Basic Analysis and Graphing - SAS

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198 Performing Contingency Analysis Chapter 6 Two Sample Test for Proportions Two Sample Test for Proportions When both the X and Y variables have two levels, you can request a two-sample test for proportions. This test determines whether the chosen response level has equal proportions across the levels of the X variable. Table 6.10 Description of the Two Sample Test for Proportions Report Description Proportion Difference Lower 95% Upper 95% Adjusted Wald Test Prob Response category of interest Shows the test being performed. Shows the difference in the proportions between the levels of the X variable. Shows the lower endpoint of the confidence interval for the difference. Based on the adjusted Wald confidence interval. Shows the upper endpoint of the confidence interval for the difference. Based on the adjusted Wald confidence interval. Shows two-tailed and one-tailed tests. Shows the p-values for the tests. Select which response level to use in the test. Related Information • “Example of a Two Sample Test for Proportions” on page 210 Measures of Association You can request several statistics that describe the association between the variables in the contingency table by selecting the Measures of Association option. For details about measures of association, see the following references: • Brown and Benedetti (1977) • Goodman and Kruskal (1979) • Kendall and Stuart (1979) • Snedecor and Cochran (1980) • Somers (1962) Table 6.11 Description of the Measures of Association Report Gamma Based on the number of concordant and discordant pairs and ignores tied pairs. Takes values in the range -1 to 1.
Chapter 6 Performing Contingency Analysis 199 Measures of Association Table 6.11 Description of the Measures of Association Report (Continued) Kendall’s Tau-b Similar to Gamma and uses a correction for ties. Takes values in the range -1 to 1. Stuart’s Tau-c Somer’s D Similar to Gamma and uses an adjustment for table size and a correction for ties. Takes values in the range -1 to 1. An asymmetric modification of Tau-b. • The C|R denotes that the row variable X is regarded as an independent variable and the column variable Y is regarded as dependent. • Similarly, the R|C denotes that the column variable Y is regarded as an independent variable and the row variable X is dependent. Somer’s D differs from Tau-b in that it uses a correction for ties only when the pair is tied on the independent variable. It takes values in the range -1 to 1. Lambda Asymmetric • For C|R, is interpreted as the probable improvement in predicting the column variable Y given knowledge of the row variable X. • For R|C, is interpreted as the probable improvement in predicting the row variable X given knowledge about the column variable Y. Takes values in the range 0 to 1. Lambda Symmetric Loosely interpreted as the average of the two Lambda Asymmetric measures. Takes values in the range 0 to 1. Uncertainty Coef • For C|R, is the proportion of uncertainty in the column variable Y that is explained by the row variable X. • For R|C, is interpreted as the proportion of uncertainty in the row variable X that is explained by the column variable Y. Takes values in the range 0 to 1. Uncertainty Coef Symmetric Symmetric version of the two Uncertainty Coef measures. Takes values in the range 0 to 1. Each statistic appears with its standard error and confidence interval. Note the following: • Gamma, Kendall’s Tau-b, Stuart’s Tau-c, and Somer’s D are measures of ordinal association that consider whether the variable Y tends to increase as X increases. They classify pairs of observations as concordant or discordant. A pair is concordant if an observation with a larger value of X also has a larger value of Y. A pair is discordant if an observation with a larger value of X has a smaller value of Y. These measures are appropriate only when both variables are ordinal. • The Lambda and Uncertainty measures are appropriate for ordinal and nominal variables.
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Version 10 Basic Analysis and Graph
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USE, DATA OR PROFITS, WHETHER IN AN
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6 Options for Categorical Variables
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8 Nonparametric Bivariate Density R
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10 Example of the Power Option . .
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12 Statistical Details for the Logi
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14 Use Categorical Variables . . .
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16 Control Animation for Dynamic Bu
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18 Launch the Scatterplot Matrix Pl
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Contents Book Conventions. . . . .
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22 Learn about JMP Chapter 1 Book C
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Additi
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32 Performing Univariate Analysis C
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Contents Overview of the Fit Y by X
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90 Introduction to the Fit Y by X P
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94 Performing Bivariate Analysis Ch
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100 Performing Bivariate Analysis C
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Contents Example of Bootstrapping .
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250 Bootstrapping Chapter 9 Example
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252 Bootstrapping Chapter 9 Unstack
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254 Bootstrapping Chapter 9 Analysi
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Contents Overview of Graph Builder
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258 Interactive Data Visualization
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Contents Example of the Chart Platf
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314 Creating Summary Charts Chapter
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340 Creating Overlay Plots Chapter
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354 Creating Three-Dimensional Scat
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374 Creating Contour Plots Chapter
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384 Creating Bubble Plots Chapter 1
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Contents Overview of Mapping . . .
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406 Creating Maps Chapter 16 Exampl
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418 Creating Maps Chapter 16 Backgr
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450 Creating Parallel Plots Chapter
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460 Creating Cell Plots Chapter 18
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468 Creating Tree Maps Chapter 19 E
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470 Creating Tree Maps Chapter 19 E
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472 Creating Tree Maps Chapter 19 T
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474 Creating Tree Maps Chapter 19 A
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486 Creating Scatterplot Matrices C
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Contents Example of a Ternary Plot
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496 Creating Ternary Plots Chapter
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504 References Becker, R.A. and Cle
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506 References Dunnett, C.W. (1955)
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508 References Hosmer, D.W. and Lem
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510 References McLachlan, G.J. and
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516 Index examples 459, 463-464 lau
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518 Index Horizontal option 268, 32
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522 Index examples 467-468, 473-482
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Basic Analysis and Graphing - SAS

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