pdf formatsmall.csv X1,X2,Y S,-0.1,19.19 S,2.53,22.74 S,4.86,23.91 M,0.26,7.07 M,2.55,7.93...

Question

pdf formatsmall.csv X1,X2,Y S,-0.1,19.19 S,2.53,22.74 S,4.86,23.91 M,0.26,7.07 M,2.55,7.93 M,4.87,8.93 L,0.08,20.63 L,2.62,23.46 L,5.09,25.75 __MACOSX/._small.csv part2.csv Month,Year,sales January,2012, February,2012, March,2012, April,2012, May,2012, June,2012, July,2012, August,2012, September,2012,1.71 October,2012,1.9 November,2012,2.74 December,2012,4.2 January,2013,1.45 February,2013,1.8 March,2013,2.03 April,2013,1.99 May,2013,2.32 June,2013,2.2 July,2013,2.13 August,2013,2.43 September,2013,1.9 October,2013,2.13 November,2013,2.56 December,2013,4.16 January,2014,2.31 February,2014,1.89 March,2014,2.02 April,2014,2.23 May,2014,2.39 June,2014,2.14 July,2014,2.27 August,2014,2.21 September,2014,1.89 October,2014,2.29 November,2014,2.83 December,2014,4.04 January,2015,2.31 February,2015,1.99 March,2015,2.42 April,2015,2.45 May,2015,2.57 June,2015,2.42 July,2015,2.4 August,2015,2.5 September,2015,2.09 October,2015,2.54 November,2015,2.97 December,2015,4.35 January,2016,2.56 February,2016,2.28 March,2016,2.69 April,2016,2.48 May,2016,2.73 June,2016,2.37 July,2016,2.31 August,2016,2.23 September,2016, October,2016, November,2016, December,2016, __MACOSX/._part2.csv categoricals.pptx CATEGORICAL VARIABLES -ENCODING- 1 We will use data visualization  To understand the results of regression models with a categorical variable And to show the model performance EXAMPLES Example 1 EXAMPLES Consider the following dataset Numerical Categorical Predictors – EXAMPLE 		X1		X2		Y 		S		-0.10		19.19 		S		2.53		22.74 		S		4.86		23.91 		M		0.26		7.07 		M		2.55		7.93 		M		4.87		8.93 		L		0.08		20.63 		L		2.62		23.46 		L		5.09		25.75 Increase size of table numbers 4 Consider the following dataset		          LABEL ENCODING 		X1		X2		Y 		S		-0.10		19.19 		S		2.53		22.74 		S		4.86		23.91 		M		0.26		7.07 		M		2.55		7.93 		M		4.87		8.93 		L		0.08		20.63 		L		2.62		23.46 		L		5.09		25.75 		X1		X2		Y 		0		-0.10		19.19 		0		2.53		22.74 		0		4.86		23.91 		1		0.26		7.07 		1		2.55		7.93 		1		4.87		8.93 		2		0.08		20.63 		2		2.62		23.46 		2		5.09		25.75 Numerical Categorical Predictors – EXAMPLE 5 X1 and X2 in the model as continuous variables R2 is close to 0.05, the explained variation of the response about the fitted equation is negligible The Adjusted R-squared is negative and equal to -0.23632 Both predictors X1 and X2 seem not to be useful for predicting Y. Coefficients: 		  Estimate Std. Error t value Pr(>|t|) (Intercept)  15.1678     5.6816   2.670    0.037 * x1            0.6019     3.4742   0.173    0.868 x2            0.7769     1.4275   0.544    0.606   Residual standard error: 8.505 on 6 degrees of freedom Multiple R-squared:  0.05259,   Adjusted R-squared:  -0.2632 F-statistic: 0.1665 on 2 and 6 DF,  p-value: 0.8504 Numerical Categorical Predictors – EXAMPLE X1 and X2 in the model as continuous variables Coefficients: 		  Estimate Std. Error t value Pr(>|t|) (Intercept)  15.1678     5.6816   2.670    0.037 * x1            0.6019     3.4742   0.173    0.868 x2            0.7769     1.4275   0.544    0.606   Residual standard error: 8.505 on 6 degrees of freedom Multiple R-squared:  0.05259,   Adjusted R-squared:  -0.2632 F-statistic: 0.1665 on 2 and 6 DF,  p-value: 0.8504 Numerical Categorical Predictors – EXAMPLE 7 X1 and X2 in the model as continuous variables Coefficients: 		  Estimate Std. Error t value Pr(>|t|) (Intercept)  15.1678     5.6816   2.670    0.037 * x1            0.6019     3.4742   0.173    0.868 x2            0.7769     1.4275   0.544    0.606   Residual standard error: 8.505 on 6 degrees of freedom Multiple R-squared:  0.05259,   Adjusted R-squared:  -0.2632 F-statistic: 0.1665 on 2 and 6 DF,  p-value: 0.8504 Numerical Categorical Predictors – EXAMPLE 8 The fitted plane is 0.7769 X2 Numerical Categorical Predictors – EXAMPLE Replace X1 with binary variables X11 and X12 	          ONE-HOT ENCODING 		X11		X12		X2		Y 		0		0		-0.10		19.19 		0		0		2.53		22.74 		0		0		4.86		23.91 		1		0		0.26		7.07 		1		0		2.55		7.93 		1		0		4.87		8.93 		0		1		0.08		20.63 		0		1		2.62		23.46 		0		1		5.09		25.75 		X1		X2		Y 		S		-0.10		19.19 		S		2.53		22.74 		S		4.86		23.91 		M		0.26		7.07 		M		2.55		7.93 		M		4.87		8.93 		L		0.08		20.63 		L		2.62		23.46 		L		5.09		25.75 Numerical Categorical Predictors – EXAMPLE 10 . Coefficients: 		  Estimate Std. Error t value Pr(>|t|) (Intercept)   19.9650     0.5802  34.413 3.90e-07 *** x11          -14.0760     0.6703 -20.998 4.54e-06 *** x12            1.1974     0.6705   1.786  0.13418 x2             0.8155     0.1378   5.920  0.00196 ** Residual standard error: 0.8207 on 5 degrees of freedom Multiple R-squared:  0.9926,   Adjusted R-squared:  0.9882 F-statistic: 225 on 3 and 5 DF,  p-value: 9.416e-06 Numerical Categorical Predictors – EXAMPLE Coefficients: 		  Estimate Std. Error t value Pr(>|t|) (Intercept)   19.9650     0.5802  34.413 3.90e-07 *** x11          -14.0760     0.6703 -20.998 4.54e-06 *** x12            1.1974     0.6705   1.786  0.13418 x2             0.8155     0.1378   5.920  0.00196 ** Residual standard error: 0.8207 on 5 degrees of freedom Multiple R-squared:  0.9926,   Adjusted R-squared:  0.9882 F-statistic: 225 on 3 and 5 DF,  p-value: 9.416e-06 Numerical Categorical Predictors – EXAMPLE The fitted equations for each level are Numerical Categorical Predictors – EXAMPLE What encoding is better? Numerical Categorical Predictors – EXAMPLE 	            LABEL ENCODING 	          	         ONE-HOT ENCODING 		X11		X12		X2		Y 		0		0		-0.10		19.19 		0		0		2.53		22.74 		0		0		4.86		23.91 		1		0		0.26		7.07 		1		0		2.55		7.93 		1		0		4.87		8.93 		0		1		0.08		20.63 		0		1		2.62		23.46 		0		1		5.09		25.75 		X1		X2		Y 		0		-0.10		19.19 		0		2.53		22.74 		0		4.86		23.91 		1		0.26		7.07 		1		2.55		7.93 		1		4.87		8.93 		2		0.08		20.63 		2		2.62		23.46 		2		5.09		25.75 Numerical Categorical Predictors – EXAMPLE 15 				  LABEL			 ONE-HOT 					ENCODING		ENCODING R-squared			0.05259		0.9926 Adjusted R-squared:  -0.2632		0.9882 Numerical Categorical Predictors – EXAMPLE 16 Why are the models different? Numerical Categorical Predictors – EXAMPLE Label encoding prediction equation   0.7769 X2 One-hot encoding prediction equations Numerical Categorical Predictors – EXAMPLE label encoding results in a regression plane one-hot encoding results in a set of regression lines (one for each category of the categorical variable) Numerical Categorical Predictors – EXAMPLE If the observations are close to a plane then label encoding and one-hot encoding results are good Numerical Categorical Predictors – EXAMPLE With a large number of variables in the model it is not possible to have a display like this We may relay on R2 or cross-validation error to choose the best model Numerical Categorical Predictors – EXAMPLE Example 2 Forecasting Consider the following demand data EXAMPLE 2 Increase size of table numbers 23 Consider the following demand data EXAMPLE 2 Increase size of table numbers 24 Wide format to long format EXAMPLE 2 Increase size of table numbers 25 Model 1 Linear Regression Use linear regression 				predict sales using time as predictor EXAMPLE 2 Increase size of table numbers 27 EXAMPLE 2 – LINEAR REGRESION MODEL 1 Increase size of table numbers 28 		sales vs Period EXAMPLE 2 – LINEAR REGRESION MODEL 1 Increase size of table numbers 29 Model 2 Categorical variable and label encoding predict sales using Year and Month EXAMPLE 2 – MODEL 2 Increase size of table numbers 31 predict sales using Year and Month label encode Month EXAMPLE 2 – MODEL 2 Increase size of table numbers 32 predict sales using Year and Month label encode Month EXAMPLE 2 – MODEL 2 Increase size of table numbers 33 predict sales using Year and Month label encode Month EXAMPLE 2 – MODEL 2 Increase size of table numbers 34 predict sales using Year and Month label encode Month model sales using Year and Period 		        (both numeric) EXAMPLE 2 – MODEL 2 Increase size of table numbers 35 EXAMPLE 2 – MODEL 2 Increase size of table numbers 36 	      sales vs Year and Period EXAMPLE 2 – MODEL 2 Increase size of table numbers 37 Model 3 Categorical variable and one-hot encoding predict sales using Year and Month one-hot encode Month EXAMPLE 2 – MODEL 3 Increase size of table numbers 39 EXAMPLE 2 – MODEL 3 Increase size of table numbers 40 	sales vs Year and Month EXAMPLE 2 – MODEL 3 Increase size of table numbers 41 __MACOSX/._categoricals.pptx Day 6.docx Day 6: Assignment  Submit Assignment · Submitting a file upload · File Types pdf, doc, and docx Instructions Write and run the code developed in class to predict the total sales from file sales.csv and submit the jupyter notebook in pdf format. Submission Click on the blue button in the top right corner to submit your assignment.    Click Next (below) to progress through the course. Rubric Assignment Rubric 		Assignment Rubric 		Criteria 		Ratings 		Pts 		This criterion is linked to a Learning OutcomeCoding 				60.0 to >30.0 pts Full Marks No coding errors 		30.0 to >20.0 pts No Marks More than three coding errors 		20.0 to >0 pts Partial Marks One to three coding errors 		60.0 pts 		This criterion is linked to a Learning OutcomeFormat & Editing 				40.0 to >20.0 pts Full Marks Code is clear and easy to follow. Plots display effective visualization.

Kshitij · Accepted Answer

day-6archive-qu3zg0my-gk010hyi/.ipynb_checkpoints/Day6-checkpoint.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraires 
",
    "import pandas as pd
",
    "import numpy as np
",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "
",
       "    .dataframe tbody tr th:only-of-type {
",
       "        vertical-align: middle;
",
       "    }
",
       "
",
       "    .dataframe tbody tr th {
",
       "        vertical-align: top;
",
       "    }
",
       "
",
       "    .dataframe thead th {
",
       "        text-align: right;
",
       "    }
",
       "
",
       "
",
       "  
",
       "    
",
       "      
",
       "      X1
",
       "      X2
",
       "      Y
",
       "    
",
       "  
",
       "  
",
       "    
",
       "      0
",
       "      S
",
       "      -0.10
",
       "      19.19
",
       "    
",
       "    
",
       "      1
",
       "      S
",
       "      2.53
",
       "      22.74
",
       "    
",
       "    
",
       "      2
",
       "      S
",
       "      4.86
",
       "      23.91
",
       "    
",
       "    
",
       "      3
",
       "      M
",
       "      0.26
",
       "      7.07
",
       "    
",
       "    
",
       "      4
",
       "      M
",
       "      2.55
",
       "      7.93
",
       "    
",
       "  
",
       "
",
       ""
      ],
      "text/plain": [
       "  X1    X2      Y
",
       "0  S -0.10  19.19
",
       "1  S  2.53  22.74
",
       "2  S  4.86  23.91
",
       "3  M  0.26   7.07
",
       "4  M  2.55   7.93"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading small data
",
    "df = pd.read_csv('small.csv')
",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the statsmodel library 
",
    "import statsmodels.formula.api as smf
",
    "import statsmodels.api as sm
",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn's label encoder
",
    "labelencoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "
",
       "    .dataframe tbody tr th:only-of-type {
",
       "        vertical-align: middle;
",
       "    }
",
       "
",
       "    .dataframe tbody tr th {
",
       "        vertical-align: top;
",
       "    }
",
       "
",
       "    .dataframe thead th {
",
       "        text-align: right;
",
       "    }
",
       "
",
       "
",
       "  
",
       "    
",
       "      
",
       "      X1
",
       "      X2
",
       "      Y
",
       "    
",
       "  
",
       "  
",
       "    
",
       "      0
",
       "      2
",
       "      -0.10
",
       "      19.19
",
       "    
",
       "    
",
       "      1
",
       "      2
",
       "      2.53
",
       "      22.74
",
       "    
",
       "    
",
       "      2
",
       "      2
",
       "      4.86
",
       "      23.91
",
       "    
",
       "    
",
       "      3
",
       "      1
",
       "      0.26
",
       "      7.07
",
       "    
",
       "    
",
       "      4
",
       "      1
",
       "      2.55
",
       "      7.93
",
       "    
",
       "    
",
       "      5
",
       "      1
",
       "      4.87
",
       "      8.93
",
       "    
",
       "    
",
       "      6
",
       "      0
",
       "      0.08
",
       "      20.63
",
       "    
",
       "    
",
       "      7
",
       "      0
",
       "      2.62
",
       "      23.46
",
       "    
",
       "    
",
       "      8
",
       "      0
",
       "      5.09
",
       "      25.75
",
       "    
",
       "  
",
       "
",
       ""
      ],
      "text/plain": [
       "   X1    X2      Y
",
       "0   2 -0.10  19.19
",
       "1   2  2.53  22.74
",
       "2   2  4.86  23.91
",
       "3   1  0.26   7.07
",
       "4   1  2.55   7.93
",
       "5   1  4.87   8.93
",
       "6   0  0.08  20.63
",
       "7   0  2.62  23.46
",
       "8   0  5.09  25.75"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encoding of labels
",
    "df['X1'] = labelencoder.fit_transform(df['X1'])
",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['X1', 'X2']] 
",
    "y = df['Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\users\kaush\appdata\local\programs\python\python36\lib\site-packages\numpy\core\fromnumeric.py:2542: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.
",
      "  return ptp(axis=axis, out=out, **kwargs)
",
      "c:\users\kaush\appdata\local\programs\python\python36\lib\site-packages\scipy\stats\stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9
",
      "  "anyway, n=%i" % int(n))
"
     ]
    },
    {
     "data": {
      "text/html": [
       "
",
       "OLS Regression Results
",
       "
",
       "  Dep. Variable:            Y          R-squared:             0.053
",
       "
",
       "
",
       "  Model:                   OLS         Adj. R-squared:       -0.263
",
       "
",
       "
",
       "  Method:             Least Squares    F-statistic:          0.1665
",
       "
",
       "
",
       "  Date:             Thu, 28 May 2020   Prob (F-statistic):   0.850 
",
       "
",
       "
",
       "  Time:                 05:12:53       Log-Likelihood:      -30.212
",
       "
",
       "
",
       "  No. Observations:           9        AIC:                   66.42
",
       "
",
       "
",
       "  Df Residuals:               6        BIC:                   67.02
",
       "
",
       "
",
       "  Df Model:                   2                                    
",
       "
",
       "
",
       "  Covariance Type:      nonrobust                                  
",
       "
",
       "
",
       "
",
       "
",
       "           coef     std err      t      P>|t|  [0.025    0.975]  
",
       "
",
       "
",
       "  const    16.3716     5.831     2.808  0.031     2.104    30.639
",
       "
",
       "
",
       "  X1       -0.6019     3.474    -0.173  0.868    -9.103     7.899
",
       "
",
       "
",
       "  X2        0.7769     1.428     0.544  0.606    -2.716     4.270
",
       "
",
       "
",
       "
",
       "
",
       "  Omnibus:        3.603   Durbin-Watson:         0.961
",
       "
",
       "
",
       "  Prob(Omnibus):  0.165   Jarque-Bera (JB):      1.553
",
       "
",
       "
",
       "  Skew:          -0.704   Prob(JB):              0.460
",
       "
",
       "
",
       "  Kurtosis:       1.530   Cond. No.               7.65
",
       "
",
       "Warnings:[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "
",
       """"
",
       "                            OLS Regression Results                            
",
       "==============================================================================
",
       "Dep. Variable:                      Y   R-squared:                       0.053
",
       "Model:                            OLS   Adj. R-squared:                 -0.263
",
       "Method:                 Least Squares   F-statistic:                    0.1665
",
       "Date:                Thu, 28 May 2020   Prob (F-statistic):              0.850
",
       "Time:                        05:12:53   Log-Likelihood:                -30.212
",
       "No. Observations:                   9   AIC:                             66.42
",
       "Df Residuals:                       6   BIC:                             67.02
",
       "Df Model:                           2                                         
",
       "Covariance Type:            nonrobust                                         
",
       "==============================================================================
",
       "                 coef    std err          t      P>|t|      [0.025      0.975]
",
       "------------------------------------------------------------------------------
",
       "const         16.3716      5.831      2.808      0.031       2.104      30.639
",
       "X1            -0.6019      3.474     -0.173      0.868      -9.103       7.899
",
       "X2             0.7769      1.428      0.544      0.606      -2.716       4.270
",
       "==============================================================================
",
       "Omnibus:                        3.603   Durbin-Watson:                   0.961
",
       "Prob(Omnibus):                  0.165   Jarque-Bera (JB):                1.553
",
       "Skew:                          -0.704   Prob(JB):                        0.460
",
       "Kurtosis:                       1.530   Cond. No.                         7.65
",
       "==============================================================================
",
       "
",
       "Warnings:
",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
",
       """""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting our model and printing the summery.
",
    "X = sm.add_constant(X)
",
    "model = sm.OLS(y, X).fit()
",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot coding for our labels
",
    "df = pd.concat([df,pd.get_dummies(df['X1'], prefix='X1',drop_first=True)],axis=1).drop(['X1'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "
",
       "    .dataframe tbody tr th:only-of-type {
",
       "        vertical-align: middle;
",
       "    }
",
       "
",
       "    .dataframe tbody tr th {
",
       "        vertical-align: top;
",
       "    }
",
       "
",
       "    .dataframe thead th {
",
       "        text-align: right;
",
       "    }
",
       "
",
       "
",
       "  
",
       "    
",
       "      
",
       "      X2
",
       "      Y
",
       "      X1_1
",
       "      X1_2
",
       "    
",
       "  
",
       "  
",
       "    
",
       "      0
",
       "      -0.10
",
       "      19.19
",
       "      0
",
       "      1
",
       "    
",
       "    
",
       "      1
",
       "      2.53
",
       "      22.74
",
       "      0
",
       "      1
",
       "    
",
       "    
",
       "      2
",
       "      4.86
",
       "      23.91
",
       "      0
",
       "      1
",
       "    
",
       "    
",
       "      3
",
       "      0.26
",
       "      7.07
",
       "      1
",
       "      0
",
       "    
",
       "    
",
       "      4
",
       "      2.55
",
       "      7.93
",
       "      1
",
       "      0
",
       "    
",
       "    
",
       "      5
",
       "      4.87
",
       "      8.93
",
       "      1
",
       "      0
",
       "    
",
       "    
",
       "      6
",
       "      0.08
",
       "      20.63
",
       "      0
",
       "      0
",
       "    
",
       "    
",
       "      7
",
       "      2.62
",
       "      23.46
",
       "      0
",
       "      0
",
       "    
",
       "    
",
       "      8
",
       "      5.09
",
       "      25.75
",
       "      0
",
       "      0
",
       "    
",
       "  
",
       "
",
       ""
      ],
      "text/plain": [
       "     X2      Y  X1_1  X1_2
",
       "0 -0.10  19.19     0     1
",
       "1  2.53  22.74     0     1
",
       "2  4.86  23.91     0     1
",
       "3  0.26   7.07     1     0
",
       "4  2.55   7.93     1     0
",
       "5  4.87   8.93     1     0
",
       "6  0.08  20.63     0     0
",
       "7  2.62  23.46     0     0
",
       "8  5.09  25.75     0     0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['X1_1', 'X1_2','X2']] 
",
    "y = df['Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "OLS Regression Results
",
       "
",
       "  Dep. Variable:            Y          R-squared:             0.993
",
       "
",
       "
",
       "  Model:                   OLS         Adj. R-squared:        0.988
",
       "
",
       "
",
       "  Method:             Least Squares    F-statistic:           225.0
",
       "
",
       "
",
       "  Date:             Thu, 28 May 2020   Prob (F-statistic): 9.42e-06
",
       "
",
       "
",
       "  Time:                 05:12:53       Log-Likelihood:      -8.3472
",
       "
",
       "
",
       "  No. Observations:           9        AIC:                   24.69
",
       "
",
       "
",
       "  Df Residuals:               5        BIC:                   25.48
",
       "
",
       "
",
       "  Df Model:                   3                                    
",
       "
",
       "
",
       "  Covariance Type:      nonrobust                                  
",
       "
",
       "
",
       "
",
       "
",
       "           coef     std err      t      P>|t|  [0.025    0.975]  
",
       "
",
       "
",
       "  const    21.1624     0.594    35.645  0.000    19.636    22.689
",
       "
",
       "
",
       "  X1_1    -15.2734     0.670   -22.792  0.000   -16.996   -13.551
",
       "
",
       "
",
       "  X1_2     -1.1974     0.671    -1.786  0.134    -2.921     0.526
",
       "
",
       "
",
       "  X2        0.8155     0.138     5.920  0.002     0.461     1.170
",
       "
",
       "
",
       "
",
       "
",
       "  Omnibus:        0.672   Durbin-Watson:         1.798
",
       "
",
       "
",
       "  Prob(Omnibus):  0.715   Jarque-Bera (JB):      0.529
",
       "
",
       "
",
       "  Skew:           0.003   Prob(JB):              0.768
",
       "
",
       "
",
       "  Kurtosis:       1.812   Cond. No.               11.4
",
       "
",
       "Warnings:[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "
",
       """"
",
       "                            OLS Regression Results                            
",
       "==============================================================================
",
       "Dep. Variable:                      Y   R-squared:                       0.993
",
       "Model:                            OLS   Adj. R-squared:                  0.988
",
       "Method:                 Least Squares   F-statistic:                     225.0
",
       "Date:                Thu, 28 May 2020   Prob (F-statistic):           9.42e-06
",
       "Time:                        05:12:53   Log-Likelihood:                -8.3472
",
       "No. Observations:                   9   AIC:                             24.69
",
       "Df Residuals:                       5   BIC:                             25.48
",
       "Df Model:                           3                                         
",
       "Covariance Type:            nonrobust                                         
",
       "==============================================================================
",
       "                 coef    std err          t      P>|t|      [0.025      0.975]
",
       "------------------------------------------------------------------------------
",
       "const         21.1624      0.594     35.645      0.000      19.636      22.689
",
       "X1_1         -15.2734      0.670    -22.792      0.000     -16.996     -13.551
",
       "X1_2          -1.1974      0.671     -1.786      0.134      -2.921       0.526
",
       "X2             0.8155      0.138      5.920      0.002       0.461       1.170
",
       "==============================================================================
",
       "Omnibus:                        0.672   Durbin-Watson:                   1.798
",
       "Prob(Omnibus):                  0.715   Jarque-Bera (JB):                0.529
",
       "Skew:                           0.003   Prob(JB):                        0.768
",
       "Kurtosis:                       1.812   Cond. No.                         11.4
",
       "==============================================================================
",
       "
",
       "Warnings:
",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
",
       """""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# again fiting data but with one hot coded labels
",
    "X = sm.add_constant(X)
",
    "model2 = sm.OLS(y, X).fit()
",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "
",
       "    .dataframe tbody tr th:only-of-type {
",
       "        vertical-align: middle;
",
       "    }
",
       "
",
       "    .dataframe tbody tr th {
",
       "        vertical-align: top;
",
       "    }
",
       "
",
       "    .dataframe thead th {
",
       "        text-align: right;
",
       "    }
",
       "
",
       "
",
       "  
",
       "    
",
       "      
",
       "      Month
",
       "      Year
",
       "      sales
",
       "    
",
       "  
",
       "  
",
       "    
",
       "      0
",
       "      January
",
       "      2012
",
       "      NaN
",
       "    
",
       "    
",
       "      1
",
       "      February
",
       "      2012
",
       "      NaN
",
       "    
",
       "    
",
       "      2
",
       "      March
",
       "      2012
",
       "      NaN
",
       "    
",
       "    
",
       "      3
",
       "      April
",
       "      2012
",
       "      NaN
",
       "    
",
       "    
",
       "      4
",
       "      May
",
       "      2012
",
       "      NaN
",
       "    
",
       "  
",
       "
",
       ""
      ],
      "text/plain": [
       "      Month  Year  sales
",
       "0   January  2012    NaN
",
       "1  February  2012    NaN
",
       "2     March  2012    NaN
",
       "3     April  2012    NaN
",
       "4       May  2012    NaN"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading data for second part
",
    "df = pd.read_csv('part2.csv')
",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gnearting period data 
",
    "k=list(range(1, len(df.Month)+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "
",
       "    .dataframe tbody tr th:only-of-type {
",
       "        vertical-align: middle;
",
       "    }
",
       "
",
       "    .dataframe tbody tr th {
",
       "        vertical-align: top;
",
       "    }
",
       "
",
       "    .dataframe thead th {
",
       "        text-align: right;
",
       "    }
",
       "
",
       "
",
       "  
",
       "    
",
       "      
",
       "      Month
",
       "      Year
",
       "      sales
",
       "      Period
",
       "    
",
       "  
",
       "  
",
       "    
",
       "      0
",
       "      January
",
       "      2012
",
       "      NaN
",
       "      1
",
       "    
",
       "    
",
       "      1
",
       "      February
",
       "      2012
",
       "      NaN
",
       "      2
",
       "    
",
       "    
",
       "      2
",
       "      March
",
       "      2012
",
       "      NaN
",
       "      3
",
       "    
",
       "    
",
       "      3
",
       "      April
",
       "      2012
",
       "      NaN
",
       "      4
",
       "    
",
       "    
",
       "      4
",
       "      May
",
       "      2012
",
       "      NaN
",
       "      5
",
       "    
",
       "  
",
       "
",
       ""
      ],
      "text/plain": [
       "      Month  Year  sales  Period
",
       "0   January  2012    NaN       1
",
       "1  February  2012    NaN       2
",
       "2     March  2012    NaN       3
",
       "3     April  2012    NaN       4
",
       "4       May  2012    NaN       5"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df["Period"]=k
",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# formulating our model based on data frame column names 
",
    "mod = smf.ols(formula='sales ~ Period', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitting our model
",
    "res = mod.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "OLS Regression Results
",
       "
",
       "  Dep. Variable:          sales        R-squared:             0.044
",
       "
",
       "
",
       "  Model:                   OLS         Adj. R-squared:        0.023
",
       "
",
       "
",
       "  Method:             Least Squares    F-statistic:           2.121
",
       "
",
       "
",
       "  Date:             Thu, 28 May 2020   Prob (F-statistic):   0.152 
",
       "
",
       "
",
       "  Time:                 05:32:38       Log-Likelihood:      -42.960
",
       "
",
       "
",
       "  No. Observations:          48        AIC:                   89.92
",
       "
",
       "
",
       "  Df Residuals:              46        BIC:                   93.66
",
       "
",
       "
",
       "  Df Model:                   1                                    
",
       "
",
       "
",
       "  Covariance Type:      nonrobust                                  
",
       "
",
       "
",
       "
",
       "
",
       "               coef     std err      t      P>|t|  [0.025    0.975]  
",
       "
",
       "
",
       "  Intercept     2.1355     0.223     9.590  0.000     1.687     2.584
",
       "
",
       "
",
       "  Period        0.0092     0.006     1.456  0.152    -0.004     0.022
",
       "
",
       "
",
       "
",
       "
",
       "  Omnibus:       34.103   Durbin-Watson:         1.733
",
       "
",
       "
",
       "  Prob(Omnibus):  0.000   Jarque-Bera (JB):     71.518
",
       "
",
       "
",
       "  Skew:           2.147   Prob(JB):           2.95e-16
",
       "
",
       "
",
       "  Kurtosis:       7.161   Cond. No.               90.2
",
       "
",
       "Warnings:[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "
",
       """"
",
       "                            OLS Regression Results                            
",
       "==============================================================================
",
       "Dep. Variable:                  sales   R-squared:                       0.044
",
       "Model:                            OLS   Adj. R-squared:                  0.023
",
       "Method:                 Least Squares   F-statistic:                     2.121
",
       "Date:                Thu, 28 May 2020   Prob (F-statistic):              0.152
",
       "Time:                        05:32:38   Log-Likelihood:                -42.960
",
       "No. Observations:                  48   AIC:                             89.92
",
       "Df Residuals:                      46   BIC:                             93.66
",
       "Df Model:                           1                                         
",
       "Covariance Type:            nonrobust                                         
",
       "==============================================================================
",
       "                 coef    std err          t      P>|t|      [0.025      0.975]
",
       "------------------------------------------------------------------------------
",
       "Intercept      2.1355      0.223      9.590      0.000       1.687       2.584
",
       "Period         0.0092      0.006      1.456      0.152      -0.004       0.022
",
       "==============================================================================
",
       "Omnibus:                       34.103   Durbin-Watson:                   1.733
",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               71.518
",
       "Skew:                           2.147   Prob(JB):                     2.95e-16
",
       "Kurtosis:                       7.161   Cond. No.                         90.2
",
       "==============================================================================
",
       "
",
       "Warnings:
",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
",
       """""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summery
",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting our sales based on period
",
    "prediction = res.predict(df['Period'])
",
    "df['Prediction']=prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "
",
       "    .dataframe tbody tr th:only-of-type {
",
       "        vertical-align: middle;
",
       "    }
",
       "
",
       "    .dataframe tbody tr th {
",
       "        vertical-align: top;
",
       "    }
",
       "
",
       "    .dataframe thead th {
",
       "        text-align: right;
",
       "    }
",
       "
",
       "
",
       "  
",
       "    
",
       "      
",
       "      Month
",
       "      Year
",
       "      sales
",
       "      Period
",
       "      Prediction
",
       "    
",
       "  
",
       "  
",
       "    
",
       "      0
",
       "      January
",
       "      2012
",
       "      NaN
",
       "      1
",
       "      2.144635
",
       "    
",
       "    
",
       "      1
",
       "      February
",
       "      2012
",
       "      NaN
",
       "      2
",
       "      2.153814
",
       "    
",
       "    
",
       "      2
",
       "      March
",
       "      2012
",
       "      NaN
",
       "      3
",
       "      2.162992
",
       "    
",
       "    
",
       "      3
",
       "      April
",
       "      2012
",
       "      NaN
",
       "      4
",
       "      2.172170
",
       "    
",
       "    
",
       "      4
",
       "      May
",
       "      2012
",
       "      NaN
",
       "      5
",
       "      2.181348
",
       "    
",
       "  
",
       "
",
       ""
      ],
      "text/plain": [
       "      Month  Year  sales  Period  Prediction
",
       "0   January  2012    NaN       1    2.144635
",
       "1  February  2012    NaN       2    2.153814
",
       "2     March  2012    NaN       3    2.162992
",
       "3     April  2012    NaN       4    2.172170
",
       "4       May  2012    NaN       5    2.181348"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploting the prediction and sales with respect to year. 
",
    "df.plot(x='Year', y=['sales', 'Prediction'], figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# model using year and period "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now formulating our model on period and year as predictors and sales as target
",
    "mod = smf.ols(formula='sales ~ Period + Year', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitting our model
",
    "res = mod.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "OLS Regression Results
",
       "
",
       "  Dep. Variable:          sales        R-squared:             0.343
",
       "
",
       "
",
       "  Model:                   OLS         Adj. R-squared:        0.314
",
       "
",
       "
",
       "  Method:             Least Squares    F-statistic:           11.75
",
       "
",
       "
",
       "  Date:             Thu, 28 May 2020   Prob (F-statistic): 7.86e-05
",
       "
",
       "
",
       "  Time:                 05:42:34       Log-Likelihood:      -33.960
",
       "
",
       "
",
       "  No. Observations:          48        AIC:                   73.92
",
       "
",
       "
",
       "  Df Residuals:              45        BIC:                   79.53
",
       "
",
       "
",
       "  Df Model:                   2                                    
",
       "
",
       "
",
       "  Covariance Type:      nonrobust                                  
",
       "
",
       "
",
       "
",
       "
",
       "               coef     std err      t      P>|t|  [0.025    0.975]  
",
       "
",
       "
",
       "  Intercept  2325.9544   513.588     4.529  0.000  1291.534  3360.374
",
       "
",
       "
",
       "  Period        0.1075     0.022     4.807  0.000     0.062     0.153
",
       "
",
       "
",
       "  Year         -1.1553     0.255    -4.525  0.000    -1.670    -0.641
",
       "
",
       "
",
       "
",
       "
",
       "  Omnibus:       12.873   Durbin-Watson:         1.313
",
       "
",
       "
",
       "  Prob(Omnibus):  0.002   Jarque-Bera (JB):     13.627
",
       "
",
       "
",
       "  Skew:           1.076   Prob(JB):            0.00110
",
       "
",
       "
",
       "  Kurtosis:       4.478   Cond. No.           1.41e+07
",
       "
",
       "Warnings:[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.[2] The condition number is large, 1.41e+07. This might indicate that there arestrong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "
",
       """"
",
       "                            OLS Regression Results                            
",
       "==============================================================================
",
       "Dep. Variable:                  sales   R-squared:                       0.343
",
       "Model:                            OLS   Adj. R-squared:                  0.314
",
       "Method:                 Least Squares   F-statistic:                     11.75
",
       "Date:                Thu, 28 May 2020   Prob (F-statistic):           7.86e-05
",
       "Time:                        05:42:34   Log-Likelihood:                -33.960
",
       "No. Observations:                  48   AIC:                             73.92
",
       "Df Residuals:                      45   BIC:                             79.53
",
       "Df Model:                           2                                         
",
       "Covariance Type:            nonrobust                                         
",
       "==============================================================================
",
       "                 coef    std err          t      P>|t|      [0.025      0.975]
",
       "------------------------------------------------------------------------------
",
       "Intercept   2325.9544    513.588      4.529      0.000    1291.534    3360.374
",
       "Period         0.1075      0.022      4.807      0.000       0.062       0.153
",
       "Year          -1.1553      0.255     -4.525      0.000      -1.670      -0.641
",
       "==============================================================================
",
       "Omnibus:                       12.873   Durbin-Watson:                   1.313
",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               13.627
",
       "Skew:                           1.076   Prob(JB):                      0.00110
",
       "Kurtosis:                       4.478   Cond. No.                     1.41e+07
",
       "==============================================================================
",
       "
",
       "Warnings:
",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
",
       "[2] The condition number is large, 1.41e+07. This might indicate that there are
",
       "strong multicollinearity or other numerical problems.
",
       """""
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summery
",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting the sales based on period and year. 
",
    "prediction2 = res.predict(df[['Period','Year']])
",
    "df['Prediction2']=prediction2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "
",
       "    .dataframe tbody tr th:only-of-type {
",
       "        vertical-align: middle;
",
       "    }
",
       "
",
       "    .dataframe tbody tr th {
",
       "        vertical-align: top;
",
       "    }
",
       "
",
       "    .dataframe thead th {
",
       "        text-align: right;
",
       "    }
",
       "
",
       "
",
       "  
",
       "    
",
       "      
",
       "      Month
",
       "      Year
",
       "      sales
",
       "      Period
",
       "      Prediction
",
       "      Prediction2
",
       "    
",
       "  
",
       "  
",
       "    
",
       "      0
",
       "      January
",
       "      2012
",
       "      NaN
",
       "      1
",
       "      2.144635
",
       "      1.550586
",
       "    
",
       "    
",
       "      1
",
       "      February
",
       "      2012
",
       "      NaN
",
       "      2
",
       "      2.153814
",
       "      1.658089
",
       "    
",
       "    
",
       "      2
",
       "      March
",
       "      2012
",
       "      NaN
",
       "      3
",
       "      2.162992
",
       "      1.765593
",
       "    
",
       "    
",
       "      3
",
       "      April
",
       "      2012
",
       "      NaN
",
       "      4
",
       "      2.172170
",
       "      1.873097
",
       "    
",
       "    
",
       "      4
",
       "      May
",
       "      2012
",
       "      NaN
",
       "      5
",
       "      2.181348
",
       "      1.980600
",
       "    
",
       "  
",
       "
",
       ""
      ],
      "text/plain": [
       "      Month  Year  sales  Period  Prediction  Prediction2
",
       "0   January  2012    NaN       1    2.144635     1.550586
",
       "1  February  2012    NaN       2    2.153814     1.658089
",
       "2     March  2012    NaN       3    2.162992     1.765593
",
       "3     April  2012    NaN       4    2.172170     1.873097
",
       "4       May  2012    NaN       5    2.181348     1.980600"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png":

small.csv X1,X2,Y S,-0.1,19.19 S,2.53,22.74 S,4.86,23.91 M,0.26,7.07 M,2.55,7.93 M,4.87,8.93 L,0.08,20.63 L,2.62,23.46 L,5.09,25.75 __MACOSX/._small.csv part2.csv Month,Year,sales January,2012,...

Answer To: small.csv X1,X2,Y S,-0.1,19.19 S,2.53,22.74 S,4.86,23.91 M,0.26,7.07 M,2.55,7.93 M,4.87,8.93...

Answer To This Question Is Available To Download

Related Questions & Answers

Submit New Assignment

	X1	X2	Y
0	2	-0.10	19.19
1	2	2.53	22.74
2	2	4.86	23.91
3	1	0.26	7.07
4	1	2.55	7.93
5	1	4.87	8.93
6	0	0.08	20.63
7	0	2.62	23.46
8	0	5.09	25.75

Dep. Variable:	Y	R-squared:	0.053
Model:	OLS	Adj. R-squared:	-0.263
Method:	Least Squares	F-statistic:	0.1665
Date:	Thu, 28 May 2020	Prob (F-statistic):	0.850
Time:	05:12:53	Log-Likelihood:	-30.212
No. Observations:	9	AIC:	66.42
Df Residuals:	6	BIC:	67.02
Df Model:	2
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	16.3716	5.831	2.808	0.031	2.104	30.639
X1	-0.6019	3.474	-0.173	0.868	-9.103	7.899
X2	0.7769	1.428	0.544	0.606	-2.716	4.270

Omnibus:	3.603	Durbin-Watson:	0.961
Prob(Omnibus):	0.165	Jarque-Bera (JB):	1.553
Skew:	-0.704	Prob(JB):	0.460
Kurtosis:	1.530	Cond. No.	7.65

Dep. Variable:	Y	R-squared:	0.993
Model:	OLS	Adj. R-squared:	0.988
Method:	Least Squares	F-statistic:	225.0
Date:	Thu, 28 May 2020	Prob (F-statistic):	9.42e-06
Time:	05:12:53	Log-Likelihood:	-8.3472
No. Observations:	9	AIC:	24.69
Df Residuals:	5	BIC:	25.48
Df Model:	3
Covariance Type:	nonrobust

Omnibus:	0.672	Durbin-Watson:	1.798
Prob(Omnibus):	0.715	Jarque-Bera (JB):	0.529
Skew:	0.003	Prob(JB):	0.768
Kurtosis:	1.812	Cond. No.	11.4

	Month	Year	sales
0	January	2012	NaN
1	February	2012	NaN
2	March	2012	NaN
3	April	2012	NaN
4	May	2012	NaN

Dep. Variable:	sales	R-squared:	0.044
Model:	OLS	Adj. R-squared:	0.023
Method:	Least Squares	F-statistic:	2.121
Date:	Thu, 28 May 2020	Prob (F-statistic):	0.152
Time:	05:32:38	Log-Likelihood:	-42.960
No. Observations:	48	AIC:	89.92
Df Residuals:	46	BIC:	93.66
Df Model:	1
Covariance Type:	nonrobust

Omnibus:	34.103	Durbin-Watson:	1.733
Prob(Omnibus):	0.000	Jarque-Bera (JB):	71.518
Skew:	2.147	Prob(JB):	2.95e-16
Kurtosis:	7.161	Cond. No.	90.2

	Month	Year	sales	Period	Prediction
0	January	2012	NaN	1	2.144635
1	February	2012	NaN	2	2.153814
2	March	2012	NaN	3	2.162992
3	April	2012	NaN	4	2.172170
4	May	2012	NaN	5	2.181348

Sun	Mon	Tue	Wed	Thu	Fri	Sat
30	31	1	2	3	4	5
6	7	8	9	10	11	12
13	14	15	16	17	18	19
20	21	22	23	24	25	26
27	28	29	30	1	2	3

Omnibus:	12.873	Durbin-Watson:	1.313
Prob(Omnibus):	0.002	Jarque-Bera (JB):	13.627
Skew:	1.076	Prob(JB):	0.00110
Kurtosis:	4.478	Cond. No.	1.41e+07