gitsupersmecher
diff --git a/‎Module_1a_Introduction.ipynb ‎Module_0_Introduction.ipynb b/‎Module_1a_Introduction.ipynb ‎Module_0_Introduction.ipynb
diff --git a/‎Module_2a_linear_algebra_inverse.ipynb ‎Module_1a_linear_algebra_inverse.ipynb b/‎Module_2a_linear_algebra_inverse.ipynb ‎Module_1a_linear_algebra_inverse.ipynb
diff --git a/‎Module_2b_linear_regression_ols.ipynb ‎Module_1b_linear_regression_ols.ipynb b/‎Module_2b_linear_regression_ols.ipynb ‎Module_1b_linear_regression_ols.ipynb
diff --git a/‎Module_2c_linear_regression_ridge.ipynb ‎Module_1c_linear_regression_ridge.ipynb b/‎Module_2c_linear_regression_ridge.ipynb ‎Module_1c_linear_regression_ridge.ipynb
diff --git a/‎Module_2d_Gradient_Descent.ipynb ‎Module_1d_Gradient_Descent.ipynb b/‎Module_2d_Gradient_Descent.ipynb ‎Module_1d_Gradient_Descent.ipynb
diff --git a/‎Module_1g_TerminologiesModule1.ipynb
+5-4 b/‎Module_1g_TerminologiesModule1.ipynb
+5-4
diff --git a/‎Module_1b_BasicMetrics.ipynb ‎Module_2a_Basic_Stat_Metrics.ipynb
+1-1 b/‎Module_1b_BasicMetrics.ipynb ‎Module_2a_Basic_Stat_Metrics.ipynb
+1-1
diff --git a/‎Module_1c_probability.ipynb ‎Module_2b_probability.ipynb
+1-1 b/‎Module_1c_probability.ipynb ‎Module_2b_probability.ipynb
+1-1
diff --git a/‎module_1d_resampling.ipynb ‎Module_2c_resampling.ipynb
+1-1 b/‎module_1d_resampling.ipynb ‎Module_2c_resampling.ipynb
+1-1
diff --git a/‎Module_1e_Distributions.ipynb ‎Module_2d_Distributions.ipynb
+1-1 b/‎Module_1e_Distributions.ipynb ‎Module_2d_Distributions.ipynb
+1-1
diff --git a/‎Module_1f_HypothesisTesting.ipynb ‎Module_2e_HypothesisTesting.ipynb
+2-1 b/‎Module_1f_HypothesisTesting.ipynb ‎Module_2e_HypothesisTesting.ipynb
+2-1
diff --git a/‎Module_4a_Terminologies.ipynb
+71 b/‎Module_4a_Terminologies.ipynb
+71
diff --git a/‎Module_4_References.ipynb ‎Module_4b_References.ipynb
+5-4 b/‎Module_4_References.ipynb ‎Module_4b_References.ipynb
+5-4
diff --git a/‎autos.csv ‎data/autos.csv b/‎autos.csv ‎data/autos.csv
diff --git a/‎bank.csv ‎data/bank.csv b/‎bank.csv ‎data/bank.csv
diff --git a/‎cars_sample.csv ‎data/cars_sample.csv b/‎cars_sample.csv ‎data/cars_sample.csv
diff --git a/‎cars_small.csv ‎data/cars_small.csv b/‎cars_small.csv ‎data/cars_small.csv
diff --git a/‎cars_v1.csv ‎data/cars_v1.csv b/‎cars_v1.csv ‎data/cars_v1.csv
diff --git a/‎loans.csv
-1 b/‎loans.csv
-1
@@ -47,22 +47,23 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
 
@@ -2820,7 +2820,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
 
@@ -451,7 +451,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
 
@@ -1064,7 +1064,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
 
@@ -717,7 +717,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
 
@@ -588,8 +588,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
 
@@ -0,0 +1,71 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Terminology\n",
+    "\n",
+    "1. Null Hypothesis\n",
+    "2. Alternate Hypothesis\n",
+    "3. p-value (Probability of observing the metric from the data at least as extreme as computed just by chance)\n",
+    "4. Bootstrap\n",
+    "5. Acceptance Region\n",
+    "6. Rejection Region\n",
+    "7. t-test\n",
+    "8. One-tailed test\n",
+    "9. Two-tailed test\n",
+    "10. Significance test\n",
+    "11. Confidence interval\n",
+    "12. Power of a test\n",
+    "13. type 1 error (Rejecting null hypothesis when it is true). Also called false positive.\n",
+    "14. type 2 error (Failing to reject null hypothesis when it is false). Also called false negative\n",
+    "\n",
+    "# Some Practical thoughts \n",
+    "\n",
+    "1. Data could be biased. Confidence intervals may then not be representative.\n",
+    "2. One way to handle biased data is to use bias-corrected-confidence-intervals. \n",
+    "3. Outliers can impact confidence intervals. \n",
+    "4. Too often, people remove outliers. But they might be encoding some necessary information.\n",
+    "5. One way to handle outliers is to use ranking, instead of actual numbers.\n",
+    "6. If sample size is small, bootstrapping underestimates the size of confidence interval. \n",
+    "7. Better to use significance testing if sample size is small.\n",
+    "8. Bootstrapping should not be used find maximum value (Eg: maximum sales of shoes, 5th largest sales of shoes, etc)\n",
+    "9. Use rank transformation when using bootstrapping, if the data has outliers\n",
+    "10. Lack of representativeness is a problem for any statistical technique\n",
+    "11. The experiment should be random. (Eg: When doing A/B testing, randomize the subjects). Experimental bias can lead to wrong inferences. \n",
+    "12. Resampling time series data is tricky. The assumption we used - that each data point is independent, doesn't hold good for time series data. \n",
+    "13. Rank transformation changes the question. For our shoe sales example, a rank transformed analysis would be: \"Do sales tend to be higher after price optimization?\". (Our analysis was: \"Does post-price-optimization sales have a higher mean sales?\")\n",
+    "14. Power of a test increases if sample size increases\n",
+    "\n",
+    "# Types of Error\n",
+    "\n",
+    "1. Sampling Bias\n",
+    "2. Measurement Error\n",
+    "3. Random Error\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
@@ -30,22 +30,23 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,