# Data Analysis: Analyzing Data - Inferential Statistics

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 WHAT IT IS Return to Table of ontents WHEN TO USE IT Inferential statistics deal with drawing conclusions and, in some cases, making predictions about the properties of a population based on information obtained from a sample. While descriptive statistics provide information about the central tendency, dispersion, skew, and kurtosis of data, inferential statistics allow making broader statements about the relationships between data. Inferential statistics are frequently used to answer cause-and-effect questions and make predictions. They are also used to investigate differences between and among groups. However, one must understand that inferential statistics by themselves do not prove causality. Such proof is always a function of a given theory, and it is vital that such theory be clearly stated prior to using inferential statistics. Otherwise, their use is little more than a fishing expedition. For example, suppose that statistical methods suggest that on average, men are paid significantly more than women for full-time work. Several competing explanations may exist for this discrepancy. Inferential statistics can provide evidence to prove one theory more accurate than the other. However, any ultimate conclusions about actual causality must come from a theory supported by both the data and sound logic. HOW TO PREPARE IT The following briefly introduces some common techniques of inferential statistics and is intended as a guide for determining when certain techniques may be appropriate. The techniques used generally depend on the kinds of variables involved, i.e. nominal, ordinal, or interval. For further information on and/or assistance with a given technique, refer to the books and in-house support listed in the Resources section at the end of this module. hi-square (P ) tests are used to identify differences between groups when all variables nominal, e.g., gender, ethnicity, salary group, political party affiliation, and so forth. Such tests are normally used with contingency tables which group observations based on common characteristics. For example, suppose one wants to determine if political party affiliation differs among ethnic groups. A contingency table which divides the sample into political parties and ethnic groups could be produced. A P test would tell if the ethnic distribution of the sample indicates differences in party affiliation. As a rule, each cell in a contingency table should have a least five observations. In those cases where this is not possible, the Fisher s Exact Test should replace the P test. Data analysis software will usually warn the user when a cell contains fewer than five observations. Analysis of variance (ANOVA) permits comparison of two or more populations when interval variables are used. ANOVA does this by comparing the dispersion of samples in order to make inferences about their means. ANOVA seeks to answer two basic questions: Texas State Auditor's Office, Methodology Manual, rev. 5/95-1

2 Accountability Modules Are the means of variables of interest different in different populations? Are the differences in the mean values statistically significant? For example, during the Welfare Reform audit, SAO staff wanted to test whether the incomes of job training program participants and nonparticipants were significantly different. ANOVA was used to do this. Analysis of covariance (AOVA) examines whether or not interval variables move together in ways that are independent of their mean values. Ideally, variables should move independently of one another, regardless of their means. Unfortunately, in the real world, groups of observations usually differ on a number of dimensions, making simple analyses of variance tests problematic since differences in other characteristics could cause observed differences in the values of the variables of interest. For example, suppose auditors/evaluators are comparing the income of job program participants and non-participants. Assuming job program participants were found to have higher incomes, could one conclude the explanation was their participation in the program? Although this may be the case, one cannot yet draw that conclusion. Other distinguishing characteristics of program participants might explain higher income. In order to control for the effects of those other variables, an analysis of covariance is necessary. During the Welfare Reform audit noted above, SAO staff found that the incomes of job training program participants and non-participants differed by gender and by target group classification (federal classification for welfare intervention purposes). An AOVA was done to determine if the wages of program participants and nonparticipants were still significantly different, after controlling for differences in gender and target group classification. Thus, here gender and target group were considered the covariates. orrelation (D), like AOVA, is used to measure the similarity in the changes of values of interval variables but is not influenced by the units of measure. Another advantage of correlation is that it is always bounded by the interval: -1 # D # 1 Here -1 indicates a perfect inverse linear relationship, i.e. y increases uniformly as x decreases, and 1 indicates a perfect direct linear relationship, i.e. x and y move uniformly together. A value of 0 indicates no relationship. Note that correlation can determine that a - Texas State Auditor's Office, Methodology Manual, rev. 5/95

3 relationship exists between variables but says nothing about the cause or directional effect. For example, a known correlation exists between muggings and ice cream sales. However, one does not cause the other. Rather, a third variable, the warm weather which puts more people on the street both to mug and buy ice cream is a more direct cause of the correlation. As a rule, it is wise to examine the correlations between all variables in a data set. This both warns auditors/evaluators about possible covariations and suggests areas for possible follow-up investigation. Regression analysis is often used to determine the effect of independent variables on a dependent variable. Regression measures the relative impact of each independent variable and is useful in forecasting. It is used most appropriately when both the independent and dependent variables are interval, though some social scientists also use regression on ordinal data. Like correlation, regression analysis assumes that the relationship between variables is linear. Regression analysis permits including multiple independent variables. For example, during the Welfare Reform audit, SAO staff wanted to predict the percentage of JOBS program clients in a given county who had gotten a job. The poverty rate and population density of the county were used as independent variables. These two variables enabled the auditors to predict almost perfectly the percentage of people who got a job. Logistic regression analysis is used to examine relationships between variables when the dependent variable is nominal, even though independent variables are nominal, ordinal, interval, or some mixture thereof. Suppose that one wanted to determine which program interventions were associated with a JOBS Program client's ability to get a job within six months of exiting the program. The outcome variable would be "job" or "no job, clearly a nominal variable. One could then use several independent variables such as GED completion, job training, post-secondary education and the like to predict the odds of getting a job. Such a method was applied to the JOBS Program audit. Discriminant analysis is similar to logistic regression in that the outcome variable is categorical. However, here the independent variables must be interval. In the audit of the Probation System, SAO staff explored how well a probationer's rating on drug abuse severity, social adjustment, and similar characteristics predicted whether or not the probationer committed another crime using continuous ratings. The predictive power of these ratings was just slightly better than chance. Of special interest was the percentage of times the model predicted the Texas State Auditor's Office, Methodology Manual, rev. 5/95-3

4 Accountability Modules probationer would not commit another crime when he or she actually did. Since that percentage was quite high, the model was a poor one. Factor analysis simultaneously examines multiple variables to determine if they reflect larger underlying dimensions. Factor analysis is commonly used when analyzing data from multi-question surveys to reduce the numerous questions to a smaller set of more global issues. For example, during the Department of Information Resources (DIR) audit, SAO staff administered a survey to agencies that used DIR services. The survey had approximately 30 questions, each of which contained three sub-parts, for a total of 90 objective questions. Reporting the responses to all 90 questions was time-consuming and risked losing continuity. Through factor analysis, the audit team found that the survey could be reduced to five central underlying questions. Thus, in many ways, factor analysis is analogous to the process of "rolling up" audit issues. Forecasting exists in many variations. The predictive power of regression analysis can be an effective forecasting tool, but time series forecasting is more common when time is a significant independent variable. Time series forecasting is based on four components: trends, as indicated by a relatively smooth pattern in the data over a long period of time, e.g., population increase cyclical effects, as indicated by a wave-like pattern in the data moving above and below a trend line of over one year in duration, e.g., recessions seasonal effects, as indicated by changes similar to cyclical effects but over a period of less than one year, e.g., expenditures for sand to apply to roads in icy conditions random variations, as indicated by any change in the data not attributable to any other component. Time series forecasting models can be expressed as an additive or multiplicative function of these four components. Once measurements of the four components exist, they can be fit to a regression line to predict future values. ADVANTAGES Inferential statistics can: provide more detailed information than descriptive statistics yield insight into relationships between variables reveal causes and effects and make predictions generate convincing support for a given theory be generally accepted due to widespread use in business and academia - 4 Texas State Auditor's Office, Methodology Manual, rev. 5/95

5 DISADVANTAGES Inferential statistics can: be quite difficult to learn and use properly be vulnerable to misuse and abuse depend more on sound theory than on implications of a data set Texas State Auditor's Office, Methodology Manual, rev. 5/95-5

### Data Analysis: Describing Data - Descriptive Statistics

WHAT IT IS Return to Table of ontents Descriptive statistics include the numbers, tables, charts, and graphs used to describe, organize, summarize, and present raw data. Descriptive statistics are most

### When to Use a Particular Statistical Test

When to Use a Particular Statistical Test Central Tendency Univariate Descriptive Mode the most commonly occurring value 6 people with ages 21, 22, 21, 23, 19, 21 - mode = 21 Median the center value the

### Statistics Review PSY379

Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses

### Module 9: Nonparametric Tests. The Applied Research Center

Module 9: Nonparametric Tests The Applied Research Center Module 9 Overview } Nonparametric Tests } Parametric vs. Nonparametric Tests } Restrictions of Nonparametric Tests } One-Sample Chi-Square Test

### Washoe County Senior Services 2013 Survey Data: Service User

Washoe County Senior Services 2013 Survey Data: Service User Profile Prepared by Zebbedia G. Gibb & Peter Reed UNR Sanford Center for Aging (Jan 2014) Overall Summary: Income was the single best predictor

EC151.02 Statistics for Business and Economics (MWF 8:00-8:50) Instructor: Chiu Yu Ko Office: 462D, 21 Campenalla Way Phone: 2-6093 Email: kocb@bc.edu Office Hours: by appointment Description This course

### II. DISTRIBUTIONS distribution normal distribution. standard scores

Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,

### Inferential Statistics

Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are

### Recall this chart that showed how most of our course would be organized:

Chapter 4 One-Way ANOVA Recall this chart that showed how most of our course would be organized: Explanatory Variable(s) Response Variable Methods Categorical Categorical Contingency Tables Categorical

### Variables and Data A variable contains data about anything we measure. For example; age or gender of the participants or their score on a test.

The Analysis of Research Data The design of any project will determine what sort of statistical tests you should perform on your data and how successful the data analysis will be. For example if you decide

### Introduction to Statistics and Quantitative Research Methods

Introduction to Statistics and Quantitative Research Methods Purpose of Presentation To aid in the understanding of basic statistics, including terminology, common terms, and common statistical methods.

### Data Analysis: Displaying Data - Graphs

Accountability Modules WHAT IT IS Return to Table of Contents WHEN TO USE IT TYPES OF GRAPHS Bar Graphs Data Analysis: Displaying Data - Graphs Graphs are pictorial representations of the relationships

### Executive Summary. 1. What is the temporal relationship between problem gambling and other co-occurring disorders?

Executive Summary The issue of ascertaining the temporal relationship between problem gambling and cooccurring disorders is an important one. By understanding the connection between problem gambling and

### Introduction to Longitudinal Data Analysis

Introduction to Longitudinal Data Analysis Longitudinal Data Analysis Workshop Section 1 University of Georgia: Institute for Interdisciplinary Research in Education and Human Development Section 1: Introduction

### Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts

### Chi Square Tests. Chapter 10. 10.1 Introduction

Contents 10 Chi Square Tests 703 10.1 Introduction............................ 703 10.2 The Chi Square Distribution.................. 704 10.3 Goodness of Fit Test....................... 709 10.4 Chi Square

### Module 3: Correlation and Covariance

Using Statistical Data to Make Decisions Module 3: Correlation and Covariance Tom Ilvento Dr. Mugdim Pašiƒ University of Delaware Sarajevo Graduate School of Business O ften our interest in data analysis

### Some Critical Information about SOME Statistical Tests and Measures of Correlation/Association

Some Critical Information about SOME Statistical Tests and Measures of Correlation/Association This information is adapted from and draws heavily on: Sheskin, David J. 2000. Handbook of Parametric and

### Simple Linear Regression Inference

Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation

### CHAPTER 15 NOMINAL MEASURES OF CORRELATION: PHI, THE CONTINGENCY COEFFICIENT, AND CRAMER'S V

CHAPTER 15 NOMINAL MEASURES OF CORRELATION: PHI, THE CONTINGENCY COEFFICIENT, AND CRAMER'S V Chapters 13 and 14 introduced and explained the use of a set of statistical tools that researchers use to measure

### Introduction to Statistics

1 Introduction to Statistics LEARNING OBJECTIVES After reading this chapter, you should be able to: 1. Distinguish between descriptive and inferential statistics. 2. Explain how samples and populations,

### Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus

Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives

### Biostatistics: Types of Data Analysis

Biostatistics: Types of Data Analysis Theresa A Scott, MS Vanderbilt University Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott Theresa A Scott, MS

### A Primer on Forecasting Business Performance

A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.

### 430 Statistics and Financial Mathematics for Business

Prescription: 430 Statistics and Financial Mathematics for Business Elective prescription Level 4 Credit 20 Version 2 Aim Students will be able to summarise, analyse, interpret and present data, make predictions

### Discussion of State Appropriation to Eastern Michigan University August 2013

Discussion of State Appropriation to Eastern Michigan University August 2013 Starting in 2012-13, the Michigan Legislature decided to condition a portion of the state appropriation on how well the 15 public

### Class 19: Two Way Tables, Conditional Distributions, Chi-Square (Text: Sections 2.5; 9.1)

Spring 204 Class 9: Two Way Tables, Conditional Distributions, Chi-Square (Text: Sections 2.5; 9.) Big Picture: More than Two Samples In Chapter 7: We looked at quantitative variables and compared the

### Association Between Variables

Contents 11 Association Between Variables 767 11.1 Introduction............................ 767 11.1.1 Measure of Association................. 768 11.1.2 Chapter Summary.................... 769 11.2 Chi

### Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com

SPSS-SA Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com SPSS-SA Training Brochure 2009 TABLE OF CONTENTS 1 SPSS TRAINING COURSES FOCUSING

### Inferential Statistics. What are they? When would you use them?

Inferential Statistics What are they? When would you use them? What are inferential statistics? Why learn about inferential statistics? Why use inferential statistics? When are inferential statistics utilized?

### Research Variables. Measurement. Scales of Measurement. Chapter 4: Data & the Nature of Measurement

Chapter 4: Data & the Nature of Graziano, Raulin. Research Methods, a Process of Inquiry Presented by Dustin Adams Research Variables Variable Any characteristic that can take more than one form or value.

There are three kinds of people in the world those who are good at math and those who are not. PSY 511: Advanced Statistics for Psychological and Behavioral Research 1 Positive Views The record of a month

### Quantitative Research Methods II. Vera E. Troeger Office: Office Hours: by appointment

Quantitative Research Methods II Vera E. Troeger Office: 0.67 E-mail: v.e.troeger@warwick.ac.uk Office Hours: by appointment Quantitative Data Analysis Descriptive statistics: description of central variables

### IPDET Module 6: Descriptive, Normative, and Impact Evaluation Designs

IPDET Module 6: Descriptive, Normative, and Impact Evaluation Designs Intervention or Policy Evaluation Questions Design Questions Elements Types Key Points Introduction What Is Evaluation Design? Connecting

### Logic Models, Human Service Programs, and Performance Measurement

Three Logic Models, Human Service Programs, and Performance Measurement Introduction Although the literature on ment has been around for over two decades now, scholars and practitioners still continue

### Business Analytics. Methods, Models, and Decisions. James R. Evans : University of Cincinnati PEARSON

Business Analytics Methods, Models, and Decisions James R. Evans : University of Cincinnati PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London

### UNDERSTANDING THE TWO-WAY ANOVA

UNDERSTANDING THE e have seen how the one-way ANOVA can be used to compare two or more sample means in studies involving a single independent variable. This can be extended to two independent variables

### Week 1. Exploratory Data Analysis

Week 1 Exploratory Data Analysis Practicalities This course ST903 has students from both the MSc in Financial Mathematics and the MSc in Statistics. Two lectures and one seminar/tutorial per week. Exam

### International Statistical Institute, 56th Session, 2007: Phil Everson

Teaching Regression using American Football Scores Everson, Phil Swarthmore College Department of Mathematics and Statistics 5 College Avenue Swarthmore, PA198, USA E-mail: peverso1@swarthmore.edu 1. Introduction

### Statistics, Research, & SPSS: The Basics

Statistics, Research, & SPSS: The Basics SPSS (Statistical Package for the Social Sciences) is a software program that makes the calculation and presentation of statistics relatively easy. It is an incredibly

### Lecture 1: Review and Exploratory Data Analysis (EDA)

Lecture 1: Review and Exploratory Data Analysis (EDA) Sandy Eckel seckel@jhsph.edu Department of Biostatistics, The Johns Hopkins University, Baltimore USA 21 April 2008 1 / 40 Course Information I Course

### ASC 076 INTRODUCTION TO SOCIAL AND CRIMINAL PSYCHOLOGY

DIPLOMA IN CRIME MANAGEMENT AND PREVENTION COURSES DESCRIPTION ASC 075 INTRODUCTION TO SOCIOLOGY AND ANTHROPOLOGY Defining Sociology and Anthropology, Emergence of Sociology, subject matter and subdisciplines.

### Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

### Parametric and Nonparametric: Demystifying the Terms

Parametric and Nonparametric: Demystifying the Terms By Tanya Hoskin, a statistician in the Mayo Clinic Department of Health Sciences Research who provides consultations through the Mayo Clinic CTSA BERD

### Statistics and research

Statistics and research Usaneya Perngparn Chitlada Areesantichai Drug Dependence Research Center (WHOCC for Research and Training in Drug Dependence) College of Public Health Sciences Chulolongkorn University,

### BNG 202 Biomechanics Lab. Descriptive statistics and probability distributions I

BNG 202 Biomechanics Lab Descriptive statistics and probability distributions I Overview The overall goal of this short course in statistics is to provide an introduction to descriptive and inferential

### Exploring Relationships using SPSS inferential statistics (Part II) Dwayne Devonish

Exploring Relationships using SPSS inferential statistics (Part II) Dwayne Devonish Reminder: Types of Variables Categorical Variables Based on qualitative type variables. Gender, Ethnicity, religious

### Data Analysis Tools. Tools for Summarizing Data

Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool

### Analyzing Research Data Using Excel

Analyzing Research Data Using Excel Fraser Health Authority, 2012 The Fraser Health Authority ( FH ) authorizes the use, reproduction and/or modification of this publication for purposes other than commercial

### 16 : Demand Forecasting

16 : Demand Forecasting 1 Session Outline Demand Forecasting Subjective methods can be used only when past data is not available. When past data is available, it is advisable that firms should use statistical

### 10- # Chapter 10 Objectives. Standard costs & the Variance Analysis Cycle. A General Model for Variance Analysis. Quantity Variance.

Chapter 10 Objectives 10-1 1. Understand the difference between ideal & practical standards (You)( 2. Calculate variances for direct labour, direct materials and variable overhead (Us)( 3. Calculate mix

### Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo

Readings: Ha and Ha Textbook - Chapters 1 8 Appendix D & E (online) Plous - Chapters 10, 11, 12 and 14 Chapter 10: The Representativeness Heuristic Chapter 11: The Availability Heuristic Chapter 12: Probability

### Descriptive Statistics

Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

### Data, Measurements, Features

Data, Measurements, Features Middle East Technical University Dep. of Computer Engineering 2009 compiled by V. Atalay What do you think of when someone says Data? We might abstract the idea that data are

### Credit Risk Models. August 24 26, 2010

Credit Risk Models August 24 26, 2010 AGENDA 1 st Case Study : Credit Rating Model Borrowers and Factoring (Accounts Receivable Financing) pages 3 10 2 nd Case Study : Credit Scoring Model Automobile Leasing

### Lecture 2. Summarizing the Sample

Lecture 2 Summarizing the Sample WARNING: Today s lecture may bore some of you It s (sort of) not my fault I m required to teach you about what we re going to cover today. I ll try to make it as exciting

### BASIC COURSE IN STATISTICS FOR MEDICAL DOCTORS, SCIENTISTS & OTHER RESEARCH INVESTIGATORS INFORMATION BROCHURE

INDIAN COUNCIL OF MEDICAL RESEARCH BASIC COURSE IN STATISTICS FOR MEDICAL DOCTORS, SCIENTISTS & OTHER RESEARCH INVESTIGATORS October 13 th 17 th 2014 INFORMATION BROCHURE COURSE OFFERED BY Division of

### Chi Square Distribution

17. Chi Square A. Chi Square Distribution B. One-Way Tables C. Contingency Tables D. Exercises Chi Square is a distribution that has proven to be particularly useful in statistics. The first section describes

### Profile of Rural Health Insurance Coverage

Profile of Rural Health Insurance Coverage A Chartbook R H R C Rural Health Research & Policy Centers Funded by the Federal Office of Rural Health Policy www.ruralhealthresearch.org UNIVERSITY OF SOUTHERN

### Gerry Hobbs, Department of Statistics, West Virginia University

Decision Trees as a Predictive Modeling Method Gerry Hobbs, Department of Statistics, West Virginia University Abstract Predictive modeling has become an important area of interest in tasks such as credit

### Demand forecasting & Aggregate planning in a Supply chain. Session Speaker Prof.P.S.Satish

Demand forecasting & Aggregate planning in a Supply chain Session Speaker Prof.P.S.Satish 1 Introduction PEMP-EMM2506 Forecasting provides an estimate of future demand Factors that influence demand and

### 10. Analysis of Longitudinal Studies Repeat-measures analysis

Research Methods II 99 10. Analysis of Longitudinal Studies Repeat-measures analysis This chapter builds on the concepts and methods described in Chapters 7 and 8 of Mother and Child Health: Research methods.

### Internal and External Validity

Internal and External Validity ScWk 240 Week 5 Slides (2 nd Set) 1 Defining Characteristics When research is designed to investigate cause and effect relationships (explanatory research) through the direct

### ONLINE AND TELECONFERENCE RESULTS PRESENTATION SEPTEMBER 2015

DATE: November 10, 2015 SUMMARY ONLINE AND TELECONFERENCE RESULTS PRESENTATION SEPTEMBER 2015 Good morning to everyone joining this teleconference. The idea is present you with the results of the third

### IBM SPSS Direct Marketing 23

IBM SPSS Direct Marketing 23 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 23, release

### The Proportional Odds Model for Assessing Rater Agreement with Multiple Modalities

The Proportional Odds Model for Assessing Rater Agreement with Multiple Modalities Elizabeth Garrett-Mayer, PhD Assistant Professor Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University 1

### Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7)

Overview Classes 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) 2-4 Loglinear models (8) 5-4 15-17 hrs; 5B02 Building and

### EBM Cheat Sheet- Measurements Card

EBM Cheat Sheet- Measurements Card Basic terms: Prevalence = Number of existing cases of disease at a point in time / Total population. Notes: Numerator includes old and new cases Prevalence is cross-sectional

### When to Use Which Statistical Test

When to Use Which Statistical Test Rachel Lovell, Ph.D., Senior Research Associate Begun Center for Violence Prevention Research and Education Jack, Joseph, and Morton Mandel School of Applied Social Sciences

### IBM SPSS Direct Marketing 22

IBM SPSS Direct Marketing 22 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 22, release

### Multivariate Normal Distribution

Multivariate Normal Distribution Lecture 4 July 21, 2011 Advanced Multivariate Statistical Methods ICPSR Summer Session #2 Lecture #4-7/21/2011 Slide 1 of 41 Last Time Matrices and vectors Eigenvalues

### Chapter 15 Multiple Choice Questions (The answers are provided after the last question.)

Chapter 15 Multiple Choice Questions (The answers are provided after the last question.) 1. What is the median of the following set of scores? 18, 6, 12, 10, 14? a. 10 b. 14 c. 18 d. 12 2. Approximately

### Unit 1: Introduction to Six Sigma I

Unit 1: Introduction to Six Sigma I Six Sigma & its Goals Six Sigma as a Performance Measure, Problem Solving Tool, & Management Philosophy Problem Solving Tools Used in Six Sigma & its Evolution Variation

### Handling attrition and non-response in longitudinal data

Longitudinal and Life Course Studies 2009 Volume 1 Issue 1 Pp 63-72 Handling attrition and non-response in longitudinal data Harvey Goldstein University of Bristol Correspondence. Professor H. Goldstein

### Outline of Topics. Statistical Methods I. Types of Data. Descriptive Statistics

Statistical Methods I Tamekia L. Jones, Ph.D. (tjones@cog.ufl.edu) Research Assistant Professor Children s Oncology Group Statistics & Data Center Department of Biostatistics Colleges of Medicine and Public

### STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE

STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE Perhaps Microsoft has taken pains to hide some of the most powerful tools in Excel. These add-ins tools work on top of Excel, extending its power and abilities

### Multivariate Analysis. Overview

Multivariate Analysis Overview Introduction Multivariate thinking Body of thought processes that illuminate the interrelatedness between and within sets of variables. The essence of multivariate thinking

### PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050

PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050 Class Hours: 2.0 Credit Hours: 3.0 Laboratory Hours: 2.0 Date Revised: Fall 2013 Catalog Course Description: Descriptive

### Official SAS Curriculum Courses

Certificate course in Predictive Business Analytics Official SAS Curriculum Courses SAS Programming Base SAS An overview of SAS foundation Working with SAS program syntax Examining SAS data sets Accessing

### Moderation. Moderation

Stats - Moderation Moderation A moderator is a variable that specifies conditions under which a given predictor is related to an outcome. The moderator explains when a DV and IV are related. Moderation

### UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010

UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 COURSE: POM 500 Statistical Analysis, ONLINE EDITION, Fall 2010 Prerequisite: Finite Math

### SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES

SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR

### In the past, the increase in the price of gasoline could be attributed to major national or global

Chapter 7 Testing Hypotheses Chapter Learning Objectives Understanding the assumptions of statistical hypothesis testing Defining and applying the components in hypothesis testing: the research and null

### Criminal Justice Professionals Attitudes Towards Offenders: Assessing the Link between Global Orientations and Specific Attributions

Criminal Justice Professionals Attitudes Towards s: Assessing the Link between Global Orientations and Specific Attributions Prepared by: Dale Willits, M.A. Lisa Broidy, Ph.D. Christopher Lyons, Ph.D.

### Change Management Dashboard: An Adaptive Approach to Lead a Change Program

Change Management Dashboard: An Adaptive Approach to Lead a Change Program By Alessio Vaccarezza and Gianluca Rizzi 46 PEOPLE & STRATEGY In present times, organizations need to change continuously in order

### Retirement routes and economic incentives to retire: a cross-country estimation approach Martin Rasmussen

Retirement routes and economic incentives to retire: a cross-country estimation approach Martin Rasmussen Welfare systems and policies Working Paper 1:2005 Working Paper Socialforskningsinstituttet The

### Descriptive Statistics. Purpose of descriptive statistics Frequency distributions Measures of central tendency Measures of dispersion

Descriptive Statistics Purpose of descriptive statistics Frequency distributions Measures of central tendency Measures of dispersion Statistics as a Tool for LIS Research Importance of statistics in research

### Organizing Your Approach to a Data Analysis

Biost/Stat 578 B: Data Analysis Emerson, September 29, 2003 Handout #1 Organizing Your Approach to a Data Analysis The general theme should be to maximize thinking about the data analysis and to minimize

### Comparing Alternate Designs For A Multi-Domain Cluster Sample

Comparing Alternate Designs For A Multi-Domain Cluster Sample Pedro J. Saavedra, Mareena McKinley Wright and Joseph P. Riley Mareena McKinley Wright, ORC Macro, 11785 Beltsville Dr., Calverton, MD 20705

### 1. Answers will vary. Students might focus on the unsanitary or disgusting nature of. Teacher Notes for Investigation #1: Did You Wash Your Hands?

Teacher Notes for Investigation #1: Did You Wash Your Hands? This investigation builds on the two hand-washing studies that were discussed in the Introduction. The questions posed here are designed to

### Unit 1: Introduction to Quality Management

Unit 1: Introduction to Quality Management Definition & Dimensions of Quality Quality Control vs Quality Assurance Small-Q vs Big-Q & Evolution of Quality Movement Total Quality Management (TQM) & its

### Analysis of Student Retention Rates and Performance Among Fall, 2013 Alternate College Option (ACO) Students

1 Analysis of Student Retention Rates and Performance Among Fall, 2013 Alternate College Option (ACO) Students A Study Commissioned by the Persistence Committee Policy Working Group prepared by Jeffrey

### Data Analysis, Research Study Design and the IRB

Minding the p-values p and Quartiles: Data Analysis, Research Study Design and the IRB Don Allensworth-Davies, MSc Research Manager, Data Coordinating Center Boston University School of Public Health IRB

### SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg

SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg IN SPSS SESSION 2, WE HAVE LEARNT: Elementary Data Analysis Group Comparison & One-way

### CONTENTS OF DAY 2. II. Why Random Sampling is Important 9 A myth, an urban legend, and the real reason NOTES FOR SUMMER STATISTICS INSTITUTE COURSE

1 2 CONTENTS OF DAY 2 I. More Precise Definition of Simple Random Sample 3 Connection with independent random variables 3 Problems with small populations 8 II. Why Random Sampling is Important 9 A myth,