fixed-effects (also known as entity or individual effects) or time-effects. Both kinds of linear models are accessed through the ols function in the pandas namespace. They all take the following arguments

fixed-effects (also known as entity or individual effects) or time-effects. Both kinds of linear models are accessed through the ols function in the pandas namespace. They all take the following arguments

fixed-effects (also known as entity or individual effects) or time-effects. Both kinds of linear models are accessed through the ols function in the pandas namespace. They all take the following arguments

k-1 levels of a categorical variable to avoid collinearity when feeding the result to statistical models. You can switch to this mode by turn on drop_first. In [102]: s = pd.Series(list('abcaa')) In [103]:

k-1 levels of a categorical variable to avoid collinearity when feeding the result to statistical models. You can switch to this mode by turn on drop_first. In [102]: s = pd.Series(list('abcaa')) In [103]: estimation while plotting, aggregating across observations and visualizing the fit of statistical models to emphasize patterns in a dataset. 30.2.3 yhat/ggplot Hadley Wickham’s ggplot2 is a foundational

k-1 levels of a categorical variable to avoid collinearity when feeding the result to statistical models. You can switch to this mode by turn on drop_first. In [102]: s = pd.Series(list('abcaa')) In [103]: estimation while plotting, aggregating across observations and visualizing the fit of statistical models to emphasize patterns in a dataset. 5.2.4 yhat/ggpy Hadley Wickham’s ggplot2 is a foundational

k-1 levels of a categorical variable to avoid collinearity when feeding the result to statistical models. You can switch to this mode by turn on drop_first. In [102]: s = pd.Series(list('abcaa')) In [103]: estimation while plotting, aggregating across observations and visualizing the fit of statistical models to emphasize patterns in a dataset. 5.2.4 yhat/ggpy Hadley Wickham’s ggplot2 is a foundational

k-1 levels of a categorical variable to avoid collinearity when feeding the result to statistical models. You can switch to this mode by turn on drop_first. In [102]: s = pd.Series(list('abcaa')) In [103]: estimation while plotting, aggregating across observations and visualizing the fit of statistical models to emphasize patterns in a dataset. 5.2.4 yhat/ggpy Hadley Wickham’s ggplot2 is a foundational

k-1 levels of a categorical variable to avoid collinearity when feeding the result to statistical models. You can switch to this mode by turn on drop_first. In [108]: s = pd.Series(list("abcaa")) In [109]: CSS relevant to the caption. [23]: s.set_caption("Confusion matrix for multiple cancer prediction models.")\ .set_table_styles([{ 'selector': 'caption', 'props': 'caption-side: bottom; font-size:1.25em;'

k-1 levels of a categorical variable to avoid collinearity when feeding the result to statistical models. You can switch to this mode by turn on drop_first. 566 Chapter 2. User Guide pandas: powerful CSS relevant to the caption. [23]: s.set_caption("Confusion matrix for multiple cancer prediction models.")\ .set_table_styles([{ 'selector': 'caption', 'props': 'caption-side: bottom; font-size:1.25em;'