# Statistics E X A M expert 80 minutes Please note that because of the COVID-19 pandemic, the course is offered online via Zoom and not face-to face in class

expert 80 minutes

Please note that because of the COVID-19 pandemic, the course is offered online via Zoom and not face-to face
in class as is the usual custom. We understand that some students may not be able to view the Zoom sessions in
real time. Consequently, classes will be available in MyCourses under the Lecture Recordings tab. Subject to
technical issues, recordings should be available roughly an hour after the completion of the online Zoom
session. It is imperative that students stay up to date with class material and complete all class work on time..
The following schedule of classes may be modified based on day to day occurrences, but in general we will
attempt to stay close to this timetable. Basically, a “unit” corresponds to a 1.5 hours class.

Tentative Schedule

o Normal Distribution

PowerPoint 1
PowerPoint 2

Unit 1
Thurs Jan 6 2022

Overview of Course

Introduction to Estimation
o Review of Least Squares Estimation (LSE)

Course Outline

PowerPoint 3, Slides 1 -9
Unit 2 & 3
Jan 11/13

Maximum Likelihood Estimation (MLE)

Estimating parameters of a normal population using maximum
likelihood estimation – A First Example MLE

o Graphical, numerical, and analytic approaches
o Estimation of parameters of the normal distribution
o Estimation of parameters of the binomial distribution

Solved problems

PowerPoint 3, Slides 10 – 54

PowerPoint 3, Slides 55 – 62
Units 4 & 5
Jan 18/20

The Lognormal Distribution

PowerPoint 4

Unit 6 & 7
Jan 25/27

Simple Linear Regression Review PowerPoint 5

Units 8 & 9
Feb 1/3

Multiple Regression
o Regression model and assumptions
o Inferences about the β parameters
o Qualitative (dummy) variables
o Coefficients of Partial Determination

o A test for comparing Nested models
o Models with Interaction
o Log transformations

PowerPoint 6

Unit 10
Feb 8

Feb 10

Stepwise regression
Using the stepwise regression option in Minitab.

MIDTERM EXAM

PowerPoint 7

PPT 1 – 6
Units 11 & 12
Feb 15/17

All-Possible Regressions
o Using the Best Subsets Regression option in Minitab
o Evaluation of criteria for selecting the best model
o Mallow’s Cp
o The PRESS statistic
o R2Pred

PowerPoint 7

Units 13 & 14
Feb 22/24

Regression pitfalls
o Multicollinearity – detection and discussion of

associated problems
o Variance Inflation factor

PowerPoint 8

Data Transformations

Residual Analysis
o Detecting unequal variances
o Checking for normality
o Outliers and Influential observations
o Cook’s D statistic

PowerPoint 9 (Residual
Analysis 1)

PowerPoint 10 (Residual
Analysis 2)

Units 15 & 16 Residual Correlation in time series data: Durbin-Watson Test

Time Series Components
o Multiplicative decomposition model
o Analysis of trend
o Seasonal indices
o Forecasting

PowerPoint 11

Units 17 & 18 Regression with Time Series Data
o Introduction to autoregressive models

Box-Jenkins ARIMA models
o Identification of ARIMA models
o Forecasting with ARIMA models

Introduction to non-seasonal Box-Jenkins methodology: ACF
and PACF diagrams

PowerPoint 12

PowerPoint 13

Units 19 & 20 Seasonal ARIMA models

Analysis of Variance for Designed Experiments
o one factor completely randomized designs
o Confidence Intervals for Means: Completely

randomized Design

Multiple Comparison Tests
o Bonferroni Test
o Tukey Test

powerPoint 14

PowerPoint 15

PowerPoint 15
Units 21 & 22 Randomized Block Designs

Two-factor Factorial Experiments

Two-Way ANOVA with replication (interaction)

PowerPoint 15

Units 23 & 24 Checking ANOVA assumptions
Detecting Unequal Variances

o Bartlett’s Test
o Levene’s Test

PowerPoint 16

Unit 25 Review
Unit 26 Review

Last updated: Jan 5, 2022