0 |
| 44 | 0 | 0 | 1 | 0 |
| 129 | 0 | 0 | 0 | 1 |
| 82 | 0 | 0 | 1 | 0 |
| 78 | 0 | 0 | 0 | 1 |
| 109 | 1 | 0 | 0 | 0 |
| 51 | 0 | 0 | 1 | 0 |
| 71 | 1 | 0 | 0 | 0 |
| 57 | 0 | 0 | 1 | 0 |
| 112 | 0 | 1 | 0 | 0 |
| 61 | 0 | 0 | 1 | 0 |
| 83 | 0 | 1 | 0 | 0 |
| 101 | 1 | 0 | 0 | 0 |
| 92 | 0 | 0 | 0 | 1 |
| 48 | 0 | 0 | 1 | 0 |
| 73 | 0 | 0 | 1 | 0 |
| 83 | 0 | 0 | 0 | 1 |
| 133 | 0 | 1 | 0 | 0 |
| 69 | 0 | 0 | 1 | 0 |
| 135 | 1 | 0 | 0 | 0 |
| 135 | 0 | 1 | 0 | 0 |
| 96 | 1 | 0 | 0 | 0 |
| 50 | 0 | 0 | 1 | 0 |
| 110 | 0 | 0 | 0 | 1 |
| 58 | 0 | 0 | 1 | 0 |
| 121 | 0 | 1 | 0 | 0 |
| 113 | 1 | 0 | 0 | 0 |
| 65 | 0 | 0 | 0 | 1 |
| 45 | 0 | 0 | 1 | 0 |
| 41 | 0 | 0 | 1 | 0 |
| 86 | 0 | 0 | 1 | 0 |
| 110 | 0 | 0 | 0 | 1 |
| 70 | 0 | 0 | 1 | 0 |
| 104 | 1 | 0 | 0 | 0 |
| 121 | 0 | 0 | 0 | 1 |
| 79 | 1 | 0 | 0 | 0 |
| 121 | 0 | 0 | 0 | 1 |
| 89 | 0 | 0 | 0 | 1 |
| 126 | 0 | 1 | 0 | 0 |
| 75 | 0 | 0 | 1 | 0 |
| 67 | 0 | 0 | 1 | 0 |
| 100 | 0 | 0 | 0 | 1 |
| 93 | 0 | 0 | 0 | 1 |
| 56 | 0 | 0 | 1 | 0 |
| 91 | 0 | 0 | 0 | 1 |
| 129 | 0 | 1 | 0 | 0 |
| 96 | 0 | 0 | 1 | 0 |
| 78 | 0 | 0 | 0 | 1 |
| 48 | 0 | 0 | 1 | 0 |
| 69 | 0 | 0 | 1 | 0 |
| 156 | 0 | 1 | 0 | 0 |
| 98 | 0 | 0 | 0 | 1 |
| 90 | 0 | 0 | 0 | 1 |
| 133 | 0 | 0 | 0 | 1 |
| 93 | 1 | 0 | 0 | 0 |
| 130 | 0 | 0 | 0 | 1 |
| 112 | 1 | 0 | 0 | 0 |
| 109 | 0 | 0 | 0 | 1 |
| 86 | 0 | 0 | 1 | 0 |
| 52 | 0 | 0 | 1 | 0 |
| 104 | 1 | 0 | 0 | 0 |
| 27 | 0 | 0 | 1 | 0 |
| 119 | 1 | 0 | 0 | 0 |
| 113 | 1 | 0 | 0 | 0 |
| 123 | 0 | 1 | 0 | 0 |
| 95 | 1 | 0 | 0 | 0 |
| 93 | 1 | 0 | 0 | 0 |
| 130 | 0 | 1 | 0 | 0 |
| 102 | 1 | 0 | 0 | 0 |
| 111 | 1 | 0 | 0 | 0 |
| 103 | 0 | 0 | 0 | 1 |
| 69 | 0 | 0 | 1 | 0 |
| 101 | 0 | 0 | 0 | 1 |
| 118 | 1 | 0 | 0 | 0 |
| 58 | 0 | 0 | 1 | 0 |
| 111 | 0 | 1 | 0 | 0 |
The regression output is as follows

I have taken all the 100 values in the Excel, though only some are visible in the image
Question (1)
The regression equation is
Predicted Customer
= 102.0435 + 0 * Morning + 20.1918 * Afternoon + (-37.7622) *
Evening + 1.9208 * Night
Question (2)
predicted number of customers during the morning shifts will be obtained by keeping Morning variable as 1 and rest all othe vairables as 0.
So predicted number of customers during the morning shift = 102.0435 + 0 * 1 + 20.1918 * 0 + (-37.7622) * 0 + 1.9208 * 0
So predicted number of customers during the morning shift = 102.0435
= 103 rounded to next hearest integer
Question (3)
predicted number of customers during the aftternoon shifts will be obtained by keeping Afternoon variable as 1 and rest all othe vairables as 0.
So predicted number of customers during the Afternoon shift = 102.0435 + 0 * 0 + 20.1918 * 1 + (-37.7622) * 0 + 1.9208 * 0
So predicted number of customers during the morning shift = 102.0435 + 20.1918
= 122.2353
= 123 rounded to next highest integer