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# Case 32 - DAYS(y 41 60 86 81 37 52 60 47 26 71 83 55 69 48...

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DAYS (y) BILL (x1) TYPE (x2) Variables: 41 215 1 DAYS = the number of days to collect th 60 205 0 BILL = amount of the overdue bill 86 79 0 TYPE = 1 for residential accounts and 0 81 97 0 37 201 1 52 302 1 60 197 0 47 288 0 26 150 1 71 158 0 83 98 0 55 225 0 69 150 0 48 273 1 25 146 1 90 50 0 94 46 0 83 95 0 84 100 0 79 140 0 47 299 0 33 187 1 47 264 1 69 180 0 19 97 1 36 179 1 30 154 1 39 310 0 63 205 0 17 110 1 85 75 0 21 100 1 49 301 1 83 95 0 13 75 1 16 79 1 53 240 0 40 197 1 47 311 0 48 299 1 70 162 0 43 240 1 59 215 0 31 158 1 30 149 1 70 154 0 34 180 1 38 205 1 42 220 1 29 162 1 83 97 0 50 311 1

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49 250 0 25 153 1 16 80 1 43 225 1 51 310 1 71 179 0 74 150 0 67 201 0 22 97 1 53 273 0 5 90 1 57 220 0 10 50 1 80 110 0 47 289 1 15 70 1 11 60 1 60 210 0 42 210 1 36 205 1 50 302 0 68 187 0 22 95 1 11 46 1 44 301 0 47 289 0 19 98 1 67 199 0 73 149 0 91 70 0 82 90 0 63 211 0 74 153 0 24 150 1 92 80 0 65 146 0 99 60 0 47 288 1 51 264 0 39 211 1 27 140 1 44 250 1 35 199 1 6 95 1
the payment 0 for commercial accounts

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Bryant/Smith manual Case 32: We want to determine whether the size of the bill ha See the "Regression" sheet. The questions are answered on the basis of the regression output. (a) Does the size of the bill somehow relate to the number of days the payment is The coefficient of multiple correlation is R = 0.797, which is moderately high. Therefore, it appears tha (b) Does your answer depend upon whether the customer is a residential or commerc Yes, The number of days the bill is late decreases between commercial accounts and residential acc (c) Conduct a regression hypothesis at = 0.05 α Ho: The number of days the bill is late does not depend either upon the bill size or the account type Ha: At least one of the variables- bill size and account type has an effect on the number of days the b t- test for the coefficients of the two variables,

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Case 32 - DAYS(y 41 60 86 81 37 52 60 47 26 71 83 55 69 48...

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