# Roadmap to Data Analysis. Introduction to the Series, and I. Introduction to Statistical Thinking-A (Very) Short Introductory Course for Agencies

Save this PDF as:

Size: px
Start display at page:

Download "Roadmap to Data Analysis. Introduction to the Series, and I. Introduction to Statistical Thinking-A (Very) Short Introductory Course for Agencies"

## Transcription

1 Roadmap to Data Analysis Introduction to the Series, and I. Introduction to Statistical Thinking-A (Very) Short Introductory Course for Agencies

2 Objectives of the Series Roadmap to Data Analysis Provide and introduction to basic statistical procedures relevant to SOT agencies Provide a foundation to analyze and interpret data on clients, services, and outcomes Provide an introduction to the use of available tools for basic analysis of data Provide an understanding of the limitations of statistical analysis, and guidelines for agency staff about when to seek statistical consultation 2

3 The Series Roadmap to Data Analysis I. Introduction to Statistical Thinking II. Primer on Measurement and Variables III. Choosing the Right Statistical Test IV. Comparing Averages The t Tests V. Comparing Counts Chi Square VI. Relationship Between Two Continuous Variables Pearson s r Correlation 3

4 Learning Objectives: I. Introduction to Statistical Thinking Understand basics of statistical thinking and inference Understand concepts of population and sample in quantitative research Understand the process by which inferences can be made to a population based on a sample Understand hypothesis testing and probability value 4

5 Inference Statistical analysis is all about understanding ( making inferences about) a population based on a sample from the population. We rarely have the opportunity to measure an entire population, such as all torture survivors in the U.S. Your agency has ready-made samples of the population of torture survivors The extent to which a sample is representative of the population can be quantified By a statistic some measure of the sample (i.e. an average), and By a quantified value that explains how well your sample statistic describes the population (i.e. a standard deviation) 5

6 Definitions: Population : the entire universe of individuals to which you seek to generalize Sample : that part of the population from which you collect data A sample should be representative of the population from which it is drawn The extent to which a sample is representative of the population can be quantified. This forms the basis for all types of statistical inference 6

7 Characteristics of the Sample A sample should be representative of the population from which it is drawn Population of Torture Survivors in the US (N=500,000) Sample of 1000 Torture Survivors Note: you can also define your population as all of your agency s clients. If you take a sample from that population, then you are inferring statistical results to the agency population. 7

8 A practical example If we say the longer torture survivors in your agency participate in socialization groups, the more they will show improved functioning, we are implying that, in the general population of torture survivors, more participation in socialization groups will result in improved functioning. How is this type of inference possible? 8

9 Minimum requirements: The sample is reasonably representative of the population to which it will generalize There is a specific research question that addresses the sample and can be answered quantitatively Is the length of participation in socialization groups related to improved functioning? There is an hypothesis implied by the research question that can be tested The analysis of data correctly matches the type of data being analyzed (i.e. using the right statistic for the data) 9

10 Hypothesis testing An hypothesis is a statement about the relationship among variables A variable is a factor, or characteristic, that varies It is hypothesized that the longer torture survivors participate in our agency s socialization group, the better the functioning. Two variables 1) Length of participation in the group, and 2) a measure of functioning Hypotheses should be generated based on the available scientific literature and some theoretical basis (i.e. what leads you to think the length of participation in socialization groups might improve functioning?) 10

11 Statistics for inference Analysis of the data results in a statistic about the sample A number representing improved functioning, such as the difference between the average pre- and postfunctioning scores A quantified value that explains how well your sample statistic describes the population (such as a standard deviation a measure of how much individuals in the sample deviate from the average), and A quantified value of how confident you can be that the sample statistic reflects the population (a probability value ) 11

12 Probability Value A p value is the mathematical probability that a relationship between variables found within a sample may have been produced by chance or error P <.05 Statistically significant at <.05 this is a typical threshold Technical note: p values are calculated based on distribution tables for the specific statistic used. The p value is provided by statistics software programs To interpret: the probability is less than 5 in 100 that improvement in functioning was the result of chance alone. In other words, the improvement in functioning from your sample likely reflects the same experience in the larger population that length of participation in the socialization group is related to improved functioning (putting aside the thorny issue of cause and effect, for now ) 12

13 Important Caveats (Partial List!) P values are useful but can t tell the whole story about treatment effects (look up effect size ). Also, non-significant findings can be valuable information as well. Statistics can t compensate for poorly written questions or instruments that are not valid Especially in torture treatment settings, there are too few instruments that have been tested with many of our current survivor groups Statistics can t compensate for a weak research design i.e. for understanding the impact of treatment it s best to have a non-treatment control or comparison group (to address that thorny causality issue) Seek consultation from statistical and content experts when considering or implementing data collection instruments They can comment on reliability and validity concerns They can guide analysis strategies, including sample size requirements and choosing the right statistical analyses They can help interpret results 13

### HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION

HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate

### CORRELATION ANALYSIS

CORRELATION ANALYSIS Learning Objectives Understand how correlation can be used to demonstrate a relationship between two factors. Know how to perform a correlation analysis and calculate the coefficient

### AP STATISTICS 2009 SCORING GUIDELINES (Form B)

AP STATISTICS 2009 SCORING GUIDELINES (Form B) Question 5 Intent of Question The primary goals of this question were to assess students ability to (1) state the appropriate hypotheses, (2) identify and

### DATA ANALYSIS. QEM Network HBCU-UP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. Howard University

DATA ANALYSIS QEM Network HBCU-UP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. Howard University Quantitative Research What is Statistics? Statistics (as a subject) is the science

### 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

### Correlational Research

Correlational Research Chapter Fifteen Correlational Research Chapter Fifteen Bring folder of readings The Nature of Correlational Research Correlational Research is also known as Associational Research.

### COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES.

277 CHAPTER VI COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. This chapter contains a full discussion of customer loyalty comparisons between private and public insurance companies

### 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

### Regression in SPSS. Workshop offered by the Mississippi Center for Supercomputing Research and the UM Office of Information Technology

Regression in SPSS Workshop offered by the Mississippi Center for Supercomputing Research and the UM Office of Information Technology John P. Bentley Department of Pharmacy Administration University of

### Lisa Rosenberg Mathematics and Statistics Department

Long Assignment for General Statistics Mathematics 110 Lisa Rosenberg Mathematics and Statistics Department Introduction for Faculty Colleagues This assignment is intended for General Statistics (MTH 110)

### Chapter 2 Probability Topics SPSS T tests

Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the One-Sample T test has been explained. In this handout, we also give the SPSS methods to perform

### Introduction to Regression and Data Analysis

Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it

### UNIVERSITY OF NAIROBI

UNIVERSITY OF NAIROBI MASTERS IN PROJECT PLANNING AND MANAGEMENT NAME: SARU CAROLYNN ELIZABETH REGISTRATION NO: L50/61646/2013 COURSE CODE: LDP 603 COURSE TITLE: RESEARCH METHODS LECTURER: GAKUU CHRISTOPHER

### CHAPTER 5 COMPARISON OF DIFFERENT TYPE OF ONLINE ADVERTSIEMENTS. Table: 8 Perceived Usefulness of Different Advertisement Types

CHAPTER 5 COMPARISON OF DIFFERENT TYPE OF ONLINE ADVERTSIEMENTS 5.1 Descriptive Analysis- Part 3 of Questionnaire Table 8 shows the descriptive statistics of Perceived Usefulness of Banner Ads. The results

### 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?

### UNDERSTANDING THE DEPENDENT-SAMPLES t TEST

UNDERSTANDING THE DEPENDENT-SAMPLES t TEST A dependent-samples t test (a.k.a. matched or paired-samples, matched-pairs, samples, or subjects, simple repeated-measures or within-groups, or correlated groups)

### TRANSCRIPT: In this lecture, we will talk about both theoretical and applied concepts related to hypothesis testing.

This is Dr. Chumney. The focus of this lecture is hypothesis testing both what it is, how hypothesis tests are used, and how to conduct hypothesis tests. 1 In this lecture, we will talk about both theoretical

### Economic Statistics (ECON2006), Statistics and Research Design in Psychology (PSYC2010), Survey Design and Analysis (SOCI2007)

COURSE DESCRIPTION Title Code Level Semester Credits 3 Prerequisites Post requisites Introduction to Statistics ECON1005 (EC160) I I None Economic Statistics (ECON2006), Statistics and Research Design

### 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

### Calculating, Interpreting, and Reporting Estimates of Effect Size (Magnitude of an Effect or the Strength of a Relationship)

1 Calculating, Interpreting, and Reporting Estimates of Effect Size (Magnitude of an Effect or the Strength of a Relationship) I. Authors should report effect sizes in the manuscript and tables when reporting

### Chapter Additional: Standard Deviation and Chi- Square

Chapter Additional: Standard Deviation and Chi- Square Chapter Outline: 6.4 Confidence Intervals for the Standard Deviation 7.5 Hypothesis testing for Standard Deviation Section 6.4 Objectives Interpret

### Simple Linear Regression Chapter 11

Simple Linear Regression Chapter 11 Rationale Frequently decision-making situations require modeling of relationships among business variables. For instance, the amount of sale of a product may be related

### Chapter 7: Simple linear regression Learning Objectives

Chapter 7: Simple linear regression Learning Objectives Reading: Section 7.1 of OpenIntro Statistics Video: Correlation vs. causation, YouTube (2:19) Video: Intro to Linear Regression, YouTube (5:18) -

### Pearson s Correlation

Pearson s Correlation Correlation the degree to which two variables are associated (co-vary). Covariance may be either positive or negative. Its magnitude depends on the units of measurement. Assumes the

### When to use a Chi-Square test:

When to use a Chi-Square test: Usually in psychological research, we aim to obtain one or more scores from each participant. However, sometimes data consist merely of the frequencies with which certain

### Sample Size Determination

Sample Size Determination Population A: 10,000 Population B: 5,000 Sample 10% Sample 15% Sample size 1000 Sample size 750 The process of obtaining information from a subset (sample) of a larger group (population)

### 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

### AMS7: WEEK 8. CLASS 1. Correlation Monday May 18th, 2015

AMS7: WEEK 8. CLASS 1 Correlation Monday May 18th, 2015 Type of Data and objectives of the analysis Paired sample data (Bivariate data) Determine whether there is an association between two variables This

### Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a

About Hypothesis Testing TABLE OF CONTENTS About Hypothesis Testing... 1 What is a HYPOTHESIS TEST?... 1 Hypothesis Testing... 1 Hypothesis Testing... 1 Steps in Hypothesis Testing... 2 Steps in Hypothesis

### Hypothesis Construction. Claude Oscar Monet: The Blue House in Zaandam, 1871.

Hypothesis Construction Claude Oscar Monet: The Blue House in Zaandam, 1871. Propositions and Hypotheses 1. Definitions Theoretical Proposition: An empirically falsifiable, abstract statement about reality.

### Correlations. MSc Module 6: Introduction to Quantitative Research Methods Kenneth Benoit. March 18, 2010

Correlations MSc Module 6: Introduction to Quantitative Research Methods Kenneth Benoit March 18, 2010 Relationships between variables In previous weeks, we have been concerned with describing variables

### Descriptive Statistics

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

### LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE

LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Pre-

### Using Excel for Statistical Analysis

Using Excel for Statistical Analysis You don t have to have a fancy pants statistics package to do many statistical functions. Excel can perform several statistical tests and analyses. First, make sure

### AIE: 85-86, 193, 217-218, 294, 339-340, 341-343, 412, 437-439, 531-533, 682, 686-687 SE: : 339, 434, 437-438, 48-454, 455-458, 680, 686

Knowledge and skills. (1) The student conducts laboratory investigations and fieldwork using safe, environmentally appropriate, and ethical practices. The student is expected to: (A) demonstrate safe practices

### 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,

### Simulation Exercises to Reinforce the Foundations of Statistical Thinking in Online Classes

Simulation Exercises to Reinforce the Foundations of Statistical Thinking in Online Classes Simcha Pollack, Ph.D. St. John s University Tobin College of Business Queens, NY, 11439 pollacks@stjohns.edu

### MATH 140 HYBRID INTRODUCTORY STATISTICS COURSE SYLLABUS

MATH 140 HYBRID INTRODUCTORY STATISTICS COURSE SYLLABUS Instructor: Mark Schilling Email: mark.schilling@csun.edu (Note: If your CSUN email address is not one you use regularly, be sure to set up automatic

### 11/20/2014. Correlational research is used to describe the relationship between two or more naturally occurring variables.

Correlational research is used to describe the relationship between two or more naturally occurring variables. Is age related to political conservativism? Are highly extraverted people less afraid of rejection

### Correlational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots

Correlational Research Stephen E. Brock, Ph.D., NCSP California State University, Sacramento 1 Correlational Research A quantitative methodology used to determine whether, and to what degree, a relationship

### MAT140: Applied Statistical Methods Summary of Calculating Confidence Intervals and Sample Sizes for Estimating Parameters

MAT140: Applied Statistical Methods Summary of Calculating Confidence Intervals and Sample Sizes for Estimating Parameters Inferences about a population parameter can be made using sample statistics for

### Recommend Continued CPS Monitoring. 63 (a) 17 (b) 10 (c) 90. 35 (d) 20 (e) 25 (f) 80. Totals/Marginal 98 37 35 170

Work Sheet 2: Calculating a Chi Square Table 1: Substance Abuse Level by ation Total/Marginal 63 (a) 17 (b) 10 (c) 90 35 (d) 20 (e) 25 (f) 80 Totals/Marginal 98 37 35 170 Step 1: Label Your Table. Label

### EFFECT SIZE, POWER, AND SAMPLE SIZE ERSH 8310

EFFECT SIZE, POWER, AND SAMPLE SIZE ERSH 8310 Today s Class Effect Size Power Sample Size Effect Size Descriptive Measures of Effect Size The report of any study should include a description of the pattern

### Econometrics and Data Analysis I

Econometrics and Data Analysis I Yale University ECON S131 (ONLINE) Summer Session A, 2014 June 2 July 4 Instructor: Doug McKee (douglas.mckee@yale.edu) Teaching Fellow: Yu Liu (dav.yu.liu@yale.edu) Classroom:

### Science Curriculum Review Worksheets

Science Curriculum Review Worksheets Table 1. ACT for Score Range 13-15 IOD 201 IOD 202 IOD 203 SIN 201 SIN 202 EMI 201 Select one piece of data from a simple data presentation (e.g., a simple food web

### " Y. Notation and Equations for Regression Lecture 11/4. Notation:

Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through

### How to Conduct a Hypothesis Test

How to Conduct a Hypothesis Test The idea of hypothesis testing is relatively straightforward. In various studies we observe certain events. We must ask, is the event due to chance alone, or is there some

### Understanding Confidence Intervals and Hypothesis Testing Using Excel Data Table Simulation

Understanding Confidence Intervals and Hypothesis Testing Using Excel Data Table Simulation Leslie Chandrakantha lchandra@jjay.cuny.edu Department of Mathematics & Computer Science John Jay College of

### 1. Comparing Two Means: Dependent Samples

1. Comparing Two Means: ependent Samples In the preceding lectures we've considered how to test a difference of two means for independent samples. Now we look at how to do the same thing with dependent

### ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE. School of Mathematical Sciences

! ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences New Revised COURSE: COS-MATH-252 Probability and Statistics II 1.0 Course designations and approvals:

### Statistical Inference

Statistical Inference Idea: Estimate parameters of the population distribution using data. How: Use the sampling distribution of sample statistics and methods based on what would happen if we used this

### Which WJ-III Subtests Should I Administer?

Which WJ-III Subtests Should I Administer? P R E S E N T E D B Y : J U D D F R E D S T R O M A R E A S P E C I A L E D U C A T I O N C O O P A S E C. N E T Woodcock-Johnson III tests of Achievement Eligibility

### 2 GENETIC DATA ANALYSIS

2.1 Strategies for learning genetics 2 GENETIC DATA ANALYSIS We will begin this lecture by discussing some strategies for learning genetics. Genetics is different from most other biology courses you have

### NPTEL STRUCTURAL RELIABILITY

NPTEL Course On STRUCTURAL RELIABILITY Module # 02 Lecture 6 Course Format: Web Instructor: Dr. Arunasis Chakraborty Department of Civil Engineering Indian Institute of Technology Guwahati 6. Lecture 06:

### Obtaining Knowledge. Lecture 7 Methods of Scientific Observation and Analysis in Behavioral Psychology and Neuropsychology.

Lecture 7 Methods of Scientific Observation and Analysis in Behavioral Psychology and Neuropsychology 1.Obtaining Knowledge 1. Correlation 2. Causation 2.Hypothesis Generation & Measures 3.Looking into

### Statistical Considerations for Experimental Design and Data Analysis

1 Statistical Considerations for Experimental Design and Data Analysis Stephen D. Kachman Department of Statistics University of Nebraska Lincoln 2 Introduction Hypothesis Design Processing Analysis Introduction

### 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

### RARITAN VALLEY COMMUNITY COLLEGE ACADEMIC COURSE OUTLINE MATH 111H STATISTICS II HONORS

RARITAN VALLEY COMMUNITY COLLEGE ACADEMIC COURSE OUTLINE MATH 111H STATISTICS II HONORS I. Basic Course Information A. Course Number and Title: MATH 111H Statistics II Honors B. New or Modified Course:

### MORE ON LOGISTIC REGRESSION

DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Posc/Uapp 816 MORE ON LOGISTIC REGRESSION I. AGENDA: A. Logistic regression 1. Multiple independent variables 2. Example: The Bell Curve 3. Evaluation

### 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

### Statistical Inference: Hypothesis Testing

Statistical Inference: Hypothesis Testing Scott Evans, Ph.D. 1 The Big Picture Populations and Samples Sample / Statistics x, s, s 2 Population Parameters μ, σ, σ 2 Scott Evans, Ph.D. 2 Statistical Inference

### Basic Research Progress

Mgt 540 Research Methods Sampling Issues 1 Basic Research Progress Explorative - Descriptive (Qualitative) 1. Framework / Domain extant knowledge for reference 2. Research Design 3. Data collection / presentation

### Fairfield Public Schools

Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity

### MCQ TESTING OF HYPOTHESIS

MCQ TESTING OF HYPOTHESIS MCQ 13.1 A statement about a population developed for the purpose of testing is called: (a) Hypothesis (b) Hypothesis testing (c) Level of significance (d) Test-statistic MCQ

### LEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016

UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION LEARNING OBJECTIVES Contrast three ways of describing results: Comparing group percentages Correlating scores Comparing group means Describe

### Analysing Tables Part V Interpreting Chi-Square

Analysing Tables Part V Interpreting Chi-Square 8.0 Interpreting Chi Square Output 8.1 the Meaning of a Significant Chi-Square Sometimes the purpose of Crosstabulation is wrongly seen as being merely about

### Diagnosis of Students Online Learning Portfolios

Diagnosis of Students Online Learning Portfolios Chien-Ming Chen 1, Chao-Yi Li 2, Te-Yi Chan 3, Bin-Shyan Jong 4, and Tsong-Wuu Lin 5 Abstract - Online learning is different from the instruction provided

### Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures

Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 35487-0348 Phone:

### CHAPTER 11 SECTION 2: INTRODUCTION TO HYPOTHESIS TESTING

CHAPTER 11 SECTION 2: INTRODUCTION TO HYPOTHESIS TESTING MULTIPLE CHOICE 56. In testing the hypotheses H 0 : µ = 50 vs. H 1 : µ 50, the following information is known: n = 64, = 53.5, and σ = 10. The standardized

BIOSTATISTICS QUIZ ANSWERS 1. When you read scientific literature, do you know whether the statistical tests that were used were appropriate and why they were used? a. Always b. Mostly c. Rarely d. Never

### SAMPLE SIZE ESTIMATION USING KREJCIE AND MORGAN AND COHEN STATISTICAL POWER ANALYSIS: A COMPARISON. Chua Lee Chuan Jabatan Penyelidikan ABSTRACT

SAMPLE SIZE ESTIMATION USING KREJCIE AND MORGAN AND COHEN STATISTICAL POWER ANALYSIS: A COMPARISON Chua Lee Chuan Jabatan Penyelidikan ABSTRACT In most situations, researchers do not have access to an

### Bivariate Regression Analysis. The beginning of many types of regression

Bivariate Regression Analysis The beginning of many types of regression TOPICS Beyond Correlation Forecasting Two points to estimate the slope Meeting the BLUE criterion The OLS method Purpose of Regression

### OLS is not only unbiased it is also the most precise (efficient) unbiased estimation technique - ie the estimator has the smallest variance

Lecture 5: Hypothesis Testing What we know now: OLS is not only unbiased it is also the most precise (efficient) unbiased estimation technique - ie the estimator has the smallest variance (if the Gauss-Markov

### Section 7.1. Introduction to Hypothesis Testing. Schrodinger s cat quantum mechanics thought experiment (1935)

Section 7.1 Introduction to Hypothesis Testing Schrodinger s cat quantum mechanics thought experiment (1935) Statistical Hypotheses A statistical hypothesis is a claim about a population. Null hypothesis

### 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

### Statistical tests for SPSS

Statistical tests for SPSS Paolo Coletti A.Y. 2010/11 Free University of Bolzano Bozen Premise This book is a very quick, rough and fast description of statistical tests and their usage. It is explicitly

### Introduction to Hypothesis Testing OPRE 6301

Introduction to Hypothesis Testing OPRE 6301 Motivation... The purpose of hypothesis testing is to determine whether there is enough statistical evidence in favor of a certain belief, or hypothesis, about

### Module 3: Multiple Regression Concepts

Contents Module 3: Multiple Regression Concepts Fiona Steele 1 Centre for Multilevel Modelling...4 What is Multiple Regression?... 4 Motivation... 4 Conditioning... 4 Data for multiple regression analysis...

### Least Squares Estimation

Least Squares Estimation SARA A VAN DE GEER Volume 2, pp 1041 1045 in Encyclopedia of Statistics in Behavioral Science ISBN-13: 978-0-470-86080-9 ISBN-10: 0-470-86080-4 Editors Brian S Everitt & David

### 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,

### Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are

### Intro to Data Analysis, Economic Statistics and Econometrics

Intro to Data Analysis, Economic Statistics and Econometrics Statistics deals with the techniques for collecting and analyzing data that arise in many different contexts. Econometrics involves the development

SCIENCE ROAD Journal SCIENCE ROAD JOURNAL Year: 2015 Volume: 03 Issue: 03 Pages: 278-285 Analyzing the impact of knowledge management on the success of customer communications with intermediary role of

### Mathematical Knowledge level of Primary Education Department students

Mathematical Knowledge level of Primary Education Department students Charalampos Lemonidis, Helen Tsakiridou, Charalampos Kapsalis Department of Primary Education University of Western Macedonia Abstract

### Likelihood Approaches for Trial Designs in Early Phase Oncology

Likelihood Approaches for Trial Designs in Early Phase Oncology Clinical Trials Elizabeth Garrett-Mayer, PhD Cody Chiuzan, PhD Hollings Cancer Center Department of Public Health Sciences Medical University

### Calculating the Probability of Returning a Loan with Binary Probability Models

Calculating the Probability of Returning a Loan with Binary Probability Models Associate Professor PhD Julian VASILEV (e-mail: vasilev@ue-varna.bg) Varna University of Economics, Bulgaria ABSTRACT The

### Foundation of Quantitative Data Analysis

Foundation of Quantitative Data Analysis Part 1: Data manipulation and descriptive statistics with SPSS/Excel HSRS #10 - October 17, 2013 Reference : A. Aczel, Complete Business Statistics. Chapters 1

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

### BA 275 Review Problems - Week 5 (10/23/06-10/27/06) CD Lessons: 48, 49, 50, 51, 52 Textbook: pp. 380-394

BA 275 Review Problems - Week 5 (10/23/06-10/27/06) CD Lessons: 48, 49, 50, 51, 52 Textbook: pp. 380-394 1. Does vigorous exercise affect concentration? In general, the time needed for people to complete

T O P I C 1 1 Introduction to statistics Preview Introduction In previous topics we have looked at ways of gathering data for research purposes and ways of organising and presenting it. In topics 11 and

### Introduction to Hypothesis Testing. Hypothesis Testing. Step 1: State the Hypotheses

Introduction to Hypothesis Testing 1 Hypothesis Testing A hypothesis test is a statistical procedure that uses sample data to evaluate a hypothesis about a population Hypothesis is stated in terms of 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

### E205 Final: Version B

Name: Class: Date: E205 Final: Version B Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The owner of a local nightclub has recently surveyed a random

### MTH 140 Statistics Videos

MTH 140 Statistics Videos Chapter 1 Picturing Distributions with Graphs Individuals and Variables Categorical Variables: Pie Charts and Bar Graphs Categorical Variables: Pie Charts and Bar Graphs Quantitative

### 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.