Data Analysis and Regression: A Second Course in Statistics
Approaching data analysis; Indication and indicators; Displays and summaries for batches; Straightening curves and plots; The practice of re-expression; Need we re-express? Hunting out the real uncertainty; A method of direct assessment; Two-and more-way tables; Robust and resistant measures; Standardizing for comparison; Regression for fitting; Woes of regression coefficients; A class of mechanisms for fitting; Guided regression; Examining regression residuals.
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Approaching Data Analysis
1A The staircase and the shortcut to inference
Wilson and Hilfertys analysis
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adjusted arithmetic arithmetic mean assessment average biweight calculation carriers centers of gravity Chapter choice choose CM CM column components compute confidence interval consider constant correlation corresponding costock cross-validation curve Data Exhibit degrees of freedom difficulty discriminant function discussion easy effect equation estimand example Exhibit 11 Exhibit 9 fractions gives Goldbach counts groups indication internal uncertainty interquartile range iteration jackknife least absolute deviation least squares logarithm look matchers mean measure median absolute deviation multiple normal distribution observations ordinary least squares pairs Panel plot points population Problem ratio re-expression regression coefficients relation residuals robust running medians sample sample mean Section shown in Exhibit shows slope smoothed standard deviation statistics stepwise stepwise regression subsamples sum of squares summary Suppose tails techniques Treatment values variables weighted least squares weights xdot zero