Handbook of Regression Analysis With Applications in R. Samprit Chatterjee

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and the coefficient of the year built variable is marginally statistically significant. The coefficients imply that given all else in the model is held fixed, one additional bathroom in a house is associated with an estimated expected price that is higher; one additional square foot of living area is associated with an estimated expected price that is higher (given the typical value of the living area variable, a more meaningful statement would probably be that an additional square feet of living area is associated with an estimated expected price that is higher); and a house being built one year later is associated with an estimated expected price that is higher.

This is a situation where the distinction between a confidence interval for a fitted value and a prediction interval (and which is of more interest to a particular person) is clear. Consider a house with bedrooms, bathroom, square feet of living area, square foot lot size, built in 1948, with in property taxes. Substituting those values into the above equation gives an estimated expected sale price of a house with these characteristics equal to . A buyer or a seller is interested in the sale price of one particular house, so a prediction interval for the sale price would provide a range for what the buyer can expect to pay and the seller expect to get. The standard error of the estimate can be used to construct a rough prediction interval, in that roughly of the time a house with these characteristics can be expected to sell for within of that estimated sale price, but a more exact interval might be required. On the other hand, a home appraiser or tax assessor is more interested in the typical (average) sale price for all homes of that type in the area, so they can give a justifiable interval estimate giving the precision of the estimate of the true expected value of the house, so a confidence interval for the fitted value is desired.

      Exact intervals for a house with these characteristics can be obtained from statistical software, and turn out to be for the prediction interval and for the confidence interval. As expected, the prediction interval is much wider than the confidence interval, since it reflects the inherent variability in sale prices in the population of houses; indeed, it is probably too wide to be of any practical value in this case, but an interval with smaller coverage (that is expected to include the actual price only of the time, say) might be useful (a interval in this case would be , so a seller could be told that there is a chance that their house will sell for a value in this range).

The validity of all of these results depends on whether the assumptions hold. Figure 1.5 gives a scatter plot of the residuals versus the fitted values and a normal plot of the residuals for this model fit. There is no apparent pattern in the plot of residuals versus fitted values, and the ordered residuals form a roughly straight line in the normal plot, so there are no apparent violations of assumptions here. The plot of residuals versus each of the predictors (Figure 1.6) also does not show any apparent patterns, other than the houses with unusual living area and year being built, respectively. It would be reasonable to omit these observations to see if they have had an effect on the regression, but we will postpone discussion of that to Chapter 3, where diagnostics for unusual observations are discussed in greater detail.

An obvious consideration at this point is that the models discussed here appear to be overspecified; that is, they include variables that do not apparently add to the predictive power of the model. As was noted earlier, this suggests the consideration of model building, where a more appropriate (simplified) model can be chosen, which will be discussed in Chapter 2.

FIGURE 1.5: Residual plots for the home price data. (a) Plot of residuals versus fitted values. (b) Normal plot of the residuals.

FIGURE 1.6: Scatter plots of residuals versus each predictor for the home price data.

1.5 Summary

      In this chapter we have laid out the basic structure of the linear regression model, including the assumptions that justify the use of least squares estimation. The three main goals of regression noted at the beginning of the chapter provide a framework for an organization of the topics covered.

      1 Modeling the relationship between and :the least squares estimates summarize the expected change in for a given change in an , accounting for all of the variables in the model;the standard error of the estimate estimates the standard deviation of the errors; and estimate the proportion of variability in accounted for by ;and the confidence interval for a fitted value provides a measure of the precision in estimating the expected target for a given set of predictor values.

      2 Prediction of the target variable:substituting specified values of into the fitted regression model gives an estimate of the value of the target for a new observation;the rough prediction interval provides a quick measure of the limits of the ability to predict a new observation;and the exact prediction interval provides a more precise measure of those limits.

      3 Testing of hypotheses:the ‐test provides a test of the statistical significance of the overall relationship;the ‐test for each slope coefficient testing whether the true value is zero provides a test of whether the variable provides additional predictive power given the other variables;and the ‐tests can be generalized to test other hypotheses of interest about the coefficients as well.

      Since

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