Why do we do regression

What is regression?

Regression helps to solve the problem of complete uncertainty and decision making and guided planning.

How do we solve the linear regression model(CLRM)?

The linear regression model is a way of examining the nature and form of the relationship between two or more variables. Also, we want to know which variable is affecting the other.

From -R

install.packages("lmPerm")

set.seed(1234)

> states <- as.data.frame(state.x77)

fit <- lmp(weight~height, data=women, perm="Prob")

summary(fit)

Call:

lmp(formula = weight ~ height, data = women, perm = "Prob")

Residuals:

    Min      1Q  Median      3Q     Max 

-1.7333 -1.1333 -0.3833  0.7417  3.1167 

Coefficients:

       Estimate Iter Pr(Prob)    

height     3.45 5000   <2e-16 ***

---

Signif. codes:  

  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’

  0.1 ‘ ’ 1

Residual standard error: 1.525 on 13 degrees of freedom

Multiple R-Squared: 0.991, Adjusted R-squared: 0.9903 

F-statistic:  1433 on 1 and 13 DF,  p-value: 1.091e-14 

> plot(fit)

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