# Additional sources Compilation of sources:

 To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video
Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: Data Analysis Brief Book (glossary) Exploratory Data Analysis Statistical Data Analysis Using Excel for Data Analysis (using Excel) 2 Copyright 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E 3 FIGURE 12.1 Data Analysis Get the feel for the data Get Mean, variance' and standard deviation on each variable See if for all items, responses range all over the scale, and not restricted to one end of the scale alone. Obtain Pearson Correlation among the variables under study. Get Frequency Distribution for all the variables. Tabulate your data. Describe your sample's key characteristics (Demographic details of sex composition, education, age, length of service, etc. ) See Histograms, Frequency Polygons, etc. 4 Quantitative Data Descriptive Statistics Describing key features of data Each type of data requires different analysis method(s): Nominal Labeling No inherent value basis Categorization purposes only Ordinal Ranking, sequence Interval Relationship basis (e.g. age) Central Tendency Mean, median mode Spread Variance, standard deviation, range Distribution (Shape ) Skewness, kurtosis 5 6 1

2 Descriptive Statistics Describing key features of data Testing Goodness of Fit Nominal Identification / categorization only Ordinal (Example on pg. 139) Non-parametric statistics Do not assume equal intervals Frequency counts Averages (median and mode) Interval Parametric Mean, Standard Deviation, variance 7 Reliability Validity Involves and Factor Analysis Split Half Internal Consistency Convergent Discriminant Factorial 8 Testing Hypotheses Use appropriate statistical analysis T-test (single or twin-tailed) Test the significance of differences of the mean of two groups ANOVA Test the significance of differences among the means of more than two different groups, using the F test. Regression (simple or multiple) Establish the variance explained in the DV by the variance in the Ivs catterp.htm 9 Statistical Power Claiming a significant difference Errors in Methodology Type 1 error Reject the null hypothesis when you should not. Called an alpha error Type 2 error Fail to reject the null hypothesis when you should. Called a beta error Statistical power refers to the ability to detect true differences avoiding type 2 errors 10 Statistical Power see discussion at Depends on 4 issues Sample size The effect size you want to detect The alpha (type 1 error rate) you specify The variability of the sample Too little power Overlook effect Too much power Any difference is significant Parametric vs. nonparametric Parametric (characteristics referring to specific population parameters) Parametric assumptions Independent samples Homogeneity of variance Data normally distributed Interval or better scale Nonparametric assumptions Sometimes independence of samples

3 t-tests (Look at t tables; p. 435) Used to compare two means or one observed mean against a guess about a hypothesized mean For large samples t and z can be considered equivalent Calculate t = - µ S Where S is the standard error of the mean, S/ n and df = n-1 13 t-tests Statistical programs will give you a choice between a matched pair and an independent t-test. Your sample and research design determine which you will use. 14 z-test for Proportions (Look at t tables; p. 435) When data are nominal Describe by counting occurrences of each value From counts, calculate proportions Compare proportion of occurrence in sample to proportion of occurrence in population Hypotheses testing allows only one of two outcomes: success or failure z-test for Proportions (Look at t tables; p. 435) Comparing sample proportion to the population proportion H 0 : π = k, where k is a value between 0 and 1 H 1 : π k z = p -π = p -π σ p (π(1- π)/n) Equivalent to χ 2 for df = Chi-Square Test(sampling distribution) One Sample Measures sample variance Squared deviations from the mean based on normal distribution Nonparametric Compare expected with observed proportion H 0 : Observed proportion = expected proportion df = number of data points categories, cells (k) minus 1 Univariate z Test Test a guess about a proportion against an observed sample; eg., MBAs constitute 35% of the managerial population H 0 : π =.35 H 1 : π 5.35 (two-tailed test suggested) χ 2 = (O E)2 E

4 Univariate Tests Some univariate tests are different in that they are among statistical procedures where you, the researcher, set the null hypothesis. In many other statistical tests the null hypothesis is implied by the test itself. 19 Contingency Tables Relationship between nominal variables Relationship between subjects' scores on two qualitative or categorical variables (Early childhood intervention) If the columns are not contingent on the rows, then the rows and column frequencies are independent. The test of whether the columns are contingent on the rows is called the chi square test of independence. The null hypothesis is that there is no relationship between row and column frequencies. 20 A statistical summary of the degree and direction of association between two variables Correlation itself does not distinguish between independent and dependent variables Most common Pearson s r You believe that a linear relationship exists between two variables The range is from 1 to +1 R 2, the coefficient of determination, is the % of variance explained in each variable by the other r = S xy /S x S y or the covariance between x and y divided by their standard deviations Calculations needed The means, x-bar and y-bar Deviations from the means, (x x-bar) and (y y-bar) for each case The squares of the deviations from the means for each case to insure positive distance measures when added, (x - x- bar) 2 and (y y-bar) 2 The cross product for each case (x x- bar) times (y y-bar) 23 The null hypothesis for correlations is H 0 : ρ = 0 and the alternative is usually H 1 : ρ 0 However, if you can justify it prior to analyzing the data you might also use H 1 : ρ > 0 or H 1 : ρ < 0, a one-tailed test 24 4

5 Alternative measures Spearman rank correlation, r ranks r ranks and r are nearly always equivalent measures for the same data (even when not the differences are trivial) Phi coefficient, r Φ, when both variables are dichotomous; again, it is equivalent to Pearson s r 25 Alternative measures Point-biserial, r pb when correlating a dichotomous with a continuous variable If a scatterplot shows a curvilinear relationship there are two options: A data transformation, or Use the correlation ratio, η 2 (etasquared) SS within 1 - SS total 26 ANOVA For two groups only the t-test and ANOVA yield the same results You must do paired comparisons when working with three or more groups to know where the means lie Multivariate Techniques Dependent variable Regression in its various forms Discriminant analysis MANOVA Classificatory or data reduction Cluster analysis Factor analysis Multidimensional scaling Linear Regression We would like to be able to predict y from x Simple linear regression with raw scores y = dependent variable s y x = independent variable s x b = regression coefficient = r xy c = a constant term The general model is y = bx + c (+e) 29 Linear Regression The statistic for assessing the overall fit of a regression model is the R 2, or the overall % of variance explained by the model R 2 = 1 = unpredictable variance total variance predictable variance total variance = 1 (s 2 e / s 2 y), where s 2 e is the variance of the error or residual 30 5

6 Linear Regression Multiple regression: more than one predictor y = b 1 x 1 + b 2 x 2 + c Each regression coefficient b is assessed independently for its statistical significance; H 0 : b = 0 So, in a statistical program s output a statistically significant b rejects the notion that the variable associated with b contributes nothing to predicting y Linear Regression Multiple regression R 2 still tells us the amount of variation in y explained by all of the predictors (x) together The F-statistic tells us whether the model as a whole is statistically significant Several other types of regression models are available for data that do not meet the assumptions needed for least-squares models (such as logistic regression for dichotomous dependent variables) Regression by SPSS & other Programs Methods for developing the model Stepwise: let s computer try to fit all chosen variables, leaving out those not significant and reexamining variables in the model at each step Enter: researcher specifies that all variables will be used in the model Forward, backward: begin with all (backward) or none (forward) of the variables and automatically adds or removes variables without reconsideration of variables already in the model 33 Multicollinearity Best regression model has uncorrelated IVs Model stability low with excessively correlated IVs Collinearity diagnostics identify problems, suggesting variables to be dropped High tolerance, low variance inflation factor are desirable 34 Discriminant Analysis Regression requires DV to be interval or ratio If DV categorical (nominal) can use discriminant analysis IVs should be interval or ratio scaled Key result is number of cases classified correctly MANOVA Compare means on two or more DVs (ANOVA limited to one DV) Pure MANOVA via SPSS only from command syntax Can use the general linear model though

7 Factor Analysis A data reduction technique a large set of variables can be reduced to a smaller set while retaining the information from the original data set Data must be on an interval or ratio scale E.g., a variable called socioeconomic status might be constructed from variables such as household income, educational attainment of the head of household, and average per capita income of the census block in which the person resides Cluster Analysis Cluster analysis seeks to group cases rather than variables; it too is a data reduction technique Data must be on an interval or ratio scale E.g., a marketing group might want to classify people into psychographic profiles regarding their tendencies to try or adopt new products pioneers or early adopters, early majority, late majority, laggards Factor vs. Cluster Analysis Factor analysis focuses on creating linear composites of variables Number of variables with which we must work is then reduced Technique begins with a correlation matrix to seed the process Cluster analysis focuses on cases 39 Potential Biases Asking the inappropriate or wrong research questions. Insufficient literature survey and hence inadequate theoretical model. Measurement problems Samples not being representative. Problems with data collection: researcher biases respondent biases instrument biases Data analysis biases: coding errors data punching & input errors inappropriate statistical analysis Biases (subjectivity) in interpretation of results. 40 Questions to ask: Adopted from Robert Niles Where did the data come from? How (Who) was the data reviewed, verified, or substantiated? How were the data collected? How is the data presented? What is the context? Cherry-picking? Be skeptical when dealing with comparisons Spurious correlations Copyright 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E 41 FIGURE

### 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

### 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

### Descriptive Statistics

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

### CHAPTER 3 COMMONLY USED STATISTICAL TERMS

CHAPTER 3 COMMONLY USED STATISTICAL TERMS There are many statistics used in social science research and evaluation. The two main areas of statistics are descriptive and inferential. The third class of

### Semester 1 Statistics Short courses

Semester 1 Statistics Short courses Course: STAA0001 Basic Statistics Blackboard Site: STAA0001 Dates: Sat. March 12 th and Sat. April 30 th (9 am 5 pm) Assumed Knowledge: None Course Description Statistical

### 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

### Data analysis process

Data analysis process Data collection and preparation Collect data Prepare codebook Set up structure of data Enter data Screen data for errors Exploration of data Descriptive Statistics Graphs Analysis

### 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

### Research Methods & Experimental Design

Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and

### 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

### Chapter Seven. Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS

Chapter Seven Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS Section : An introduction to multiple regression WHAT IS MULTIPLE REGRESSION? Multiple

### 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

### 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,

### Analysis of Data. Organizing Data Files in SPSS. Descriptive Statistics

Analysis of Data Claudia J. Stanny PSY 67 Research Design Organizing Data Files in SPSS All data for one subject entered on the same line Identification data Between-subjects manipulations: variable to

### Study Guide for the Final Exam

Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make

### Basic Statistcs Formula Sheet

Basic Statistcs Formula Sheet Steven W. ydick May 5, 0 This document is only intended to review basic concepts/formulas from an introduction to statistics course. Only mean-based procedures are reviewed,

### Objective of the course The main objective is to teach students how to conduct quantitative data analysis in SPSS for research purposes.

COURSE DESCRIPTION The course Data Analysis with SPSS was especially designed for students of Master s Programme System and Software Engineering. The content and teaching methods of the course correspond

### How to choose a statistical test. Francisco J. Candido dos Reis DGO-FMRP University of São Paulo

How to choose a statistical test Francisco J. Candido dos Reis DGO-FMRP University of São Paulo Choosing the right test One of the most common queries in stats support is Which analysis should I use There

### Introduction to Quantitative Methods

Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................

### 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,

### 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

### Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk

Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk Structure As a starting point it is useful to consider a basic questionnaire as containing three main sections:

### TABLE OF CONTENTS. About Chi Squares... 1. What is a CHI SQUARE?... 1. Chi Squares... 1. Hypothesis Testing with Chi Squares... 2

About Chi Squares TABLE OF CONTENTS About Chi Squares... 1 What is a CHI SQUARE?... 1 Chi Squares... 1 Goodness of fit test (One-way χ 2 )... 1 Test of Independence (Two-way χ 2 )... 2 Hypothesis Testing

### Instructions for SPSS 21

1 Instructions for SPSS 21 1 Introduction... 2 1.1 Opening the SPSS program... 2 1.2 General... 2 2 Data inputting and processing... 2 2.1 Manual input and data processing... 2 2.2 Saving data... 3 2.3

### SPSS Tests for Versions 9 to 13

SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list

### Technology Step-by-Step Using StatCrunch

Technology Step-by-Step Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate

### Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.

Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing

### business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar

business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel

### A Guide for a Selection of SPSS Functions

A Guide for a Selection of SPSS Functions IBM SPSS Statistics 19 Compiled by Beth Gaedy, Math Specialist, Viterbo University - 2012 Using documents prepared by Drs. Sheldon Lee, Marcus Saegrove, Jennifer

### SPSS Explore procedure

SPSS Explore procedure One useful function in SPSS is the Explore procedure, which will produce histograms, boxplots, stem-and-leaf plots and extensive descriptive statistics. To run the Explore procedure,

### Chapter 14: Analyzing Relationships Between Variables

Chapter Outlines for: Frey, L., Botan, C., & Kreps, G. (1999). Investigating communication: An introduction to research methods. (2nd ed.) Boston: Allyn & Bacon. Chapter 14: Analyzing Relationships Between

### Quantitative Data Analysis: Choosing a statistical test Prepared by the Office of Planning, Assessment, Research and Quality

Quantitative Data Analysis: Choosing a statistical test Prepared by the Office of Planning, Assessment, Research and Quality 1 To help choose which type of quantitative data analysis to use either before

### The Dummy s Guide to Data Analysis Using SPSS

The Dummy s Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April, 2001 Amy Gamble 4/30/01 All Rights Rerserved TABLE OF CONTENTS PAGE Helpful Hints for All Tests...1 Tests

### SPSS ADVANCED ANALYSIS WENDIANN SETHI SPRING 2011

SPSS ADVANCED ANALYSIS WENDIANN SETHI SPRING 2011 Statistical techniques to be covered Explore relationships among variables Correlation Regression/Multiple regression Logistic regression Factor analysis

### T-test & factor analysis

Parametric tests T-test & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives Continue

### 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.

### Semester 2 Statistics Short courses

Semester 2 Statistics Short courses Course: STAA0001 - Basic Statistics Blackboard Site: STAA0001 Dates: Sat 10 th Sept and 22 Oct 2016 (9 am 5 pm) Room EN409 Assumed Knowledge: None Day 1: Exploratory

### 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

### 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

### An introduction to IBM SPSS Statistics

An introduction to IBM SPSS Statistics Contents 1 Introduction... 1 2 Entering your data... 2 3 Preparing your data for analysis... 10 4 Exploring your data: univariate analysis... 14 5 Generating descriptive

### Yiming Peng, Department of Statistics. February 12, 2013

Regression Analysis Using JMP Yiming Peng, Department of Statistics February 12, 2013 2 Presentation and Data http://www.lisa.stat.vt.edu Short Courses Regression Analysis Using JMP Download Data to Desktop

### 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

### Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics

Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This

### MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS

MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS MSR = Mean Regression Sum of Squares MSE = Mean Squared Error RSS = Regression Sum of Squares SSE = Sum of Squared Errors/Residuals α = Level of Significance

### Class 6: Chapter 12. Key Ideas. Explanatory Design. Correlational Designs

Class 6: Chapter 12 Correlational Designs l 1 Key Ideas Explanatory and predictor designs Characteristics of correlational research Scatterplots and calculating associations Steps in conducting a correlational

### SPSS: Descriptive and Inferential Statistics. For Windows

For Windows August 2012 Table of Contents Section 1: Summarizing Data...3 1.1 Descriptive Statistics...3 Section 2: Inferential Statistics... 10 2.1 Chi-Square Test... 10 2.2 T tests... 11 2.3 Correlation...

### 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

### 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

Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology Step-by-Step - Excel Microsoft Excel is a spreadsheet software application

### Descriptive Analysis

Research Methods William G. Zikmund Basic Data Analysis: Descriptive Statistics Descriptive Analysis The transformation of raw data into a form that will make them easy to understand and interpret; rearranging,

### 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

### 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

### 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

### Projects Involving Statistics (& SPSS)

Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,

### Final Exam Practice Problem Answers

Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal

### Inferential Statistics. Probability. From Samples to Populations. Katie Rommel-Esham Education 504

Inferential Statistics Katie Rommel-Esham Education 504 Probability Probability is the scientific way of stating the degree of confidence we have in predicting something Tossing coins and rolling dice

ESSENTIALS OF Business Research Methods SECOND EDITION Joseph F. Hair Jr. Mary Wolfinbarger Celsi Arthur H. Money Phillip Samouel Michael J. Page am.e.sharpe Armonk, New York London, England Detailed Table

### 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

### Using Excel for inferential statistics

FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied

### Course Description. Learning Objectives

STAT X400 (2 semester units in Statistics) Business, Technology & Engineering Technology & Information Management Quantitative Analysis & Analytics Course Description This course introduces students to

### Lecture - 32 Regression Modelling Using SPSS

Applied Multivariate Statistical Modelling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Lecture - 32 Regression Modelling Using SPSS (Refer

### SPSS TUTORIAL & EXERCISE BOOK

UNIVERSITY OF MISKOLC Faculty of Economics Institute of Business Information and Methods Department of Business Statistics and Economic Forecasting PETRA PETROVICS SPSS TUTORIAL & EXERCISE BOOK FOR BUSINESS

### 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

### Introduction to Statistics with SPSS for Social Science

New Introduction to Statistics with SPSS for Social Science Gareth Norris Faiza Qureshi Dennis Howitt Duncan Cramer Aberystwyth University City University London University of Loughborough University of

### Figure 1. IBM SPSS Statistics Base & Associated Optional Modules

IBM SPSS Statistics: A Guide to Functionality IBM SPSS Statistics is a renowned statistical analysis software package that encompasses a broad range of easy-to-use, sophisticated analytical procedures.

### 12/31/2016. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2

PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Understand when to use multiple Understand the multiple equation and what the coefficients represent Understand different methods

### 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

### Applications of Intermediate/Advanced Statistics in Institutional Research

Applications of Intermediate/Advanced Statistics in Institutional Research Edited by Mary Ann Coughlin THE ASSOCIATION FOR INSTITUTIONAL RESEARCH Number Sixteen Resources in Institional Research 2005 Association

### Sydney Roberts Predicting Age Group Swimmers 50 Freestyle Time 1. 1. Introduction p. 2. 2. Statistical Methods Used p. 5. 3. 10 and under Males p.

Sydney Roberts Predicting Age Group Swimmers 50 Freestyle Time 1 Table of Contents 1. Introduction p. 2 2. Statistical Methods Used p. 5 3. 10 and under Males p. 8 4. 11 and up Males p. 10 5. 10 and under

### 1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96

1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years

### CRJ Doctoral Comprehensive Exam Statistics Friday August 23, :00pm 5:30pm

CRJ Doctoral Comprehensive Exam Statistics Friday August 23, 23 2:pm 5:3pm Instructions: (Answer all questions below) Question I: Data Collection and Bivariate Hypothesis Testing. Answer the following

### Hypothesis Testing & Data Analysis. Statistics. Descriptive Statistics. What is the difference between descriptive and inferential statistics?

2 Hypothesis Testing & Data Analysis 5 What is the difference between descriptive and inferential statistics? Statistics 8 Tools to help us understand our data. Makes a complicated mess simple to understand.

### DATA INTERPRETATION AND STATISTICS

PholC60 September 001 DATA INTERPRETATION AND STATISTICS Books A easy and systematic introductory text is Essentials of Medical Statistics by Betty Kirkwood, published by Blackwell at about 14. DESCRIPTIVE

### DEPARTMENT OF HEALTH AND HUMAN SCIENCES HS900 RESEARCH METHODS

DEPARTMENT OF HEALTH AND HUMAN SCIENCES HS900 RESEARCH METHODS Using SPSS Session 2 Topics addressed today: 1. Recoding data missing values, collapsing categories 2. Making a simple scale 3. Standardisation

### Simple Predictive Analytics Curtis Seare

Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use

### The Statistics Tutor s Quick Guide to

statstutor community project encouraging academics to share statistics support resources All stcp resources are released under a Creative Commons licence The Statistics Tutor s Quick Guide to Stcp-marshallowen-7

### STA-201-TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance

Principles of Statistics STA-201-TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis

### DATA COLLECTION AND ANALYSIS

DATA COLLECTION AND ANALYSIS Quality Education for Minorities (QEM) Network HBCU-UP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. August 23, 2013 Objectives of the Discussion 2 Discuss

### Chi-Square Test. Contingency Tables. Contingency Tables. Chi-Square Test for Independence. Chi-Square Tests for Goodnessof-Fit

Chi-Square Tests 15 Chapter Chi-Square Test for Independence Chi-Square Tests for Goodness Uniform Goodness- Poisson Goodness- Goodness Test ECDF Tests (Optional) McGraw-Hill/Irwin Copyright 2009 by The

### LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING

LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING In this lab you will explore the concept of a confidence interval and hypothesis testing through a simulation problem in engineering setting.

### Multiple Regression in SPSS STAT 314

Multiple Regression in SPSS STAT 314 I. The accompanying data is on y = profit margin of savings and loan companies in a given year, x 1 = net revenues in that year, and x 2 = number of savings and loan

### Univariate Regression

Univariate Regression Correlation and Regression The regression line summarizes the linear relationship between 2 variables Correlation coefficient, r, measures strength of relationship: the closer r is

### Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear.

Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear. In the main dialog box, input the dependent variable and several predictors.

### DESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.

DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,

### Chapter 13 Introduction to Linear Regression and Correlation Analysis

Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing

### Part 2: Analysis of Relationship Between Two Variables

Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable

### Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1

Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1 Calculate counts, means, and standard deviations Produce

### CHAPTER 11 CHI-SQUARE: NON-PARAMETRIC COMPARISONS OF FREQUENCY

CHAPTER 11 CHI-SQUARE: NON-PARAMETRIC COMPARISONS OF FREQUENCY The hypothesis testing statistics detailed thus far in this text have all been designed to allow comparison of the means of two or more samples

### 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

### Multivariate analysis of variance

21 Multivariate analysis of variance In previous chapters, we explored the use of analysis of variance to compare groups on a single dependent variable. In many research situations, however, we are interested

### INTERPRETING THE REPEATED-MEASURES ANOVA

INTERPRETING THE REPEATED-MEASURES ANOVA USING THE SPSS GENERAL LINEAR MODEL PROGRAM RM ANOVA In this scenario (based on a RM ANOVA example from Leech, Barrett, and Morgan, 2005) each of 12 participants

### 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

### When to use Excel. When NOT to use Excel 9/24/2014

Analyzing Quantitative Assessment Data with Excel October 2, 2014 Jeremy Penn, Ph.D. Director When to use Excel You want to quickly summarize or analyze your assessment data You want to create basic visual

### Bivariate Statistics Session 2: Measuring Associations Chi-Square Test

Bivariate Statistics Session 2: Measuring Associations Chi-Square Test Features Of The Chi-Square Statistic The chi-square test is non-parametric. That is, it makes no assumptions about the distribution

### 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.

### 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

### Two Related Samples t Test

Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person