![]() This is equal to $\beta_0$, the intercept of the model where our regression line intersects with the y axis when $x$ is zero. $_i$, is our dependent variable of the model that we are predicting with four independent variables of a specific observation $i$. $Y_i=\ \beta_0 \ \beta_1x_1 \beta_2x_2\ldots \beta_kx_k \varepsilon$įormula 2 is specific to our analysis that includes our dependent variable wages and our independent variables age, sex, education, and language.Ģ.There are two formulas below a general linear regression formula and the specific formula for our example.įormula 1 below, is a general linear regression formula that does not specify our variables and is a good starting place for building a linear regression model. Language is coded as 1= English, 2= French, and 3= Other. This is a nominal level variable measuring the language that each respondent speaks. This is a continuous level variable measuring the number of years of education each respondent has. Education of respondent in years ( education).This is a nominal level variable measuring the sex of each respondent and is coded as 1= FEMALE and 2=MALE. This is a continuous level variable measuring the age of each respondent. This is a continuous variable that ranges from a score of 2.30 to 49.92, which is a large range! If you would like to investigate this variable more use the code for the descriptive statistics to better understand the distribution, which is very important for a linear regression model. ![]() ![]() Below is a breakdown of the variables included in our model to help us keep track of the types of variables we are working with. ![]()
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