Applied linear statistical methods
Regression and correlation models for two variables; Regression and correlation models with several independent variables; Further inference for regression models; Polynomial models for time series; Choosing a set of independent variables; Some methods for time series; Some further topics in regression analysis; The analysis of variance for the one-way layout; The analysis of variance for higher-way layout; The analysis of covariance.
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Regression and Correlation Models with Several
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analysis of variance average bivariate normal blood lead level Bonferroni cell Chapter chi-squared distribution CN CN CN coefficient of determination column effects common variance components computed confidence intervals contrasts correlation coefficient covariance matrix critical value d.f. Mean square degrees of freedom dependent disturbance terms example F-distribution F-ratio F-statistic given hypothesis H0 independent variables inferences interaction intercept least squares estimators linear function linear model linear regression method multinormal multiple comparisons normal distribution observations orthogonal polynomials pairs partial correlation plot prediction intervals quadratic forms random variables regression analysis regression coefficients regression equation regression function reject H0 residuals row and column sample Scheffe significant simultaneous tests Source Sum squares and products squares d.f. Mean standard deviation subset sum of squares Table test scores test statistic test the hypothesis tests and confidence theorem tion treatment effects two-sided variance a1 variation zero