PPD 404
Mark Velez Office- RGL 107a Office Hours: Thur. 5-6 pm Class Web site: https:/blackboard.usc.edu/ E-mail: mvelez@pvestates.org
Textbooks
Sirkin, R. Mark. 1999. Statistics for the Social Sciences. Second Edition. Thousand Oaks, CA: Sage. (Pa

Analysis of Covariance
ANOVA is a class of statistics developed to evaluate controlled experiments. Experimental control, random selection of subjects, and random assignment of subjects to subgroups are devices to control or hold constant all the ot

Analysis of Covariance
ANOVA is a class of statistics developed to evaluate controlled experiments. Experimental control, random selection of subjects, and random assignment of subjects to subgroups are devices to control or hold constant all the ot

Linear Regression with Two Variables
We return to the unfinished task of association between two continuous variables. The key to building measures of association for continuous variables is the properties of this type of variable. (1) They have equ

The Pearson Product-Moment Correlation Coefficient
The regression coefficient is an asymmetrical statistic, one that gives different values for the model Y = f(X) and the model X = f(Y). The other major measure of bivariate association is the Pearso

Significance Tests for Regression Models and Their Coefficients
A. Testing the Significance of the Regression Coefficient The null hypothesis in the significance test for the regression coefficient (i.e., slope) is: H0 : = 0.0
This simple symbolic

Multiple Contingency-Table Analysis
A. Philosophical Introduction
We are now in position to begin dealing with cause and effect, that is, causality. Let's take a look at what we are saying and what we are NOT saying when we describe something as th

Introduction to Multiple Regression Analysis
You will recall that the general linear model used in least squares regression is: Yi = + bXi +
I
where b is the regression coefficient describing the average change in Y per one unit increase (or decrea

Assumptions Underlying Multiple Regression Analysis
Multiple regression analysis requires meeting several assumptions. We will:
(1) identify some of these assumptions;
(2) describe how to tell if they have been met; and (3) suggest how to overcome

Assumptions Underlying Multiple Regression Analysis
Multiple regression analysis requires meeting several assumptions. We will:
(1) identify some of these assumptions;
(2) describe how to tell if they have been met; and (3) suggest how to overcome

University of Southern California School of Policy, Planning, and Development PPD 404 Instructor: Mark Velez Email: mvelez@pvestates.org Name: _ Email: _
First Excel Assignment Part I. Using the data set (Excel spreadsheet) nations.xls, write formul

University of Southern California School of Policy, Planning, and Development PPD 404 Instructor: Mark Velez Email: mvelez@pvestaes.org Name: _ Aaron Dan Email: aarondan@usc.edu
Second Excel Assignment Using the Excel spreadsheet Nations.xls, write

Aaron Dan PPD 404 Prof. Velez
Research Project: Fast Food
From The Official Journal of the American Academy of Pediatrics "Effects of Fast-Food Consumption on Energy Intake and Diet Quality Among Children in a National Household Survey" Shanthy A.

One-Way Analysis of Variance
Recapitulation
1. Comparing differences among three or more subsamples requires a different statistical test than either z-tests or t-tests. 2. The solution is to perform an analysis of variance (ANOVA). 3. ANOVA involve

Testing Hypotheses about Differences among Several Means
The t-test compares independent means TWO AT A TIME. Whenever we encounter a research situation in which we need to compare THREE or MORE independent-sample means, we would have to make multip

The Distribution of Single Variables
Two Types of Variables
Continuous variables
Equal intervals of measurement Known zero-point that is meaningful
Discrete variables
Simply counts of attributes Generate category frequencies
For continuous v

Measures of Central Tendency
For Discrete Variables
These are variables having categories: a. which are mutually exclusive; b. and exhaustive. Identify the category having the greatest frequency. This is known as the modal category (after another st

Two statistical tasks
Description Central Tendency Variability Association Inference
Measures of Variability
Discrete Variables Index of Qualitative Variation Continuous Variables Range Interquartile Range Variance Standard Deviation
For Discre

Crosstabulation
Describing Association To what extent are two variables, X and Y, related? We begin with the concept of statistical independence
Room Temperature
72
Mean Exam Score
75
72 72
98 85
Room Temperature
72
Mean Exam Score
75
85 98

Describing Association for Discrete Variables
Discrete variables can have one of two different qualities: 1. ordered categories 2. non-ordered categories
1. Ordered categories e.g., "High," "Medium," and "Low" 2. Non-ordered categories e.g., "Yes"

The Chi-Square Distributions (Statistical Inference)
Two Statistical Tasks 1. Description 2. Inference
Thus far, we have completed: 1. Descriptive Statistics
a. Central tendency
i. discrete variables ii. continuous variables
b. Variation
i. discre

The Central Limit Theorem and the Normal Distribution
Recapitulation from Last Time
1. Statistical inference involves generalizing from a sample to a (statistical) universe. 2. Statistical inference is only possible with random samples. 3. Statistic

Estimation
Let's return to our example of the random sample of 200 USC undergraduates. Remember that this is both a large and a random sample, and therefore the Central Limit Theorem applies to any statistic that we calculate from it. We ask these 2

Hypothesis Testing (Statistical Significance Testing)
Two Points to Emphasize:
1. Hypothesis testing ALWAYS involves a null hypothesis (H0) whether one is explicitly stated or not. 2. The significance level (i.e., -level) is chosen BEFORE the sample

Testing Hypotheses about the Difference between Two Means
Random samples, partitioned into two independent subsamples (e.g., men and women). Question: Are the means of some variable (such as salary) significantly different between the two subsamples

The t-test: Independent and Paired (Dependent) Samples
Recapitulation
1. Still dealing with random samples. 2. However, they are partitioned into two subsamples. 3. Interest is in whether the means of some variable differ significantly between the t

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