# statsmodels.stats.power.FTestPower¶

class statsmodels.stats.power.FTestPower(**kwds)[source]

Statistical Power calculations for generic F-test of a constraint

This class is not recommended, use FTestPowerF2 with corrected interface.

This is based on Cohen’s f as effect size measure.

Warning: Methods in this class have the names df_num and df_denom reversed.

`FTestPowerF2`

Class with Cohen’s f-squared as effect size, corrected keyword names.

Examples

Sample size and power for multiple regression base on R-squared

Compute effect size from R-squared

``````>>> r2 = 0.1
>>> f2 = r2 / (1 - r2)
>>> f = np.sqrt(f2)
>>> r2, f2, f
(0.1, 0.11111111111111112, 0.33333333333333337)
``````

Find sample size by solving for denominator df, wrongly named `df_num`

``````>>> df1 = 1  # number of constraints in hypothesis test
>>> df2 = FTestPower().solve_power(effect_size=f, alpha=0.1, power=0.9,
df_denom=df1)
>>> ncc = 1  # default
>>> nobs = df2 + df1 + ncc
>>> df2, nobs
(76.46459758305376, 78.46459758305376)
``````

verify power at df2

``````>>> FTestPower().power(effect_size=f, alpha=0.1, df_denom=df1, df_num=df2)
0.8999999972109698
``````

Methods

 `plot_power`([dep_var, nobs, effect_size, ...]) Plot power with number of observations or effect size on x-axis `power`(effect_size, df_num, df_denom, alpha) Calculate the power of a F-test. `solve_power`([effect_size, df_num, df_denom, ...]) solve for any one parameter of the power of a F-test

Last update: Sep 01, 2023