thesis

regression model assignment

36-401, Modern Regression, Section B

Welcome to the community of statistical inquiry

This course is an introduction to the real world of statistics and data analysis. We will explore real data sets, examine various models for the data, assess the validity of their assumptions, and determine which conclusions we can make (if any). Data analysis is a bit of an art; there may be several valid approaches. We will strongly emphasize the importance of critical thinking about the data and the question of interest. Our overall goal is to use a basic set of modeling tools to explore and analyze data and to present the results in a scientific report. A minimum grade of C in any one of the pre-requisites is required. A grade of C is required to move on to 36-402 or any 36-46x course.

Prerequisites

Instructors, topics, notes, readings, course mechanics.

Grades will not go away if you avert your eyes (photo by browntj on Flicker)

Theory Exams

Data analysis projects, formats and submission of assignments, office hours, collaboration, cheating and plagiarism.

Cheating leads to desolation and ruin

Physically Disabled and Learning Disabled Students

Computational work, r resources.

Surrounded by thickets of syntax

Other Iterations of the Class

Identifying significant features from background (photo by Udoy Bhaskar Borah on Flickr)

Browse Course Material

Course info.

  • Prof. Dimitris Bertsimas

Departments

  • Sloan School of Management

As Taught In

  • Operations Management
  • Probability and Statistics

Learning Resource Types

The analytics edge, 2 linear regression.

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ContinueWelcome to Unit 2

2.1 Welcome to Unit 2

  • 2.1.1 Welcome to Unit 2

2.2 The Statistical Sommelier: An Introduction to Linear Regression

  • 2.2.1 Video 1: Predicting the Quality of Wine
  • 2.2.2 Quick Question
  • 2.2.3 Video 2: One-Variable Linear Regression
  • 2.2.4 Quick Question
  • 2.2.5 Video 3: Multiple Linear Regression
  • 2.2.6 Quick Question
  • 2.2.7 Video 4: Linear Regression in R
  • 2.2.8 Quick Question
  • 2.2.9 Video 5: Understanding the Model
  • 2.2.10 Quick Question
  • 2.2.11 Video 6: Correlation and Multicollinearity
  • 2.2.12 Quick Question
  • 2.2.13 Video 7: Making Predictions
  • 2.2.14 Quick Question
  • 2.2.15 Video 8: Comparing the Model to the Experts

2.3 Moneyball: The Power of Sports Analytics

  • 2.3.1 A Quick Introduction to Baseball
  • 2.3.2 Video 1: The Story of Moneyball
  • 2.3.3 Video 2: Making it to the Playoffs
  • 2.3.4 Quick Question
  • 2.3.5 Video 3: Predicting Runs
  • 2.3.6 Quick Question
  • 2.3.7 Video 4: Using the Models to Make Predictions
  • 2.3.8 Quick Question
  • 2.3.9 Video 5: Winning the World Series
  • 2.3.10 Quick Question
  • 2.3.11 Video 6: The Analytics Edge in Sports
  • 2.3.12 Quick Question

2.4 Playing Moneyball in the NBA (Recitation)

  • 2.4.1 Welcome to Recitation 2
  • 2.4.2 Video 1: The Data
  • 2.4.3 Video 2: Playoffs and Wins
  • 2.4.4 Video 3: Points Scored
  • 2.4.5 Video 4: Making Predictions

2.5 Assignment 2

2.5.1 Climate Change

2.5.2 Reading Test Scores

2.5.3 Detecting Flu Epidemics via Search Engine Query Data

2.5.4 State Data

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IMAGES

  1. Building a Regression Model

    regression model assignment

  2. What Is And How To Use A Multiple Regression Equation Model Example

    regression model assignment

  3. MBA 643 Multiple Regression Model Analysis

    regression model assignment

  4. Regression analysis: What it means and how to interpret the outcome

    regression model assignment

  5. Performance Metrics: Regression Model

    regression model assignment

  6. Linear Regression model sample illustration

    regression model assignment

VIDEO

  1. Linear Regression Assignment

  2. Lecture 3: Panel Data Regression Model III

  3. LINEAR REGRESSION DEK3033

  4. MULTIPLE REGRESSION MODEL ACTIVITY (FINAL)

  5. Lesson 3.7 Assessing a Regression Model

  6. Variable Selection Methods for Regression Model Building in JMP

COMMENTS

  1. 36-401, Modern Regression (2015)

    36-401, Modern Regression, Section B. Fall 2015. Section B: Tuesdays and Thursdays, 3:00--4:20, Baker Hall 136A. Here's the official description: This course is an introduction to the real world of statistics and data analysis. We will explore real data sets, examine various models for the data, assess the validity of their assumptions, and ...

  2. 2 Linear Regression

    2.2.15 Video 8: Comparing the Model to the Experts; 2.3 Moneyball: The Power of Sports Analytics. 2.3.1 A Quick Introduction to Baseball; 2.3.2 Video 1: The Story of Moneyball; 2.3.3 Video 2: Making it to the Playoffs; 2.3.4 Quick Question; 2.3.5 Video 3: Predicting Runs; 2.3.6 Quick Question; 2.3.7 Video 4: Using the Models to Make Predictions ...