Summary
Regression is the backbone of any analysis and the reader cannot go ahead without touching on it. In this chapter, I have presented linear regression and multivariate regression and how they are used for prediction. The R function lm()
is used to implement both simple and multivariate linear regression. I also presented significance testing along with residual calculations and the normality plot, which tests residuals for normality using a qq plot. Analysis of variance (ANOVA) is used to select the difference means of two or more samples. Multivariate linear regression involves many variables, and the coefficient of each variable is different, which varies the importance of each variable and is ranked accordingly. Stepwise regression is used to select variables which are important in the regression. Time series analysis does not represent the complete information sometimes. It becomes necessary to explore frequency analysis, which can be done with wavelet, fast Fourier and Hilbert...