{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Inferential Statistics\n", "\n", "## Introduction to Probability\n", "\n", "* Normal Distribution\n", "* Normal Distribution & Standard Deviation\n", "* Concept of Z-score\n", "\n", "## Introduction to Inference\n", "\n", "* Sample Mean & Population Mean\n", "* Statistical Inference\n", "* Central Limit Theorem\n", "* Confidence Intervals\n", "* Interpretation Of Confidence Interval\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# importing all the basic libraries\n", "\n", "# for using division module\n", "from __future__ import division\n", "\n", "# for basic operations\n", "import pandas as pd\n", "import numpy as np\n", "\n", "# for data visualizations\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# for avoiding warnings\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1460, 81)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading the data\n", "data = pd.read_csv('Datasets/train.csv')\n", "\n", "# lets check the shape of the dataset\n", "data.shape" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
\n", "
" ], "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", "0 1 60 RL 65.0 8450 Pave NaN Reg \n", "1 2 20 RL 80.0 9600 Pave NaN Reg \n", "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", "\n", " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", "0 Lvl AllPub Inside Gtl CollgCr Norm \n", "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", "2 Lvl AllPub Inside Gtl CollgCr Norm \n", "3 Lvl AllPub Corner Gtl Crawfor Norm \n", "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", "\n", " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", "0 Norm 1Fam 2Story 7 5 2003 \n", "1 Norm 1Fam 1Story 6 8 1976 \n", "2 Norm 1Fam 2Story 7 5 2001 \n", "3 Norm 1Fam 2Story 7 5 1915 \n", "4 Norm 1Fam 2Story 8 5 2000 \n", "\n", " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", "1 1976 Gable CompShg MetalSd MetalSd None \n", "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", "\n", " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", "0 196.0 Gd TA PConc Gd TA No \n", "1 0.0 TA TA CBlock Gd TA Gd \n", "2 162.0 Gd TA PConc Gd TA Mn \n", "3 0.0 TA TA BrkTil TA Gd No \n", "4 350.0 Gd TA PConc Gd TA Av \n", "\n", " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", "0 GLQ 706 Unf 0 150 856 \n", "1 ALQ 978 Unf 0 284 1262 \n", "2 GLQ 486 Unf 0 434 920 \n", "3 ALQ 216 Unf 0 540 756 \n", "4 GLQ 655 Unf 0 490 1145 \n", "\n", " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", "0 GasA Ex Y SBrkr 856 854 0 \n", "1 GasA Ex Y SBrkr 1262 0 0 \n", "2 GasA Ex Y SBrkr 920 866 0 \n", "3 GasA Gd Y SBrkr 961 756 0 \n", "4 GasA Ex Y SBrkr 1145 1053 0 \n", "\n", " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", "0 1710 1 0 2 1 3 \n", "1 1262 0 1 2 0 3 \n", "2 1786 1 0 2 1 3 \n", "3 1717 1 0 1 0 3 \n", "4 2198 1 0 2 1 4 \n", "\n", " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", "0 1 Gd 8 Typ 0 NaN \n", "1 1 TA 6 Typ 1 TA \n", "2 1 Gd 6 Typ 1 TA \n", "3 1 Gd 7 Typ 1 Gd \n", "4 1 Gd 9 Typ 1 TA \n", "\n", " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", "0 Attchd 2003.0 RFn 2 548 TA \n", "1 Attchd 1976.0 RFn 2 460 TA \n", "2 Attchd 2001.0 RFn 2 608 TA \n", "3 Detchd 1998.0 Unf 3 642 TA \n", "4 Attchd 2000.0 RFn 3 836 TA \n", "\n", " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", "0 TA Y 0 61 0 0 \n", "1 TA Y 298 0 0 0 \n", "2 TA Y 0 42 0 0 \n", "3 TA Y 0 35 272 0 \n", "4 TA Y 192 84 0 0 \n", "\n", " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", "0 0 0 NaN NaN NaN 0 2 2008 \n", "1 0 0 NaN NaN NaN 0 5 2007 \n", "2 0 0 NaN NaN NaN 0 9 2008 \n", "3 0 0 NaN NaN NaN 0 2 2006 \n", "4 0 0 NaN NaN NaN 0 12 2008 \n", "\n", " SaleType SaleCondition SalePrice \n", "0 WD Normal 208500 \n", "1 WD Normal 181500 \n", "2 WD Normal 223500 \n", "3 WD Abnorml 140000 \n", "4 WD Normal 250000 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# lets check the head of the dataset\n", "pd.set_option('max_columns', 82)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n", " 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n", " 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", " 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n", " 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n", " 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n", " 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n", " 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n", " 'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n", " 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n", " 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n", " 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n", " 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n", " 'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n", " 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n", " 'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n", " 'SaleCondition', 'SalePrice'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NAmes 225\n", "CollgCr 150\n", "OldTown 113\n", "Edwards 100\n", "Somerst 86\n", "Gilbert 79\n", "NridgHt 77\n", "Sawyer 74\n", "NWAmes 73\n", "SawyerW 59\n", "BrkSide 58\n", "Crawfor 51\n", "Mitchel 49\n", "NoRidge 41\n", "Timber 38\n", "IDOTRR 37\n", "ClearCr 28\n", "StoneBr 25\n", "SWISU 25\n", "Blmngtn 17\n", "MeadowV 17\n", "BrDale 16\n", "Veenker 11\n", "NPkVill 9\n", "Blueste 2\n", "Name: Neighborhood, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# lets check the different neighborhoods\n", "data['Neighborhood'].value_counts()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Number of Houses in the Neighborhood : 1460\n" ] } ], "source": [ "# total number of houses in the neighborhood\n", "all_houses = data.shape[0]\n", "print(\"Total Number of Houses in the Neighborhood :\", all_houses)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Number of Houses in the Old Town Road : 113\n" ] } ], "source": [ "# total number of houses in the Old town neighborhood\n", "houses_in_OldTown = data[data['Neighborhood'] == 'OldTown'].shape[0]\n", "print(\"Total Number of Houses in the Old Town Road :\", houses_in_OldTown)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probability of picking a house in OldTown: 7.74%\n" ] } ], "source": [ "# lets find the probability of picking a House in the Old Town\n", "probability = (houses_in_OldTown/all_houses)*100\n", "print('Probability of picking a house in OldTown: {0:.2f}'.format(probability )+'%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Normal Distrution\n", "\n", "* Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checking for Skewness of the data\n", "\n", "* We Generally check Askewness in the Target Columns of the data.\n", "* Skewness is a state of distribution where the distribution is highly biased towards the right or left side of the plot." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = (11, 4)\n", "plt.style.use('fivethirtyeight')\n", "\n", "plt.xticks(rotation=30)\n", "sns.distplot(data['SalePrice'])\n", "plt.title('Distribution of Target Column')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The distribution for our target variable aka SalePrice doesn't resemble a normal distribution, it is skewed to the right\n", "* If we remove the outliers, it'd somewhat resemble a Normal Dstribution" ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "## Z-Score\n", "\n", "* The number of standard deviations from the mean is also called the \"Standard Score\", \"sigma\" or \"z-score\".\n", "![image.png](attachment:image.png)\n", "* Let's take an example to better understand the meaning of z-score\n", " * Let's Suppose the average height of a Student in a class is 1.4 meters\n", " * In that same class one of the students is 1.85m tall\n", " * You can see on the bell curve that 1.85m is 3 standard deviations from the mean of 1.4.\n", " * so, the student with 1.85m height has a **z-score\" of 3.0**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sample Mean and population Mean\n", "\n", "* Let's consider a sample of 500 houses at random from 1460 houses and plot its mean\n", "* But the mean of these 500 houses can be near or pretty far away from the mean of the 1460 houses calculated earlier." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample mean: 177499.802\n", "Population mean: 180921.19589041095\n" ] } ], "source": [ "# lets take seed so that everytime the random values come out to be constant\n", "np.random.seed(6)\n", "\n", "# lets take 500 sample values from the dataset of 1460 values\n", "sample_ages = np.random.choice(a= data['SalePrice'], size=500)\n", "\n", "# getting the sample mean\n", "print (\"Sample mean:\", sample_ages.mean() ) \n", "\n", "# getting the population mean\n", "print(\"Population mean:\", data['SalePrice'].mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statistical Inference\n", "\n", "* This **subset** of the population is nothing but the Sample data\n", "\n", "* We carry out various tests on the Sample to gain insight on the larger population out there!\n", "\n", "* Therefore Statistical inference is the process of analyzing sample data to gain insight into the population from which the data was collected and to investigate differences between different data samples.\n", "\n", "The sample mean is usually not exactly the same as the population mean. This difference can be caused by many factors including poor survey design, biased sampling methods and the randomness inherent to drawing a sample from a population.\n", "\n" ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "## Confidence Interval\n", "\n", "![image.png](attachment:image.png)\n", "\n", "\n", "**Confidence Interval (CI)** is a type of estimate computed from the statistics of the observed data. This proposes a range of plausible values for an unknown parameter (for example, the mean). The interval has an associated confidence level that the true parameter is in the proposed range.\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The 95% confidence interval defines a range of values that you can be 95% certain contains the population mean. With large samples, you know that mean with much more precision than you do with a small sample, so the confidence interval is quite narrow when computed from a large sample." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "z-critical value: 1.6448536269514722\n", "Confidence interval: (178338.05201966673, 186602.42998033328)\n", "True mean: 180921.19589041095\n" ] } ], "source": [ "# lets import the scipy package\n", "import scipy.stats as stats\n", "import math\n", "\n", "# lets seed the random values\n", "np.random.seed(10)\n", "\n", "# lets take a sample size\n", "sample_size = 1000\n", "sample = np.random.choice(a= data['SalePrice'],\n", " size = sample_size)\n", "sample_mean = sample.mean()\n", "\n", "# Get the z-critical value*\n", "z_critical = stats.norm.ppf(q = 0.95) \n", "\n", " # Check the z-critical value \n", "print(\"z-critical value: \",z_critical) \n", "\n", "# Get the population standard deviation\n", "pop_stdev = data['SalePrice'].std() \n", "\n", "# checking the margin of error\n", "margin_of_error = z_critical * (pop_stdev/math.sqrt(sample_size)) \n", "\n", "# defining our confidence interval\n", "confidence_interval = (sample_mean - margin_of_error,\n", " sample_mean + margin_of_error) \n", "\n", "# lets print the results\n", "print(\"Confidence interval:\",end=\" \")\n", "print(confidence_interval)\n", "print(\"True mean: {}\".format(data['SalePrice'].mean()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Notice that the true mean is contained in our interval.\n", "* A confidence interval of 95% would mean that if we take many samples and create confidence intervals for each of them, 95% of our samples' confidence intervals will contain the true population mean.\n", "* Now, let's create several confidence intervals and plot them to get a better sense of what it means to \"capture\" the true mean" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(12)\n", "\n", "sample_size = 500\n", "\n", "intervals = []\n", "sample_means = []\n", "\n", "for sample in range(25):\n", " sample = np.random.choice(a= data['SalePrice'], size = sample_size)\n", " sample_mean = sample.mean()\n", " sample_means.append(sample_mean)\n", "\n", " # Get the z-critical value* \n", " z_critical = stats.norm.ppf(q = 0.975) \n", "\n", " # Get the population standard deviation\n", " pop_stdev = data['SalePrice'].std() \n", "\n", " stats.norm.ppf(q = 0.025)\n", "\n", " margin_of_error = z_critical * (pop_stdev/math.sqrt(sample_size))\n", "\n", " confidence_interval = (sample_mean - margin_of_error,\n", " sample_mean + margin_of_error) \n", " \n", " intervals.append(confidence_interval)\n", " \n", "\n", "plt.figure(figsize=(13, 9))\n", "\n", "plt.errorbar(x=np.arange(0.1, 25, 1), \n", " y=sample_means, \n", " yerr=[(top-bot)/2 for top,bot in intervals],\n", " fmt='o')\n", "\n", "plt.hlines(xmin=0, xmax=25,\n", " y=data['SalePrice'].mean(), \n", " linewidth=2.0,\n", " color=\"red\")\n", "plt.title('Confidence Intervals for 25 Trials', fontsize = 20)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* It is easily visible that 95% of the times the blue lines(the sample meean) overlaps with the red line(the true mean), also 5% of the times it is expected to not overlap with the red line(the true mean)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hypothesis Testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* $Statistical Hypothesis$, sometimes called confirmatory data analysis, is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. A statistical hypothesis test is a method of statistical inference.\n", "\n", "### Null Hypothesis\n", "\n", "* In Inferential Statistics, **The Null Hypothesis is a general statement or default position that there is no relationship between two measured phenomena or no association among groups.**\n", "\n", "* Statistical hypothesis tests are based on a statement called the null hypothesis that assumes nothing interesting is going on between whatever variables you are testing.\n", "\n", "* Therefore, in our case the Null Hypothesis would be:\n", "**The Mean of House Prices in OldTown is not different from the houses of other neighborhoods**\n", "\n", "### Alternate Hypothesis\n", "\n", "* The alternate hypothesis is just an alternative to the null. For example, if your null is **I'm going to win up to 1000** then your alternate is **I'm going to win more than 1000.** Basically, you're looking at whether there's enough change (with the alternate hypothesis) to be able to reject the null hypothesis\n", "\n", "### The Null Hypothesis is assumed to be true and Statistical evidence is required to reject it in favor of an Alternative Hypothesis." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "1. Once you have the null and alternative hypothesis in hand, you choose a significance level (often denoted by the Greek letter α). The significance level is a probability threshold that determines when you reject the null hypothesis.\n", "\n", "2. After carrying out a test, if the probability of getting a result as extreme as the one you observe due to chance is lower than the significance level, you reject the null hypothesis in favor of the alternative.\n", "\n", "3. This probability of seeing a result as extreme or more extreme than the one observed is known as the p-value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### P Value\n", "\n", "* In statistical hypothesis testing, **the p-value or probability value** is the probability of obtaining test results at least as extreme as the results actually observed during the test, assuming that the null hypothesis is correct. \n", "\n", "* So now say that we have put a significance (α) = 0.05\n", "* This means that if we see a p-value of lesser than 0.05, we reject our Null and accept the Alternative to be true\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**Are house prices in OldTown really different from the House Prices of Other Neighborhoods?**" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Z-statistic is :-10.639294263334575\n", "P-value is :0.00000000000000000000000001956052602626001826532572\n" ] } ], "source": [ "# lets import z test from statsmodels\n", "from statsmodels.stats.weightstats import ztest\n", "\n", "z_statistic, p_value = ztest(x1 = data[data['Neighborhood'] == 'OldTown']['SalePrice'],\n", " value = data['SalePrice'].mean())\n", "\n", "# lets print the Results\n", "print('Z-statistic is :{}'.format(z_statistic))\n", "print('P-value is :{:.50f}'.format(p_value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* If the P value if less than 0.05, then we can reject our null hypothesis against the alternate hypothesis.\n", "\n", "* **The Probability of getting the given distribution of houseprices in OldTown under the assumption that its mean, is the same as the mean of all house prices.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Another way to test: Gosset's (Student's) t-test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The T-test is a statistical test used to determine whether a numeric data sample differs significantly from the population or whether two samples differ from one another.\n", "* A z-test assumes a sample size >30 to work, but what if our sample is less than 30?\n", "* A t-test solves this problem and gives us a way to do a hypothesis test on a smaller sample.\n", "* Now, let's also see if house prices in Stone Brook neighborhood are different from the houses in the rest of the neighborhoods." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now, let's also see if house prices in Stone Brook neighborhood are different from the houses in the rest of the neighborhoods." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No of houses in Stone Brook: 25\n" ] } ], "source": [ "print('No of houses in Stone Brook: {}'\\\n", " .format(data['Neighborhood'].value_counts()['StoneBr']))\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_1sampResult(statistic=5.735070151700397, pvalue=6.558704101036394e-06)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.ttest_1samp(a= data[data['Neighborhood'] == 'StoneBr']['SalePrice'], # Sample data\n", " popmean= data['SalePrice'].mean()) # Pop mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The p-value in this case again is low and we can reject our null hypothesis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Type 1 and Type 2 Error\n", "\n", "* In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis, while a type II error is the non-rejection of a false null hypothesis\n", "\n", "### Type 1 and Type 2 Error Example\n", "\n", "For example, let's look at the trail of an accused criminal. The null hypothesis is that the person is innocent, while the alternative is guilty. \n", "* A Type 1 error in this case would mean that the person is not found innocent and is sent to jail, despite actually being innocent.\n", "* A Type 2 Erroe Example In this case would be, the person is found innocent and not sent to jail despite of him being guilty in real.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Chi Square Test\n", "\n", "The term \"chi-squared test,\" also written as χ² test, refers to certain types of statistical hypothesis tests that are valid to perform when the test statistic is chi-squared distributed under the null hypothesis. Often, however, the term is used to refer to Pearson's chi-squared test and variants thereof.\n", "\n", "***A chi-squared goodness of fit tests whether the distribution of sample categorical data matches an expected distribution.***\n", "\n", "For example, \n", "* *you could use a chi-squared goodness-of-fit test to check whether the race demographics of members at your church or school match that of the entire population of your country*.\n", "* *you could check whether the computer browser preferences of your friends match those of Internet uses as a whole.*\n", "\n", "* *When working with categorical data the values the observations themselves aren't of much use for statistical testing because categories like \"male\", \"female,\" and \"other\" have no mathematical meaning.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Let's generate some fake demographic data for U.S. and Minnesota and walk through the chi-square goodness of fit test to check whether they are different:\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chi-Squared Goodness of fit Test" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "National\n", "col_0 count\n", "0 \n", "asian 15000\n", "black 50000\n", "hispanic 60000\n", "other 35000\n", "white 100000\n", " \n", "Minnesota\n", "col_0 count\n", "0 \n", "asian 75\n", "black 250\n", "hispanic 300\n", "other 150\n", "white 600\n" ] } ], "source": [ "national = pd.DataFrame([\"white\"]*100000 + [\"hispanic\"]*60000 +\\\n", " [\"black\"]*50000 + [\"asian\"]*15000 + [\"other\"]*35000) \n", "\n", "minnesota = pd.DataFrame([\"white\"]*600 + [\"hispanic\"]*300 + \\\n", " [\"black\"]*250 +[\"asian\"]*75 + [\"other\"]*150)\n", "\n", "national_table = pd.crosstab(index=national[0], columns=\"count\")\n", "minnesota_table = pd.crosstab(index=minnesota[0], columns=\"count\")\n", "\n", "print( \"National\")\n", "print(national_table)\n", "print(\" \")\n", "print( \"Minnesota\")\n", "print(minnesota_table)" ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "![image.png](attachment:image.png)\n", "\n", "* **Good Fit**: If the significance value that is p-value associated with chi-square statistics is 0.002, there is very strong evidence of rejecting the null hypothesis of no fit. It means good fit." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "col_0\n", "count 18.194805\n", "dtype: float64\n" ] } ], "source": [ "observed = minnesota_table\n", "\n", "national_ratios = national_table/len(national) # Get population ratios\n", "\n", "expected = national_ratios * len(minnesota) # Get expected counts\n", "\n", "chi_squared_stat = (((observed-expected)**2)/expected).sum()\n", "\n", "print(chi_squared_stat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chi-Sqaured Test of Independence" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Independence is a key concept in probability that describes a situation where knowing the value of one variable tells you nothing about the value of another.\n", "\n", "For instance, the month you were born probably doesn't tell you anything which web browser you use, so we'd expect birth month and browser preference to be independent.\n", "\n", "On the other hand, your month of birth might be related to whether you excelled at sports in school, so month of birth and sports performance might not be independent.\n", "\n", "The chi-squared test of independence tests whether two categorical variables are independent." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Effect of LandContour on SalePrice" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Frequency table\n", "============================\n", "SalePrice High Medium Low\n", "LandContour \n", "Bnk 32 20 11\n", "HLS 10 12 28\n", "Low 8 11 17\n", "Lvl 437 447 427\n", "============================\n", "ChiSquare test statistic: 26.252544346201447\n", "p-value: 0.00019976918050008285\n" ] } ], "source": [ "# Let's test if knowing LandContour which is the overall flatness of the property tells us anything about the price\n", "\n", "# For this let's divide the SalePrice in three buckets - High, Medium, Low\n", "\n", "import scipy.stats as sp\n", "def compute_freq_chi2(x,y):\n", " freqtab = pd.crosstab(x,y)\n", " print(\"Frequency table\")\n", " print(\"============================\")\n", " print(freqtab)\n", " print(\"============================\")\n", " chi2, pval, dof, expected = sp.chi2_contingency(freqtab)\n", " print(\"ChiSquare test statistic: \",chi2)\n", " print(\"p-value: \",pval)\n", " return\n", "\n", "\n", "price = pd.qcut(data['SalePrice'], 3, labels = ['High', 'Medium', 'Low'])\n", "compute_freq_chi2(data.LandContour, price)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The low p-value tells us that the two variables aren't independent and knowing the LandContour of a house does tells us something about its SalePrice.\n", "\n", "**The frequency distribution reflects this**\n", "* Houses that are Near Flat/Level(Lvl) have an equal distribution of SalePrice.\n", "* On the other hand houses that are at a Hillside i.e., Significant slope from side to side (HLS) have almost thrice as much houses with low price than high prices." ] } ], "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }