Regression analysis of R language

Data metrics modeling refers to the use of several arguments and the creation of formulas to predict target variables. If the target variable of the study is continuous type, it is called regression analysis.

One or one-yuan linear regression analysis

Data.lm<-LM (height~weight,women) Calculation Model Summary (DATA.LM) lists the model details CALL:LM (Formula=Height~Weight, data=women) Residuals:Min1Q Median 3QMax -0.83233 -0.26249  0.08314  0.34353  0.49790coefficients:estimate Std. Error t value Pr (>|T|) (Intercept)25.723456   1.043746   24.64 2.68e- A ***Weight0.287249   0.007588   37.85 1.09e- - ***---Signif. Codes0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1residual standard error:0.44  on  - degrees  offreedommultiple R-Squared:0.991, Adjusted R-Squared:0.9903F-Statistic:1433  on 1  and  -DF, p-Value1.091e- -Additional information: (1) correlation coefficient R, R^2multiple R-Squared Get: summary (DATA.LM) $r. Squared (2) correction of correlation coefficient r^2, eliminating the effect of the number of independent variables adjusted R-Squared Get: summary (DATA.LM) $adj. r.squared (3the significance of regression coefficients test T-Test: Verify that each model parameter equals 0 and calculates the probability that it equals 0 o'clock

Regression analysis of R language