how to find simple linear regression equation in r studio

> y=predict(fit,newdata=list(x=seq(from=1,to=n,length. 2 will be needed shortly for the variance.). Store the p-value and keep the regressor with a p-value lower than a defined threshold (0.1 by default). Step 1: Regress each predictor on y separately. means that a linear model of the form y0+1x is to be fitted (if x is not a factor variable). 3.06157 -0.69437 -0.00174 0.76330 2.63033 The computational formulas for Sxy and Sxx require only the summary statistics xi, yi, xi. The algorithm works as follow: Stepwise Linear Regression in R. This example models y as a cubic function of x: lm (y poly (x, 3, raw TRUE )) The example’s formula corresponds to the following cubic regression equation: yi 0 + 1xi + 2xi2 + 3xi3 + i. 1 2 3 4 simple.fit lm(SalesSpend, datadataset) summary(simple.fit) multi.fit lm(SalesSpend+Month, datadataset) summary(multi.fit) Notices on the multi.fit line the Spend variables is accompanied by the Month variable and a plus sign (+). The first argument of the function to be specified is, similar to plot(), the regression formula with the basic syntax y x where y is the dependent variable. In my opinion, it's a good strategy to transform your data before performing linear regression model as your data show good log relation: > #generating the data Use the poly (x,n) function in your regression formula to regress on an n -degree polynomial of x. We’ll use SalesSpend, datadataset and we’ll call the resulting linear model fit.

How to find simple linear regression equation in r studio