BloomTech’s Downfall: A Long Time Coming

The Analytics Edge (Spring 2017)

The Analytics Edge (Spring 2017)

Prof. Dimitris Bertsimas via MIT OpenCourseWare Direct link

3.2.6 Introduction to Logistical Regression - Video 4: Logistic Regression in R

46 of 193
Previous
Next

46 of 193

3.2.6 Introduction to Logistical Regression - Video 4: Logistic Regression in R

Class Central Classrooms beta

YouTube playlists curated by Class Central.

Classroom Contents

The Analytics Edge (Spring 2017)

Automatically move to the next video in the Classroom when playback concludes
  1. 1 1.1.1 Welcome to Unit 1: An Introduction to Analytics
  2. 2 1.2.1 The Analytics Edge - Video 1: Introduction to The Analytics Edge
  3. 3 1.2.2 The Analytics Edge - Video 2: Example 1 - IBM Watson
  4. 4 1.2.3 The Analytics Edge - Video 3: Example 2 - eHarmony
  5. 5 1.2.4 The Analytics Edge - Video 4: Example 3 - The Framingham Heart Study
  6. 6 1.2.5 The Analytics Edge - Video 5: Example 4 - D2Hawkeye
  7. 7 1.2.6 The Analytics Edge - Video 6: This Class
  8. 8 1.3.2 Working with Data - Video 1: History of R
  9. 9 1.3.4 Working with Data - Video 2: Getting Started in R
  10. 10 1.3.6 Working with Data - Video 3: Vectors and Data Frames
  11. 11 1.3.8 Working with Data - Video 4: Loading Data Files
  12. 12 1.3.10 Working with Data - Video 5: Data Analysis - Summary Statistics and Scatterplots
  13. 13 1.3.12 Working with Data - Video 6: Data Analysis - Plots and Summary Tables
  14. 14 1.3.14 Working with Data - Video 7: Saving with Script Files
  15. 15 1.4.1 Welcome to Recitation 1 - Understanding Food: Nutritional Education with Data
  16. 16 1.4.2 R1. Understanding Food - Video 1: The Importance of Food and Nutrition
  17. 17 1.4.3 R1. Understanding Food - Video 2: Working with Data in R
  18. 18 1.4.4 R1. Understanding Food - Video 3: Data Analysis
  19. 19 1.4.5 R1. Understanding Food - Video 4: Creating Plots in R
  20. 20 1.4.6 R1. Understanding Food - Video 5: Adding Variables
  21. 21 1.4.7 R1. Understanding Food - Video 6: Summary Tables
  22. 22 2.1.1 Welcome to Unit 2 - An Introduction to Linear Regression
  23. 23 2.2.1 An Introduction to Linear Regression - Video 1: Predicting the Quality of Wine
  24. 24 2.2.3 An Introduction to Linear Regression - Video 2: One-variable Linear Regression
  25. 25 2.2.5 An Introduction to Linear Regression - Video 3: Multiple Linear Regression
  26. 26 2.2.7 An Introduction to Linear Regression - Video 4: Linear Regression in R
  27. 27 2.2.9 An Introduction to Linear Regression - Video 5: Understanding the Model
  28. 28 2.2.11 An Introduction to Linear Regression - Video 6: Correlation and Multicollinearity
  29. 29 2.2.13 An Introduction to Linear Regression - Video 7: Making Predictions
  30. 30 2.2.15 An Introduction to Linear Regression - Video 8: Comparing the Model to the Experts
  31. 31 2.3.2 Sports Analytics - Video 1: The Story of Moneyball
  32. 32 2.3.3 Sports Analytics - Video 2: Making It to the Playoffs
  33. 33 2.3.5 Sports Analytics - Video 3: Predicting Runs
  34. 34 2.3.7 Sports Analytics - Video 4: Using the Model to Make Predictions
  35. 35 2.3.9 Sports Analytics - Video 5: Winning the World Series
  36. 36 2.3.11 Sports Analytics - Video 6: The Analytics Edge in Sports
  37. 37 2.4.1 R2. Playing Moneyball in the NBA - Welcome to Recitation 2
  38. 38 2.4.2 R2. Moneyball in the NBA - Video 1: The Data
  39. 39 2.4.3 R2. Moneyball in the NBA - Video 2: Playoffs and Wins
  40. 40 2.4.4 R2. Moneyball in the NBA - Video 3: Points Scored
  41. 41 2.4.5 R2. Moneyball in the NBA - Video 4: Making Predictions
  42. 42 3.1.1 Welcome to Unit 3: Modeling the Expert - An Introduction to Logistical Regression
  43. 43 3.2.1 Introduction to Logistical Regression - Video 1: Replicating Expert Assessment
  44. 44 3.2.2 Introduction to Logistical Regression - Video 2: Building the Dataset
  45. 45 3.2.4 Introduction to Logistical Regression - Video 3: Logistic Regression
  46. 46 3.2.6 Introduction to Logistical Regression - Video 4: Logistic Regression in R
  47. 47 3.2.8 Introduction to Logistical Regression - Video 5: Thresholding
  48. 48 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves
  49. 49 3.2.12 Introduction to Logistical Regression - Video 7: Interpreting the Model
  50. 50 3.2.14 Introduction to Logistical Regression - Video 8: The Analytics Edge
  51. 51 3.3.1 The Framingham Heart Study - Video 1: Evaluating Risk Factors to Save Lives
  52. 52 3.3.3 The Framingham Heart Study - Video 2: Risk Factors
  53. 53 3.3.5 The Framingham Heart Study - Video 3: A Logistical Regression Model
  54. 54 3.3.7 The Framingham Heart Study - Video 4: Validating the Model
  55. 55 3.3.9 The Framingham Heart Study - Video 5: Interventions
  56. 56 3.3.11 The Framingham Heart Study - Video 6: Overall Impact
  57. 57 3.4.1 Recitation 3 - Election Forecasting: Predicting the Winner Before Any Votes Are Cast
  58. 58 3.4.2 R3. Election Forecasting - Video 1: Election Prediction
  59. 59 3.4.3 R3. Election Forecasting - Video 2: Dealing with Missing Data
  60. 60 3.4.4 R3. Election Forecasting - Video 3: A Sophisticated Baseline Method
  61. 61 3.4.5 R3. Election Forecasting - Video 4: Logistic Regression Models
  62. 62 3.4.6 R3. Election Forecasting - Video 5: Test Set Predictions
  63. 63 4.1.1 Welcome to Unit 4 - Judge, Jury, and Classifier: An Introduction to Trees
  64. 64 4.2.1 An Introduction to Trees - Video 1: The Supreme Court
  65. 65 4.2.3 An Introduction to Trees - Video 2: CART
  66. 66 4.2.5 An Introduction to Trees - Video 3: Splitting and Predictions
  67. 67 4.2.7 An Introduction to Trees - Video 4: CART in R
  68. 68 4.2.9 An Introduction to Trees - Video 5: Random Forests
  69. 69 4.2.11 An Introduction to Trees - Video 6: Cross-Validation
  70. 70 4.2.13 An Introduction to Trees - Video 7: The Model v. The Experts
  71. 71 4.3.1 Healthcare Costs - Video 1: The Story of D2Hawkeye
  72. 72 4.3.3 Healthcare Costs - Video 2: Claims Data
  73. 73 4.3.5 Healthcare Costs - Video 3: The Variables
  74. 74 4.3.7 Healthcare Costs- Video 4: Error Measures
  75. 75 4.3.9 Healthcare Costs - Video 5: CART to Predict Cost
  76. 76 4.3.11 Healthcare Costs - Video 6: Claims Data in R
  77. 77 4.3.13 Healthcare Costs - Video 7: Baseline Method and Penalty Matrix
  78. 78 4.3.15 Healthcare Costs - Video 8: Predicting Healthcare Cost in R
  79. 79 4.3.17 Healthcare Costs - Video 9: Results
  80. 80 4.4.1 Welcome to Recitation 4 - Location, Location, Location: Regression Trees for Housing Data
  81. 81 4.4.2 R4. Regression Trees - Video 1: Boston Housing Data
  82. 82 4.4.3 R4. Regression Trees- Video 2: The Data
  83. 83 4.4.4 R4. Regression Trees - Video 3: Geographical Predictions
  84. 84 4.4.5 R4. Regression Trees - Video 4: Regression Trees
  85. 85 4.4.6 R4. Regression Trees - Video 5: Putting it all Together
  86. 86 4.4.7 R4. Regression Trees - Video 6: The CP Parameter
  87. 87 4.4.8 R4. Regression Trees - Video 7: Cross-Validation
  88. 88 5.1.1 Welcome to Unit 5 - Turning Tweets into Knowledge: An Introduction to Text Analytics
  89. 89 5.2.1 An Introduction to Text Analytics - Video 1: Twitter
  90. 90 5.2.2 An Introduction to Text Analytics - Video 2: Text Analytics
  91. 91 5.2.4 An Introduction to Text Analytics - Video 3: Creating the Dataset
  92. 92 5.2.6 An Introduction to Text Analytics - Video 4: Bag of Words
  93. 93 5.2.8 An Introduction to Text Analytics - Video 5: Pre-Processing in R
  94. 94 5.2.10 An Introduction to Text Analytics - Video 6: Bag of Words in R
  95. 95 5.2.12 An Introduction to Text Analytics - Video 7: Predicting Sentiment
  96. 96 5.2.14 An Introduction to Text Analytics - Video 8: Conclusion
  97. 97 5.3.1 How IBM Built a Jeopardy Champion - Video 1: IBM Watson
  98. 98 5.3.3 How IBM Built a Jeopardy Champion - Video 2: The Game of Jeopardy
  99. 99 5.3.5 How IBM Built a Jeopardy Champion - Video 3: Watson's Database and Tools
  100. 100 5.3.7 How IBM Built a Jeopardy Champion - Video 4: How Watson Works - Steps 1 and 2
  101. 101 5.3.9 How IBM Built a Jeopardy Champion - Video 5: How Watson Works - Steps 3 and 4
  102. 102 5.3.11 How IBM Built a Jeopardy Champion - Video 6: The Results
  103. 103 5.4.1 Welcome to Recitation 5 - Predictive Coding: Bringing Text Analytics to the Courtroom
  104. 104 5.4.2 R5. Predictive Coding - Video 1: The Story of Enron
  105. 105 5.4.3 R5. Predictive Coding - Video 2: The Data
  106. 106 5.4.4 R5. Predictive Coding - Video 3: Pre-Processing
  107. 107 5.4.5 R5. Predictive Coding - Video 4: Bag of Words
  108. 108 5.4.6 R5. Predictive Coding - Video 5: Building Models
  109. 109 5.4.7 R5. Predictive Coding - Video 6: Evaluating the Model
  110. 110 5.4.8 R5. Predictive Coding - Video 7: The ROC Curve
  111. 111 5.4.9 R5. Predictive Coding - Video 8: Predictive Coding Today
  112. 112 6.1.1 Welcome to Unit 6 - An Introduction to Clustering
  113. 113 6.2.1 An Introduction to Clustering - Video 1: Introduction to Netflix
  114. 114 6.2.3 An Introduction to Clustering - Video 2: Recommendation Systems
  115. 115 6.2.5 An Introduction to Clustering - Video 3: Movie Data and Clustering
  116. 116 6.2.7 An Introduction to Clustering - Video 4: Computing Distances
  117. 117 6.2.9 An Introduction to Clustering - Video 5: Hierarchical Clustering
  118. 118 6.2.11 An Introduction to Clustering - Video 6: Getting the Data
  119. 119 6.2.13 An Introduction to Clustering - Video 7: Hierarchical Clustering in R
  120. 120 6.2.15 An Introduction to Clustering - Video 8: The Analytics Edge of Recommendation Systems
  121. 121 6.3.1 Predictive Diagnosis - Video 1: Heart Attacks
  122. 122 6.3.3 Predictive Diagnosis - Video 2: The Data
  123. 123 6.3.5 Predictive Diagnosis - Video 3: Predicting Heart Attacks Using Clustering
  124. 124 6.3.7 Predictive Diagnosis - Video 4: Understanding Cluster Patterns
  125. 125 6.3.9 Predictive Diagnosis - Video 5: The Analytics Edge
  126. 126 6.4.1 Welcome to Recitation 6 - Seeing the Big Picture: Segmenting Images to Create Data
  127. 127 6.4.2 Recitation 6 - Video 1: Image Segmentation
  128. 128 6.4.3 R6. Segmenting Images - Video 2: Clustering Pixels
  129. 129 6.4.4 R6. Segmenting Images - Video 3: Hierarchical Clustering
  130. 130 6.4.6 R6. Segmenting Images - Video 4: MRI Image
  131. 131 6.4.7 R6. Segmenting Images - Video 5: K-Means Clustering
  132. 132 6.4.8 R6. Segmenting Images - Video 6: Detecting Tumors
  133. 133 6.4.9 R6. Segmenting Images - Video 7: Comparing Methods
  134. 134 7.1.1 Welcome to Unit 7 - Visualizing the World: An Introduction to Visualization
  135. 135 7.2.1 An Introduction to Visualization - Video 1: The Power of Visualizations
  136. 136 7.2.3 An Introduction to Visualization - Video 2: The World Health Organization (WHO)
  137. 137 7.2.5 An Introduction to Visualization - Video 3: What is Data Visualization?
  138. 138 7.2.7 An Introduction to Visualization - Video 4: Basic Scatterplots Using ggplot
  139. 139 7.2.9 An Introduction to Visualization - Video 5: Advanced Scatterplots Using ggplot
  140. 140 7.3.1 Visualization for Law and Order - Video 1: Predictive Policing
  141. 141 7.3.3 Visualization for Law and Order - Video 2: Visualizing Crime Over Time
  142. 142 7.3.5 Visualization for Law and Order - Video 3: A Line Plot
  143. 143 7.3.7 Visualization for Law and Order - Video 4: A Heatmap
  144. 144 7.3.9 Visualization for Law and Order - Video 5: A Geographical Hot Spot Map
  145. 145 7.3.11 Visualization for Law and Order - Video 6: A Heatmap on the United States
  146. 146 7.3.13 Visualization for Law and Order - Video 7: The Analytics Edge
  147. 147 7.4.1 Welcome to Recitation 7 - The Good, the Bad, and the Ugly in Visualization
  148. 148 7.4.2 R7. Visualization - Video 1: Introduction
  149. 149 7.4.3 R7. Visualization - Video 2: Pie Charts
  150. 150 7.4.4 R7. Visualization - Video 3: Bar Charts in R
  151. 151 7.4.5 R7. Visualization - Video 4: A Better Visualization
  152. 152 7.4.6 R7. Visualization - Video 5: World Maps in R
  153. 153 7.4.7 R7. Visualization - Video 6: Scales
  154. 154 7.4.8 R7. Visualization - Video 7: Using Line Charts Instead
  155. 155 8.1.1 Welcome to Unit 8 - Airline Revenue Management: An Introduction to Linear Optimization
  156. 156 8.2.1 An Introduction to Linear Optimization - Video 1: Introduction
  157. 157 8.2.2 An Introduction to Linear Optimization - Video 2: A Single Flight
  158. 158 8.2.4 An Introduction to Linear Optimization - Video 3: The Problem Formulation
  159. 159 8.2.6 An Introduction to Linear Optimization - Video 4: Solving the Problem
  160. 160 8.2.8 An Introduction to Linear Optimization - Video 5: Visualizing the Problem
  161. 161 8.2.10 An Introduction to Linear Optimization - Video 6: Sensitivity Analysis
  162. 162 8.2.12 An Introduction to Linear Optimization - Video 7: Connecting Flights
  163. 163 8.2.14 An Introduction to Linear Optimization - Video 8: The Edge of Revenue Management
  164. 164 8.3.1 An Application of Linear Optimization - Video 1: Introduction to Radiation Therapy
  165. 165 8.3.3 Radiation Therapy - Video 2: An Optimization Problem
  166. 166 8.3.5 Radiation Therapy - Video 3: Solving the Problem
  167. 167 8.3.7 Radiation Therapy - Video 4: A Head and Neck Case
  168. 168 8.3.9 Radiation Therapy - Video 5: Sensitivity Analysis
  169. 169 8.3.11 Radiation Therapy - Video 6: The Analytics Edge
  170. 170 8.4.1 Welcome to Recitation 8 - Google AdWords: Optimizing Online Advertising
  171. 171 8.4.2 R8. Google AdWords - Video 1: Introduction
  172. 172 8.4.3 R8. Google AdWords - Video 2: How Online Advertising Works
  173. 173 8.4.4 R8. Google AdWords - Video 3: Prices and Queries
  174. 174 8.4.5 R8. Google AdWords - Video 4: Modeling the Problem
  175. 175 8.4.6 R8. Google AdWords - Video 5: Solving the Problem
  176. 176 8.4.7 R8. Google AdWords - Video 6: A Greedy Approach
  177. 177 8.4.8 R8. Google AdWords - Video 7: Sensitivity Analysis
  178. 178 8.4.9 R8. Google AdWords - Video 8: Extensions and the Edge
  179. 179 9.1.1 Welcome to Unit 9: An Introduction to Integer Optimization
  180. 180 9.2.1 Sports Scheduling - Video 1: Introduction
  181. 181 9.2.3 Sports Scheduling - Video 2: The Optimization Problem
  182. 182 9.2.5 Sports Scheduling - Video 3: Solving the Problem
  183. 183 9.2.7 Sports Scheduling - Video 4: Logical Constraints
  184. 184 9.2.9 Sports Scheduling - Video 5: The Edge
  185. 185 9.3.1 eHarmony - Video 1: The Goal of eHarmony
  186. 186 9.3.3 eHarmony - Video 2: Using Integer Optimization
  187. 187 9.3.5 eHarmony - Video 3: Predicting Compatibility Scores
  188. 188 9.3.7 eHarmony - Video 4: The Analytics Edge
  189. 189 9.4.1 Welcome to Recitation 9 - Operating Room Scheduling: Making Hospitals Run Smoothly
  190. 190 9.4.2 R9. Operating Room Scheduling - Video 1: The Problem
  191. 191 9.4.3 R9. Operating Room Scheduling - Video 2: An Optimization Model
  192. 192 9.4.4 R9. Operating Room Scheduling - Video 3: Solving the Problem
  193. 193 9.4.5 R9. Operating Room Scheduling - Video 4: The Solution

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.