By Simon Sheather

ISBN-10: 0387096078

ISBN-13: 9780387096070

This publication specializes in instruments and methods for development legitimate regression versions utilizing real-world facts. A key subject through the e-book is that it in simple terms is sensible to base inferences or conclusions on legitimate types.

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Additional info for A Modern Approach to Regression with R (Springer Texts in Statistics)

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3. , Y*). We base our prediction of Y when X = x* (that is of Y*) on yˆ* = bˆ 0 + bˆ1 x * The error in our prediction is Y * − yˆ* = b 0 + b1 x * + e * − yˆ* = E(Y | X = x*) − yˆ * + e * that is, the deviation between E(Y | X = x*) and yˆ * plus the random fluctuation e* (which represents the deviation of Y* from E(Y | X = x*)). Thus the variability in the error for predicting a single value of Y will exceed the variability for estimating the expected value of Y (because of the random error e*).

Here the notation ŷi is used to denote the value of the line of best fit in order to distinguish it from the observed values of y, that is, yi. We shall refer to ŷi as the ith predicted value or the fitted value of yi. Residuals In practice, we wish to minimize the difference between the actual value of y (yi) and the predicted value of y (ŷi). This difference is called the residual, êi, that is, êi = yi– ŷi. 2 shows a hypothetical situation based on six data points. Marked on this plot is a line of best fit, ŷi along with the residuals.

Thus, the random error term does not depend on x, nor does it contain any information about Y (otherwise it would be a systematic error). We shall begin by assuming that Var(Y | X = x ) = s 2 . 2) In Chapter 4 we shall see how this last assumption can be relaxed. Estimating the population slope and intercept Suppose for example that X = height and Y = weight of a randomly selected individual from some population, then for a straight line regression model the mean weight of individuals of a given height would be a linear function of that height.

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A Modern Approach to Regression with R (Springer Texts in Statistics) by Simon Sheather


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