R program regression

It finds the line of best fit through your data by searching for the value of the regression coefficient s that minimizes the total error of the model. In this step-by-step guide, we will walk you through linear regression in R using two sample datasets. Simple regression dataset Multiple regression dataset. Table of contents Getting started in R Load the data into R Make sure your data meet the assumptions Perform the linear regression analysis Check for homoscedasticity Visualize the results with a graph Report your results.

Start by downloading R and RStudio. As we go through each step , you can copy and paste the code from the text boxes directly into your script. To install the packages you need for the analysis, run this code you only need to do this once :. Next, load the packages into your R environment by running this code you need to do this every time you restart R :. Because both our variables are quantitative , when we run this function we see a table in our console with a numeric summary of the data.

This tells us the minimum, median, mean, and maximum values of the independent variable income and dependent variable happiness :. Again, because the variables are quantitative, running the code produces a numeric summary of the data for the independent variables smoking and biking and the dependent variable heart disease :.

See an example. We can use R to check that our data meet the four main assumptions for linear regression. If you know that you have autocorrelation within variables i. Use a structured model, like a linear mixed-effects model, instead. To check whether the dependent variable follows a normal distribution , use the hist function.

The observations are roughly bell-shaped more observations in the middle of the distribution, fewer on the tails , so we can proceed with the linear regression. The relationship between the independent and dependent variable must be linear. We can test this visually with a scatter plot to see if the distribution of data points could be described with a straight line. We can test this assumption later, after fitting the linear model. Slope: Depicts the steepness of the line.

Intercept: The location where the line cuts the axis. This means if x is increased by a unit, y gets increased by 5. Coefficient — Estimate: In this, the intercept denotes the average value of the output variable when all input becomes zero.

So, in our case, salary in lakhs will be Here slope represents the change in the output variable with a unit change in the input variable. In turn, this tells about the confidence for relating input and output variables. Coefficient — t value: This value gives the confidence to reject the null hypothesis.

The greater the value away from zero, the bigger the confidence to reject the null hypothesis and establishing the relationship between output and input variable. In our case value is away from zero as well. The closer it is to zero, the easier we can to reject the null hypothesis. The line we see in our case, this value is near to zero; we can say there exists a relationship between salary package, satisfaction score and year of experience.

R-squared is a very important statistical measure in understanding how close the data has fitted into the model. Next we can predict the value of the response variable for a given set of predictor variables using these coefficients. Consider the data set "mtcars" available in the R environment. It gives a comparison between different car models in terms of mileage per gallon mpg , cylinder displacement "disp" , horse power "hp" , weight of the car "wt" and some more parameters.

The goal of the model is to establish the relationship between "mpg" as a response variable with "disp","hp" and "wt" as predictor variables. We create a subset of these variables from the mtcars data set for this purpose. Asif Hussain. R - Linear Regression Advertisements. Previous Page. Next Page. Live Demo.