Howie’s Bakery is one of the most popular bakeries in town,and the favorite at Howie’s is French bread.Each day of the week,Howie’s bakes a number of loaves of French bread,more or less according to a daily schedule. To maintain its fine reputation, Howie’s gives to charity any loaves not sold on the day they are baked. Although this occurs frequently,it is also common for Howie’s to run out of French bread on any given day—more demand than supply.In this case,no extra loaves are baked that day;the customers have to go elsewhere (or come back to Howie’s the next day) for their French bread. Although French bread at Howie’s is always popular,Howie’s stimulates demand by running occasional 10% off sales. Howie’s has collected data for 20 consecutive weeks, 140 days in all. These data are listed in the file Howies Bakery.xlsx. The variables are Day (Monday–Sunday), Supply (number of loaves baked that day), OnSale (whether French bread is on sale that day), and Demand (loaves actually sold that day). Howie’s wants to see whether regression can be used successfully to estimate Demand from the other data in the file. Howie reasons that if these other variables can be used to predict Demand, then he might be able to determine his daily supply (number of loaves to bake) in a more cost-effective way. How successful is regression with these data? Is Howie correct that regression can help him determine his daily supply? Is any information missing that would be useful? How would you obtain it? How would you use it? Is this extra information really necessary?

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Howie’s Bakery is one of the most popular bakeries in town,and the favorite at Howie’s is French bread.Each day of the week,Howie’s bakes a number of loaves of French bread,more or less according to a daily schedule. To maintain its fine reputation, Howie’s gives to charity any loaves not sold on the day they are baked. Although this occurs frequently,it is also common for Howie’s to run out of French bread on any given day—more demand than supply.In this case,no extra loaves are baked that day;the customers have to go elsewhere (or come back to Howie’s the next day) for their French bread. Although French bread at Howie’s is always popular,Howie’s stimulates demand by running occasional 10% off sales. Howie’s has collected data for 20 consecutive weeks, 140 days in all. These data are listed in the file Howies Bakery.xlsx. The variables are Day (Monday–Sunday), Supply (number of loaves baked that day), OnSale (whether French bread is on sale that day), and Demand (loaves actually sold that day). Howie’s wants to see whether regression can be used successfully to estimate Demand from the other data in the file. Howie reasons that if these other variables can be used to predict Demand, then he might be able to determine his daily supply (number of loaves to bake) in a more cost-effective way. How successful is regression with these data? Is Howie correct that regression can help him determine his daily supply? Is any information missing that would be useful? How would you obtain it? How would you use it? Is this extra information really necessary?

copy paste the data table given below in excel and solve this regression problem. is regression useful? Review R^2 and S.E. and comment Can regression help in deciding daily supply? Explain your understanding and opinion. is any information missing which will be useful? Be specific. How do we obtain it? How would you use it? Is this extra information really necessary?

Demand for French bread at Howie's
       
Day Supply OnSale Demand
Monday 46 No 44
Tuesday 48 Yes 27
Wednesday 48 No 48
Thursday 53 No 29
Friday 99 Yes 88
Saturday 134 No 112
Sunday 98 No 64
Monday 52 Yes 47
Tuesday 47 Yes 46
Wednesday 51 No 43
Thursday 49 No 36
Friday 103 Yes 102
Saturday 132 Yes 107
Sunday 98 Yes 77
Monday 47 No 37
Tuesday 46 No 46
Wednesday 45 No 44
Thursday 45 No 45
Friday 97 No 81
Saturday 128 No 102
Sunday 103 No 87
Monday 54 Yes 54
Tuesday 48 Yes 46
Wednesday 47 No 43
Thursday 45 No 44
Friday 99 Yes 99
Saturday 135 Yes 123
Sunday 101 Yes 101
Monday 50 No 42
Tuesday 46 Yes 41
Wednesday 45 Yes 43
Thursday 52 Yes 35
Friday 104 Yes 104
Saturday 134 Yes 97
Sunday 95 Yes 95
Monday 46 Yes 45
Tuesday 48 No 45
Wednesday 51 Yes 39
Thursday 54 No 52
Friday 97 No 97
Saturday 133 No 54
Sunday 96 No 96
Monday 55 No 55
Tuesday 48 No 38
Wednesday 46 No 46
Thursday 54 No 54
Friday 105 Yes 105
Saturday 132 Yes 132
Sunday 104 Yes 82
Monday 51 Yes 51
Tuesday 53 Yes 53
Wednesday 53 Yes 53
Thursday 48 No 48
Friday 98 No 88
Saturday 134 Yes 134
Sunday 96 Yes 78
Monday 52 Yes 52
Tuesday 45 No 35
Wednesday 54 No 31
Thursday 48 No 46
Friday 96 No 58
Saturday 134 No 70
Sunday 102 No 69
Monday 52 No 26
Tuesday 51 No 51
Wednesday 51 No 30
Thursday 53 No 40
Friday 96 No 89
Saturday 129 Yes 129
Sunday 102 Yes 84
Monday 47 Yes 47
Tuesday 48 No 48
Wednesday 45 Yes 41
Thursday 49 Yes 49
Friday 95 Yes 59
Saturday 132 No 109
Sunday 100 No 100
Monday 47 No 24
Tuesday 45 No 30
Wednesday 53 No 53
Thursday 55 No 50
Friday 102 No 102
Saturday 126 No 94
Sunday 96 Yes 74
Monday 48 No 48
Tuesday 49 No 46
Wednesday 55 No 44
Thursday 47 No 37
Friday 103 Yes 103
Saturday 126 No 103
Sunday 105 No 89
Monday 49 Yes 49
Tuesday 47 Yes 35
Wednesday 54 Yes 49
Thursday 45 No 40
Friday 103 No 66
Saturday 132 No 132
Sunday 102 No 69
Monday 54 No 50
Tuesday 54 Yes 53
Wednesday 48 Yes 42
Thursday 49 Yes 49
Friday 104 Yes 104
Saturday 127 Yes 127
Sunday 103 Yes 59
Monday 46 Yes 46
Tuesday 52 No 52
Wednesday 48 Yes 28
Thursday 47 No 42
Friday 104 Yes 104
Saturday 133 Yes 133
Sunday 95 Yes 62
Monday 47 Yes 47
Tuesday 47 No 31
Wednesday 49 No 45
Thursday 49 Yes 48
Friday 102 Yes 88
Saturday 135 Yes 135
Sunday 97 Yes 97
Monday 51 Yes 51
Tuesday 46 No 46
Wednesday 52 No 52
Thursday 48 No 48
Friday 105 No 34
Saturday 133 Yes 133
Sunday 97 Yes 97
Monday 48 Yes 48
Tuesday 45 Yes 42
Wednesday 47 Yes 37
Thursday 55 Yes 53
Friday 96 Yes 96
Saturday 126 No 126
Sunday 102 Yes 102
Monday 52 Yes 42
Tuesday 47 Yes 47
Wednesday 50 No 37
Thursday 45 No 37
Friday 99 Yes 83
Saturday 127 Yes 77
Sunday 102 Yes 92
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