Box plot: To spot any outlier observations in the variable. 
Scatter plot: Visualize the linear relationship between the predictor and response. Typically, for each of the independent variables (predictors), the following plots are drawn to visualize the following behavior: But before jumping in to the syntax, lets try to understand these variables graphically. The aim of this exercise is to build a simple regression model that we can use to predict Distance (dist) by establishing a statistically significant linear relationship with Speed (speed). The graphical analysis and correlation study below will help with this. Head(cars) # display the first 6 observations #> speed dist #> 1 4 2 #> 2 4 10 #> 3 7 4 #> 4 7 22 #> 5 8 16 #> 6 9 10īefore we begin building the regression model, it is a good practice to analyze and understand the variables. Lets print out the first six observations here. You will find that it consists of 50 observations(rows) and 2 variables (columns) – dist and speed. You can access this dataset simply by typing in cars in your R console. cars is a standard built-in dataset, that makes it convenient to demonstrate linear regression in a simple and easy to understand fashion. ϵ is the error term, the part of Y the regression model is unable to explain.įor this analysis, we will use the cars dataset that comes with R by default. Collectively, they are called regression coefficients. 
Where, β 1 is the intercept and β 2 is the slope. This mathematical equation can be generalized as follows: The aim of linear regression is to model a continuous variable Y as a mathematical function of one or more X variable(s), so that we can use this regression model to predict the Y when only the X is known. The aim is to establish a linear relationship (a mathematical formula) between the predictor variable(s) and the response variable, so that, we can use this formula to estimate the value of the response Y, when only the predictors ( X s) values are known.
Linear regression is used to predict the value of an outcome variable Y based on one or more input predictor variables X.
0 kommentar(er)
