Week

Date

Topic

Reading

Note

1

2/20

Course Introduction

Data: Type, Source, Applications, analysis

1. Traditional Data

• Elements, variables, and observations


• Data type


• Cross sectional and time series data


• Data sources

2. Descriptive Statistics: Single variable

• Frequency distributions


• Bar-graphs, pie-charts, histogram, etc.


• Measures of central tendency


• Measure of dispersion

Chapters 1-3

Lab session

2

2/27

3. Descriptive Statistics: Two variables

• Scatter plot


• Covariance and correlation


• Crosstabulation and scatter plots

Probability Distributions

• Normal distribution


• Standard normal distribution


• t-distribution


• c² distribution


• F distribution

Chapters 1-3

Chapter 6: sections 6.2

3

3/5

Statistical Inference: Hypothesis Testing

• Basic concepts and procedure


• Types I and II errors


• Sampling distribution


• Single population/process hypothesis testing


• p-value

Chapter 8: sections 8.2, 8.3, 8.4

Chapter 9:

sections 9.4, 9.5

4

3/12

Statistical Inference: Estimation

• Sampling methods


• Point and interval estimation


• Sample size selection

Chapter 7: sections 7.2, 7.3, 7.4, 7.5, 7.6, 7.8

5

3/19

Comparisons of Multiple Populations: Two Populations

• Paired samples


• Independent samples

Chapter 10:

Sections 10.2, 10.3, 10.4

6

3/26

Comparisons of Multiple Populations: ANOVA

• Partition of the sum of squares


• One-way analysis of variance


• Multiple comparisons

Chapter 13: sections 13.1, 13.2, 13.3, 13.5

4/2

Holiday

7

4/9

Predictive modeling: Regression

• Basic concept


• Least-squares estimation


• R²


• Hypothesis testing


• Residual analysis


• Partition of the sum of squares


• Partial F-test and t-test


• Indicator (Dummy) variables


• Build a regression model

Chapters 14, 15, 16

8

4/16

Regression Analysis (continued)

Predictive modeling: Classification

• Logistic regression


• Classification tree

Chapter 13: sections 13.1, 13.2, Handouts

9

4/23

Classification (continued)

Handouts

10

4/30

Mid-term Exam

11

5/7

Process and bottleneck analysis

• Overview of a process


• Throughput and bottleneck


• Resource capacity and Process capacity


• Inventory and waiting time


• Face game

Reference:

Chapters 3-4

12

5/14

Process and bottleneck analysis

• Capacity planning


• Analysis of Kristen Cookie


• Little’s Law


• M/M/1 queue


• M/M/N queue

Reference:

Chapters 5

Case assignment: Kristen Cookie

13

5/21

Linear Programming

• Problem formation


• Excel solver


• Sensitivity analysis


• Scheduling


• Portfolio analysis

14

5/28

Statistical process control

• Introduction to quality


• Control chart concepts


• chart


• R chart


• p and np chart


• Lean operations

Chapter 19: 19.1, 19.2

15

6/4

Epilogue and Final Exam