how to find simple linear regression equation in r studio

In this case, 83.1% of the variation in exam scores can be explained by the number of hours studied. In general, the larger the R-squared value of a regression model the better the predictor variables are able to predict the value of the response variable. The R-Square value tells us the percentage of variation in the exam scores that can be explained by the number of hours studied. In other words, hours is a useful variable for predicting exam score. 05, we conclude that the regression model as a whole is statistically significant. The overall F-value of the regression model is 63.91 and the corresponding p-value is <.0001. Here’s how to interpret the most important values from each table in the output: Next, we’ll use proc reg to fit the simple linear regression model: /*fit simple linear regression model*/ Step 2: Fit the Simple Linear Regression Model The following code shows how to create this dataset in SAS: /*create dataset*/ We’ll to fit a simple linear regression model using hours as the predictor variable and score as the response variable. Step 1: Create the Dataįor this example, we’ll create a dataset that contains the total hours studied and final exam score for 15 students. The following step-by-step example shows how to perform simple linear regression in SAS. This equation helps us understand the relationship between the predictor variable and the response variable.

b 0: The intercept of the regression line.

This technique finds a line that best “fits” the data and takes on the following form: Simple linear regression is a technique that we can use to understand the relationship between one predictor variable and a response variable.

How to find simple linear regression equation in r studio