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John wiley sons data mining techniques for marketing sales_6

Tham khảo tài liệu 'john wiley sons data mining techniques for marketing sales_6', kinh doanh - tiếp thị, quản trị kinh doanh phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | f w Team-Ffỉj Table 5.2 The 95 Percent Confidence Interval Bounds for the champion Group 1 RESPONSE SIZE SEP 95 CONF 95 CONF SEP LOWER UPPER 1 4.5 900 000 0.0219 1.96 0.0219 1.96 0.0429 4.46 4.54 4.6 900 000 0.0221 1.96 0.0221 .96 0.0433 4.56 4.64 4.7 900 000 0.0223 1.96 0.0223 .96 0.0437 4.66 4.74 4.8 900 000 0.0225 1.96 0.0225 1.96 0.0441 4.76 4.84 4.9 900 000 0.0228 1.96 0.0228 .96 0.0447 4.86 4.94 5.0 900 000 0.0230 1.96 0.0230 1.96 0.0451 4.95 5.05 5.1 900 000 0.0232 1.96 0.0232 .96 0.0455 5.05 5.15 5.2 900 000 0.0234 1.96 0.0234 .96 0.0459 5.15 5.25 5.3 900 000 0.0236 1.96 0.0236 .96 0.0463 5.25 5.35 5.4 900 000 0.0238 1.96 0.0238 .96 0.0466 5.35 5.45 5.5 900 000 0.0240 1.96 0.0240 .96 0.0470 5.45 5.55 Response rates vary from 4.5 o to 5.5 o. The bounds for the 95 o confidence level are calculated usingl .96 standard deviations from the mean. Chapter 5 The Lure of Statistics Data Mining Using Familiar Tools 143 Based on these possible response rates it is possible to tell if the confidence bounds overlap. The 95 percent confidence bounds for the challenger model were from about 4.86 percent to 5.14 percent. These bounds overlap the confidence bounds for the champion model when its response rates are 4.9 percent 5.0 percent or 5.1 percent. For instance the confidence interval for a response rate of 4.9 percent goes from 4.86 percent to 4.94 percent this does overlap 4.86 percent 5.14 percent. Using the overlapping bounds method we would consider these statistically the same. Comparing Results Using Difference of Proportions Overlapping bounds is easy but its results are a bit pessimistic. That is even though the confidence intervals overlap we might still be quite confident that the difference is not due to chance with some given level of confidence. Another approach is to look at the difference between response rates rather than the rates themselves. Just as there is a formula for the standard error of a proportion there is a formula for the standard error of