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From Data to Decisions: Measurement, Uncertainty, Analysis, and Modeling
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Start on. Show related SlideShares at end. WordPress Shortcode. Published in: Education. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique.
The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. Convert currency. Add to Basket. Compare all 4 new copies. Book Description Wiley, Condition: New. Never used!. Seller Inventory P More information about this seller Contact this seller. Book Description Wiley. Seller Inventory NEW Ships with Tracking Number! Buy with confidence, excellent customer service!. Main article: Linear least squares. Main article: Trend estimation.
Main article: Econometrics. This section needs expansion. You can help by adding to it. January Statistics portal. Analysis of variance Blinder—Oaxaca decomposition Censored regression model Cross-sectional regression Curve fitting Empirical Bayes methods Errors and residuals Lack-of-fit sum of squares Line fitting Linear classifier Linear equation Logistic regression M-estimator Multivariate adaptive regression splines Nonlinear regression Nonparametric regression Normal equations Projection pursuit regression Segmented linear regression Stepwise regression Structural break Support vector machine Truncated regression model.
Freedman Statistical Models: Theory and Practice. Cambridge University Press. A simple regression equation has on the right hand side an intercept and an explanatory variable with a slope coefficient. Seal The earliest form of the linear regression was the least squares method, which was published by Legendre in , and by Gauss in Legendre and Gauss both applied the method to the problem of determining, from astronomical observations, the orbits of bodies about the sun.
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The Variation of Animals and Plants under Domestication. Darwin uses the term "reversion". Draper, N.
Applied Regression Analysis 3rd ed. John Wiley. Francis Galton. Facsimile at:  Robert S.
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Pindyck and Daniel L. Rubinfeld , 4h ed. Econometric Models and Economic Forecasts , ch. Least squares and regression analysis. Least squares Linear least squares Non-linear least squares Iteratively reweighted least squares. Pearson product-moment correlation Rank correlation Spearman's rho Kendall's tau Partial correlation Confounding variable.
Ordinary least squares Partial least squares Total least squares Ridge regression. Simple linear regression Ordinary least squares Generalized least squares Weighted least squares General linear model. Polynomial regression Growth curve statistics Segmented regression Local regression. Generalized linear model Binomial Poisson Logistic. Mean and predicted response Gauss—Markov theorem Errors and residuals Goodness of fit Studentized residual Minimum mean-square error. Response surface methodology Optimal design Bayesian design. Numerical analysis Approximation theory Numerical integration Gaussian quadrature Orthogonal polynomials Chebyshev polynomials Chebyshev nodes.
Curve fitting Calibration curve Numerical smoothing and differentiation System identification Moving least squares. Regression analysis category Statistics category Statistics portal Statistics outline Statistics topics. Outline Index. Descriptive statistics. Mean arithmetic geometric harmonic Median Mode. Central limit theorem Moments Skewness Kurtosis L-moments. Index of dispersion. Grouped data Frequency distribution Contingency table.
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Pearson product-moment correlation Rank correlation Spearman's rho Kendall's tau Partial correlation Scatter plot.