Suppose a real estate agent wants to understand the relationship between square footage and house price. To analyze this relationship, he collects data on square footage and house price for houses in a particular city. In this scenario, the real estate agent should use a simple linear regression model to analyze the relationship between these two variables because the predictor variable square footage is continuous.
Using simple linear regression, the real estate agent can fit the following regression model:. This will allow the real estate agent to quantify the relationship between square footage and house price. The real estate agent can then fit the following multiple linear regression model:.
Check out the following tutorials to see how to create dummy variables in different statistical software:. The following tutorials offer an in-depth introduction to linear regression models:. Your email address will not be published. Skip to content Menu. Posted on May 7, by Zach. These two types of models share the following similarity: The response variable in each model is continuous.
Examples of continuous variables include weight, height, length, width, time, age, etc. However, these two types of models share the following difference : ANOVA models are used when the predictor variables are categorical. It performs analysis of data from a wide variety of experimental designs. In this process, a continuous response variable, known as a dependent variable, is measured under experimental conditions identified by classification variables, known as independent variables.
The variation in the response is assumed to be due to effects in the classification, with random error accounting for the remaining variation. Here we study the dependence between the variables car type and their horsepower. As the car type is a variable with categorical values, we take it as class variable and use both these variables in the MODEL. We can also extend the model by applying the MEANS statement in which we use Turkey's Studentized method to compare the mean values of various car types.
The category of car types are listed with the mean value of horsepower in each category along with some additional values like error mean square etc.
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