How to interpret the summary of linear regression with log-transformed variable

How should we interpret the coefficients of linear regression when we use log-transformation?

On the area of econometrics and data science, we sometimes use log-transformed weights for linear regression. Usually, one of the advantages of linear regression is that we can easily interpret the outcome. But by log-transformation, how should we interpret the outcome?

Overview

In many cases, we adopt linear regression to analyze data. That lets us understand how influential each feature is.

So when we use it, to make the way of interpretation easy, we want as simple features as possible. If you transform the features, you need to adjust your interpretation to that.

How to deal with heteroscedasticity

On the article below, I wrote about heteroscedasticity.

What is heteroscedasticity and How to check it on R

Linear regression with OLS is simple and strong method to analyze data. By the coefficients, we can know the influence each variables have. Although it looks easy to use linear regression with OLS because of the simple system from the viewpoint of necessary code and mathematics, it has some important conditions which should be kept to get proper coefficients and characteristics.

Roughly, with heteroscedasticity, we can’t get OLS’s nice feature, unbiasedness. And plot and some tests such as Breusch-Pagan test reveal the existence of heteroscedasticity.

After knowing the problem, of course we need to know how to solve it.
Here on this article, I’ll write about how to deal with this heteroscedasticity.

I’ll use same data here as the article above.

What is heteroscedasticity and How to check it on R

Linear regression with OLS is simple and strong method to analyze data. By the coefficients, we can know the influence each variables have.

Although it looks easy to use linear regression with OLS because of the simple system from the viewpoint of necessary code and mathematics, it has some important conditions which should be kept to get proper coefficients and characteristics.

On this article, I’ll show the way to check heteroscedasticity.

Making linear regression model by R

Overview

When we make model by data science, machine learning method, it’s not simple process such as “just throw data into SVM”. Getting, checking, processing, modeling, evaluation. There are many steps you need to care about.

On this article, by making regression model on R, I’ll show the example of part of the process.