Here is how to interpret the most interesting numbers in the output: Type the following into the Command box to perform a simple linear regression using weight as an explanatory variable and mpg as a response variable. Step 4: Perform simple linear regression. To quantify this relationship, we will now perform a simple linear regression. We can see that cars with higher weights tend to have lower miles per gallon. Type the following into the Command box to create a scatterplot: mpg so we can visualize the relationship between these two variables and check for any obvious outliers. We can see that there are 12 different variables in the dataset, but the only two that we care about are mpg and weight.īefore we perform simple linear regression, let’s first create a scatterplot of weight vs. Gain a quick understanding of the data you’re working with by typing the following into the Command box: Load the data by typing the following into the Command box: Perform the following steps in Stata to conduct a simple linear regression using the dataset called auto, which contains data on 74 different cars. Suppose we are interested in understanding the relationship between the weight of a car and its miles per gallon. To explore this relationship, we can perform simple linear regression using weight as an explanatory variable and miles per gallon as a response variable. Example: Simple Linear Regression in Stata This tutorial explains how to perform simple linear regression in Stata. © W.Simple linear regressionis a method you can use to understand the relationship between an explanatory variable, x, and a response variable, y. Here's a list of the most important operators: & Regress income education if household=1 & nchildren > 1 For instance, the same analysis as before, but restricted to mothers with more than one child, is produced by: Most notably, you may combine several conditions with "and" or "or". If clauses can be more complicated than that. Regress income education if household=1, betaĪs you can see, the if clause is placed at the end of the command, before the option(s). Let's assume that single mothers are coded as "1" in variable "household". For instance, you might wish to do regress income on education for single mothers only. With Stata, you may also do statistical analyses IF certain conditions are given. Perhaps you will find some examples at a later stage in the "data transformation" section. A household will belong to the category of "unmarried heterosexual, without children" IF there are two adults of different sex (who should have declared each other as partners, because otherwise they might just share a flat for the fun of it) and there are no children. So, a household will belong to the category of "single mothers" IF one single female parent and one or more children are present. For instance, you may wish to create types of household. In most other statistical software I know (admittedly, this is not much more than a handful), if clauses are important for creating or changing data. Multiple Imputation: Analysis and Pooling StepsĪll Stata commands (or nearly) can be accompanied by an if clause.Confidence Intervals with ci and centile.Changing the Look of Lines, Symbols etc.
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