This can dilute the significance of a departure from the null hypothesis. We can focus the test on departures of a particular form.


 Charles Parker
 2 years ago
 Views:
Transcription
1 OneDegreeofFreedom Tests Test for group occasion interactions has (number of groups 1) number of occasions 1) degrees of freedom. This can dilute the significance of a departure from the null hypothesis. We can focus the test on departures of a particular form. 1
2 For example: if we expect all responses after the baseline to respond similarly: µ after baseline = µ baseline + shift, where shift depends on treatment group, we estimate the difference of (µ 1 + µ 4 + µ 6 )/3 µ 0 between treated and placebo groups: [(µ S,1 + µ S,4 + µ S,6 )/3 µ S,0 ] [(µ P,1 + µ P,4 + µ P,6 )/3 µ P,0 ] or we can focus on the area under the curve 2 (µ 1 µ 0 ) (µ 4 µ 0 ) + 1 (µ 6 µ 0 ) and again estimate the difference between the groups. 2
3 Test these hypotheses in PROC MIXED by adding the statements contrast response vs baseline treatment * week / chisq; contrast AUC treatment * week / chisq; The additional output is Contrasts Num Den Label DF DF ChiSquare F Value Pr > ChiSq Pr > F response vs baseline <.0001 <.0001 AUC <.0001 <
4 Exploiting the Baseline Profile analysis tests for any differences in change over time. With baseline data and randomized groups, we know that the baseline responses can differ between groups only by sampling variation. We can exploit this knowledge by converting the later responses into differences from baseline, or treating the baseline measurement as a covariate instead of a response. 4
5 Convert to differences:... data tlc_uni; /* Convert the data from multivariate form (one record per subject, with * 4 responses), to univariate form (one record per response, with a new * variable week to identify the occasion; differences from baseline */ set tlc; drop y0  y6; week = 1; y = y1  y0; output; week = 4; y = y4  y0; output; week = 6; y = y6  y0; output; run; proc sort data = tlc_uni; by treatment; proc mixed data = tlc_uni order = data; class child treatment week; model y = treatment week treatment * week / solution chisq; repeated week / subject = child type = un; run; 5
6 The key output is Mixed Model Analysis of TLC data 8 Differences from baseline Type 3 Tests of Fixed Effects Num Den Effect DF DF ChiSquare F Value Pr > ChiSq Pr > F treatment <.0001 <.0001 week <.0001 <.0001 treatment*week <.0001 <.0001 Note that the main effect of treatment is now of substantive interest. In fact, it is the same as the simpler onedegreeoffreedom test described above. These treatment and treatment week ChiSquares sum to the interaction term in the previous analysis. 6
7 Covariate approach:... data tlc_uni; /* Convert the data from multivariate form (one record per subject, with * 4 responses), to univariate form (one record per response, with a new * variable week to identify the occasion; keep y0 as a covariate */ set tlc; drop y1  y6; week = 1; y = y1; output; week = 4; y = y4; output; week = 6; y = y6; output; run; proc sort data = tlc_uni; by treatment; proc mixed data = tlc_uni order = data; class child treatment week; model y = treatment week treatment * week y0 / solution chisq; repeated week / subject = child type = un; run; 7
8 The key output is Mixed Model Analysis of TLC data 8 Use baseline data as a covariate The Mixed Procedure Solution for Fixed Effects Standard Effect treatment week Estimate Error DF t Value Pr > t y <.0001 Type 3 Tests of Fixed Effects Num Den Effect DF DF ChiSquare F Value Pr > ChiSq Pr > F treatment <.0001 <.0001 week <.0001 <.0001 treatment*week <.0001 <.0001 y <.0001 <
9 The week and treatment week tests are the same as in the deviation from baseline analysis, but the main effect of treatment is slightly more significant. The y0 coefficient of is more than 2 standard deviations less than 1 (significant at the 5% level). 9
10 Constraining baseline means to be equal:... data tlc_uni; set tlc; drop y0  y6; week = 1; y = y1; output; week = 4; y = y4; output; week = 6; y = y6; output; /* put baseline in its own group: */ week = 0; y = y0; treatment = Z ; output; run; proc sort data = tlc_uni; by treatment; proc mixed data = tlc_uni order = data; class child treatment week; model y = week treatment * week /* no main effect of treatment */ / solution chisq; repeated week / subject = child type = un; run; 10
11 The key output is Mixed Model Analysis of TLC data, baseline means equal 8 The Mixed Procedure Solution for Fixed Effects Standard Effect treatment week Estimate Error DF t Value Pr > t Intercept <.0001 week week week week treatment*week A <.0001 treatment*week A <.0001 treatment*week A treatment*week P treatment*week P treatment*week P treatment*week Z
12 Type 3 Tests of Fixed Effects Num Den Effect DF DF ChiSquare F Value Pr > ChiSq Pr > F week <.0001 <.0001 treatment*week <.0001 <.0001 This approach: exploits the information about baseline data; is as powerful as any other; uses data for a subject even when the baseline is missing. 12
SAS Syntax and Output for Data Manipulation:
Psyc 944 Example 5 page 1 Practice with Fixed and Random Effects of Time in Modeling WithinPerson Change The models for this example come from Hoffman (in preparation) chapter 5. We will be examining
More informationChapter 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
More informationChapter 5 Analysis of variance SPSS Analysis of variance
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means Oneway ANOVA To test the null hypothesis that several population means are equal,
More informationCalculating PValues. Parkland College. Isela Guerra Parkland College. Recommended Citation
Parkland College A with Honors Projects Honors Program 2014 Calculating PValues Isela Guerra Parkland College Recommended Citation Guerra, Isela, "Calculating PValues" (2014). A with Honors Projects.
More informationRandom effects and nested models with SAS
Random effects and nested models with SAS /************* classical2.sas ********************* Three levels of factor A, four levels of B Both fixed Both random A fixed, B random B nested within A ***************************************************/
More informationThe GoodnessofFit Test
on the Lecture 49 Section 14.3 HampdenSydney College Tue, Apr 21, 2009 Outline 1 on the 2 3 on the 4 5 Hypotheses on the (Steps 1 and 2) (1) H 0 : H 1 : H 0 is false. (2) α = 0.05. p 1 = 0.24 p 2 = 0.20
More informationUse of deviance statistics for comparing models
A likelihoodratio test can be used under full ML. The use of such a test is a quite general principle for statistical testing. In hierarchical linear models, the deviance test is mostly used for multiparameter
More informationUnit 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
More informationUsing An Ordered Logistic Regression Model with SAS Vartanian: SW 541
Using An Ordered Logistic Regression Model with SAS Vartanian: SW 541 libname in1 >c:\=; Data first; Set in1.extract; A=1; PROC LOGIST OUTEST=DD MAXITER=100 ORDER=DATA; OUTPUT OUT=CC XBETA=XB P=PROB; MODEL
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 informationIndices of Model Fit STRUCTURAL EQUATION MODELING 2013
Indices of Model Fit STRUCTURAL EQUATION MODELING 2013 Indices of Model Fit A recommended minimal set of fit indices that should be reported and interpreted when reporting the results of SEM analyses:
More informationLogistic regression diagnostics
Logistic regression diagnostics Biometry 755 Spring 2009 Logistic regression diagnostics p. 1/28 Assessing model fit A good model is one that fits the data well, in the sense that the values predicted
More informationVI. Introduction to Logistic Regression
VI. Introduction to Logistic Regression We turn our attention now to the topic of modeling a categorical outcome as a function of (possibly) several factors. The framework of generalized linear models
More informationChapter 9, Part A Hypothesis Tests. Learning objectives
Chapter 9, Part A Hypothesis Tests Slide 1 Learning objectives 1. Understand how to develop Null and Alternative Hypotheses 2. Understand Type I and Type II Errors 3. Able to do hypothesis test about population
More information12.5: CHISQUARE GOODNESS OF FIT TESTS
125: ChiSquare Goodness of Fit Tests CD121 125: CHISQUARE GOODNESS OF FIT TESTS In this section, the χ 2 distribution is used for testing the goodness of fit of a set of data to a specific probability
More informationEPS 625 ANALYSIS OF COVARIANCE (ANCOVA) EXAMPLE USING THE GENERAL LINEAR MODEL PROGRAM
EPS 6 ANALYSIS OF COVARIANCE (ANCOVA) EXAMPLE USING THE GENERAL LINEAR MODEL PROGRAM ANCOVA One Continuous Dependent Variable (DVD Rating) Interest Rating in DVD One Categorical/Discrete Independent Variable
More informationElectronic Thesis and Dissertations UCLA
Electronic Thesis and Dissertations UCLA Peer Reviewed Title: A Multilevel Longitudinal Analysis of Teaching Effectiveness Across Five Years Author: Wang, Kairong Acceptance Date: 2013 Series: UCLA Electronic
More information861 Example SPLH. 5 page 1. prefer to have. New data in. SPSS Syntax FILE HANDLE. VARSTOCASESS /MAKE rt. COMPUTE mean=2. COMPUTE sal=2. END IF.
SPLH 861 Example 5 page 1 Multivariate Models for Repeated Measures Response Times in Older and Younger Adults These data were collected as part of my masters thesis, and are unpublished in this form (to
More informationBasic Statistical and Modeling Procedures Using SAS
Basic Statistical and Modeling Procedures Using SAS OneSample Tests The statistical procedures illustrated in this handout use two datasets. The first, Pulse, has information collected in a classroom
More informationAn Introduction to Statistical Tests for the SAS Programmer Sara Beck, Fred Hutchinson Cancer Research Center, Seattle, WA
ABSTRACT An Introduction to Statistical Tests for the SAS Programmer Sara Beck, Fred Hutchinson Cancer Research Center, Seattle, WA Often SAS Programmers find themselves in situations where performing
More informationLecture 14: GLM Estimation and Logistic Regression
Lecture 14: GLM Estimation and Logistic Regression Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology Medical University of South
More informationLesson 1: Comparison of Population Means Part c: Comparison of Two Means
Lesson : Comparison of Population Means Part c: Comparison of Two Means Welcome to lesson c. This third lesson of lesson will discuss hypothesis testing for two independent means. Steps in Hypothesis
More informationSydney 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
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 informationSurvey, Statistics and Psychometrics Core Research Facility University of NebraskaLincoln. LogRank Test for More Than Two Groups
Survey, Statistics and Psychometrics Core Research Facility University of NebraskaLincoln LogRank Test for More Than Two Groups Prepared by Harlan Sayles (SRAM) Revised by Julia Soulakova (Statistics)
More information1.5 Oneway Analysis of Variance
Statistics: Rosie Cornish. 200. 1.5 Oneway Analysis of Variance 1 Introduction Oneway analysis of variance (ANOVA) is used to compare several means. This method is often used in scientific or medical experiments
More informationAn Introduction to Modeling Longitudinal Data
An Introduction to Modeling Longitudinal Data Session I: Basic Concepts and Looking at Data Robert Weiss Department of Biostatistics UCLA School of Public Health robweiss@ucla.edu August 2010 Robert Weiss
More informationNew SAS Procedures for Analysis of Sample Survey Data
New SAS Procedures for Analysis of Sample Survey Data Anthony An and Donna Watts, SAS Institute Inc, Cary, NC Abstract Researchers use sample surveys to obtain information on a wide variety of issues Many
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 informationIndependent t Test (Comparing Two Means)
Independent t Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent ttest when to use the independent ttest the use of SPSS to complete an independent
More informationIntroduction 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
More informationExperimental Design for Influential Factors of Rates on Massive Open Online Courses
Experimental Design for Influential Factors of Rates on Massive Open Online Courses December 12, 2014 Ning Li nli7@stevens.edu Qing Wei qwei1@stevens.edu Yating Lan ylan2@stevens.edu Yilin Wei ywei12@stevens.edu
More informationBiostatistics Short Course Introduction to Longitudinal Studies
Biostatistics Short Course Introduction to Longitudinal Studies Zhangsheng Yu Division of Biostatistics Department of Medicine Indiana University School of Medicine Zhangsheng Yu (Indiana University) Longitudinal
More informationNormality Testing in Excel
Normality Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com
More informationThe Relationship Between Rodent Offspring Blood Lead Levels and Maternal Diet
The Relationship Between Rodent Offspring Blood Lead Levels and Maternal Diet Allison Crawford, Xiahong Li, Mira Shapiro 1, Ruitao Zhang Introduction A study was undertaken to understand the effect of
More information93.4 Likelihood ratio test. NeymanPearson lemma
93.4 Likelihood ratio test NeymanPearson lemma 91 Hypothesis Testing 91.1 Statistical Hypotheses Statistical hypothesis testing and confidence interval estimation of parameters are the fundamental
More informationElementary Statistics Sample Exam #3
Elementary Statistics Sample Exam #3 Instructions. No books or telephones. Only the supplied calculators are allowed. The exam is worth 100 points. 1. A chi square goodness of fit test is considered to
More informationSUGI 29 Statistics and Data Analysis
Paper 19429 Head of the CLASS: Impress your colleagues with a superior understanding of the CLASS statement in PROC LOGISTIC Michelle L. Pritchard and David J. Pasta Ovation Research Group, San Francisco,
More informationStatistics, Data Analysis & Econometrics
Using the LOGISTIC Procedure to Model Responses to Financial Services Direct Marketing David Marsh, Senior Credit Risk Modeler, Canadian Tire Financial Services, Welland, Ontario ABSTRACT It is more important
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 informationFinal 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
More informationUnit 29 ChiSquare GoodnessofFit Test
Unit 29 ChiSquare GoodnessofFit Test Objectives: To perform the chisquare hypothesis test concerning proportions corresponding to more than two categories of a qualitative variable To perform the Bonferroni
More informationADVANCED FORECASTING MODELS USING SAS SOFTWARE
ADVANCED FORECASTING MODELS USING SAS SOFTWARE Girish Kumar Jha IARI, Pusa, New Delhi 110 012 gjha_eco@iari.res.in 1. Transfer Function Model Univariate ARIMA models are useful for analysis and forecasting
More informationGeneral Procedure for Hypothesis Test. Five types of statistical analysis. 1. Formulate H 1 and H 0. General Procedure for Hypothesis Test
Five types of statistical analysis General Procedure for Hypothesis Test Descriptive Inferential Differences Associative Predictive What are the characteristics of the respondents? What are the characteristics
More informationOutline. Topic 4  Analysis of Variance Approach to Regression. Partitioning Sums of Squares. Total Sum of Squares. Partitioning sums of squares
Topic 4  Analysis of Variance Approach to Regression Outline Partitioning sums of squares Degrees of freedom Expected mean squares General linear test  Fall 2013 R 2 and the coefficient of correlation
More informationData Mining and Data Warehousing. Henryk Maciejewski. Data Mining Predictive modelling: regression
Data Mining and Data Warehousing Henryk Maciejewski Data Mining Predictive modelling: regression Algorithms for Predictive Modelling Contents Regression Classification Auxiliary topics: Estimation of prediction
More information1. 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
More information6 Variables: PD MF MA K IAH SBS
options pageno=min nodate formdlim=''; title 'Canonical Correlation, Journal of Interpersonal Violence, 10: 354366.'; data SunitaPatel; infile 'C:\Users\Vati\Documents\StatData\Sunita.dat'; input Group
More informationTwoSample TTests Allowing Unequal Variance (Enter Difference)
Chapter 45 TwoSample TTests Allowing Unequal Variance (Enter Difference) Introduction This procedure provides sample size and power calculations for one or twosided twosample ttests when no assumption
More information6. Statistical Inference: Significance Tests
6. Statistical Inference: Significance Tests Goal: Use statistical methods to check hypotheses such as Women's participation rates in elections in France is higher than in Germany. (an effect) Ethnic divisions
More informationTwo 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
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 informationSPSS Guide: Regression Analysis
SPSS Guide: Regression Analysis I put this together to give you a stepbystep guide for replicating what we did in the computer lab. It should help you run the tests we covered. The best way to get familiar
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 informationChapter Five: Paired Samples Methods 1/38
Chapter Five: Paired Samples Methods 1/38 5.1 Introduction 2/38 Introduction Paired data arise with some frequency in a variety of research contexts. Patients might have a particular type of laser surgery
More informationCHAPTER 11 CHISQUARE AND F DISTRIBUTIONS
CHAPTER 11 CHISQUARE AND F DISTRIBUTIONS CHISQUARE TESTS OF INDEPENDENCE (SECTION 11.1 OF UNDERSTANDABLE STATISTICS) In chisquare tests of independence we use the hypotheses. H0: The variables are independent
More informationPart 3. Comparing Groups. Chapter 7 Comparing Paired Groups 189. Chapter 8 Comparing Two Independent Groups 217
Part 3 Comparing Groups Chapter 7 Comparing Paired Groups 189 Chapter 8 Comparing Two Independent Groups 217 Chapter 9 Comparing More Than Two Groups 257 188 Elementary Statistics Using SAS Chapter 7 Comparing
More informationTechnology StepbyStep Using StatCrunch
Technology StepbyStep 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
More informationPart 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
More informationCATEGORICAL DATA ChiSquare Tests for Univariate Data
CATEGORICAL DATA ChiSquare Tests For Univariate Data 1 CATEGORICAL DATA ChiSquare Tests for Univariate Data Recall that a categorical variable is one in which the possible values are categories or groupings.
More informationStatistical Models in R
Statistical Models in R Some Examples Steven Buechler Department of Mathematics 276B Hurley Hall; 16233 Fall, 2007 Outline Statistical Models Linear Models in R Regression Regression analysis is the appropriate
More informationOneWay Analysis of Variance (ANOVA) Example Problem
OneWay Analysis of Variance (ANOVA) Example Problem Introduction Analysis of Variance (ANOVA) is a hypothesistesting technique used to test the equality of two or more population (or treatment) means
More information5. Ordinal regression: cumulative categories proportional odds. 6. Ordinal regression: comparison to single reference generalized logits
Lecture 23 1. Logistic regression with binary response 2. Proc Logistic and its surprises 3. quadratic model 4. HosmerLemeshow test for lack of fit 5. Ordinal regression: cumulative categories proportional
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 informationFamily economics data: total family income, expenditures, debt status for 50 families in two cohorts (A and B), annual records from 1990 1995.
Lecture 18 1. Random intercepts and slopes 2. Notation for mixed effects models 3. Comparing nested models 4. Multilevel/Hierarchical models 5. SAS versions of R models in Gelman and Hill, chapter 12 1
More informationPearson's Correlation Tests
Chapter 800 Pearson's Correlation Tests Introduction The correlation coefficient, ρ (rho), is a popular statistic for describing the strength of the relationship between two variables. The correlation
More informationChapter 15. Mixed Models. 15.1 Overview. A flexible approach to correlated data.
Chapter 15 Mixed Models A flexible approach to correlated data. 15.1 Overview Correlated data arise frequently in statistical analyses. This may be due to grouping of subjects, e.g., students within classrooms,
More informationGeneral Method: Difference of Means. 3. Calculate df: either WelchSatterthwaite formula or simpler df = min(n 1, n 2 ) 1.
General Method: Difference of Means 1. Calculate x 1, x 2, SE 1, SE 2. 2. Combined SE = SE1 2 + SE2 2. ASSUMES INDEPENDENT SAMPLES. 3. Calculate df: either WelchSatterthwaite formula or simpler df = min(n
More informationWe extended the additive model in two variables to the interaction model by adding a third term to the equation.
Quadratic Models We extended the additive model in two variables to the interaction model by adding a third term to the equation. Similarly, we can extend the linear model in one variable to the quadratic
More informationMultiple Linear Regression
Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is
More informationBinomial Distribution n = 20, p = 0.3
This document will describe how to use R to calculate probabilities associated with common distributions as well as to graph probability distributions. R has a number of built in functions for calculations
More information2013 MBA Jump Start Program. Statistics Module Part 3
2013 MBA Jump Start Program Module 1: Statistics Thomas Gilbert Part 3 Statistics Module Part 3 Hypothesis Testing (Inference) Regressions 2 1 Making an Investment Decision A researcher in your firm just
More informationChapter 8 Introduction to Hypothesis Testing
Chapter 8 Student Lecture Notes 81 Chapter 8 Introduction to Hypothesis Testing Fall 26 Fundamentals of Business Statistics 1 Chapter Goals After completing this chapter, you should be able to: Formulate
More informationHLM software has been one of the leading statistical packages for hierarchical
Introductory Guide to HLM With HLM 7 Software 3 G. David Garson HLM software has been one of the leading statistical packages for hierarchical linear modeling due to the pioneering work of Stephen Raudenbush
More informationRecommend 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
More informationEcon 424/Amath 462 Hypothesis Testing in the CER Model
Econ 424/Amath 462 Hypothesis Testing in the CER Model Eric Zivot July 23, 2013 Hypothesis Testing 1. Specify hypothesis to be tested 0 : null hypothesis versus. 1 : alternative hypothesis 2. Specify significance
More informationGetting Correct Results from PROC REG
Getting Correct Results from PROC REG Nathaniel Derby, Statis Pro Data Analytics, Seattle, WA ABSTRACT PROC REG, SAS s implementation of linear regression, is often used to fit a line without checking
More informationNovember 08, 2010. 155S8.6_3 Testing a Claim About a Standard Deviation or Variance
Chapter 8 Hypothesis Testing 8 1 Review and Preview 8 2 Basics of Hypothesis Testing 8 3 Testing a Claim about a Proportion 8 4 Testing a Claim About a Mean: σ Known 8 5 Testing a Claim About a Mean: σ
More informationTwoSample TTests Assuming Equal Variance (Enter Means)
Chapter 4 TwoSample TTests Assuming Equal Variance (Enter Means) Introduction This procedure provides sample size and power calculations for one or twosided twosample ttests when the variances of
More informationProbability Calculator
Chapter 95 Introduction Most statisticians have a set of probability tables that they refer to in doing their statistical wor. This procedure provides you with a set of electronic statistical tables that
More informationLecture 19: Conditional Logistic Regression
Lecture 19: Conditional Logistic Regression Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology Medical University of South Carolina
More informationComparing Multiple Proportions, Test of Independence and Goodness of Fit
Comparing Multiple Proportions, Test of Independence and Goodness of Fit Content Testing the Equality of Population Proportions for Three or More Populations Test of Independence Goodness of Fit Test 2
More informationDifference of Means and ANOVA Problems
Difference of Means and Problems Dr. Tom Ilvento FREC 408 Accounting Firm Study An accounting firm specializes in auditing the financial records of large firm It is interested in evaluating its fee structure,particularly
More informationClass 19: Two Way Tables, Conditional Distributions, ChiSquare (Text: Sections 2.5; 9.1)
Spring 204 Class 9: Two Way Tables, Conditional Distributions, ChiSquare (Text: Sections 2.5; 9.) Big Picture: More than Two Samples In Chapter 7: We looked at quantitative variables and compared the
More informationUnderstand the role that hypothesis testing plays in an improvement project. Know how to perform a two sample hypothesis test.
HYPOTHESIS TESTING Learning Objectives Understand the role that hypothesis testing plays in an improvement project. Know how to perform a two sample hypothesis test. Know how to perform a hypothesis test
More informationRegression. Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question.
Class: Date: Regression Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Given the least squares regression line y8 = 5 2x: a. the relationship between
More informationANOVA  Analysis of Variance
ANOVA  Analysis of Variance ANOVA  Analysis of Variance Extends independentsamples t test Compares the means of groups of independent observations Don t be fooled by the name. ANOVA does not compare
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 informatione = random error, assumed to be normally distributed with mean 0 and standard deviation σ
1 Linear Regression 1.1 Simple Linear Regression Model The linear regression model is applied if we want to model a numeric response variable and its dependency on at least one numeric factor variable.
More informationEXST SAS Lab Lab #9: Twosample ttests
EXST700X Lab Spring 014 EXST SAS Lab Lab #9: Twosample ttests Objectives 1. Input a CSV file (data set #1) and do a onetailed twosample ttest. Input a TXT file (data set #) and do a twotailed twosample
More informationHypothesis. Testing Examples and Case Studies. Chapter 23. Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc.
Hypothesis Chapter 23 Testing Examples and Case Studies Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc. 23.1 How Hypothesis Tests Are Reported in the News 1. Determine the null hypothesis
More informationPearson s Correlation
Pearson s Correlation Correlation the degree to which two variables are associated (covary). Covariance may be either positive or negative. Its magnitude depends on the units of measurement. Assumes the
More informationHypothesis Testing and Confidence Interval Estimation
Biostatistics for Health Care Researchers: A Short Course Hypothesis Testing and Confidence Interval Estimation Presented ed by: Susan M. Perkins, Ph.D. Division of Biostatistics Indiana University School
More informationStatistics for Clinical Trial SAS Programmers 1: paired ttest Kevin Lee, Covance Inc., Conshohocken, PA
Statistics for Clinical Trial SAS Programmers 1: paired ttest Kevin Lee, Covance Inc., Conshohocken, PA ABSTRACT This paper is intended for SAS programmers who are interested in understanding common statistical
More informationCHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression
Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the
More informationLogistic (RLOGIST) Example #1
Logistic (RLOGIST) Example #1 SUDAAN Statements and Results Illustrated EFFECTS RFORMAT, RLABEL REFLEVEL EXP option on MODEL statement HosmerLemeshow Test Input Data Set(s): BRFWGT.SAS7bdat Example Using
More informationQuantitative Methods for Finance
Quantitative Methods for Finance Module 1: The Time Value of Money 1 Learning how to interpret interest rates as required rates of return, discount rates, or opportunity costs. 2 Learning how to explain
More informationHypothesis Testing  One Mean
Hypothesis Testing  One Mean A hypothesis is simply a statement that something is true. Typically, there are two hypotheses in a hypothesis test: the null, and the alternative. Null Hypothesis The hypothesis
More informationChi Square Tests. Chapter 10. 10.1 Introduction
Contents 10 Chi Square Tests 703 10.1 Introduction............................ 703 10.2 The Chi Square Distribution.................. 704 10.3 Goodness of Fit Test....................... 709 10.4 Chi Square
More informationCool Tools for PROC LOGISTIC
Cool Tools for PROC LOGISTIC Paul D. Allison Statistical Horizons LLC and the University of Pennsylvania March 2013 www.statisticalhorizons.com 1 New Features in LOGISTIC ODDSRATIO statement EFFECTPLOT
More information