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Applied Statistics Using SAS and SPSS

Be humble in our attribute, be loving and varying in our attitude, that is the way to live in heaven. Applied Statistics Using SAS and SPSS. Topic: One Way ANOVA By Prof Kelly Fan, Cal State Univ, East Bay. Statistical Tools vs. Variable Types. Example: Battery Lifetime.

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Applied Statistics Using SAS and SPSS

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  1. Be humble in our attribute, be loving and varying in our attitude, that is the way to live in heaven.

  2. Applied Statistics Using SAS and SPSS Topic: One Way ANOVA By Prof Kelly Fan, Cal State Univ, East Bay

  3. Statistical Tools vs. Variable Types

  4. Example: Battery Lifetime • 8 brands of battery are studied. We would like to find out whether or not the brand of a battery will affect its lifetime. If so, of which brand the batteries can last longer than the other brands. • Data collection: For each brand, 3 batteries are tested for their lifetime. • What is Y variable? X variable?

  5. 1 2 3 4 5 6 7 8 1.8 4.2 8.6 7.0 4.2 4.2 7.8 9.0 5.0 5.4 4.6 5.0 7.8 4.2 7.0 7.4 1.0 4.2 4.2 9.0 6.6 5.4 9.8 5.8 5.8 2.6 4.6 5.8 7.0 6.2 4.6 8.2 7.4 Data: Y = LIFETIME (HOURS) BRAND 3 replications per level

  6. Statistical Model (Brand is, of course, represented as “categorical”) “LEVEL” OF BRAND 1 2 • • •  •  •  • • • C 1 2 • • • • n Y11 Y12 • • • • • • •Y1c Yij = i + ij i = 1, . . . . . , C j = 1, . . . . . , n Y21 • • • • • • YnI • • • • • Yij Ync •   •  •   •    •   •    •    • 

  7. Hypotheses Setup HO: Level of X has no impact on Y HI: Level of X does have impact on Y HO: 1 = 2 = • • • • 8 HI: not all j are EQUAL

  8. ONE WAY ANOVA Analysis of Variance for life Source DF SS MS F P brand 7 69.12 9.87 3.38 0.021 Error 16 46.72 2.92 Total 23 115.84 Estimate of the common variances^2 S = 1.709 R-Sq = 59.67% R-Sq(adj) = 42.02%

  9. Review • Fitted value = Predicted value • Residual = Observed value – fitted value

  10. Diagnosis: Normality • The points on the normality plot must more or less follow a line to claim “normal distributed”. • There are statistic tests to verify it scientifically. • The ANOVA method we learn here is not sensitive to the normality assumption. That is, a mild departure from the normal distribution will not change our conclusions much. Normality plot: normal scores vs. residuals

  11. From the Battery lifetime data:

  12. Diagnosis: Equal Variances • The points on the residual plot must be more or less within a horizontal band to claim “constant variances”. • There are statistic tests to verify it scientifically. • The ANOVA method we learn here is not sensitive to the constant variances assumption. That is, slightly different variances within groups will not change our conclusions much. Residual plot: fitted values vs. residuals

  13. From the Battery lifetime data:

  14. Multiple Comparison Procedures Once we reject H0: ==...c in favor of H1: NOT all ’s are equal, we don’t yet know the way in which they’re not all equal, but simply that they’re not all the same. If there are 4 columns, are all 4 ’s different? Are 3 the same and one different? If so, which one? etc.

  15. These “more detailed” inquiries into the process are called MULTIPLE COMPARISON PROCEDURES. Errors (Type I): We set up “” as the significance level for a hypothesis test. Suppose we test 3 independent hypotheses, each at = .05; each test has type I error (rej H0 when it’s true) of .05. However, P(at least one type I error in the 3 tests) = 1-P( accept all ) = 1 - (.95)3 .14 3, given true

  16. In other words, Probability is .14 that at least one type one error is made. For 5 tests, prob = .23. Question - Should we choose = .05, and suffer (for 5 tests) a .23 OVERALL Error rate (or “a” or aexperimentwise)? OR Should we choose/control the overall error rate, “a”, to be .05, and find the individual test  by 1 - (1-)5 = .05, (which gives us  = .011)?

  17. The formula 1 - (1-)5 = .05 would be valid only if the tests are independent; often they’re not. [ e.g., 1=22=3, 1= 3 IF accepted & rejected, isn’t it more likely that rejected? ] 2 3 1 1 2 3

  18. When the tests are not independent, it’s usually very difficult to arrive at the correct for an individual test so that a specified value results for the overall error rate.

  19. Categories of multiple comparison tests - “Planned”/ “a priori” comparisons (stated in advance, usually a linear combination of the column means equal to zero.) “Post hoc”/ “a posteriori” comparisons (decided after a look at the data - which comparisons “look interesting”) “Post hoc” multiple comparisons (every column mean compared with each other column mean)

  20. There are many multiple comparison procedures. We’ll cover only a few. • Post hoc multiple comparisons • Pairwise comparisons: Do a series of pairwise tests; Duncan and SNK tests • (Optional) Comparisons to control: Dunnett tests

  21. Example: Broker Study A financial firm would like to determine if brokers they use to execute trades differ with respect to their ability to provide a stock purchase for the firm at a low buying price per share. To measure cost, an index, Y, is used. Y=1000(A-P)/A where P=per share price paid for the stock; A=average of high price and low price per share, for the day. “The higher Y is the better the trade is.”

  22. CoL: broker 1 12 3 5 -1 12 5 6 2 7 17 13 11 7 17 12 3 8 1 7 4 3 7 5 4 21 10 15 12 20 6 14 5 24 13 14 18 14 19 17 } R=6 Five brokers were in the study and six trades were randomly assigned to each broker.

  23. SPSS Output Analyze>>General Linear Model>>Univariate…

  24. Homogeneous Subsets

  25. Conclusion : 3, 1 2 4 5 ??? Conclusion : 3, 1 2, 4, 5

  26. Broker 1 and 3 are not significantly different but they are significantly different to the other 3 brokers. Broker 2 and 4 are not significantly different, and broker 4 and 5 are not significantly different, but broker 2 is different to (smaller than) broker 5 significantly. Conclusion : 3, 1 2 4 5

  27. Comparisons to Control Dunnett’s test Designed specifically for (and incorporating the interdependencies of) comparing several “treatments” to a “control.” Col Example: 1 2 3 4 5 } R=6 6 12 5 14 17 CONTROL

  28. CONTROL 1 2 3 4 5 In our example: 6 12 5 14 17 - Cols 4 and 5 differ from the control [ 1 ]. - Cols 2 and 3 are not significantly different from control.

  29. Exercise: Sales Data Sales

  30. Exercise. • Find the Anova table. • Perform SNK tests at a = 5% to group treatments . • Perform Duncan tests at a = 5% to group treatments. • Which treatment would you use?

  31. Post Hoc and Priori comparisons • F test for linear combination of column means (contrast) • Scheffe test: To test all linear combinations at once. Very conservative; not to be used for a few of comparisons.

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