1/15/2013
Categorical Variable
Unit 1 Section 1
Response falls into one or more
categories.
Categorical Data
Examples
Eye Color
Stage of Colon Cancer
Blue
Brown
Green/Hazel
Examples
Examples
I
II
III
5/5/2013
Variables
Unit 2 Section 6
Categorical Variable
categories
Poisson Regression Analysis of
Multi-Dimensional Contingency
Tables
Data
Random sample of size from
population.
Cross-classify based
4/3/2013
Space Transportation System (STS)
Unit 2 Section 2
Better known as the Space Shuttle.
NASAs manned spacecraft.
135 missions from 1981 to 2011.
The Special Case of the
Space Shuttle Challenger
Technology Guide: Unit 2: Sections 1 - 3
Stat 457
Spring 2013
Below is an explanation of the R commands and functions needed to analysis and fit simple and
multiple logistic regression models. Before
Review for the Final Exam
SOC / CCJ 3020
The final exam consists of 40 multiple choice questions and covers material from the first day of
class. As in previous tests, there will be a mix of conceptua
1/10/2016
Random Variables
Unit 1 Section 2
Variable whose value is determined
based on random event.
Bernoulli, Binomial and
Multinomial Random Variables
Random Variables
Two Types
Discrete finite or
1/10/2016
Categorical Variable
Unit 1 Section 1
Value for variable falls into one or more
categories.
Numerical and Graphical
Summaries of Categorical
Variables
Examples
Eye Color
Blue
Brown
Green
Haz
STATISTICS 457
Technology Guide
Unit 1 - Section 1
Below is an explanation of the R commands and functions needed to analyze categorical variables. Before
starting your data analysis, you should chang
STATISTICS 457
Technology Guide
Unit 1 - Section 2
Below is an explanation of the R commands and functions needed to work with bernoulli, binomial, and
multinomial random variables in R.
Bernoulli Di
1/27/2016
Outline
Unit 1 Section 5
Confidence Intervals for a
Population Proportion
Binomial Random Variables
Random event with 2 outcomes
2 Outcomes = Success and Failure
Success = Category of Intere
1/20/2016
Outline
Unit 1 Section 4
Hypothesis Test for
a Population Proportion
Binomial Random Variables
Random event with 2 outcomes
2 Outcomes = Success and Failure
Success = Category of Interest
Fa
1/20/2016
Binomial Random Variable
Unit 1 Section 3
Random event with 2 outcomes
2 Outcomes = Success and Failure
Sampling Distribution for a
Sample Proportion
Binomial Random Variable
number of succe
2/3/2016
Multinomial Random Variables
Random event with outcomes
Probability of each Outcome =
Unit 1 Section 6
1
Goodness of Fit Test for
One Categorical Variable
Multinomial Random Variables
number
STATISTICS 457
Technology Guide
Unit 1 - Section 3
Below is an explanation of the R commands and functions needed to investigate the sampling distribution
of the sample proportion p.
^
For larger samp
5/5/2013
Data
Models for Matched Data
Two Related Questions
Same Categorical Responses (Yes, No)
Same Respondents for Both Questions
McNemars Test
Data
Probabilities
Q2 = Yes
Q2 = No
Total
Probability
5/5/2013
Variables
Unit 2 Section 5
Data
categories
Categorical Variable
Poisson Regression Analysis of
Two-Dimensional Contingency Tables
Categorical Variable
categories
Data Example 3x4 Table
Rando
1/22/2013
Categorical Variable
Unit 1 Section 4A
Two Categories
Inference for a Population
Proportion - Hypothesis Tests
Inference for
is a specific value or
*Hypothesis Test for .
If
10 and
1
,
10:
1/24/2013
Categorical Variable
Unit 1 Section 4B
Two Categories
Inference for a Population
Proportion Confidence Intervals
Inference for
Determine if
population proportion in category
of interest
Data
1/16/2013
Categorical Variable
Unit 1 Section 5
Categorical Variable has categories.
= population proportion of category .
1.
Goodness of Fit Test for
One Categorical Variable
Model for Categorical V
1/15/2013
Categorical Variables
Unit #1 Section 2
Variable with categories as values.
Descriptive Statistics for One
Categorical Variable
Numerical Summaries
Gender (Male, Female)
Marital Status (Marr
Whatisyourgender?
67%
BabyPicturesPartI
One of the babies pictured
at right is the daughter of
the mother pictured below.
1.
33%
2.
Male
Female
Whichbabyisthedaughterofthemotherpictured?
37%
26%
32%
5
2/11/2013
Variables
Unit 1 Section 7b
Response Variable
Categorical with 2 categories.
Testing the Equality of Multiple
Population Proportions
Explanatory Variable (Grouping)
Two Cases
Population Prop
1/17/2013
Categorical Variable
Unit 1 Section 3
Two Categories
Category of Interest (Success)
Everything else (Failure)
Models for a Categorical Variable
Population
Data
proportion of population in
ca
1/31/2013
Variables
Unit 1 Section 6
Cross-classify data according to
categories for response and explanatory
variables.
number of observations in the th
category of explanatory variable and th
catego
2/11/2013
Variables
Unit 1 Section 9
Random sample size from population.
Gather information about response and
explanatory variables.
Cross-classify data according to
categories of response and explan
2/11/2013
Variables
Unit 1 Section 7a
Response Variable
Categorical with 2 categories.
Testing the Equality of Two
Population Proportions
Explanatory Variable (Grouping)
Two Cases
Population Proportio
2/11/2013
Variables
Unit 1 Section 8
Response Variable
2 categories
Relative Risk and the Odds Ratio
Explanatory Variable (Grouping)
Population Proportions
= population proportion in the
category of i
2/20/2013
Measures of Association
Unit 1 Section 10
Measures of Association
Correlation Coefficient
Population Proportions
Response Variable
Correlation Coefficient
Coefficient
Cramers V
Goodman-Krusk
4/3/2013
Format
Unit 2 Section 1
Response Variable
2 Categories
Simple Logistic Regression
Explanatory Variable
Motivating Example
Does the temperature at incubation
affect the sex of turtles? Turtle