{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chapter 16: Models with several factors\n",
"\n",
"Load the packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"import seaborn as sns\n",
"from scipy import stats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Two factors with no replication\n",
"\n",
"Load in the data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" strength | \n",
" laser | \n",
" tape | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 25.66 | \n",
" 40W | \n",
" slow | \n",
"
\n",
" \n",
" 2 | \n",
" 29.15 | \n",
" 50W | \n",
" slow | \n",
"
\n",
" \n",
" 3 | \n",
" 35.73 | \n",
" 60W | \n",
" slow | \n",
"
\n",
" \n",
" 4 | \n",
" 28.00 | \n",
" 40W | \n",
" medium | \n",
"
\n",
" \n",
" 5 | \n",
" 35.09 | \n",
" 50W | \n",
" medium | \n",
"
\n",
" \n",
" 6 | \n",
" 39.56 | \n",
" 60W | \n",
" medium | \n",
"
\n",
" \n",
" 7 | \n",
" 20.65 | \n",
" 40W | \n",
" fast | \n",
"
\n",
" \n",
" 8 | \n",
" 29.79 | \n",
" 50W | \n",
" fast | \n",
"
\n",
" \n",
" 9 | \n",
" 35.66 | \n",
" 60W | \n",
" fast | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" strength laser tape\n",
"1 25.66 40W slow\n",
"2 29.15 50W slow\n",
"3 35.73 60W slow\n",
"4 28.00 40W medium\n",
"5 35.09 50W medium\n",
"6 39.56 60W medium\n",
"7 20.65 40W fast\n",
"8 29.79 50W fast\n",
"9 35.66 60W fast"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"composite = pd.read_csv(\"data/composite.csv\", index_col=0)\n",
"composite"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the interaction plots:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(x=\"laser\", y=\"strength\", hue=\"tape\", data=composite, kind=\"point\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(x=\"tape\", y=\"strength\", hue=\"laser\", data=composite, kind=\"point\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit a saturated model. Don't show the error messages (get complaints about small datasets)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | strength | R-squared: | 1.000 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | nan | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 0.000 | \n",
"
\n",
"\n",
" Date: | Wed, 26 Sep 2018 | Prob (F-statistic): | nan | \n",
"
\n",
"\n",
" Time: | 10:15:24 | Log-Likelihood: | 274.42 | \n",
"
\n",
"\n",
" No. Observations: | 9 | AIC: | -530.8 | \n",
"
\n",
"\n",
" Df Residuals: | 0 | BIC: | -529.1 | \n",
"
\n",
"\n",
" Df Model: | 8 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" Intercept | 20.6500 | inf | 0 | nan | nan | nan | \n",
"
\n",
"\n",
" tape[T.medium] | 7.3500 | inf | 0 | nan | nan | nan | \n",
"
\n",
"\n",
" tape[T.slow] | 5.0100 | inf | 0 | nan | nan | nan | \n",
"
\n",
"\n",
" laser[T.50W] | 9.1400 | inf | 0 | nan | nan | nan | \n",
"
\n",
"\n",
" laser[T.60W] | 15.0100 | inf | 0 | nan | nan | nan | \n",
"
\n",
"\n",
" tape[T.medium]:laser[T.50W] | -2.0500 | inf | -0 | nan | nan | nan | \n",
"
\n",
"\n",
" tape[T.slow]:laser[T.50W] | -5.6500 | inf | -0 | nan | nan | nan | \n",
"
\n",
"\n",
" tape[T.medium]:laser[T.60W] | -3.4500 | inf | -0 | nan | nan | nan | \n",
"
\n",
"\n",
" tape[T.slow]:laser[T.60W] | -4.9400 | inf | -0 | nan | nan | nan | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 4.500 | Durbin-Watson: | 0.628 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.105 | Jarque-Bera (JB): | 2.010 | \n",
"
\n",
"\n",
" Skew: | 1.157 | Prob(JB): | 0.366 | \n",
"
\n",
"\n",
" Kurtosis: | 2.925 | Cond. No. | 13.9 | \n",
"
\n",
"
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: strength R-squared: 1.000\n",
"Model: OLS Adj. R-squared: nan\n",
"Method: Least Squares F-statistic: 0.000\n",
"Date: Wed, 26 Sep 2018 Prob (F-statistic): nan\n",
"Time: 10:15:24 Log-Likelihood: 274.42\n",
"No. Observations: 9 AIC: -530.8\n",
"Df Residuals: 0 BIC: -529.1\n",
"Df Model: 8 \n",
"Covariance Type: nonrobust \n",
"===============================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------------------\n",
"Intercept 20.6500 inf 0 nan nan nan\n",
"tape[T.medium] 7.3500 inf 0 nan nan nan\n",
"tape[T.slow] 5.0100 inf 0 nan nan nan\n",
"laser[T.50W] 9.1400 inf 0 nan nan nan\n",
"laser[T.60W] 15.0100 inf 0 nan nan nan\n",
"tape[T.medium]:laser[T.50W] -2.0500 inf -0 nan nan nan\n",
"tape[T.slow]:laser[T.50W] -5.6500 inf -0 nan nan nan\n",
"tape[T.medium]:laser[T.60W] -3.4500 inf -0 nan nan nan\n",
"tape[T.slow]:laser[T.60W] -4.9400 inf -0 nan nan nan\n",
"==============================================================================\n",
"Omnibus: 4.500 Durbin-Watson: 0.628\n",
"Prob(Omnibus): 0.105 Jarque-Bera (JB): 2.010\n",
"Skew: 1.157 Prob(JB): 0.366\n",
"Kurtosis: 2.925 Cond. No. 13.9\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%capture --no-display\n",
"lmod = smf.ols(\"strength ~ tape * laser\", composite).fit()\n",
"lmod.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit main effects only model:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Intercept 22.437778\n",
"tape[T.medium] 5.516667\n",
"tape[T.slow] 1.480000\n",
"laser[T.50W] 6.573333\n",
"laser[T.60W] 12.213333\n",
"dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lmod = smf.ols(\"strength ~ tape + laser\", composite).fit()\n",
"lmod.params"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 6.57, 12.21, 0. , 6.57, 12.21, 0. , 6.57, 12.21])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lasercoefs = np.tile([0, 6.57, 12.21],3)\n",
"lasercoefs"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1.48, 1.48, 1.48, 5.52, 5.52, 5.52, 0. , 0. , 0. ])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tapecoefs = np.repeat([1.48, 5.52, 0], 3)\n",
"tapecoefs"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | strength | R-squared: | 0.968 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.914 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 18.03 | \n",
"
\n",
"\n",
" Date: | Wed, 26 Sep 2018 | Prob (F-statistic): | 0.0191 | \n",
"
\n",
"\n",
" Time: | 10:15:24 | Log-Likelihood: | -12.837 | \n",
"
\n",
"\n",
" No. Observations: | 9 | AIC: | 37.67 | \n",
"
\n",
"\n",
" Df Residuals: | 3 | BIC: | 38.86 | \n",
"
\n",
"\n",
" Df Model: | 5 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" Intercept | 21.9483 | 1.491 | 14.717 | 0.001 | 17.202 | 26.694 | \n",
"
\n",
"\n",
" tape[T.medium] | 6.6746 | 2.239 | 2.982 | 0.059 | -0.449 | 13.799 | \n",
"
\n",
"\n",
" tape[T.slow] | 1.7905 | 1.498 | 1.195 | 0.318 | -2.977 | 6.558 | \n",
"
\n",
"\n",
" laser[T.50W] | 7.0870 | 1.618 | 4.381 | 0.022 | 1.939 | 12.235 | \n",
"
\n",
"\n",
" laser[T.60W] | 13.1680 | 2.014 | 6.538 | 0.007 | 6.759 | 19.578 | \n",
"
\n",
"\n",
" crossp | -0.0335 | 0.050 | -0.671 | 0.550 | -0.193 | 0.126 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 0.288 | Durbin-Watson: | 2.095 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.866 | Jarque-Bera (JB): | 0.368 | \n",
"
\n",
"\n",
" Skew: | 0.306 | Prob(JB): | 0.832 | \n",
"
\n",
"\n",
" Kurtosis: | 2.220 | Cond. No. | 146. | \n",
"
\n",
"
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: strength R-squared: 0.968\n",
"Model: OLS Adj. R-squared: 0.914\n",
"Method: Least Squares F-statistic: 18.03\n",
"Date: Wed, 26 Sep 2018 Prob (F-statistic): 0.0191\n",
"Time: 10:15:24 Log-Likelihood: -12.837\n",
"No. Observations: 9 AIC: 37.67\n",
"Df Residuals: 3 BIC: 38.86\n",
"Df Model: 5 \n",
"Covariance Type: nonrobust \n",
"==================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"----------------------------------------------------------------------------------\n",
"Intercept 21.9483 1.491 14.717 0.001 17.202 26.694\n",
"tape[T.medium] 6.6746 2.239 2.982 0.059 -0.449 13.799\n",
"tape[T.slow] 1.7905 1.498 1.195 0.318 -2.977 6.558\n",
"laser[T.50W] 7.0870 1.618 4.381 0.022 1.939 12.235\n",
"laser[T.60W] 13.1680 2.014 6.538 0.007 6.759 19.578\n",
"crossp -0.0335 0.050 -0.671 0.550 -0.193 0.126\n",
"==============================================================================\n",
"Omnibus: 0.288 Durbin-Watson: 2.095\n",
"Prob(Omnibus): 0.866 Jarque-Bera (JB): 0.368\n",
"Skew: 0.306 Prob(JB): 0.832\n",
"Kurtosis: 2.220 Cond. No. 146.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%capture --no-display\n",
"composite['crossp'] = tapecoefs * lasercoefs\n",
"tmod = smf.ols(\"strength ~ tape + laser + crossp\", composite).fit()\n",
"tmod.summary()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" df | \n",
" sum_sq | \n",
" mean_sq | \n",
" F | \n",
" PR(>F) | \n",
"
\n",
" \n",
" \n",
" \n",
" tape | \n",
" 2.0 | \n",
" 48.918689 | \n",
" 24.459344 | \n",
" 8.033725 | \n",
" 0.062408 | \n",
"
\n",
" \n",
" laser | \n",
" 2.0 | \n",
" 224.183822 | \n",
" 112.091911 | \n",
" 36.816831 | \n",
" 0.007746 | \n",
"
\n",
" \n",
" crossp | \n",
" 1.0 | \n",
" 1.369294 | \n",
" 1.369294 | \n",
" 0.449748 | \n",
" 0.550466 | \n",
"
\n",
" \n",
" Residual | \n",
" 3.0 | \n",
" 9.133750 | \n",
" 3.044583 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" df sum_sq mean_sq F PR(>F)\n",
"tape 2.0 48.918689 24.459344 8.033725 0.062408\n",
"laser 2.0 224.183822 112.091911 36.816831 0.007746\n",
"crossp 1.0 1.369294 1.369294 0.449748 0.550466\n",
"Residual 3.0 9.133750 3.044583 NaN NaN"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.stats.anova_lm(tmod)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fix the order of the categories"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"cat_type = pd.api.types.CategoricalDtype(categories=['slow','medium','fast'],ordered=True)\n",
"composite['tape'] = composite.tape.astype(cat_type)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | strength | R-squared: | 0.963 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.926 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 26.00 | \n",
"
\n",
"\n",
" Date: | Wed, 26 Sep 2018 | Prob (F-statistic): | 0.00401 | \n",
"
\n",
"\n",
" Time: | 10:15:24 | Log-Likelihood: | -13.465 | \n",
"
\n",
"\n",
" No. Observations: | 9 | AIC: | 36.93 | \n",
"
\n",
"\n",
" Df Residuals: | 4 | BIC: | 37.92 | \n",
"
\n",
"\n",
" Df Model: | 4 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" Intercept | 31.0322 | 0.540 | 57.452 | 0.000 | 29.533 | 32.532 | \n",
"
\n",
"\n",
" C(tape, Poly).Linear | -1.0465 | 0.936 | -1.119 | 0.326 | -3.644 | 1.551 | \n",
"
\n",
"\n",
" C(tape, Poly).Quadratic | -3.9001 | 0.936 | -4.169 | 0.014 | -6.498 | -1.303 | \n",
"
\n",
"\n",
" C(laser, Poly).Linear | 8.6361 | 0.936 | 9.231 | 0.001 | 6.039 | 11.234 | \n",
"
\n",
"\n",
" C(laser, Poly).Quadratic | -0.3810 | 0.936 | -0.407 | 0.705 | -2.979 | 2.216 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 0.284 | Durbin-Watson: | 1.929 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.868 | Jarque-Bera (JB): | 0.411 | \n",
"
\n",
"\n",
" Skew: | -0.128 | Prob(JB): | 0.814 | \n",
"
\n",
"\n",
" Kurtosis: | 1.985 | Cond. No. | 1.73 | \n",
"
\n",
"
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: strength R-squared: 0.963\n",
"Model: OLS Adj. R-squared: 0.926\n",
"Method: Least Squares F-statistic: 26.00\n",
"Date: Wed, 26 Sep 2018 Prob (F-statistic): 0.00401\n",
"Time: 10:15:24 Log-Likelihood: -13.465\n",
"No. Observations: 9 AIC: 36.93\n",
"Df Residuals: 4 BIC: 37.92\n",
"Df Model: 4 \n",
"Covariance Type: nonrobust \n",
"============================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"--------------------------------------------------------------------------------------------\n",
"Intercept 31.0322 0.540 57.452 0.000 29.533 32.532\n",
"C(tape, Poly).Linear -1.0465 0.936 -1.119 0.326 -3.644 1.551\n",
"C(tape, Poly).Quadratic -3.9001 0.936 -4.169 0.014 -6.498 -1.303\n",
"C(laser, Poly).Linear 8.6361 0.936 9.231 0.001 6.039 11.234\n",
"C(laser, Poly).Quadratic -0.3810 0.936 -0.407 0.705 -2.979 2.216\n",
"==============================================================================\n",
"Omnibus: 0.284 Durbin-Watson: 1.929\n",
"Prob(Omnibus): 0.868 Jarque-Bera (JB): 0.411\n",
"Skew: -0.128 Prob(JB): 0.814\n",
"Kurtosis: 1.985 Cond. No. 1.73\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%capture --no-display\n",
"from patsy.contrasts import Poly\n",
"lmod = smf.ols(\"strength ~ C(tape,Poly) + C(laser,Poly)\", composite).fit()\n",
"lmod.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check out the design matrix"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , -0.71, 0.41, -0.71, 0.41],\n",
" [ 1. , -0.71, 0.41, -0. , -0.82],\n",
" [ 1. , -0.71, 0.41, 0.71, 0.41],\n",
" [ 1. , -0. , -0.82, -0.71, 0.41],\n",
" [ 1. , -0. , -0.82, -0. , -0.82],\n",
" [ 1. , -0. , -0.82, 0.71, 0.41],\n",
" [ 1. , 0.71, 0.41, -0.71, 0.41],\n",
" [ 1. , 0.71, 0.41, -0. , -0.82],\n",
" [ 1. , 0.71, 0.41, 0.71, 0.41]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import patsy\n",
"dm = patsy.dmatrix('~ C(tape,Poly) + C(laser,Poly)', composite)\n",
"np.asarray(dm).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"composite['Nlaser'] = np.tile([40,50,60],3)\n",
"composite['Ntape'] = np.repeat([6.42,13,27], 3)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | strength | R-squared: | 0.961 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.938 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 41.55 | \n",
"
\n",
"\n",
" Date: | Wed, 26 Sep 2018 | Prob (F-statistic): | 0.000587 | \n",
"
\n",
"\n",
" Time: | 10:15:24 | Log-Likelihood: | -13.648 | \n",
"
\n",
"\n",
" No. Observations: | 9 | AIC: | 35.30 | \n",
"
\n",
"\n",
" Df Residuals: | 5 | BIC: | 36.09 | \n",
"
\n",
"\n",
" Df Model: | 3 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" Intercept | -55.0499 | 13.338 | -4.127 | 0.009 | -89.336 | -20.763 | \n",
"
\n",
"\n",
" np.log(Ntape) | 46.5929 | 10.499 | 4.438 | 0.007 | 19.605 | 73.581 | \n",
"
\n",
"\n",
" I(np.log(Ntape) ** 2) | -9.2378 | 2.028 | -4.554 | 0.006 | -14.452 | -4.024 | \n",
"
\n",
"\n",
" Nlaser | 0.6107 | 0.060 | 10.113 | 0.000 | 0.455 | 0.766 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 0.659 | Durbin-Watson: | 1.972 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.719 | Jarque-Bera (JB): | 0.553 | \n",
"
\n",
"\n",
" Skew: | -0.205 | Prob(JB): | 0.758 | \n",
"
\n",
"\n",
" Kurtosis: | 1.857 | Cond. No. | 1.76e+03 | \n",
"
\n",
"
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.76e+03. This might indicate that there are
strong multicollinearity or other numerical problems."
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: strength R-squared: 0.961\n",
"Model: OLS Adj. R-squared: 0.938\n",
"Method: Least Squares F-statistic: 41.55\n",
"Date: Wed, 26 Sep 2018 Prob (F-statistic): 0.000587\n",
"Time: 10:15:24 Log-Likelihood: -13.648\n",
"No. Observations: 9 AIC: 35.30\n",
"Df Residuals: 5 BIC: 36.09\n",
"Df Model: 3 \n",
"Covariance Type: nonrobust \n",
"=========================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-----------------------------------------------------------------------------------------\n",
"Intercept -55.0499 13.338 -4.127 0.009 -89.336 -20.763\n",
"np.log(Ntape) 46.5929 10.499 4.438 0.007 19.605 73.581\n",
"I(np.log(Ntape) ** 2) -9.2378 2.028 -4.554 0.006 -14.452 -4.024\n",
"Nlaser 0.6107 0.060 10.113 0.000 0.455 0.766\n",
"==============================================================================\n",
"Omnibus: 0.659 Durbin-Watson: 1.972\n",
"Prob(Omnibus): 0.719 Jarque-Bera (JB): 0.553\n",
"Skew: -0.205 Prob(JB): 0.758\n",
"Kurtosis: 1.857 Cond. No. 1.76e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.76e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%capture --no-display\n",
"lmodn = smf.ols(\"strength ~ np.log(Ntape) + I(np.log(Ntape)**2) + Nlaser\", composite).fit()\n",
"lmodn.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Two factors with replication\n",
"\n",
"Read in the data while making sure the predictors are treated as categorical variables:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" psize | \n",
" operator | \n",
" resin | \n",
"
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" \n",
" \n",
" \n",
" 1 | \n",
" 36.2 | \n",
" 1 | \n",
" 1 | \n",
"
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" \n",
" 2 | \n",
" 36.3 | \n",
" 1 | \n",
" 1 | \n",
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" 3 | \n",
" 35.3 | \n",
" 1 | \n",
" 2 | \n",
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" 4 | \n",
" 35.0 | \n",
" 1 | \n",
" 2 | \n",
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" 5 | \n",
" 30.8 | \n",
" 1 | \n",
" 3 | \n",
"
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" \n",
"
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"
"
],
"text/plain": [
" psize operator resin\n",
"1 36.2 1 1\n",
"2 36.3 1 1\n",
"3 35.3 1 2\n",
"4 35.0 1 2\n",
"5 30.8 1 3"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pvc = pd.read_csv(\"data/pvc.csv\", index_col=0, dtype={'psize':'float','operator':'category','resin':'category'})\n",
"pvc.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the group means"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" psize | \n",
"
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" \n",
" operator | \n",
" | \n",
"
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" \n",
" \n",
" \n",
" 1 | \n",
" 32.94375 | \n",
"
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" \n",
" 2 | \n",
" 32.68125 | \n",
"
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" \n",
" 3 | \n",
" 31.43750 | \n",
"
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" \n",
"
\n",
"
"
],
"text/plain": [
" psize\n",
"operator \n",
"1 32.94375\n",
"2 32.68125\n",
"3 31.43750"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pvc.groupby('operator')[['psize']].mean()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(x='resin',y='psize',hue='operator', data=pvc, kind=\"point\",ci=None)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = sns.catplot(x='operator',y='psize',hue='resin', data=pvc, ci=None,scale=0.5,kind=\"point\")\n",
"ax = sns.swarmplot(x='operator',y='psize',hue='resin', data=pvc)\n",
"ax.legend_.remove()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the ANOVA table"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" df | \n",
" sum_sq | \n",
" mean_sq | \n",
" F | \n",
" PR(>F) | \n",
"
\n",
" \n",
" \n",
" \n",
" operator | \n",
" 2.0 | \n",
" 20.718 | \n",
" 10.359 | \n",
" 7.007 | \n",
" 0.004 | \n",
"
\n",
" \n",
" resin | \n",
" 7.0 | \n",
" 283.946 | \n",
" 40.564 | \n",
" 27.439 | \n",
" 0.000 | \n",
"
\n",
" \n",
" operator:resin | \n",
" 14.0 | \n",
" 14.335 | \n",
" 1.024 | \n",
" 0.693 | \n",
" 0.760 | \n",
"
\n",
" \n",
" Residual | \n",
" 24.0 | \n",
" 35.480 | \n",
" 1.478 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" df sum_sq mean_sq F PR(>F)\n",
"operator 2.0 20.718 10.359 7.007 0.004\n",
"resin 7.0 283.946 40.564 27.439 0.000\n",
"operator:resin 14.0 14.335 1.024 0.693 0.760\n",
"Residual 24.0 35.480 1.478 NaN NaN"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lmod = smf.ols('psize ~ operator*resin', pvc).fit()\n",
"sm.stats.anova_lm(lmod).round(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now remove the interaction term:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" df | \n",
" sum_sq | \n",
" mean_sq | \n",
" F | \n",
" PR(>F) | \n",
"
\n",
" \n",
" \n",
" \n",
" operator | \n",
" 2.0 | \n",
" 20.718 | \n",
" 10.359 | \n",
" 7.902 | \n",
" 0.001 | \n",
"
\n",
" \n",
" resin | \n",
" 7.0 | \n",
" 283.946 | \n",
" 40.564 | \n",
" 30.943 | \n",
" 0.000 | \n",
"
\n",
" \n",
" Residual | \n",
" 38.0 | \n",
" 49.815 | \n",
" 1.311 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" df sum_sq mean_sq F PR(>F)\n",
"operator 2.0 20.718 10.359 7.902 0.001\n",
"resin 7.0 283.946 40.564 30.943 0.000\n",
"Residual 38.0 49.815 1.311 NaN NaN"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lmod = smf.ols('psize ~ operator+resin', pvc).fit()\n",
"sm.stats.anova_lm(lmod).round(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make the diagnostic plots:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = sm.qqplot(lmod.resid, line=\"q\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.residplot(lmod.fittedvalues, lmod.resid)\n",
"plt.xlabel(\"Fitted values\")\n",
"plt.ylabel(\"Residuals\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.swarmplot(pvc['operator'], lmod.resid)\n",
"plt.axhline(0)\n",
"plt.xlabel(\"Operator\")\n",
"plt.ylabel(\"Residuals\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test for equality of variance:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sum_sq | \n",
" df | \n",
" F | \n",
" PR(>F) | \n",
"
\n",
" \n",
" \n",
" \n",
" Intercept | \n",
" 0.263 | \n",
" 1.0 | \n",
" 5.326 | \n",
" 0.037 | \n",
"
\n",
" \n",
" operator | \n",
" 1.490 | \n",
" 2.0 | \n",
" 15.117 | \n",
" 0.000 | \n",
"
\n",
" \n",
" resin | \n",
" 0.638 | \n",
" 7.0 | \n",
" 1.848 | \n",
" 0.155 | \n",
"
\n",
" \n",
" Residual | \n",
" 0.690 | \n",
" 14.0 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" sum_sq df F PR(>F)\n",
"Intercept 0.263 1.0 5.326 0.037\n",
"operator 1.490 2.0 15.117 0.000\n",
"resin 0.638 7.0 1.848 0.155\n",
"Residual 0.690 14.0 NaN NaN"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lmod = smf.ols('psize ~ operator*resin', pvc).fit()\n",
"ii = np.arange(0,48,2)\n",
"pvce = pvc.iloc[ii,:].copy()\n",
"pvce['res'] = np.sqrt(abs(lmod.resid.loc[ii+1]))\n",
"vmod = smf.ols('res ~ operator+resin', pvce).fit()\n",
"sm.stats.anova_lm(vmod,typ=3).round(3)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | psize | R-squared: | 0.859 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.826 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 25.82 | \n",
"
\n",
"\n",
" Date: | Wed, 26 Sep 2018 | Prob (F-statistic): | 1.47e-13 | \n",
"
\n",
"\n",
" Time: | 10:15:25 | Log-Likelihood: | -69.000 | \n",
"
\n",
"\n",
" No. Observations: | 48 | AIC: | 158.0 | \n",
"
\n",
"\n",
" Df Residuals: | 38 | BIC: | 176.7 | \n",
"
\n",
"\n",
" Df Model: | 9 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" Intercept | 36.2396 | 0.523 | 69.345 | 0.000 | 35.182 | 37.298 | \n",
"
\n",
"\n",
" operator[T.2] | -0.2625 | 0.405 | -0.648 | 0.521 | -1.082 | 0.557 | \n",
"
\n",
"\n",
" operator[T.3] | -1.5063 | 0.405 | -3.721 | 0.001 | -2.326 | -0.687 | \n",
"
\n",
"\n",
" resin[T.2] | -1.0333 | 0.661 | -1.563 | 0.126 | -2.372 | 0.305 | \n",
"
\n",
"\n",
" resin[T.3] | -5.8000 | 0.661 | -8.774 | 0.000 | -7.138 | -4.462 | \n",
"
\n",
"\n",
" resin[T.4] | -6.1833 | 0.661 | -9.354 | 0.000 | -7.522 | -4.845 | \n",
"
\n",
"\n",
" resin[T.5] | -4.8000 | 0.661 | -7.261 | 0.000 | -6.138 | -3.462 | \n",
"
\n",
"\n",
" resin[T.6] | -5.4500 | 0.661 | -8.245 | 0.000 | -6.788 | -4.112 | \n",
"
\n",
"\n",
" resin[T.7] | -2.9167 | 0.661 | -4.412 | 0.000 | -4.255 | -1.578 | \n",
"
\n",
"\n",
" resin[T.8] | -0.1833 | 0.661 | -0.277 | 0.783 | -1.522 | 1.155 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 13.065 | Durbin-Watson: | 2.517 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.001 | Jarque-Bera (JB): | 17.339 | \n",
"
\n",
"\n",
" Skew: | 0.885 | Prob(JB): | 0.000172 | \n",
"
\n",
"\n",
" Kurtosis: | 5.353 | Cond. No. | 9.82 | \n",
"
\n",
"
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: psize R-squared: 0.859\n",
"Model: OLS Adj. R-squared: 0.826\n",
"Method: Least Squares F-statistic: 25.82\n",
"Date: Wed, 26 Sep 2018 Prob (F-statistic): 1.47e-13\n",
"Time: 10:15:25 Log-Likelihood: -69.000\n",
"No. Observations: 48 AIC: 158.0\n",
"Df Residuals: 38 BIC: 176.7\n",
"Df Model: 9 \n",
"Covariance Type: nonrobust \n",
"=================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------\n",
"Intercept 36.2396 0.523 69.345 0.000 35.182 37.298\n",
"operator[T.2] -0.2625 0.405 -0.648 0.521 -1.082 0.557\n",
"operator[T.3] -1.5063 0.405 -3.721 0.001 -2.326 -0.687\n",
"resin[T.2] -1.0333 0.661 -1.563 0.126 -2.372 0.305\n",
"resin[T.3] -5.8000 0.661 -8.774 0.000 -7.138 -4.462\n",
"resin[T.4] -6.1833 0.661 -9.354 0.000 -7.522 -4.845\n",
"resin[T.5] -4.8000 0.661 -7.261 0.000 -6.138 -3.462\n",
"resin[T.6] -5.4500 0.661 -8.245 0.000 -6.788 -4.112\n",
"resin[T.7] -2.9167 0.661 -4.412 0.000 -4.255 -1.578\n",
"resin[T.8] -0.1833 0.661 -0.277 0.783 -1.522 1.155\n",
"==============================================================================\n",
"Omnibus: 13.065 Durbin-Watson: 2.517\n",
"Prob(Omnibus): 0.001 Jarque-Bera (JB): 17.339\n",
"Skew: 0.885 Prob(JB): 0.000172\n",
"Kurtosis: 5.353 Cond. No. 9.82\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lmod = smf.ols('psize ~ operator+resin', pvc).fit()\n",
"lmod.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the Tukey critical value:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(3.45)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from statsmodels.sandbox.stats.multicomp import get_tukeyQcrit\n",
"get_tukeyQcrit(3,38)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the first pairwise difference:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-1.25002748, 0.72502748])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lmod.params[1] + np.array([-1,1]) * get_tukeyQcrit(3,38) * lmod.bse[1] / np.sqrt(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Two factors with an interaction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read in the data:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" breaks | \n",
" wool | \n",
" tension | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 26 | \n",
" A | \n",
" L | \n",
"
\n",
" \n",
" 2 | \n",
" 30 | \n",
" A | \n",
" L | \n",
"
\n",
" \n",
" 3 | \n",
" 54 | \n",
" A | \n",
" L | \n",
"
\n",
" \n",
" 4 | \n",
" 25 | \n",
" A | \n",
" L | \n",
"
\n",
" \n",
" 5 | \n",
" 70 | \n",
" A | \n",
" L | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" breaks wool tension\n",
"1 26 A L\n",
"2 30 A L\n",
"3 54 A L\n",
"4 25 A L\n",
"5 70 A L"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"warpbreaks = pd.read_csv(\"data/warpbreaks.csv\", index_col=0, dtype={'breaks':'int','wool':'category','tension':'category'})\n",
"warpbreaks.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEgBJREFUeJzt3XGsXvV93/H3h2toDIQmGON5l4CTXi800haS3GZsaTslwOSlUfGkUIVVidUiudU6x11bdaTaP5GqLlGlpI4nbXPLtouWllLaFFQhr45DGrpmaW3iFBLYuKXg4bi2Y0ICwSXYfPfHPW6Mc+37OPg85zG/90u6Os8595x7PjJXfO7vnOd3nlQVkqR2nTd0AEnSsCwCSWqcRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuOWDR1gFJdddlmtWbNm6BiSdE7ZvXv316pq5VL7nRNFsGbNGnbt2jV0DEk6pyR5YpT9vDQkSY2zCCSpcRaBJDXOIpCkxlkEjTl8+DAf/OAHOXz48NBRJE2I3oogyRuT7Dnh65tJfj7JpUl2JHm0W762rwz6bnNzczz44IPcfvvtQ0eRNCF6K4Kq+j9VdU1VXQO8DXgO+BRwK7CzqtYCO7t1jcHhw4fZvn07VcX27dsdFUgCxndp6Drgr6rqCeBGYK7bPgesH1OG5s3NzfHiiy8CcOzYMUcFkoDxFcH7gN/pXq+qqv0A3fLyxQ5IsjHJriS7Dh06NKaYr2yf/vSnOXr0KABHjx5lx44dAyeSNAl6L4IkFwA/DvzemRxXVduqaraqZleuXHKGtEZw/fXXs2zZwmTyZcuWccMNNwycSNIkGMeI4F8AD1TVgW79QJLVAN3y4BgyCNiwYQPnnbfwn3xqaooPfOADAyeSNAnGUQQ3853LQgD3ABu61xuAu8eQQcCKFStYt24dSVi3bh0rVqwYOpKkCdDrQ+eSXAjcAPzMCZs/AtyZ5BZgL3BTnxn0Uhs2bODxxx93NCDp76Sqhs6wpNnZ2fLpo5J0ZpLsrqrZpfZzZnFjnFks6WQWQWOcWSzpZBZBQ5xZLGkxFkFDnFksaTEWQUOcWSxpMRZBQ5xZLGkxFkFDnFksaTEWQUOcWSxpMb3OLNbkcWaxpJNZBI1ZsWIFn/jEJ4aOIWmCeGlIkhpnEUhS4ywCSWqcRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcRaBJDWu1yJI8pokdyV5JMnDSf5JkkuT7EjyaLd8bZ8ZJEmn1/eIYAuwvaquBt4MPAzcCuysqrXAzm5dkjSQ3oogySXAjwK3AVTVt6vqaeBGYK7bbQ5Y31cGSdLS+hwRvAE4BPy3JF9M8ltJLgJWVdV+gG55+WIHJ9mYZFeSXYcOHeoxpiS1rc8iWAa8FfhPVfUW4FucwWWgqtpWVbNVNbty5cq+MkpS8/osgieBJ6vqC936XSwUw4EkqwG65cEeM0iSltBbEVTV3wD/L8kbu03XAV8B7gE2dNs2AHf3lUGStLRlPf/8TcAnk1wAPAb8FAvlc2eSW4C9wE09Z5AknUavRVBVe4DZRb51XZ/nlSSNzpnFktQ4i0CSGmcRSFLjLAJJapxFIEmNswgkqXEWgSQ1ziKQpMZZBJLUOItAkhpnEUhS4ywCSWqcRSBJjev7MdTqbN26lfn5+aFjsG/fPgCmp6cHzTEzM8OmTZsGzSBpgUXQmCNHjgwdQdKEsQjGZFL++t28eTMAW7ZsGTiJpEnhPQJJapxFIEmNswgkqXEWgSQ1ziKQpMZZBJLUOItAkhrX6zyCJI8DzwDHgKNVNZvkUuB3gTXA48BPVNXX+8whSTq1cYwI3llV11TVbLd+K7CzqtYCO7t1SdJAhrg0dCMw172eA9YPkEGS1Om7CAr44yS7k2zstq2qqv0A3fLynjNIkk6j72cNvaOqvprkcmBHkkdGPbArjo0AV155ZV/5JKl5vY4Iquqr3fIg8Cng7cCBJKsBuuXBUxy7rapmq2p25cqVfcaUpKb1VgRJLkry6uOvgX8OPATcA2zodtsA3N1XBknS0vq8NLQK+FSS4+f57aranuQvgDuT3ALsBW7qMYMkaQm9FUFVPQa8eZHth4Hr+jqvJOnMOLNYkhpnEUhS4ywCSWqcRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcWdcBEnOS3JJH2EkSeM30mOok/w28LPAMWA38P1JPlZVv95nOEn927p1K/Pz80PHYN++fQBMT08PmmNmZoZNmzYNmmHcRh0RvKmqvgmsB+4FrgTe31sqSc05cuQIR44cGTpGk0b9YJrzk5zPQhH8x6p6ofvkMUnnuEn563fz5s0AbNmyZeAk7Rl1RPBfgMeBi4DPJbkK+EZfoSRJ4zNyEVTVdFW9u6qKhc8aXt9jLknSmIxaBH+Q5MTLSH8P+OMe8kiSxmzUIvhD4K4kU0nWAP8T+FBfoSRJ4zPSzeKq+s0kF7BQCGuAn6mqP+szmCRpPE5bBEl+4cRV4HXAHuDaJNdW1cf6DCdJ6t9SI4JXn7T+qVNslySdo05bBFX14Zd7giRTwC5gX1W9J8nrgTuAS4EHgPdX1bdf7nkkSd+bkW4WJ1mZ5NeT3JvkM8e/RjzHZuDhE9Y/Cny8qtYCXwduObPIkqSzadR3DX0SeAR4PfBhFiaX/cVSByW5Avgx4Le69QDvAu7qdpnD+QiSNKhRi2BFVd0GvFBVf1JVPw1cO8JxvwH8MvDi8Z8DPF1VR7v1J4FhnzAlSY0btQhe6Jb7k/xYkrcAV5zugCTvAQ5W1e4TNy+ya53i+I1JdiXZdejQoRFjSpLO1KgPnfvVJN8P/CKwFbgE+LdLHPMO4MeTvBt4VXfMbwCvSbKsGxVcAXx1sYOrahuwDWB2dnbRspAkvXwjjQiq6o+q6htV9VBVvbOq3lZV9yxxzIeq6oqqWgO8D/hMVf0kcB/w3m63DcDdLyO/JOllGvVdQ/8gyc4kD3Xr/yjJv/8ez/nvgF9IMs/CPYPbvsefI0k6C0a9R/CbLDxb6AWAqvpLFv7KH0lVfbaq3tO9fqyq3l5VM1V1U1U9f6ahJUlnz6hFcGFV/flJ244uuqck6ZwyahF8LckP0L3DJ8l7gf29pZIkjc2o7xr6ORbewXN1kn3AXwM/2VsqSdLYLFkESc4DZqvq+iQXAedV1TP9R5MkjcOSl4aq6kXg33Svv2UJSNIry6j3CHYk+aUkr0ty6fGvXpNJksZi1HsEP83CjeJ/fdL2N5zdOJKkcRu1CN7EQgn8MAuFcD/wn/sKdbZt3bqV+fn5oWNMhOP/Dps3bx44yfBmZmbYtGnT0DGkwY1aBHPAN4FPdOs3d9t+oo9QZ9v8/Dx7HnqYYxd6Neu8by88tmn3YwcGTjKsqeeeGjqCNDFGLYI3VtWbT1i/L8mX+gjUl2MXXsqRq989dAxNiOWP3Dt0BGlijHqz+ItJ/u7zB5L8Y+B/9RNJkjROpx0RJHmQhXsC5wMfSLK3W78K+Er/8SRJfVvq0tB7xpJCkjSY0xZBVT0xriCSpGGMeo9AkvQKZRFIUuMsAklqnEUgSY2zCCSpcRaBJDXOIpCkxlkEktQ4i0CSGmcRSFLjeiuCJK9K8udJvpTky0k+3G1/fZIvJHk0ye8muaCvDJKkpfU5IngeeFf3OQbXAOu6R1l/FPh4Va0Fvg7c0mMGSdISeiuCWvBst3p+91XAu4C7uu1zwPq+MkiSltbrPYIkU0n2AAeBHcBfAU9X1dFulyeB6T4zSJJOr9ciqKpjVXUNcAXwduAHF9ttsWOTbEyyK8muQ4cO9RlTkpo2lncNVdXTwGeBa4HXJDn+OQhXAF89xTHbqmq2qmZXrlw5jpiS1KRRP7z+jCVZCbxQVU8nWQ5cz8KN4vuA9wJ3ABuAu/vKIE26rVu3Mj8/P3SMiXD832Hz5s0DJ5kMMzMzbNq0aSzn6q0IgNXAXJIpFkYed1bVHyX5CnBHkl8Fvgjc1mMGaaLNz8/z6Je/yJUXHxs6yuAueGHhAsXzT+waOMnw9j47Ndbz9VYEVfWXwFsW2f4YC/cLJAFXXnyMX3nrN4eOoQnyaw9cMtbzObNYkhpnEUhS4ywCSWqcRSBJjbMIJKlxFoEkNc4ikKTG9TmhbGLs27ePqee+wfJH7h06iibE1HOH2bfv6NI7Sg1wRCBJjWtiRDA9Pc3fPL+MI1e/e+gomhDLH7mX6elVQ8eQJoIjAklqnEUgSY2zCCSpcRaBJDWuiZvF0qTat28f33pmauyPHdZke+KZKS7at29s53NEIEmNc0QgDWh6eprnj+73g2n0Er/2wCV83/T02M7niECSGmcRSFLjLAJJapxFIEmNswgkqXEWgSQ1ziKQpMb1VgRJXpfkviQPJ/lyks3d9kuT7EjyaLd8bV8ZJElL63NEcBT4xar6QeBa4OeSvAm4FdhZVWuBnd26JGkgvRVBVe2vqge6188ADwPTwI3AXLfbHLC+rwySpKWN5R5BkjXAW4AvAKuqaj8slAVw+TgySJIW13sRJLkY+H3g56tq5AeqJNmYZFeSXYcOHeovoCQ1rteHziU5n4US+GRV/UG3+UCS1VW1P8lq4OBix1bVNmAbwOzsbL3cLFPPPcXyR+59uT/mnHfe3y508Yuvavuxx1PPPQX4mcUS9FgESQLcBjxcVR874Vv3ABuAj3TLu/vKcNzMzEzfpzhnzM8/A8DMG1r/n+Aqfy+kTp8jgncA7wceTLKn2/YrLBTAnUluAfYCN/WYAYBNmzb1fYpzxubNmwHYsmXLwEkkTYreiqCq/hTIKb59XV/nlSSdGWcWS1Lj/IQyaWB7n/UziwEOPLfwd+mqC18cOMnw9j47xdoxns8ikAbkDevv+Pb8PADfd5X/JmsZ7++GRSANyDcyfIdvZBiO9wgkqXEWgSQ1ziKQpMZZBJLUOItAkhpnEUhS4ywCSWqcRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIa59NHpcZt3bqV+e4R0EM6nuH4U0iHMjMz09xTYS0CSRNh+fLlQ0dolkUgNa61v3713bxHIEmNswgkqXEWgSQ1ziKQpMb1VgRJ/muSg0keOmHbpUl2JHm0W762r/NLkkbT54jgvwPrTtp2K7CzqtYCO7t1SdKAeiuCqvoc8NRJm28E5rrXc8D6vs4vSRrNuOcRrKqq/QBVtT/J5WM+/2CcvflSLc7elCbVxE4oS7IR2Ahw5ZVXDpzmlcPZm5JONu4iOJBkdTcaWA0cPNWOVbUN2AYwOztb4wrYF//6lTSpxv320XuADd3rDcDdYz6/JOkkfb599HeAzwNvTPJkkluAjwA3JHkUuKFblyQNqLdLQ1V18ym+dV1f55QknTlnFktS4ywCSWqcRSBJjbMIJKlxFoEkNS5Vkz9XK8kh4Imhc7yCXAZ8begQ0iL83Ty7rqqqlUvtdE4Ugc6uJLuqanboHNLJ/N0chpeGJKlxFoEkNc4iaNO2oQNIp+Dv5gC8RyBJjXNEIEmNswgakuRfJqkkVw+dRTouybEke5J8KckDSf7p0JlaYxG05WbgT4H3DR1EOsGRqrqmqt4MfAj4D0MHao1F0IgkFwPvAG7BItDkugT4+tAhWjOxn1mss249sL2q/m+Sp5K8taoeGDqUBCxPsgd4FbAaeNfAeZrjiKAdNwN3dK/v6NalSXD80tDVwDrg9iQZOlRLfPtoA5KsAJ4EDgIFTHXLq8pfAA0sybNVdfEJ6weAf1hVBweM1RRHBG14L3B7VV1VVWuq6nXAXwM/PHAu6SW6d7RNAYeHztIS7xG04WbgIydt+33gXwH3jz+O9BLH7xEABNhQVceGDNQaLw1JUuO8NCRJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQOpZkseTXDZ0DulULAJJapxFIJ0kyS8n+WD3+uNJPtO9vi7J/0hyc5IHkzyU5KMnHLfodmnSWQTSd/sc8CPd61ng4iTns/BIjkeBj7LwhMxrgB9Ksj7J319s+9iTS98Di0D6bruBtyV5NfA88HkWCuFHgKeBz1bVoao6CnwS+FHgh06xXZp4FoF0kqp6AXgc+Cngz1h4HtM7gR8A9p7iMB+brHOWRSAt7nPAL3XL+4GfBfYA/xv4Z0kuSzLFwgP9/gT4wim2SxPPIpAWdz8Ln5b1+ao6APwtcH9V7Wfhc3XvA74EPFBVd59q+zDRpTPj00clqXGOCCSpcRaBJDXOIpCkxlkEktQ4i0CSGmcRSFLjLAJJapxFIEmN+/9QmNcT1ZN9AwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x=\"wool\", y=\"breaks\", data=warpbreaks)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(x='wool',y='breaks',hue='tension', data=warpbreaks, ci=None,kind=\"point\")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"warpbreaks['nwool'] = warpbreaks['wool'].cat.codes"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = sns.catplot(x='wool',y='breaks',hue='tension', data=warpbreaks, ci=None,kind=\"point\")\n",
"ax = sns.swarmplot(x='wool',y='breaks',hue='tension', data=warpbreaks)\n",
"ax.legend_.remove()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = sns.catplot(x='tension',y='breaks',hue='wool', data=warpbreaks, ci=None, kind=\"point\")\n",
"ax = sns.swarmplot(x='tension',y='breaks',hue='wool', data=warpbreaks)\n",
"ax.legend_.remove()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'Residuals')"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"lmod = smf.ols('breaks ~ wool * tension', warpbreaks).fit()\n",
"sns.regplot(lmod.fittedvalues, lmod.resid,scatter_kws={'alpha':0.3}, ci=None)\n",
"plt.xlabel(\"Fitted values\")\n",
"plt.ylabel(\"Residuals\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'Residuals')"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"lmod = smf.ols('np.sqrt(breaks) ~ wool * tension', warpbreaks).fit()\n",
"sns.regplot(lmod.fittedvalues, lmod.resid,scatter_kws={'alpha':0.3}, ci=None)\n",
"plt.xlabel(\"Fitted values\")\n",
"plt.ylabel(\"Residuals\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" df | \n",
" sum_sq | \n",
" mean_sq | \n",
" F | \n",
" PR(>F) | \n",
"
\n",
" \n",
" \n",
" \n",
" wool | \n",
" 1.0 | \n",
" 2.901924 | \n",
" 2.901924 | \n",
" 3.022232 | \n",
" 0.088542 | \n",
"
\n",
" \n",
" tension | \n",
" 2.0 | \n",
" 15.891615 | \n",
" 7.945807 | \n",
" 8.275225 | \n",
" 0.000817 | \n",
"
\n",
" \n",
" wool:tension | \n",
" 2.0 | \n",
" 7.201396 | \n",
" 3.600698 | \n",
" 3.749976 | \n",
" 0.030674 | \n",
"
\n",
" \n",
" Residual | \n",
" 48.0 | \n",
" 46.089229 | \n",
" 0.960192 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" df sum_sq mean_sq F PR(>F)\n",
"wool 1.0 2.901924 2.901924 3.022232 0.088542\n",
"tension 2.0 15.891615 7.945807 8.275225 0.000817\n",
"wool:tension 2.0 7.201396 3.600698 3.749976 0.030674\n",
"Residual 48.0 46.089229 0.960192 NaN NaN"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.stats.anova_lm(lmod)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | np.sqrt(breaks) | R-squared: | 0.361 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.294 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 5.415 | \n",
"
\n",
"\n",
" Date: | Wed, 26 Sep 2018 | Prob (F-statistic): | 0.000500 | \n",
"
\n",
"\n",
" Time: | 10:15:26 | Log-Likelihood: | -72.346 | \n",
"
\n",
"\n",
" No. Observations: | 54 | AIC: | 156.7 | \n",
"
\n",
"\n",
" Df Residuals: | 48 | BIC: | 168.6 | \n",
"
\n",
"\n",
" Df Model: | 5 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" Intercept | 4.8564 | 0.327 | 14.868 | 0.000 | 4.200 | 5.513 | \n",
"
\n",
"\n",
" wool[T.B] | -0.5542 | 0.462 | -1.200 | 0.236 | -1.483 | 0.375 | \n",
"
\n",
"\n",
" tension[T.L] | 1.6912 | 0.462 | 3.661 | 0.001 | 0.762 | 2.620 | \n",
"
\n",
"\n",
" tension[T.M] | -0.0304 | 0.462 | -0.066 | 0.948 | -0.959 | 0.898 | \n",
"
\n",
"\n",
" wool[T.B]:tension[T.L] | -0.7553 | 0.653 | -1.156 | 0.253 | -2.069 | 0.558 | \n",
"
\n",
"\n",
" wool[T.B]:tension[T.M] | 1.0269 | 0.653 | 1.572 | 0.123 | -0.287 | 2.340 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 4.119 | Durbin-Watson: | 2.147 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.127 | Jarque-Bera (JB): | 1.882 | \n",
"
\n",
"\n",
" Skew: | 0.034 | Prob(JB): | 0.390 | \n",
"
\n",
"\n",
" Kurtosis: | 2.088 | Cond. No. | 9.77 | \n",
"
\n",
"
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: np.sqrt(breaks) R-squared: 0.361\n",
"Model: OLS Adj. R-squared: 0.294\n",
"Method: Least Squares F-statistic: 5.415\n",
"Date: Wed, 26 Sep 2018 Prob (F-statistic): 0.000500\n",
"Time: 10:15:26 Log-Likelihood: -72.346\n",
"No. Observations: 54 AIC: 156.7\n",
"Df Residuals: 48 BIC: 168.6\n",
"Df Model: 5 \n",
"Covariance Type: nonrobust \n",
"==========================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------------------\n",
"Intercept 4.8564 0.327 14.868 0.000 4.200 5.513\n",
"wool[T.B] -0.5542 0.462 -1.200 0.236 -1.483 0.375\n",
"tension[T.L] 1.6912 0.462 3.661 0.001 0.762 2.620\n",
"tension[T.M] -0.0304 0.462 -0.066 0.948 -0.959 0.898\n",
"wool[T.B]:tension[T.L] -0.7553 0.653 -1.156 0.253 -2.069 0.558\n",
"wool[T.B]:tension[T.M] 1.0269 0.653 1.572 0.123 -0.287 2.340\n",
"==============================================================================\n",
"Omnibus: 4.119 Durbin-Watson: 2.147\n",
"Prob(Omnibus): 0.127 Jarque-Bera (JB): 1.882\n",
"Skew: 0.034 Prob(JB): 0.390\n",
"Kurtosis: 2.088 Cond. No. 9.77\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lmod.summary()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | np.sqrt(breaks) | R-squared: | 0.361 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.294 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 5.415 | \n",
"
\n",
"\n",
" Date: | Wed, 26 Sep 2018 | Prob (F-statistic): | 0.000500 | \n",
"
\n",
"\n",
" Time: | 10:15:26 | Log-Likelihood: | -72.346 | \n",
"
\n",
"\n",
" No. Observations: | 54 | AIC: | 156.7 | \n",
"
\n",
"\n",
" Df Residuals: | 48 | BIC: | 168.6 | \n",
"
\n",
"\n",
" Df Model: | 5 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" wool[A]:tension[H] | 4.8564 | 0.327 | 14.868 | 0.000 | 4.200 | 5.513 | \n",
"
\n",
"\n",
" wool[B]:tension[H] | 4.3022 | 0.327 | 13.171 | 0.000 | 3.645 | 4.959 | \n",
"
\n",
"\n",
" wool[A]:tension[L] | 6.5476 | 0.327 | 20.046 | 0.000 | 5.891 | 7.204 | \n",
"
\n",
"\n",
" wool[B]:tension[L] | 5.2381 | 0.327 | 16.037 | 0.000 | 4.581 | 5.895 | \n",
"
\n",
"\n",
" wool[A]:tension[M] | 4.8259 | 0.327 | 14.775 | 0.000 | 4.169 | 5.483 | \n",
"
\n",
"\n",
" wool[B]:tension[M] | 5.2987 | 0.327 | 16.222 | 0.000 | 4.642 | 5.955 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 4.119 | Durbin-Watson: | 2.147 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.127 | Jarque-Bera (JB): | 1.882 | \n",
"
\n",
"\n",
" Skew: | 0.034 | Prob(JB): | 0.390 | \n",
"
\n",
"\n",
" Kurtosis: | 2.088 | Cond. No. | 1.00 | \n",
"
\n",
"
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: np.sqrt(breaks) R-squared: 0.361\n",
"Model: OLS Adj. R-squared: 0.294\n",
"Method: Least Squares F-statistic: 5.415\n",
"Date: Wed, 26 Sep 2018 Prob (F-statistic): 0.000500\n",
"Time: 10:15:26 Log-Likelihood: -72.346\n",
"No. Observations: 54 AIC: 156.7\n",
"Df Residuals: 48 BIC: 168.6\n",
"Df Model: 5 \n",
"Covariance Type: nonrobust \n",
"======================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"--------------------------------------------------------------------------------------\n",
"wool[A]:tension[H] 4.8564 0.327 14.868 0.000 4.200 5.513\n",
"wool[B]:tension[H] 4.3022 0.327 13.171 0.000 3.645 4.959\n",
"wool[A]:tension[L] 6.5476 0.327 20.046 0.000 5.891 7.204\n",
"wool[B]:tension[L] 5.2381 0.327 16.037 0.000 4.581 5.895\n",
"wool[A]:tension[M] 4.8259 0.327 14.775 0.000 4.169 5.483\n",
"wool[B]:tension[M] 5.2987 0.327 16.222 0.000 4.642 5.955\n",
"==============================================================================\n",
"Omnibus: 4.119 Durbin-Watson: 2.147\n",
"Prob(Omnibus): 0.127 Jarque-Bera (JB): 1.882\n",
"Skew: 0.034 Prob(JB): 0.390\n",
"Kurtosis: 2.088 Cond. No. 1.00\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lmod = smf.ols('np.sqrt(breaks) ~ wool:tension-1', warpbreaks).fit()\n",
"lmod.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Width of Tukey HSD bands is ( qt/sqrt(2) * se * sqrt(2))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.3725048838137048"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_tukeyQcrit(6,48) * lmod.bse[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute all the pairwise differences:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Difference | \n",
" lb | \n",
" ub | \n",
"
\n",
" \n",
" \n",
" \n",
" wool[A]:tension[H]-wool[B]:tension[H] | \n",
" 0.554178 | \n",
" -0.818327 | \n",
" 1.926683 | \n",
"
\n",
" \n",
" wool[B]:tension[H]-wool[A]:tension[L] | \n",
" -2.245378 | \n",
" -3.617883 | \n",
" -0.872873 | \n",
"
\n",
" \n",
" wool[B]:tension[H]-wool[B]:tension[L] | \n",
" -0.935944 | \n",
" -2.308449 | \n",
" 0.436561 | \n",
"
\n",
" \n",
" wool[A]:tension[M]-wool[B]:tension[M] | \n",
" -0.472707 | \n",
" -1.845212 | \n",
" 0.899798 | \n",
"
\n",
" \n",
" wool[B]:tension[H]-wool[A]:tension[M] | \n",
" -0.523746 | \n",
" -1.896251 | \n",
" 0.848759 | \n",
"
\n",
" \n",
" wool[B]:tension[H]-wool[B]:tension[M] | \n",
" -0.996453 | \n",
" -2.368958 | \n",
" 0.376051 | \n",
"
\n",
" \n",
" wool[A]:tension[L]-wool[A]:tension[M] | \n",
" 1.721631 | \n",
" 0.349127 | \n",
" 3.094136 | \n",
"
\n",
" \n",
" wool[A]:tension[H]-wool[B]:tension[M] | \n",
" -0.442276 | \n",
" -1.814781 | \n",
" 0.930229 | \n",
"
\n",
" \n",
" wool[A]:tension[L]-wool[B]:tension[L] | \n",
" 1.309434 | \n",
" -0.063071 | \n",
" 2.681939 | \n",
"
\n",
" \n",
" wool[A]:tension[L]-wool[B]:tension[M] | \n",
" 1.248924 | \n",
" -0.123580 | \n",
" 2.621429 | \n",
"
\n",
" \n",
" wool[A]:tension[H]-wool[A]:tension[M] | \n",
" 0.030431 | \n",
" -1.342073 | \n",
" 1.402936 | \n",
"
\n",
" \n",
" wool[A]:tension[H]-wool[B]:tension[L] | \n",
" -0.381766 | \n",
" -1.754271 | \n",
" 0.990739 | \n",
"
\n",
" \n",
" wool[B]:tension[L]-wool[A]:tension[M] | \n",
" 0.412198 | \n",
" -0.960307 | \n",
" 1.784702 | \n",
"
\n",
" \n",
" wool[A]:tension[H]-wool[A]:tension[L] | \n",
" -1.691200 | \n",
" -3.063705 | \n",
" -0.318695 | \n",
"
\n",
" \n",
" wool[B]:tension[L]-wool[B]:tension[M] | \n",
" -0.060510 | \n",
" -1.433014 | \n",
" 1.311995 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Difference lb ub\n",
"wool[A]:tension[H]-wool[B]:tension[H] 0.554178 -0.818327 1.926683\n",
"wool[B]:tension[H]-wool[A]:tension[L] -2.245378 -3.617883 -0.872873\n",
"wool[B]:tension[H]-wool[B]:tension[L] -0.935944 -2.308449 0.436561\n",
"wool[A]:tension[M]-wool[B]:tension[M] -0.472707 -1.845212 0.899798\n",
"wool[B]:tension[H]-wool[A]:tension[M] -0.523746 -1.896251 0.848759\n",
"wool[B]:tension[H]-wool[B]:tension[M] -0.996453 -2.368958 0.376051\n",
"wool[A]:tension[L]-wool[A]:tension[M] 1.721631 0.349127 3.094136\n",
"wool[A]:tension[H]-wool[B]:tension[M] -0.442276 -1.814781 0.930229\n",
"wool[A]:tension[L]-wool[B]:tension[L] 1.309434 -0.063071 2.681939\n",
"wool[A]:tension[L]-wool[B]:tension[M] 1.248924 -0.123580 2.621429\n",
"wool[A]:tension[H]-wool[A]:tension[M] 0.030431 -1.342073 1.402936\n",
"wool[A]:tension[H]-wool[B]:tension[L] -0.381766 -1.754271 0.990739\n",
"wool[B]:tension[L]-wool[A]:tension[M] 0.412198 -0.960307 1.784702\n",
"wool[A]:tension[H]-wool[A]:tension[L] -1.691200 -3.063705 -0.318695\n",
"wool[B]:tension[L]-wool[B]:tension[M] -0.060510 -1.433014 1.311995"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import itertools\n",
"dp = set(itertools.combinations(range(0,6),2))\n",
"dcoef = []\n",
"namdiff = []\n",
"for cp in dp:\n",
" dcoef.append(lmod.params[cp[0]] - lmod.params[cp[1]])\n",
" namdiff.append(lmod.params.index[cp[0]] + '-' + lmod.params.index[cp[1]])\n",
"thsd = pd.DataFrame({'Difference':dcoef},index=namdiff)\n",
"thsd[\"lb\"] = thsd.Difference - get_tukeyQcrit(6,48) * lmod.bse[0]\n",
"thsd[\"ub\"] = thsd.Difference + get_tukeyQcrit(6,48) * lmod.bse[0]\n",
"thsd"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['wool[A]:tension[H]', 'wool[B]:tension[H]', 'wool[A]:tension[L]',\n",
" 'wool[B]:tension[L]', 'wool[A]:tension[M]', 'wool[B]:tension[M]'],\n",
" dtype='object')"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lmod.params.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Larger factorial experiments"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read in the data"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
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},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"speedo = pd.read_csv(\"data/speedo.csv\", index_col=0)\n",
"speedo"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | y | R-squared: | 1.000 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | nan | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 0.000 | \n",
"
\n",
"\n",
" Date: | Wed, 26 Sep 2018 | Prob (F-statistic): | nan | \n",
"
\n",
"\n",
" Time: | 10:15:26 | Log-Likelihood: | 547.43 | \n",
"
\n",
"\n",
" No. Observations: | 16 | AIC: | -1063. | \n",
"
\n",
"\n",
" Df Residuals: | 0 | BIC: | -1051. | \n",
"
\n",
"\n",
" Df Model: | 15 | | | \n",
"
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"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
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"\n",
" Intercept | 0.5825 | inf | 0 | nan | nan | nan | \n",
"
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"\n",
" h[T.-] | -0.0622 | inf | -0 | nan | nan | nan | \n",
"
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"\n",
" d[T.-] | -0.0609 | inf | -0 | nan | nan | nan | \n",
"
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" l[T.-] | -0.0272 | inf | -0 | nan | nan | nan | \n",
"
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" b[T.-] | 0.0559 | inf | 0 | nan | nan | nan | \n",
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" j[T.-] | 0.0009 | inf | 0 | nan | nan | nan | \n",
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" m[T.-] | -0.0278 | inf | -0 | nan | nan | nan | \n",
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"\n",
"\n",
" Omnibus: | 17.995 | Durbin-Watson: | 0.602 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 18.033 | \n",
"
\n",
"\n",
" Skew: | -1.744 | Prob(JB): | 0.000121 | \n",
"
\n",
"\n",
" Kurtosis: | 6.859 | Cond. No. | 9.90 | \n",
"
\n",
"
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 1.000\n",
"Model: OLS Adj. R-squared: nan\n",
"Method: Least Squares F-statistic: 0.000\n",
"Date: Wed, 26 Sep 2018 Prob (F-statistic): nan\n",
"Time: 10:15:26 Log-Likelihood: 547.43\n",
"No. Observations: 16 AIC: -1063.\n",
"Df Residuals: 0 BIC: -1051.\n",
"Df Model: 15 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 0.5825 inf 0 nan nan nan\n",
"h[T.-] -0.0622 inf -0 nan nan nan\n",
"d[T.-] -0.0609 inf -0 nan nan nan\n",
"l[T.-] -0.0272 inf -0 nan nan nan\n",
"b[T.-] 0.0559 inf 0 nan nan nan\n",
"j[T.-] 0.0009 inf 0 nan nan nan\n",
"f[T.-] -0.0741 inf -0 nan nan nan\n",
"n[T.-] -0.0066 inf -0 nan nan nan\n",
"a[T.-] -0.0678 inf -0 nan nan nan\n",
"i[T.-] -0.0428 inf -0 nan nan nan\n",
"e[T.-] -0.2453 inf -0 nan nan nan\n",
"m[T.-] -0.0278 inf -0 nan nan nan\n",
"c[T.-] -0.0897 inf -0 nan nan nan\n",
"k[T.-] -0.0684 inf -0 nan nan nan\n",
"g[T.-] 0.1403 inf 0 nan nan nan\n",
"o[T.-] -0.0059 inf -0 nan nan nan\n",
"==============================================================================\n",
"Omnibus: 17.995 Durbin-Watson: 0.602\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 18.033\n",
"Skew: -1.744 Prob(JB): 0.000121\n",
"Kurtosis: 6.859 Cond. No. 9.90\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%capture --no-display\n",
"lmod = smf.ols('y ~ h+d+l+b+j+f+n+a+i+e+m+c+k+g+o', speedo).fit()\n",
"lmod.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Look at the design matrix:"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 1., 1., 0., 1., 0., 0., 1., 1., 0., 0., 1., 0., 1., 1., 0.],\n",
" [1., 0., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1.],\n",
" [1., 1., 0., 1., 1., 0., 1., 0., 1., 0., 1., 0., 0., 1., 0., 1.],\n",
" [1., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n",
" [1., 1., 1., 0., 0., 1., 1., 0., 1., 0., 0., 1., 1., 0., 0., 1.],\n",
" [1., 0., 1., 1., 0., 0., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0.],\n",
" [1., 1., 0., 1., 0., 1., 0., 1., 1., 0., 1., 0., 1., 0., 1., 0.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
" [1., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 0., 1., 0., 0., 1.],\n",
" [1., 0., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 1., 0., 0.],\n",
" [1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 0., 1., 0.],\n",
" [1., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
" [1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1., 0.],\n",
" [1., 0., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1.],\n",
" [1., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dm = patsy.dmatrix('~ h+d+l+b+j+f+n+a+i+e+m+c+k+g+o', speedo)\n",
"np.asarray(dm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make a QQ plot of the coefficients:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ii = np.argsort(lmod.params[1:])\n",
"scoef = np.array(lmod.params[1:])[ii]\n",
"lcoef = (speedo.columns[:-1])[ii]\n",
"n = len(scoef)\n",
"qq = stats.norm.ppf(np.arange(1,n+1)/(n+1))\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(qq, scoef,s=1)\n",
"\n",
"for i in range(len(qq)):\n",
" ax.annotate(lcoef[i], (qq[i],scoef[i]))\n",
" \n",
"plt.xlabel(\"Normal Quantiles\")\n",
"plt.ylabel(\"Sorted coefficients\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"n = len(scoef)\n",
"qq = stats.norm.ppf((n + np.arange(1,n+1))/(2*n+1))\n",
"acoef = np.abs(lmod.params[1:])\n",
"ii = np.argsort(acoef)\n",
"acoef = acoef[ii]\n",
"lcoef = (speedo.columns[:-1])[ii]\n",
" \n",
"fig, ax = plt.subplots()\n",
"ax.scatter(qq, acoef,s=1)\n",
"\n",
"for i in range(len(qq)):\n",
" ax.annotate(lcoef[i], (qq[i],acoef[i]))\n",
" \n",
"plt.xlabel(\"Normal Half Quantiles\")\n",
"plt.ylabel(\"Sorted absolute coefficients\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"application/json": {
"Software versions": [
{
"module": "Python",
"version": "3.7.0 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]"
},
{
"module": "IPython",
"version": "6.5.0"
},
{
"module": "OS",
"version": "Darwin 17.7.0 x86_64 i386 64bit"
},
{
"module": "pandas",
"version": "0.23.4"
},
{
"module": "numpy",
"version": "1.15.1"
},
{
"module": "matplotlib",
"version": "2.2.3"
},
{
"module": "seaborn",
"version": "0.9.0"
},
{
"module": "scipy",
"version": "1.1.0"
},
{
"module": "patsy",
"version": "0.5.0"
},
{
"module": "statsmodels",
"version": "0.9.0"
}
]
},
"text/html": [
"Software | Version |
---|
Python | 3.7.0 64bit [Clang 4.0.1 (tags/RELEASE_401/final)] |
IPython | 6.5.0 |
OS | Darwin 17.7.0 x86_64 i386 64bit |
pandas | 0.23.4 |
numpy | 1.15.1 |
matplotlib | 2.2.3 |
seaborn | 0.9.0 |
scipy | 1.1.0 |
patsy | 0.5.0 |
statsmodels | 0.9.0 |
Wed Sep 26 10:15:26 2018 BST |
"
],
"text/latex": [
"\\begin{tabular}{|l|l|}\\hline\n",
"{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
"Python & 3.7.0 64bit [Clang 4.0.1 (tags/RELEASE\\_401/final)] \\\\ \\hline\n",
"IPython & 6.5.0 \\\\ \\hline\n",
"OS & Darwin 17.7.0 x86\\_64 i386 64bit \\\\ \\hline\n",
"pandas & 0.23.4 \\\\ \\hline\n",
"numpy & 1.15.1 \\\\ \\hline\n",
"matplotlib & 2.2.3 \\\\ \\hline\n",
"seaborn & 0.9.0 \\\\ \\hline\n",
"scipy & 1.1.0 \\\\ \\hline\n",
"patsy & 0.5.0 \\\\ \\hline\n",
"statsmodels & 0.9.0 \\\\ \\hline\n",
"\\hline \\multicolumn{2}{|l|}{Wed Sep 26 10:15:26 2018 BST} \\\\ \\hline\n",
"\\end{tabular}\n"
],
"text/plain": [
"Software versions\n",
"Python 3.7.0 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]\n",
"IPython 6.5.0\n",
"OS Darwin 17.7.0 x86_64 i386 64bit\n",
"pandas 0.23.4\n",
"numpy 1.15.1\n",
"matplotlib 2.2.3\n",
"seaborn 0.9.0\n",
"scipy 1.1.0\n",
"patsy 0.5.0\n",
"statsmodels 0.9.0\n",
"Wed Sep 26 10:15:26 2018 BST"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%load_ext version_information\n",
"%version_information pandas, numpy, matplotlib, seaborn, scipy, patsy, statsmodels"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}