R linear regression
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Linear regression is a statistical tool to model the relationship between a predictor variable and a response variable using the equation y = ax + b. The lm() function in R is used to create this relationship, while the predict() function forecasts outcomes based on new predictor values. The document also touches on related topics such as multiple regression and logistic regression.
R linear regression
- 1. Swipe R - Linear Regression
- 2. Regression analysis is a very widely used statistical tool to establish a relationship model between two variables. One of these variable is called predictor variable whose value is gathered through experiments. The other variable is called response variable whose value is derived from the predictor variable In Linear Regression these two variables are related through an equation, where exponent (power) of both these variables is 1. R - Vectors
- 3. Mathematically a linear relationship represents a straight line when plotted as a graph. A non-linear relationship where the exponent of any variable is not equal to 1 creates a curve. The general mathematical equation for a linear regression is:- y = ax + b Following is the description of the parameters used:- y is the response variable. x is the predictor variable. a and b are constants which are called the coefficients.
- 4. Steps to Establish a Regression A simple example of regression is predicting weight of a person when his height is known. To do this we need to have the relationship between height and weight of a person. The steps to create the relationship is:- Carry out the experiment of gathering a sample of observed values of height and corresponding weight. Create a relationship model using the lm() functions in R.
- 5. Find the coefficients from the model created and create the mathematical equation using these Get a summary of the relationship model to know the average error in prediction. Also called residuals. To predict the weight of new persons, use the predict() function in R.
- 6. lm() Function This function creates the relationship model between the predictor and the response variable. The basic syntax for lm() function in linear regression is:- lm(formula,data) Following is the description of the parameters used − formula is a symbol presenting the relation between x and y. data is the vector on which the formula will be applied.
- 7. predict() Function The basic syntax for predict() in linear regression is:- predict(object, newdata) Following is the description of the parameters used − object is the formula which is already created using the lm() function. newdata is the vector containing the new value for predictor variable.
- 8. R - Multiple Regression R - Logistic Regression R - Normal Distribution Stay Tuned with Topics for next Post