test

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": {}
   },

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