diff --git a/content/08-regression/regressions.ipynb b/content/08-regression/regressions.ipynb index 3826e30..098bd20 100644 --- a/content/08-regression/regressions.ipynb +++ b/content/08-regression/regressions.ipynb @@ -315,7 +315,7 @@ " result = model.fit() \n", " result.summary() \n", " \n", - "In the above code, you begin by selecting your x-variables as a DataFrame and your y-variable as a Series. You then initialize an OLS model, adding an intercept term (with `sm.add_constant()`) if necessary. Finally, you fit the OLS model and display the results. For example, below we run a regression where we estimate people's log wage (`logwage`) based on their number of years of educ (`educ`), race (`hispanic`, `black`, `asian`) and sex (`female`). Note how we deliberately do not include the sex `male` and the race `white` in our regression to avoid [linear dependency](https://stats.stackexchange.com/questions/143324/what-is-the-significance-of-a-linear-dependency-in-a-polynomial-regression)" + "In the above code, you begin by selecting your x-variables as a DataFrame and your y-variable as a Series. You then initialize an OLS model, adding an intercept term (with `sm.add_constant()`) if necessary. Finally, you fit the OLS model and display the results. For example, below we run a regression where we estimate people's log wage (`logwage`) based on their number of years of educ (`educ`), race (`hispanic`, `black`, `asian`) and sex (`female`). Note how we deliberately do not include the sex `male` and the race `white` in our regression to avoid [linear dependency](https://stats.stackexchange.com/questions/143324/what-is-the-significance-of-a-linear-dependency-in-a-polynomial-regression)." ], "metadata": {} }, diff --git a/requirements.txt b/requirements.txt index c28fc68..eee34c1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,5 +5,4 @@ pandas==1.5.3 plotly==5.13.0 regex==2023.5.5 requests==2.28.1 -scipy seaborn==0.12.2 \ No newline at end of file