Overview
Dummy Variable
Regression
Dummy variables are ones that take on either a 1 or a zero,
where 1 indicates the presence of some attribute.
Dr Tom Ilvento
Department of Food and Resource Economics
With a categorical variable with j classes or categor

Overview
Regression Inference
We will look at the main components of inference in the
regression model
t-tests for individual coefficients
Most of these ideas should seem familiar to you
Dr Tom Ilvento
Department of Food and Resource Economics
F-test for

Overview
Regression 2: the
Output
Department of Food and Resource Economics
SUMMARY OUTPUT of SALES Regressed on SALARY
Regression Statistics
Multiple R
0.700
R Square
0.489
Adjusted R Square
0.489
Standard Error
687.068
Observations
1000
Intercept
SALARY

Overview
Introduction to
Regression
The last part of the course will focus on Regression Analysis
This is one of the more powerful statistical techniques
Dr Tom Ilvento
Department of Food and Resource Economics
Provides estimates from a model
Allows for i

Overview
Chi-square Tests and
Measures of Association
This lecture will continue the discussion of Chisquares tests for table data
We will discuss some Measures of Association
relevant to table data
We will look at more complex tables
Dr Tom Ilvento
Depar

Overview
ANalysis Of VAriance II
Lets continue our discussion of the ANOVA Model
See how software displays the results
We will solve for the sum of squares for a basic model with
two means
We will look at the Basic Test for ANOVA
Dr Tom Ilvento
Department

Overview
ANalysis Of VAriance III
Next we will discuss two variations of the ANOVA model
introducing a third variable
Dr Tom Ilvento
Department of Food and Resource Economics
Two-way ANOVA
Now we have two factors that influence the response
variable
Plus

Overview
The Basics of a
Hypothesis Test
Alternative way to make inferences from a sample to
the Population is via a Hypothesis Test
A hypothesis test is based upon
Dr Tom Ilvento
Department of Food and Resource Economics
Newspaper Print Problem
(BLACKNES

What is Next?
Correlation and Regression
Correlation and Covariance
Correlation
A measure of association between two variables
Can be shown in a visual way via a scatterplot
Expressed as a linear relationship
Based on the Co-variance - how two variables v

Overview
ANalysis Of VAriance II
Lets continue our journey through the ANOVA
approach to data
Dr Tom Ilvento
Department of Food and Resource Economics
Focus on Single Factor Models
Terms for the ANOVA Table
R-square
More single factor models
Strategies fo

Overview
Confidence Intervals
for Large Sample Means
Lets continue the discussion of Confidence
Intervals (C.I.)
And I will shift to the C.I. for means
Dr Tom Ilvento
In this case, we traditionally used the standard
normal table to contruct a confidence i

Overview
Chi-square Tests and
Table Data
Dr Tom Ilvento
Next we will look at approaches when we have two
or more variables - a step further than difference of
means or proportions
We will start with contingency table data two
variables that are cross-tabu

Overview
Introduction to
ANOVA
Dr Tom Ilvento
Department of Food and Resource Economics
Our next set of lectures provides an introduction to ANOVA
and Experimental Design
This is a direct extension of the difference of means test we
focused on earlier - A

Overview
p-values, Type I and
Type II Error
Dr Tom Ilvento
Department of Food and Resource Economics
This lecture will focus on
p-values as an alternative to the Critical Value
and a Rejection Region
Type I Error, referred to as !
Type II Error, referred

Overview
Examples of Hypothesis
Testing
Dr Tom Ilvento
Department of Food and Resource Economics
Lets continue with some examples of hypothesis
tests
introduce computer output
See what happens with an outlier
compare hypothesis test to confidence interval

Overview
Confidence Intervals for
Large Sample Proportions
Confidence Intervals C.I.
We will start with large sample C.I. for proportions,
using the normal approximation of the binomial
distribution
Dr Tom Ilvento
and p or q is not too extreme
New terms: