# Methods of Correlation and Regression Analysis, Linear and Curvilinear

By Mordecai Ezekiel; Karl A. Fox | Go to book overview

CHAPTER 7
Measuring accuracy of estimate and degree of correlation

The methods developed up to this point may be used to estimate the values of one variable when the values of another are known or given. They also furnish an explicit statement of the average difference or change in the values of the estimated or dependent variable for each particular difference or change in the value of the known or independent variable. But that is not enough. In addition it is frequently desirable to answer three queries: (1) How closely can values of the dependent variable be estimated from the values of the independent variable? (2) How important is the relation of the dependent variable to the independent variable? (3) How far are the regression curve and these relations, as shown by the particular sample, likely to depart from the true values for the universe from which the sample was drawn? Special statistical devices, termed (1) the standard error of estimate and (2) the coefficient and index of correlation, have been developed to meet the need indicated by the first two questions. Error formulas and knowledge of the distributions of these coefficients, and standard errors for the regression line or curve, provide approximate answers for the third, under the assumption that certain conditions of sampling are met.

The Closeness of Estimate--Standard Error of Estimate

Attention has previously been called to the fact that when some dependent variable, such as the distance required for an automobile to stop after the brake is applied or the protein content in wheat samples, is estimated from another variable, such as the speed at which the car is moving or the proportion of vitreous kernels in the sample, the estimated values in many cases will not be the same as the values of the dependent

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