95-796, Statistics for IT Managers

Course Description

This introductory course in data analysis and statistical inference requires no background in statistics. Its objective is to provide individuals who aspire to enter IT management positions with the basic statistical tools for analyzing and interpreting data. The course is divided into three distinct modules: descriptive statistics, statistical inference, and regression analysis. The emphasis of the classes on descriptive statistics is the calculation and interpretation of summary statistical measures for describing raw data. The sessions on statistical inference are designed to provide you with the background for executing and interpreting hypothesis tests and confidence intervals. The final component of the course focuses on regression analysis, a widely used statistical methodology. Throughout the course you will regularly analyze data relevant to IT management using the statistical software package Minitab.

Textbook

Statistics for Business and Economics, 10th Edition
(McClave, Benson, and Sincich)

Lecture slides

Module I: Descriptive Statistics and Probability (pdf)
Module II: Hypothesis Testing and Inference (pdf)
Module III: Regression (pdf)

Grading

Three homework assignments: 30% (10% each)
Two mini-projects: 30% (15% each)
Final exam: 40%

Sample syllabus

Lecture 1: Descriptive statistics
Course overview
What is statistics (1.1-1.3)
Types of data (1.5)
Random sampling (1.6)
Histograms (2.2)
Measures of central tendency (2.4)
Measures of variability (2.5-2.6)
Box plots (2.8)
Using Minitab for descriptive statistics

Lecture 2: Probability
Basic probability (3.1-3.4)
Conditional probability (3.5-3.6)
Bayes' Theorem (3.8)

Lecture 3: Random variables
Discrete random variables (4.1-4.3)
Continuous random variables (4.5)
The Uniform distribution (4.6)

Lecture 4: Normal distributions and sampling
The Normal distribution (4.7-4.8)
Sampling distributions (4.10)
Central Limit Theorem (4.11)
Using Minitab for random variables and sampling

Lecture 5: Confidence intervals
Large-sample confidence intervals for the mean (5.2)
Small-sample confidence intervals for the mean (5.3)
Large-sample confidence intervals for the population proportion (5.4)
Determining the sample size (5.5)
Using Minitab for confidence intervals

Lecture 6: Hypothesis testing
Introduction to hypothesis testing (6.1)
Large-sample hypothesis tests for the mean (6.2)
Small-sample hypothesis tests for the mean (6.4)
Large-sample hypothesis tests for the population proportion (6.5)

Lecture 7: More hypothesis testing
Type I and Type II errors (6.1)
p-values (6.3)
Using Minitab for 1-sample hypothesis testing

Lecture 8: Comparing two populations
Comparing two population means (7.2)
Comparing two population proportions (7.4)
Using Minitab for 2-sample hypothesis testing

Lecture 9: Comparing two populations, continued
Comparing two population means using paired differences (7.3)
Using Minitab for paired differences

Lecture 10: Simple regression
Linear models (10.1, 10.3)
Least squares linear regression (10.2)
Estimating the error of the model (10.4)
Making inferences using the model (10.5)

Lecture 11: More simple regression
Coefficients of correlation and determination (10.6-10.7)
Using the model for estimation and prediction (10.8)
Using Minitab for simple regression

Lecture 12: Multiple regression
Multivariate linear models (11.1)
Least squares linear regression (11.2)
Estimating the error of the model (11.2)
Multiple coefficient of determination (11.3)
Making inferences using the model (11.3)

Lecture 13: More multiple regression
Using the model for estimation and prediction (11.4)
Advanced topics in model building (11.5-11.7)
Using Minitab for multiple regression

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