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This tells us the average price of a car when both mpg and weight are zero. Since this value is less than 0.05, we have sufficient evidence to say that weight has a statistically significant relationship with price.Ĭoef (_cons): 1946.069. This is the p-value associated with the test statistic for weight.
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If car A weighs one pound more than car B, then car A is expected to cost $1.74 more. This tells us the average change in price that is associated with a one unit increase in weight, assuming mpg is held constant. In this example, each one unit increase in weight is associated with an average increase of about $1.74 in price, assuming mpg is held constant.įor example, suppose cars A and B both get 20 mpg. Since this value is not less than 0.05, we don’t have evidence to say that mpg has a statistically significant relationship with price.Ĭoef (weight): 1.746. This is the p-value associated with the test statistic for mpg. If car A gets 20 mpg and car B only gets 19 mpg, we would expect the price of car A to be $49.51 less than the price of car B. This tells us the average change in price that is associated with a one unit increase in the mpg, assuming weight is held constant. In this example, each one unit increase in mpg is associated with an average decrease of about $49.51 in price, assuming weight is held constant.įor example, suppose cars A and B both weigh 2,000 pounds. In this example, 29.34% of the variation in price can be explained by mpg and weight.Ĭoef (mpg): -49.512. R-squared: 0.2934. This is the proportion of the variance in the response variable that can be explained by the explanatory variables. Since this value is less than 0.05, this indicates that the combined explanatory variables of mpg and weight have a statistically significant relationship with the response variable price. This is the p-value for the overall regression.
Simple linear regression equation from stata how to#
Here is how to interpret the most interesting numbers in the output: Type the following into the Command box to perform a multiple linear regression using mpg and weight as explanatory variables and price as a response variable. Step 3: Perform multiple linear regression. We can see the following basic summary statistics about these three variables: We can see that there are 12 different variables in the dataset, but the only ones we care about are mpg, weight, and price. 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 multiple linear regression using the dataset called auto, which contains data on 74 different cars. To test this, we can perform a multiple linear regression using miles per gallon and weight as the two explanatory variables and price as the response variable. Suppose we want to know if miles per gallon and weight impact the price of a car.
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Example: Multiple Linear Regression in Stata This tutorial explains how to perform multiple linear regression in Stata. Multiple linear regression is a method you can use to understand the relationship between several explanatory variables and a response variable.