#6
| ID | Spending ($) | Duration (minutes) | Age | Gender (0=male, 1=female) | Travel (minutes) | Dietary Preference | Member? |
| 1 | 65 | 21 | 18 | 0 | 15 | Regular | Yes |
| 2 | 26 | 4 | 25 | 0 | 20 | Regular | No |
| 3 | 44 | 31 | 17 | 0 | 15 | Regular | No |
| 4 | 31 | 33 | 48 | 0 | 10 | Low-carb | No |
| 5 | 5 | 9 | 28 | 0 | 25 | Low-fat | Yes |
| 6 | 95 | 47 | 55 | 0 | 40 | Regular | No |
| 7 | 12 | 35 | 33 | 0 | 20 | Low-fat | Yes |
| 8 | 125 | 43 | 51 | 0 | 35 | Low-carb | No |
| 9 | 8 | 7 | 25 | 0 | 5 | Regular | Yes |
| 10 | 85 | 18 | 32 | 0 | 10 | Low-carb | No |
| 11 | 80 | 49 | 29 | 0 | 25 | Regular | No |
| 12 | 102 | 61 | 28 | 0 | 50 | Regular | No |
| 13 | 89 | 36 | 30 | 0 | 15 | Low-carb | No |
| 14 | 68 | 48 | 19 | 0 | 60 | Low-fat | No |
| 15 | 122 | 57 | 62 | 0 | 10 | Low-carb | No |
| 16 | 73 | 56 | 30 | 1 | 35 | Low-fat | Yes |
| 17 | 188 | 68 | 40 | 1 | 70 | Regular | Yes |
| 18 | 256 | 47 | 51 | 1 | 25 | Low-carb | No |
| 19 | 113 | 53 | 45 | 1 | 30 | Low-fat | Yes |
| 20 | 88 | 39 | 68 | 1 | 5 | Low-fat | Yes |
| 21 | 188 | 57 | 29 | 1 | 10 | Low-carb | Yes |
| 22 | 76 | 27 | 30 | 1 | 30 | Low-fat | No |
| 23 | 197 | 67 | 47 | 1 | 45 | Low-carb | Yes |
| 24 | 99 | 30 | 33 | 1 | 20 | Regular | Yes |
| 25 | 238 | 61 | 90 | 1 | 15 | Low-carb | No |
| 26 | 47 | 52 | 16 | 1 | 30 | Low-fat | Yes |
| 27 | 166 | 26 | 43 | 1 | 40 | Low-carb | Yes |
| 28 | 85 | 51 | 36 | 1 | 60 | Low-fat | Yes |
| 29 | 159 | 64 | 71 | 1 | 45 | Regular | Yes |
| 30 | 200 | 50 | 48 | 1 | 20 | Low-fat | No |
| Regular | Low-fat | Low-carb |
| 65 | 5 | 31 |
| 26 | 12 | 125 |
| 44 | 68 | 85 |
| 95 | 73 | 89 |
| 8 | 113 | 122 |
| 80 | 88 | 256 |
| 102 | 76 | 188 |
| 188 | 47 | 197 |
| 99 | 85 | 238 |
| 159 | 200 | 166 |
Bumble, after watching Bimble’s analysis, wants to predict the amount of spending ($) from customers’ duration of stay (minutes), age (years), and gender (0 = male, 1 = female) according to the sample data.
6-1 Run an appropriate regression model and report the coefficients below. (select from the dropdown)
*If a variable should not be used, select the number "0" (zero).
Spending ($) = [ Select ] ["0", "-27.00", "-20.59", "-19.77", "-2.776", "5.0422"] + [ Select ] ["0", "1.048", "1.056", "1.236", "1.266", "1.282"] Duration + [ Select ] ["0", "1.008", "1.098", "1.281", "1.299", "1.364"] Age + [ Select ] ["0", "45.23", "45.38", "46.40", "70.30", "77.24"] Gender + [ Select ] ["0", "-0.052", "0.036", "0.140", "0.950", "18.71"] Travel + [ Select ] ["0", "-48.93", "-40.64", "-2.607", "-2.181", "18.64"] Member
6-2 Bimble spends 40 minutes shopping at Bumble’s store. She is 22 years old and has a membership at the store.
What is her estimated amount of spending, approximately? (select from the dropdown)
$ [ Select ] ["0", "20", "40", "60", "80", "100", "120", "140", "160", "180", "200", "220", "240", "260", "280", "300"]
6-3 Bumble finds a series of t-test results next to the slope values in the Excel regression output.
What can we conclude for each of the following variables according to the output at α = 0.05?
Duration has [ Select ] ["no linear relationship", "a positive linear relationship", "a negative linear relationship"] with the amount of spending while holding the other predictors constant.
Age has [ Select ] ["no linear relationship", "a positive linear relationship", "a negative linear relationship"] with the amount of spending while holding the other predictors constant.
Travel has [ Select ] ["no linear relationship", "a positive linear relationship", "a negative linear relationship"] with the amount of spending while holding the other predictors constant.