28. “ Analysis of variance, t-tests, confidence intervals, and other statistical techniques taught in books,….., are inappropriate because they provide no basis for prediction and because they bury the information contained in the order of production.” W.E. Deming, Out of the Crisis Traditional statistical methods have their place, but are widely abused in the real world. When this is the case, statistics do more to cloud the issue than to enlighten.
29. PARC ANALYSIS P ractical A ccumulated R ecords C ompilation P assive A nalysis (by) R egression C orrelations P lanning A fter R esearch C ompleted P rofound A nalysis R elying (on) C omputers note inverse relationship with C ontinuous R ecording (of) A dministrative P rocedures C onstant R epetition (of) A necdotal P erceptions
32. WHAT’S SIGNIFICANT? Two-sample T for C1 vs C2 N Mean StDev SE Mean A 5 13.652 0.487 0.22 B 5 14.369 0.646 0.29 Difference = mu (C1) - mu (C2) Estimate for difference: -0.716615 95% CI for difference: (-1.551531, 0.118301) T-Test of difference = 0 (vs not =): T-Value = -1.98 P-Value = 0.083 DF = 8 Both use Pooled StDev = 0.5725 Two-sample T for C3 vs C4 N Mean StDev SE Mean A 200 13.510 0.501 0.035 A 200 13.667 0.492 0.035 Difference = mu (C3) - mu (C4) Estimate for difference: -0.157292 95% CI for difference: (-0.254935, -0.059649) T-Test of difference = 0 (vs not =): T-Value = -3.17 P-Value = 0.002 DF = 398 Both use Pooled StDev = 0.4967 Mean A = 13.7, Mean B = 14.4 Not significant? Mean A = 13.5, Mean B = 13.7 Significant?
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35. “ REGRESSION” WITH EXCEL Relationship is clearly not linear, and should not be presented as such
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Editor's Notes
Non-Linear Relationships - Look at the data first Influential Points - Look for outliers and large residuals. Plot the regression model on the original data set Extrapolating - Predicting beyond the range of actual data. Lurking Variables . Unknown variables that influence both the explanatory and response variable. Lurking variables may cause a relationship to appear strong when in fact the variables are not directly related. Summary Data . Averaging a lot of data will cause the strength of a relationship to appear greater. Assuming Causation . Cause and effect can only be determined by a controlled experiment. Here, we are simply identifying a relationship exists.
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