Statistics for Sports Medicine


 Ralf Benson
 4 years ago
 Views:
Transcription
1 Statistics for Sports Medicine Suzanne Hecht, MD University of Minnesota Fellow s Research Conference July 2012: Philadelphia
2
3 GOALS Try not to bore you to death!! Try to teach you something useful Introduce concepts Give you a stats reference guide Encourage sports med research
4
5 QUIZ What is the appropriate stats test to apply?. 50 soccer players wore head gear & 40 did not. Players were followed for diagnosis of concussion over one season. 1. Paired two tailed ttest 2. ANOVA 3. Chisquare analysis 4. McNemar test
6 MY TOP 10 STATS TIP LIST
7 OVERVIEW Introduction Variables Normal distribution Hypothesis testing Comparing means Measuring association Scatterplots & Correlation Regression
8 PURPOSE Stats is just a tool to analyze data you collect Learn the basics Add to your foundation over time Lots of names of tests, just like Sports Medicine!! You wouldn t talk about a Jobe s test during a knee exam Mt Stats
9 PURPOSE Infer something about a population based on information from a sample of that population Use probability concepts Describe how reliable the conclusions are ie: You have all this data & is it useful in someway?
10 MY TOP 10 STATS TIP LIST
11 Variables Discrete Examples Gender (m/f); Fracture (y/n) Nominal or Ordinal Nominal: Set of categories, no ordering ie: m/f Ordinal: Ordering, but no meaning to differences in scores ie Compare 1 st & 2 nd place finishers (ranking) without using actual times Continuous Examples Weight, race time Differences between values has meaning
12 USE FOR FUTURE REFERENCE Variable Summary Statistics Comparing 2 groups Measuring Association Nominal Mode Chisquare Contingency Coefficient Ordinal Median Chisquare Nonparametric Kappa Spearman r Kendall s tao Continuous Mean Median & SD ttest Nonparametric Spearman r Pearson r
13 SAMPLE SIZE & POWER Important to calculate Do this prior to the study Avoid expenses, time, resources, etc. Calculations available in stats software Let s you know that you have enough subjects to detect a meaningful change
14 HYPOTHESIS TESTING Null hypothesis (H 0 ) No difference between groups (groups are the same) Alternative hypothesis (H 1 ) There is a difference between groups Type I error Saying groups are different when they aren t Type II error Saying groups are the same when they are different
15 MY TOP 10 STATS TIP LIST
16 Normal Distribution Applies to continuous variables Mean=median=mode Many stats tests assume nl distr ttest; ANOVA; regression Ways to test to see if a nl distribution Use nonparametric tests or transform data (ie log) if not a nl distribution Methods that assume nl distr Robust to moderate departures of nl distr assumption if n is large enough!
17 Normal Distribution Symmetrical about the mean BLUE= 68.2% of values w/in 1 SD BLUE+ BROWN= 95.4% of values w/in 2 SD BLUE + BROWN + GREEN= 99.7% of values w/in 3 SD
18 PValue = the probability of obtaining results by chance alone p=0.05 (5% chance) May not tell whole story Statistically significant Clinically significant Small or large n s Small n: Type II error Give both: pvalue & CI
19 MY TOP 10 STATS TIP LIST
20
21 Comparing 2 groups or rxs Type of Outcome Continuous Binary (y/n) Nl Distribution Paired Unpaired Paired ttest Yes Parametric Unpaired ttest Sign test No Nonparametric Paired Sign rank test McNemar s test Unpaired Wilcoxon rank sum test Yes Large Sample Size ChiSquared No Fischer s Exact Test
22 Comparing 3 or > groups Type of Outcome Continuous Binary (y/n) Nl Distribution Yes Parametric No Nonparametric Frequency Tables Chisquared Methods ANOVA Kruskal Wallis Test
23 Comparing 2 groups or rxs Type of Outcome Continuous Binary (y/n) Nl Distribution Yes No Parametric Nonparametric Paired Unpaired Paired Unpaired ttest ttest Sign test Sign rank test Wilcoxon rank sum test
24 Comparing Group Means ttest ANOVA Assumptions Data is continuous & nl distributed Methods 2 indep samples: 2 sample ttest Paired data: Paired ttest >2 indep samples: ANOVA Includes Confidence intervals Hypothesis testing
25 3 types 2 sample ttest Student s ttest ttests Independent samples ttest Paired samples ttest Paired data: 2 measurements on same subject or test unit One sample ttest Compare to a known (norm) value
26 ttests Onetailed vs twotailed Almost always use twotailed Results could be higher or lower not just one way
27 95% CI Confidence Intervals 95% confident that the true value falls in the interval. Wide CI suggests uncertainty about data Does the CI contain a value that implies no change or no effect? Mean: 0 Odds ratio: 1 Does the confidence interval lie partly or entirely within a range of clinical indifference?
28 Example: Confidence Intervals Survey 19 millionaires Mean income donation=15% +/ 2 SD CI: +/ 2.4% Interpretation We are 95% confident that millionaires donate between % of their income.
29 Comparing 2 groups or rxs Type of Outcome Continuous Binary (y/n) Nl Distribution Yes No Parametric Nonparametric Paired Unpaired Paired Unpaired ttest ttest Sign test Sign rank test Wilcoxon rank sum test
30 SIGN TEST Nonparametric test Not a nl distribution Alternative to paired ttest Good for small sample size Test the difference for matched pairs on before & after data Method: Calculate diffs Throwout zero diff Test for # of + diff H 1 is true: median does not = 0
31 WILCOXON SIGN RANK TEST Same application as Sign Test Uses the ranks & the signs of diff More powerful test than Sign Test Method: Calculate differences in pairs Throw away zero differences Rank from smallest to largest difference w/out regard to +/ Test: sum of ranks of + diff
32 Wilcoxon Rank Sum Test Also known as: MannWhitney U test Comparing 2 independent samples Not nl distribution Good for detecting changes in medians Method: Combine data from 2 gps Rank smallest to largest Add ranks in the gp with smaller sample size Add ranks in gp with larger N Test: sum of ranks for smaller gp compared to larger gp
33 EXAMPLE: RankSum Test Team Cheetah 5 team members Team Impala 7 team members Results TC: 3, 4, 7, 12, 13 (min) Results TI: 2, 5, 6, 8, 9, 10, 11 (min) Combine data & then rank: 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Sum ranks of smaller gp = 34 Test if sum ranks of smaller gp is the same or different from other group
34 MY TOP 10 STATS TIP LIST
35 Comparing 3 or > groups Type of Outcome Continuous Binary (y/n) Nl Distribution Yes Parametric No Nonparametric Frequency Tables Chisquared Methods ANOVA Kruskal Wallis Test
36 ANOVA Analysis of variance Comparing means of >2 groups Assumes Continuous Nl distrib Same variance w/in each group Benefits compared to ttests Efficiency Avoids multiple testing problem Problem Sign F test tells you that at least 2 gps are different, but not which ones!
37 ANOVAProblem Multiple Comparisons Procedures Used to tell which groups differ Stricter levels for accepting/rejecting that the means are the same 4 methods Bonferroni Tukey NeumanKeuls Scheffe
38 KruskalWallis Test Nonparametric test Use for comparing 3 or > independent groups Think of as a nonparametric ANOVA test Good for detecting changes in median
39 MY TOP 10 STATS TIP LIST
40 Comparing 2 groups or rxs Type of Outcome Continuous Binary (y/n) Paired Unpaired McNemar s test Yes Large Sample Size No ChiSquared Fischer s Exact Test
41 Comparing Frequency Data Binary outcome (yes/no) Paired method McNemar s Test Nonpaired methods Pearson s Chisquare Fisher s Exact Test
42 Assumes Pearson s Chisquare Random samples from 2 groups Compares expected with observed All samples sizes are large enough All frequencies must be > 5 2x2 table: Standard New Helmet Helmet Concussion No Concussion TOTAL n 1 =25 n 2 =19 p 1 =18/25 =0.72 (72%) p 2 =6/19 =0.32 (32%)
43 Pearson s Chisquare OBSERVED Standard Helmet New Helmet TOTAL Concussion No Concussion 7 13 TOTAL n 1 =25 n 2 = X 2 =7.1 (p=0.0077) EXPECTED (if not different) Concussion No Concussion Standard Helmet 24/44 x 25 = /44 x 25 =11.36 New Helmet 24/44 x 19 = /44 x 19 =8.64
44 Fisher s Exact Test Use this test when 1 or more of frequencies is < 5
45 McNemar s Test Use for paired binary data Same subject before & after rx Crossover study
46 MY TOP 10 STATS TIP LIST
47 RISK Risk difference Absolute difference in risk proportions Can be difficult to interpret Relative Risk (RR) Also known as Risk Ratio Risk in 1 gp/risk in other gps Odds Ratio (OR) Probability or Odds of an event OR= odds of exposed gp/odds of control gp OR=1 means no difference
48 RELATIVE RISK Relative risk (RR) is the risk of an event relative to exposure. Risk of having a boy if mom took testosterone during pregnancy 75/100=75% Risk (probability) of having a boy= 51/100= 51% Risk Ratio=.75/.51=1.5 Easier to understand Risk ratio =0.5 =risk is half Risk ratio=2=risk is double
49 CALCULATING ODDS Odds of an event =# of events/# of nonevents 51 boys born for every 100 births Odds of any randomly chosen delivery being a boy=51/10051=1.04 Odds>1: Event is more likely to happen than not Odds of certain event= Odds<1: Event is not likely to happen Odds of an impossible event=0
50 ODDS RATIO Testosterone example 75/ /10051= 3/1.04= 2.9 The odds of having a boy is 2.9x higher in moms using testosterone vs mom s not using testosterone.
51 ODDS RATIO: Benefits No upper limit RR range varies depending on baseline prevalence When events are low (rare dz) OR approx RR OR ok to use with case control Don t use RR with case control
52 Calculating OR Cross Product Factor (Event) Group 1 Group 2 a b No Factor (No Event) c OR= a/c b/d d = a x d b x c Concussion No Concussion Standard New Helmet Helmet x 13 = x 7
53
54
55 MY TOP 10 STATS TIP LIST
56 SCATTERPLOT Can help answer the following Are variables X & Y related? Are X & Y linearly related? Are X & Y nonlinearly related? Does the variation in Y change depending on X? Are there outliers? 1. Linear relationship 2. Small scatter (strong correlation) 3. + slope (+ correlation)
57 SCATTERPLOTS No relationship 1. Linear 2. Small scatter (strong correlation) 3.  slope (neg correlation)
58 SCATTERPLOTS Outlier Nonlinear
59 CORRELATION: PEARSON Measures the strength of (linear) association between 2 variables Ranges from 1 to 1 1= 1= 0= Examples: r=0.8 r=0.3 r=0.7 perfect + correlation perfect correlation no correlation strong + correlation weak + correlation moderate correlation
60 MY TOP 10 STATS TIP LIST
61 REGRESSION A straight line that describes the dependence of one variable on another is called a regression line Y=response variable ie finishing time X=explanatory variable ie body fat percentage Is finishing time predicted by body fat percentage?
62 Linear REGRESSION TYPES Data: Normal distribution Simple or Multiple Logistical Data: binary (y/n) Simple or Multiple Multiple Regression Models Allow estimation of the indep effect of each X after controlling for other variables in the model.
63 Simple LINEAR REGRESSION Use to predict Y given X Determine best fitting equation Test whether there is a relationship between X & Y
64 Linear Regression R 2 value =% of variance in Y explained by X If R 2 =1 then x can predict y 100% of the time F test for significance If p >0.05 then no significant relationship (slope of line =zero) exists between x & Y
65 Multiple Linear Regression Model that explains how a single dependent variable (Y) relates to several independent variables (x). Example: Test if age, gender, body fat %, prior triathlon competitions, & occupation predict finishing time.
66 Multiple Linear Regression How many variables to use? Recommend that you have 1020x # of cases to variables tested. Test lots of variables Increase random chance of stat sign Model becomes unstable
67 Multiple Linear Regression Example cont: Model predicts 90% of variance in performance Now test for which variable or combinations of variables is most predictive Body fat %: 15% Age: 10% Gender: 30% Body fat & gender 35% Occupation 0% Prior triathlon 40%
68 MY TOP 10 STATS TIP LIST
69 QUIZ What is the appropriate stats test to apply?. 50 soccer players wore head gear & 40 did not. Players were followed for diagnosis of concussion over one season. 1. Paired two tailed ttest 2. ANOVA 3. Chisquare analysis 4. McNemar test
70 OTHER TIPS Stats support at Universities Usually charge per hour MS cheaper than PhD Authorship If stats person willing to: (International Committee of Medical Journal Editors (ICMJE) guidelines) Help design study Analyze data Format tables, graphs, etc Write a portion of article May be able to get small grant to cover $ of stats analysis Online support
71
72 REFERENCES 1. Applied Biostatistics in Clinical Research Course Book; CaseWestern Reserve General Clinical Research Center Biostatistics 100B Course Book; UCLA The Essentials of Clinical Investigation Course Book; UCLA Clinical Research Center Moore, McCabe, Craig (2009) Introduction to the Practice of Statistics, Sixth Edition. WH Freeman and Company, New York. ISBN13:
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:
More informationDescriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
More informationThe 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
More informationSection 3 Part 1. Relationships between two numerical variables
Section 3 Part 1 Relationships between two numerical variables 1 Relationship between two variables The summary statistics covered in the previous lessons are appropriate for describing a single variable.
More informationNONPARAMETRIC STATISTICS 1. depend on assumptions about the underlying distribution of the data (or on the Central Limit Theorem)
NONPARAMETRIC STATISTICS 1 PREVIOUSLY parametric statistics in estimation and hypothesis testing... construction of confidence intervals computing of pvalues classical significance testing depend on assumptions
More informationII. 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,
More informationIntroduction 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.................
More informationBiostatistics: 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
More informationSPSS Explore procedure
SPSS Explore procedure One useful function in SPSS is the Explore procedure, which will produce histograms, boxplots, stemandleaf plots and extensive descriptive statistics. To run the Explore procedure,
More informationX X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1)
CORRELATION AND REGRESSION / 47 CHAPTER EIGHT CORRELATION AND REGRESSION Correlation and regression are statistical methods that are commonly used in the medical literature to compare two or more variables.
More informationCorrelation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables 2
Lesson 4 Part 1 Relationships between two numerical variables 1 Correlation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables
More informationCome scegliere un test statistico
Come scegliere un test statistico Estratto dal Capitolo 37 of Intuitive Biostatistics (ISBN 0195086074) by Harvey Motulsky. Copyright 1995 by Oxfd University Press Inc. (disponibile in Iinternet) Table
More informationOverview of NonParametric Statistics PRESENTER: ELAINE EISENBEISZ OWNER AND PRINCIPAL, OMEGA STATISTICS
Overview of NonParametric Statistics PRESENTER: ELAINE EISENBEISZ OWNER AND PRINCIPAL, OMEGA STATISTICS About Omega Statistics Private practice consultancy based in Southern California, Medical and Clinical
More informationUNIVERSITY 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
More informationNonparametric Statistics
Nonparametric Statistics J. Lozano University of Goettingen Department of Genetic Epidemiology Interdisciplinary PhD Program in Applied Statistics & Empirical Methods Graduate Seminar in Applied Statistics
More informationUsing 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
More informationProjects 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,
More informationDESCRIPTIVE 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,
More informationTHE KRUSKAL WALLLIS TEST
THE KRUSKAL WALLLIS TEST TEODORA H. MEHOTCHEVA Wednesday, 23 rd April 08 THE KRUSKALWALLIS TEST: The nonparametric alternative to ANOVA: testing for difference between several independent groups 2 NON
More informationStudy 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
More informationData Analysis, Research Study Design and the IRB
Minding the pvalues p and Quartiles: Data Analysis, Research Study Design and the IRB Don AllensworthDavies, MSc Research Manager, Data Coordinating Center Boston University School of Public Health IRB
More informationWe are often interested in the relationship between two variables. Do people with more years of fulltime education earn higher salaries?
Statistics: Correlation Richard Buxton. 2008. 1 Introduction We are often interested in the relationship between two variables. Do people with more years of fulltime education earn higher salaries? Do
More informationSPSS 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
More informationComparing Means in Two Populations
Comparing Means in Two Populations Overview The previous section discussed hypothesis testing when sampling from a single population (either a single mean or two means from the same population). Now we
More informationThe 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 Stcpmarshallowen7
More informationStatistics. Measurement. Scales of Measurement 7/18/2012
Statistics Measurement Measurement is defined as a set of rules for assigning numbers to represent objects, traits, attributes, or behaviors A variableis something that varies (eye color), a constant does
More informationQUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NONPARAMETRIC TESTS
QUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NONPARAMETRIC TESTS This booklet contains lecture notes for the nonparametric work in the QM course. This booklet may be online at http://users.ox.ac.uk/~grafen/qmnotes/index.html.
More informationRankBased NonParametric Tests
RankBased NonParametric Tests Reminder: Student Instructional Rating Surveys You have until May 8 th to fill out the student instructional rating surveys at https://sakai.rutgers.edu/portal/site/sirs
More information1. 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 1propZint 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
More informationDATA 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
More informationIntroduction 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.
More informationAnalysis of Variance ANOVA
Analysis of Variance ANOVA Overview We ve used the t test to compare the means from two independent groups. Now we ve come to the final topic of the course: how to compare means from more than two populations.
More informationSection 13, Part 1 ANOVA. Analysis Of Variance
Section 13, Part 1 ANOVA Analysis Of Variance Course Overview So far in this course we ve covered: Descriptive statistics Summary statistics Tables and Graphs Probability Probability Rules Probability
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationStatistical 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
More informationDifference tests (2): nonparametric
NST 1B Experimental Psychology Statistics practical 3 Difference tests (): nonparametric Rudolf Cardinal & Mike Aitken 10 / 11 February 005; Department of Experimental Psychology University of Cambridge
More informationCHAPTER 14 NONPARAMETRIC TESTS
CHAPTER 14 NONPARAMETRIC TESTS Everything that we have done up until now in statistics has relied heavily on one major fact: that our data is normally distributed. We have been able to make inferences
More informationEPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST
EPS 625 INTERMEDIATE STATISTICS The Friedman test is an extension of the Wilcoxon test. The Wilcoxon test can be applied to repeatedmeasures data if participants are assessed on two occasions or conditions
More informationSimple 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
More informationBasic Statistics and Data Analysis for Health Researchers from Foreign Countries
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma siersma@sund.ku.dk The Research Unit for General Practice in Copenhagen Dias 1 Content Quantifying association
More informationThe correlation coefficient
The correlation coefficient Clinical Biostatistics The correlation coefficient Martin Bland Correlation coefficients are used to measure the of the relationship or association between two quantitative
More informationChisquare test Fisher s Exact test
Lesson 1 Chisquare test Fisher s Exact test McNemar s Test Lesson 1 Overview Lesson 11 covered two inference methods for categorical data from groups Confidence Intervals for the difference of two proportions
More informationAnalyzing 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
More informationStatistics 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
More informationPermutation Tests for Comparing Two Populations
Permutation Tests for Comparing Two Populations Ferry Butar Butar, Ph.D. JaeWan Park Abstract Permutation tests for comparing two populations could be widely used in practice because of flexibility of
More informationSTA201TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance
Principles of Statistics STA201TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis
More informationSPSS 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
More informationResearch 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
More informationNonParametric Tests (I)
Lecture 5: NonParametric Tests (I) KimHuat LIM lim@stats.ox.ac.uk http://www.stats.ox.ac.uk/~lim/teaching.html Slide 1 5.1 Outline (i) Overview of DistributionFree Tests (ii) Median Test for Two Independent
More informationUnivariate 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
More informationBusiness 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, McGrawHill/Irwin, 2008, ISBN: 9780073319889. Required Computing
More informationModule 5: Multiple Regression Analysis
Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College
More informationTypes of Data, Descriptive Statistics, and Statistical Tests for Nominal Data. Patrick F. Smith, Pharm.D. University at Buffalo Buffalo, New York
Types of Data, Descriptive Statistics, and Statistical Tests for Nominal Data Patrick F. Smith, Pharm.D. University at Buffalo Buffalo, New York . NONPARAMETRIC STATISTICS I. DEFINITIONS A. Parametric
More informationSCHOOL 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
More informationNonparametric TwoSample Tests. Nonparametric Tests. Sign Test
Nonparametric TwoSample Tests Sign test MannWhitney Utest (a.k.a. Wilcoxon twosample test) KolmogorovSmirnov Test Wilcoxon SignedRank Test TukeyDuckworth Test 1 Nonparametric Tests Recall, nonparametric
More informationLinear Models in STATA and ANOVA
Session 4 Linear Models in STATA and ANOVA Page Strengths of Linear Relationships 42 A Note on NonLinear Relationships 44 Multiple Linear Regression 45 Removal of Variables 48 Independent Samples
More informationSimple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the
More informationPrinciples of Hypothesis Testing for Public Health
Principles of Hypothesis Testing for Public Health Laura Lee Johnson, Ph.D. Statistician National Center for Complementary and Alternative Medicine johnslau@mail.nih.gov Fall 2011 Answers to Questions
More informationbusiness 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
More information1 Nonparametric Statistics
1 Nonparametric Statistics When finding confidence intervals or conducting tests so far, we always described the population with a model, which includes a set of parameters. Then we could make decisions
More informationTesting Group Differences using Ttests, ANOVA, and Nonparametric Measures
Testing Group Differences using Ttests, ANOVA, and Nonparametric Measures Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 354870348 Phone:
More informationSection 14 Simple Linear Regression: Introduction to Least Squares Regression
Slide 1 Section 14 Simple Linear Regression: Introduction to Least Squares Regression There are several different measures of statistical association used for understanding the quantitative relationship
More informationHypothesis testing  Steps
Hypothesis testing  Steps Steps to do a twotailed test of the hypothesis that β 1 0: 1. Set up the hypotheses: H 0 : β 1 = 0 H a : β 1 0. 2. Compute the test statistic: t = b 1 0 Std. error of b 1 =
More informationTwo Correlated Proportions (McNemar Test)
Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with
More informationOnce saved, if the file was zipped you will need to unzip it. For the files that I will be posting you need to change the preferences.
1 Commands in JMP and Statcrunch Below are a set of commands in JMP and Statcrunch which facilitate a basic statistical analysis. The first part concerns commands in JMP, the second part is for analysis
More informationBowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition
Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology StepbyStep  Excel Microsoft Excel is a spreadsheet software application
More informationANALYSING LIKERT SCALE/TYPE DATA, ORDINAL LOGISTIC REGRESSION EXAMPLE IN R.
ANALYSING LIKERT SCALE/TYPE DATA, ORDINAL LOGISTIC REGRESSION EXAMPLE IN R. 1. Motivation. Likert items are used to measure respondents attitudes to a particular question or statement. One must recall
More informationSection Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini
NEW YORK UNIVERSITY ROBERT F. WAGNER GRADUATE SCHOOL OF PUBLIC SERVICE Course Syllabus Spring 2016 Statistical Methods for Public, Nonprofit, and Health Management Section Format Day Begin End Building
More informationStudy Design and Statistical Analysis
Study Design and Statistical Analysis Anny H Xiang, PhD Department of Preventive Medicine University of Southern California Outline Designing Clinical Research Studies Statistical Data Analysis Designing
More informationThe Wilcoxon RankSum Test
1 The Wilcoxon RankSum Test The Wilcoxon ranksum test is a nonparametric alternative to the twosample ttest which is based solely on the order in which the observations from the two samples fall. We
More informationPermutation & NonParametric Tests
Permutation & NonParametric Tests Statistical tests Gather data to assess some hypothesis (e.g., does this treatment have an effect on this outcome?) Form a test statistic for which large values indicate
More informationHYPOTHESIS TESTING: CONFIDENCE INTERVALS, TTESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, TTESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
More informationIntroduction. Hypothesis Testing. Hypothesis Testing. Significance Testing
Introduction Hypothesis Testing Mark Lunt Arthritis Research UK Centre for Ecellence in Epidemiology University of Manchester 13/10/2015 We saw last week that we can never know the population parameters
More informationMASTER COURSE SYLLABUSPROTOTYPE PSYCHOLOGY 2317 STATISTICAL METHODS FOR THE BEHAVIORAL SCIENCES
MASTER COURSE SYLLABUSPROTOTYPE THE PSYCHOLOGY DEPARTMENT VALUES ACADEMIC FREEDOM AND THUS OFFERS THIS MASTER SYLLABUSPROTOTYPE ONLY AS A GUIDE. THE INSTRUCTORS ARE FREE TO ADAPT THEIR COURSE SYLLABI
More informationAn 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
More informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 15 scale to 0100 scores When you look at your report, you will notice that the scores are reported on a 0100 scale, even though respondents
More informationINTRODUCTORY STATISTICS
INTRODUCTORY STATISTICS FIFTH EDITION Thomas H. Wonnacott University of Western Ontario Ronald J. Wonnacott University of Western Ontario WILEY JOHN WILEY & SONS New York Chichester Brisbane Toronto Singapore
More informationErik Parner 14 September 2016. Basic Biostatistics  Day 221 September, 2016 1
PhD course in Basic Biostatistics Day Erik Parner, Department of Biostatistics, Aarhus University Logtransformation of continuous data Exercise.+.4+Standard (Triglyceride) Logarithms and exponentials
More informationSimple linear regression
Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between
More informationBill 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
More informationDATA ANALYSIS. QEM Network HBCUUP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. Howard University
DATA ANALYSIS QEM Network HBCUUP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. Howard University Quantitative Research What is Statistics? Statistics (as a subject) is the science
More informationCourse 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, McGrawHill/Irwin, 2010, ISBN: 9780077384470 [This
More informationtraining programme in pharmaceutical medicine Clinical Data Management and Analysis
training programme in pharmaceutical medicine Clinical Data Management and Analysis 1921 may 2011 Clinical Data Management and Analysis 19 21 MAY 2011 LocaL: University of Aveiro, Campus Universitário
More informationMULTIPLE 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
More informationData 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
More informationStatistiek II. John Nerbonne. October 1, 2010. Dept of Information Science j.nerbonne@rug.nl
Dept of Information Science j.nerbonne@rug.nl October 1, 2010 Course outline 1 Oneway ANOVA. 2 Factorial ANOVA. 3 Repeated measures ANOVA. 4 Correlation and regression. 5 Multiple regression. 6 Logistic
More informationStat 5102 Notes: Nonparametric Tests and. confidence interval
Stat 510 Notes: Nonparametric Tests and Confidence Intervals Charles J. Geyer April 13, 003 This handout gives a brief introduction to nonparametrics, which is what you do when you don t believe the assumptions
More informationParametric and nonparametric statistical methods for the life sciences  Session I
Why nonparametric methods What test to use? Rank Tests Parametric and nonparametric statistical methods for the life sciences  Session I Liesbeth Bruckers Geert Molenberghs Interuniversity Institute
More informationCorrelational 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
More information" 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
More informationTwosample inference: Continuous data
Twosample inference: Continuous data Patrick Breheny April 5 Patrick Breheny STA 580: Biostatistics I 1/32 Introduction Our next two lectures will deal with twosample inference for continuous data As
More informationBivariate Statistics Session 2: Measuring Associations ChiSquare Test
Bivariate Statistics Session 2: Measuring Associations ChiSquare Test Features Of The ChiSquare Statistic The chisquare test is nonparametric. That is, it makes no assumptions about the distribution
More informationChapter 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) 
More informationMathematics within the Psychology Curriculum
Mathematics within the Psychology Curriculum Statistical Theory and Data Handling Statistical theory and data handling as studied on the GCSE Mathematics syllabus You may have learnt about statistics and
More informationSPSS Guide Howto, Tips, Tricks & Statistical Techniques
SPSS Guide Howto, Tips, Tricks & Statistical Techniques Support for the course Research Methodology for IB Also useful for your BSc or MSc thesis March 2014 Dr. Marijke Leliveld Jacob Wiebenga, MSc CONTENT
More informationChapter 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
More informationHomework 11. Part 1. Name: Score: / null
Name: Score: / Homework 11 Part 1 null 1 For which of the following correlations would the data points be clustered most closely around a straight line? A. r = 0.50 B. r = 0.80 C. r = 0.10 D. There is
More informationStatistics. Onetwo sided test, Parametric and nonparametric test statistics: one group, two groups, and more than two groups samples
Statistics Onetwo sided test, Parametric and nonparametric test statistics: one group, two groups, and more than two groups samples February 3, 00 Jobayer Hossain, Ph.D. & Tim Bunnell, Ph.D. Nemours
More informationHYPOTHESIS TESTING WITH SPSS:
HYPOTHESIS TESTING WITH SPSS: A NONSTATISTICIAN S GUIDE & TUTORIAL by Dr. Jim Mirabella SPSS 14.0 screenshots reprinted with permission from SPSS Inc. Published June 2006 Copyright Dr. Jim Mirabella CHAPTER
More information