1. How will an increase in the sample size affect the width of the confidence interval?
|
|
- Ann Rice
- 5 years ago
- Views:
Transcription
1 Study Guide Concept Questions 1. How will an increase in the sample size affect the width of the confidence interval? 2. How will an increase in the sample size affect the power of a statistical test? 3. How will an increase in the sample size affect the p-value? 4. When planning a future study that will report a confidence interval result, how does decreasing the desired confidence interval width affect the sample size requirement? 5. When planning a future study that will report a confidence interval result, how does increasing the desired level of confidence affect the sample size requirement? 6. When planning a future study that will report a hypothesis testing result, how does increasing the desired power affect the sample size requirement? 7. When planning a future study that will report a hypothesis testing result, how does decreasing the alpha level affect the sample size requirement? 8. When planning a future study that will report a hypothesis testing result, how does decreasing the value of the expected effect size affect the sample size requirement? k 9. When planning a future study to estimate μ, μ 1 μ 2, or j=1 c j μ j, how does using a larger value of σ 2 affect the sample size requirement? 10. When planning a future study to test H0: μ = h, H0: μ 1 = μ 2, or H0: j=1 c j μ j = 0, how does using a larger value of σ 2 affect the sample size requirement? 11. When planning a future study to estimate δ, how does using a larger value of δ 2 affect the sample size requirement? 12. Why are narrower confidence intervals preferred over wider confidence intervals? k
2 13. Why would a 95% confidence interval be preferable to a 90% confidence interval? 14. Why is higher power desirable? 15. What are some ways to obtain a planning value for σ? 16. How do you modify the sample size formulas for testing or estimating a difference in means or a difference in proportions when planning a future study that will report v pairwise tests or confidence intervals? 17. How does the planning value of the correlation between the measurements in a paired-samples designs affect the sample size requirement for a confidence interval for μ 1 μ 2 or a test of H0: μ 1 = μ 2? 18. How does the planning value of ρ yx affect the sample size requirement for estimating ρ yx with desired precision or testing a hypothesis regarding the value of ρ yx with desired power? 19. How does the planning value of π affect the sample size requirement for estimating π with desired precision or testing H0: π = h with desired power? 20. What planning value of π will give the largest sample size requirement? 21. How does the number of control variables affect the sample size requirement for a confidence interval or hypothesis test regarding the value of a partial correlation? 22. Why should researchers avoid using unnecessarily large samples? 23. What are some consequences of using a sample size that is too small? 24. What are the sample size implications of sampling from a diverse study population rather than a more homogeneous study population? 25. What are the advantages of using equal sample sizes in a multiple group design? 26. Why are unequal sample sizes in a multiple group design sometimes justified? 27. How does the range of x values affect the sample size requirement for testing or estimating a slope in a fixed-x model?
3 28. When is a two-sage sample size analysis useful? 29. What is the effect of using a larger assurance probability on the sample size requirement? 30. What is the advantage of using an upper confidence limit for a population variance rather than a sample variance as a variance planning value? Computation Problems 1. How large of a sample is needed to obtain a 95% confidence interval for μ with a width of 5.0 based on a variance planning value of 38? 2. A researcher plans to test H0: μ = 200 at α =.05 using a sample size of n = 30. What is the power of the test if μ = 240 and σ = 60? 3. What is the expected width of a 90% confidence interval for μ in a sample size of 100 and a variance planning value of 36? 4. What sample size is required to test H0: μ = 50 with power =.90, σ 2 = 40, α =.05, and μ = 45? 5. For a 2-group design, what is the sample size requirement per group to obtain a 95% confidence interval for μ 1 μ 2 with a width of 8.0, and a variance planning value of 50? 6. For a 2-group design, what sample size is required per group to test H0: μ 1 = μ 2 with power =.80, σ 2 = 100, α =.05, and μ 1 μ 2 = 7? 7. A researcher plans to test H0: μ 1 = μ 2 at α =.05 using a 2-group design and sample sizes of n 1 = 10 and n 2 = 20. What is the power of the test if μ 1 μ 2 = 5 and σ 1 = σ 2 = 15? 8. A researcher plans to compute a 95% confidence interval for μ 1 μ 2 using a 2-group design and sample sizes of n 1 = 30 and n 2 = 30. What is expected confidence interval width with σ = 8? 9. For a 2-group design, what is the sample size requirement per group to obtain a 95% confidence interval for δ with a width of 0.5 and δ = 0.8?
4 10. For a between-subjects design, what is the sample size requirement per group to obtain a 95% confidence interval for (μ 1 + μ 2 )/2 μ 3 with a width of 3.0 and a variance planning value of 10? 11. A researcher plans to test H0: μ 1 μ 2 μ 3 + μ 4 at α =.01 using a between-subjects design and 12 participants per group. What is the power of the test if μ 1 μ 2 μ 3 + μ 4 = 8 and all within-group standard deviations are assumed to be 15? 12. A researcher plans to compute a 99% confidence interval for (μ 1 + μ 2 )/2 (μ 3 + μ 4 )/2. What is the expected confidence interval width for σ = 50? 13. For a between-subjects design, what sample size per group is required to test H0: (μ 1 + μ 2 )/2 (μ 3 + μ 4 )/2 with (μ 1 + μ 2)/2 (μ 3 + μ 4)/2 = 4, power =.90, σ 2 = 50, and α = For a between-subjects design, what is the sample size requirement per group to obtain a 95% confidence interval for a standardized contrast of (μ 1 + μ 2 )/2 μ 3 with a width of 0.6 and φ = 1.0? 15. Suppose a researcher obtained a 95% confidence interval for φ in a between-subjects design using 50 participants per group in a first-stage sample and obtained a confidence interval width of 1.3. How many participants should be sampled per group in the second stage to reduce the 95% confidence interval width to 0.8? 16. For a 2-level with-subjects design, what is the sample size requirement to obtain a 95% confidence interval for μ 1 μ 2 with a width of 1.0, a variance planning value of 3, and a correlation planning value of.75? 17. For a 2-level with-subjects design, what is the sample size requirement to obtain a 95% confidence interval for δ with a width of 0.4, δ = 1.5, and ρ 12 =.80? 18. A researcher plans to compute a 95% confidence interval for μ 1 μ 2 using a withinsubjects design and a sample size of 25. What is the expected confidence interval width with σ 2 = 15 and ρ 12 =.75? 19. For a 2-level with-subjects design, what sample size is required to test H0: μ 1 = μ 2 with power =.85, σ 2 = 180, α =.05, ρ 12 =.80, and μ 1 μ 2 = 5? 20. What sample size is required to test H0: π =.5 with power =.95, α =.05, and π =.75?
5 21. What sample size is required to obtain a 95% confidence interval for π with a width of.2 and π =.6? 22. A researcher is planning to test H0: π =.25 at α =.05 in a sample of n = 50. What is the expected power of this test for π =.45? 23. A researcher is planning to compute a 95% confidence interval for π in a sample of n = 50. What is the expected confidence interval width for π =.75? 24. For a 2-group design, what is the sample size requirement per group to obtain a 95% confidence for π 1 π 2 with a width of 0.3 using π 1 =.3 and π 1 =.5? 25. For a 2-group design, what sample size is required per group to test H0: π 1 = π 2 with power =.90, α =.05, for π 1 =.6 and π 1 =.75? 26. A researcher plans to test H0: π 1 = π 2 at α =.05 using a 2-group design and sample sizes of n 1 = 50 and n 2 = 50. What is the power of the test if π 1 =.25 and π 2 =.4? 27. For a 2-level within-subject design, what is the sample size requirement to test H0: π 1 = π 2 at α =.05 with power of.8 using π 1 =.1, π 1 =.2, and ρ =.6? 28. For a 2-level within-subject design, what is the sample size requirement to obtain a 95% confidence for π 1 π 2 with a width of 0.25 using π 1 =.3, π 1 =.5, and ρ =.5? 29. For a between-subjects design, what is the sample size requirement per group to obtain a 95% confidence interval for (π 1 + π 2 )/2 (π 3 + π 4 + π 5 )/3 with a width of 0.2 and planning values for π 1, π 2, π 3, π 4, and π 5 equal to.2,.2,.4,.4, and.4, respectively. 30. What sample size is required to test H0: ρ yx =.4 with power =.90, α =.05, and ρ yx =.6? 31. How large of a sample is needed to obtain a 95% confidence interval for ρ yx with a width of.2 using ρ yx =.5? 32. A researcher is planning to test H0: ρ yx = 0 using a sample size of 50. What is the expected power of this test at α =.05 and ρ yx =.2? 33. A researcher plans to compute a 95% confidence interval for ρ yx in a sample of n = 100. What is the expected confidence interval width with ρ yx =.4?
6 34. How large of a sample is needed to obtain a 95% confidence interval for squared multiple correlation for 3 predictor variables with a desired confidence interval width of.2 and a squared multiple correlation planning value of.25? 35. How large of a sample is needed to test the null hypothesis that Cronbach s alpha reliability for the average of two raters is equal to.7 with power =.90, α =.05, and a reliability planning value of.8? 36. How large of a sample is needed to obtain a 95% confidence interval for Cronbach s alpha reliability of a scale with 6 items with a lower planning limit of.7 and an upper planning limit of.9? 37. Suppose a researcher obtained a 95% confidence interval for μ using a first-stage sample of n = 20 and obtained a confidence interval width of 6.4. How many participants should be sampled in the second stage to reduce the 95% confidence interval width to 4.0? 38. Suppose a researcher obtained a 95% confidence interval for a difference in two correlations in a 2-group design using a first-stage sample per group of 50 and obtained a confidence interval width of 0.4. How many participants should be sampled per group in the second stage to reduce the 95% confidence interval width to 0.3? 39. Suppose a researcher obtained a 95% confidence interval for φ in a within-subjects design using 40 participants in a first-stage sample and obtained a confidence interval width of 1.1. How many participants should be sampled in the second stage to reduce the 95% confidence interval width to 0.75? 40. For a 2-group design, what is the sample size requirement per group to obtain a 95% confidence interval for μ 1 μ 2 with a width of 8.0, a variance planning value of 50, and a sample size in group 2 that is twice as large as the sample size in group 1? 41. How large of a sample is needed to obtain a 95% confidence interval for slope coefficient in a fixed-x model with a desired confidence interval width of 2, σ x 2 = 10, and a within-group variance planning value of 80? 42. How large of a sample is needed to test H0: β 1 = 1 in a fixed-x model with a desired power of.9, σ x 2 = 25, and a within-group variance planning value of 250?
7 43. How large of a sample is needed to obtain a 95% confidence interval for residual variance that has an upper to lower limit ratio of 1.5 in a linear model with one predictor variable? 44. What sample size is required in a sign test of H0: τ = 50 with power =.9, α =.05, and π =.75? 45. What sample size is required in a Mann-Whitney test of H0: π =.5 with power =.80, α =.05, and π =.7?
8 Answers to Selected Computational Problems
Analysis of Covariance. The following example illustrates a case where the covariate is affected by the treatments.
Analysis of Covariance In some experiments, the experimental units (subjects) are nonhomogeneous or there is variation in the experimental conditions that are not due to the treatments. For example, a
More informationMultiple Regression. More Hypothesis Testing. More Hypothesis Testing The big question: What we really want to know: What we actually know: We know:
Multiple Regression Ψ320 Ainsworth More Hypothesis Testing What we really want to know: Is the relationship in the population we have selected between X & Y strong enough that we can use the relationship
More informationChapter 7 Comparison of two independent samples
Chapter 7 Comparison of two independent samples 7.1 Introduction Population 1 µ σ 1 1 N 1 Sample 1 y s 1 1 n 1 Population µ σ N Sample y s n 1, : population means 1, : population standard deviations N
More informationTests for Two Coefficient Alphas
Chapter 80 Tests for Two Coefficient Alphas Introduction Coefficient alpha, or Cronbach s alpha, is a popular measure of the reliability of a scale consisting of k parts. The k parts often represent k
More informationHypothesis Testing for Var-Cov Components
Hypothesis Testing for Var-Cov Components When the specification of coefficients as fixed, random or non-randomly varying is considered, a null hypothesis of the form is considered, where Additional output
More informationChapter 10. Regression. Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania
Chapter 10 Regression Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania Scatter Diagrams A graph in which pairs of points, (x, y), are
More informationGROUPED DATA E.G. FOR SAMPLE OF RAW DATA (E.G. 4, 12, 7, 5, MEAN G x / n STANDARD DEVIATION MEDIAN AND QUARTILES STANDARD DEVIATION
FOR SAMPLE OF RAW DATA (E.G. 4, 1, 7, 5, 11, 6, 9, 7, 11, 5, 4, 7) BE ABLE TO COMPUTE MEAN G / STANDARD DEVIATION MEDIAN AND QUARTILES Σ ( Σ) / 1 GROUPED DATA E.G. AGE FREQ. 0-9 53 10-19 4...... 80-89
More informationT.I.H.E. IT 233 Statistics and Probability: Sem. 1: 2013 ESTIMATION AND HYPOTHESIS TESTING OF TWO POPULATIONS
ESTIMATION AND HYPOTHESIS TESTING OF TWO POPULATIONS In our work on hypothesis testing, we used the value of a sample statistic to challenge an accepted value of a population parameter. We focused only
More informationBasic Statistical Analysis
indexerrt.qxd 8/21/2002 9:47 AM Page 1 Corrected index pages for Sprinthall Basic Statistical Analysis Seventh Edition indexerrt.qxd 8/21/2002 9:47 AM Page 656 Index Abscissa, 24 AB-STAT, vii ADD-OR rule,
More informationTypes of Statistical Tests DR. MIKE MARRAPODI
Types of Statistical Tests DR. MIKE MARRAPODI Tests t tests ANOVA Correlation Regression Multivariate Techniques Non-parametric t tests One sample t test Independent t test Paired sample t test One sample
More information3 Joint Distributions 71
2.2.3 The Normal Distribution 54 2.2.4 The Beta Density 58 2.3 Functions of a Random Variable 58 2.4 Concluding Remarks 64 2.5 Problems 64 3 Joint Distributions 71 3.1 Introduction 71 3.2 Discrete Random
More informationLAB 2. HYPOTHESIS TESTING IN THE BIOLOGICAL SCIENCES- Part 2
LAB 2. HYPOTHESIS TESTING IN THE BIOLOGICAL SCIENCES- Part 2 Data Analysis: The mean egg masses (g) of the two different types of eggs may be exactly the same, in which case you may be tempted to accept
More information" M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2
Notation and Equations for Final Exam Symbol Definition X The variable we measure in a scientific study n The size of the sample N The size of the population M The mean of the sample µ The mean of the
More informationInstrumentation (cont.) Statistics vs. Parameters. Descriptive Statistics. Types of Numerical Data
Norm-Referenced vs. Criterion- Referenced Instruments Instrumentation (cont.) October 1, 2007 Note: Measurement Plan Due Next Week All derived scores give meaning to individual scores by comparing them
More informationSix Sigma Black Belt Study Guides
Six Sigma Black Belt Study Guides 1 www.pmtutor.org Powered by POeT Solvers Limited. Analyze Correlation and Regression Analysis 2 www.pmtutor.org Powered by POeT Solvers Limited. Variables and relationships
More informationExam details. Final Review Session. Things to Review
Exam details Final Review Session Short answer, similar to book problems Formulae and tables will be given You CAN use a calculator Date and Time: Dec. 7, 006, 1-1:30 pm Location: Osborne Centre, Unit
More informationdf=degrees of freedom = n - 1
One sample t-test test of the mean Assumptions: Independent, random samples Approximately normal distribution (from intro class: σ is unknown, need to calculate and use s (sample standard deviation)) Hypotheses:
More informationDESIGNING EXPERIMENTS AND ANALYZING DATA A Model Comparison Perspective
DESIGNING EXPERIMENTS AND ANALYZING DATA A Model Comparison Perspective Second Edition Scott E. Maxwell Uniuersity of Notre Dame Harold D. Delaney Uniuersity of New Mexico J,t{,.?; LAWRENCE ERLBAUM ASSOCIATES,
More informationSimple Linear Regression for the Climate Data
Prediction Prediction Interval Temperature 0.2 0.0 0.2 0.4 0.6 0.8 320 340 360 380 CO 2 Simple Linear Regression for the Climate Data What do we do with the data? y i = Temperature of i th Year x i =CO
More informationT. Mark Beasley One-Way Repeated Measures ANOVA handout
T. Mark Beasley One-Way Repeated Measures ANOVA handout Profile Analysis Example In the One-Way Repeated Measures ANOVA, two factors represent separate sources of variance. Their interaction presents an
More informationEXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY
EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE IN STATISTICS, 009 MODULE 4 : Linear models Time allowed: One and a half hours Candidates should answer THREE questions. Each question carries
More informationGlossary. The ISI glossary of statistical terms provides definitions in a number of different languages:
Glossary The ISI glossary of statistical terms provides definitions in a number of different languages: http://isi.cbs.nl/glossary/index.htm Adjusted r 2 Adjusted R squared measures the proportion of the
More informationReview: General Approach to Hypothesis Testing. 1. Define the research question and formulate the appropriate null and alternative hypotheses.
1 Review: Let X 1, X,..., X n denote n independent random variables sampled from some distribution might not be normal!) with mean µ) and standard deviation σ). Then X µ σ n In other words, X is approximately
More informationNon-parametric tests, part A:
Two types of statistical test: Non-parametric tests, part A: Parametric tests: Based on assumption that the data have certain characteristics or "parameters": Results are only valid if (a) the data are
More informationLecture 30. DATA 8 Summer Regression Inference
DATA 8 Summer 2018 Lecture 30 Regression Inference Slides created by John DeNero (denero@berkeley.edu) and Ani Adhikari (adhikari@berkeley.edu) Contributions by Fahad Kamran (fhdkmrn@berkeley.edu) and
More information36-309/749 Experimental Design for Behavioral and Social Sciences. Dec 1, 2015 Lecture 11: Mixed Models (HLMs)
36-309/749 Experimental Design for Behavioral and Social Sciences Dec 1, 2015 Lecture 11: Mixed Models (HLMs) Independent Errors Assumption An error is the deviation of an individual observed outcome (DV)
More informationTutorial 2: Power and Sample Size for the Paired Sample t-test
Tutorial 2: Power and Sample Size for the Paired Sample t-test Preface Power is the probability that a study will reject the null hypothesis. The estimated probability is a function of sample size, variability,
More informationTutorial 3: Power and Sample Size for the Two-sample t-test with Equal Variances. Acknowledgements:
Tutorial 3: Power and Sample Size for the Two-sample t-test with Equal Variances Anna E. Barón, Keith E. Muller, Sarah M. Kreidler, and Deborah H. Glueck Acknowledgements: The project was supported in
More informationBiostatistics. Correlation and linear regression. Burkhardt Seifert & Alois Tschopp. Biostatistics Unit University of Zurich
Biostatistics Correlation and linear regression Burkhardt Seifert & Alois Tschopp Biostatistics Unit University of Zurich Master of Science in Medical Biology 1 Correlation and linear regression Analysis
More informationSimple Linear Regression
Simple Linear Regression ST 370 Regression models are used to study the relationship of a response variable and one or more predictors. The response is also called the dependent variable, and the predictors
More informationPrerequisite Material
Prerequisite Material Study Populations and Random Samples A study population is a clearly defined collection of people, animals, plants, or objects. In social and behavioral research, a study population
More informationIntroduction to Analysis of Variance (ANOVA) Part 2
Introduction to Analysis of Variance (ANOVA) Part 2 Single factor Serpulid recruitment and biofilms Effect of biofilm type on number of recruiting serpulid worms in Port Phillip Bay Response variable:
More informationSimple and Multiple Linear Regression
Sta. 113 Chapter 12 and 13 of Devore March 12, 2010 Table of contents 1 Simple Linear Regression 2 Model Simple Linear Regression A simple linear regression model is given by Y = β 0 + β 1 x + ɛ where
More informationy ˆ i = ˆ " T u i ( i th fitted value or i th fit)
1 2 INFERENCE FOR MULTIPLE LINEAR REGRESSION Recall Terminology: p predictors x 1, x 2,, x p Some might be indicator variables for categorical variables) k-1 non-constant terms u 1, u 2,, u k-1 Each u
More informationMeasuring the fit of the model - SSR
Measuring the fit of the model - SSR Once we ve determined our estimated regression line, we d like to know how well the model fits. How far/close are the observations to the fitted line? One way to do
More informationChapte The McGraw-Hill Companies, Inc. All rights reserved.
12er12 Chapte Bivariate i Regression (Part 1) Bivariate Regression Visual Displays Begin the analysis of bivariate data (i.e., two variables) with a scatter plot. A scatter plot - displays each observed
More informationAN INTRODUCTION TO STRUCTURAL EQUATION MODELING WITH AN APPLICATION TO THE BLOGOSPHERE
AN INTRODUCTION TO STRUCTURAL EQUATION MODELING WITH AN APPLICATION TO THE BLOGOSPHERE Dr. James (Jim) D. Doyle March 19, 2014 Structural equation modeling or SEM 1971-1980: 27 1981-1990: 118 1991-2000:
More informationModel II (or random effects) one-way ANOVA:
Model II (or random effects) one-way ANOVA: As noted earlier, if we have a random effects model, the treatments are chosen from a larger population of treatments; we wish to generalize to this larger population.
More informationItem Reliability Analysis
Item Reliability Analysis Revised: 10/11/2017 Summary... 1 Data Input... 4 Analysis Options... 5 Tables and Graphs... 5 Analysis Summary... 6 Matrix Plot... 8 Alpha Plot... 10 Correlation Matrix... 11
More informationHarvard University. Rigorous Research in Engineering Education
Statistical Inference Kari Lock Harvard University Department of Statistics Rigorous Research in Engineering Education 12/3/09 Statistical Inference You have a sample and want to use the data collected
More informationInter Item Correlation Matrix (R )
7 1. I have the ability to influence my child s well-being. 2. Whether my child avoids injury is just a matter of luck. 3. Luck plays a big part in determining how healthy my child is. 4. I can do a lot
More informationHYPOTHESIS TESTING II TESTS ON MEANS. Sorana D. Bolboacă
HYPOTHESIS TESTING II TESTS ON MEANS Sorana D. Bolboacă OBJECTIVES Significance value vs p value Parametric vs non parametric tests Tests on means: 1 Dec 14 2 SIGNIFICANCE LEVEL VS. p VALUE Materials and
More informationInference for Regression Inference about the Regression Model and Using the Regression Line
Inference for Regression Inference about the Regression Model and Using the Regression Line PBS Chapter 10.1 and 10.2 2009 W.H. Freeman and Company Objectives (PBS Chapter 10.1 and 10.2) Inference about
More informationA discussion on multiple regression models
A discussion on multiple regression models In our previous discussion of simple linear regression, we focused on a model in which one independent or explanatory variable X was used to predict the value
More informationMy data doesn t look like that..
Testing assumptions My data doesn t look like that.. We have made a big deal about testing model assumptions each week. Bill Pine Testing assumptions Testing assumptions We have made a big deal about testing
More informationModule 3. Latent Variable Statistical Models. y 1 y2
Module 3 Latent Variable Statistical Models As explained in Module 2, measurement error in a predictor variable will result in misleading slope coefficients, and measurement error in the response variable
More informationsphericity, 5-29, 5-32 residuals, 7-1 spread and level, 2-17 t test, 1-13 transformations, 2-15 violations, 1-19
additive tree structure, 10-28 ADDTREE, 10-51, 10-53 EXTREE, 10-31 four point condition, 10-29 ADDTREE, 10-28, 10-51, 10-53 adjusted R 2, 8-7 ALSCAL, 10-49 ANCOVA, 9-1 assumptions, 9-5 example, 9-7 MANOVA
More informationPsychology 282 Lecture #4 Outline Inferences in SLR
Psychology 282 Lecture #4 Outline Inferences in SLR Assumptions To this point we have not had to make any distributional assumptions. Principle of least squares requires no assumptions. Can use correlations
More informationLatent Trait Reliability
Latent Trait Reliability Lecture #7 ICPSR Item Response Theory Workshop Lecture #7: 1of 66 Lecture Overview Classical Notions of Reliability Reliability with IRT Item and Test Information Functions Concepts
More informationAnalysis of Variance: Part 1
Analysis of Variance: Part 1 Oneway ANOVA When there are more than two means Each time two means are compared the probability (Type I error) =α. When there are more than two means Each time two means are
More informationInference for Regression Simple Linear Regression
Inference for Regression Simple Linear Regression IPS Chapter 10.1 2009 W.H. Freeman and Company Objectives (IPS Chapter 10.1) Simple linear regression p Statistical model for linear regression p Estimating
More informationContents. Acknowledgments. xix
Table of Preface Acknowledgments page xv xix 1 Introduction 1 The Role of the Computer in Data Analysis 1 Statistics: Descriptive and Inferential 2 Variables and Constants 3 The Measurement of Variables
More informationStats Review Chapter 14. Mary Stangler Center for Academic Success Revised 8/16
Stats Review Chapter 14 Revised 8/16 Note: This review is meant to highlight basic concepts from the course. It does not cover all concepts presented by your instructor. Refer back to your notes, unit
More informationPSY 307 Statistics for the Behavioral Sciences. Chapter 20 Tests for Ranked Data, Choosing Statistical Tests
PSY 307 Statistics for the Behavioral Sciences Chapter 20 Tests for Ranked Data, Choosing Statistical Tests What To Do with Non-normal Distributions Tranformations (pg 382): The shape of the distribution
More information4.1. Introduction: Comparing Means
4. Analysis of Variance (ANOVA) 4.1. Introduction: Comparing Means Consider the problem of testing H 0 : µ 1 = µ 2 against H 1 : µ 1 µ 2 in two independent samples of two different populations of possibly
More informationMeasurement Error. Martin Bland. Accuracy and precision. Error. Measurement in Health and Disease. Professor of Health Statistics University of York
Measurement in Health and Disease Measurement Error Martin Bland Professor of Health Statistics University of York http://martinbland.co.uk/ Accuracy and precision In this lecture: measurements which are
More informationDETAILED CONTENTS PART I INTRODUCTION AND DESCRIPTIVE STATISTICS. 1. Introduction to Statistics
DETAILED CONTENTS About the Author Preface to the Instructor To the Student How to Use SPSS With This Book PART I INTRODUCTION AND DESCRIPTIVE STATISTICS 1. Introduction to Statistics 1.1 Descriptive and
More informationChapter 12: Estimation
Chapter 12: Estimation Estimation In general terms, estimation uses a sample statistic as the basis for estimating the value of the corresponding population parameter. Although estimation and hypothesis
More informationData Analysis and Statistical Methods Statistics 651
y 1 2 3 4 5 6 7 x Data Analysis and Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasini/teaching.html Lecture 32 Suhasini Subba Rao Previous lecture We are interested in whether a dependent
More informationCorrelation and Regression
Correlation and Regression Dr. Bob Gee Dean Scott Bonney Professor William G. Journigan American Meridian University 1 Learning Objectives Upon successful completion of this module, the student should
More informationComparing Several Means: ANOVA
Comparing Several Means: ANOVA Understand the basic principles of ANOVA Why it is done? What it tells us? Theory of one way independent ANOVA Following up an ANOVA: Planned contrasts/comparisons Choosing
More informationPrepared by: Prof. Dr Bahaman Abu Samah Department of Professional Development and Continuing Education Faculty of Educational Studies Universiti
Prepared by: Prof. Dr Bahaman Abu Samah Department of Professional Development and Continuing Education Faculty of Educational Studies Universiti Putra Malaysia Serdang Use in experiment, quasi-experiment
More informationIntroduction to Statistics for the Social Sciences Review for Exam 4 Homework Assignment 27
Introduction to Statistics for the Social Sciences Review for Exam 4 Homework Assignment 27 Name: Lab: The purpose of this worksheet is to review the material to be represented in Exam 4. Please answer
More informationReview for Final. Chapter 1 Type of studies: anecdotal, observational, experimental Random sampling
Review for Final For a detailed review of Chapters 1 7, please see the review sheets for exam 1 and. The following only briefly covers these sections. The final exam could contain problems that are included
More informationArea1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed)
Institutional Assessment Report Texas Southern University College of Pharmacy and Health Sciences "An Analysis of 2013 NAPLEX, P4-Comp. Exams and P3 courses The following analysis illustrates relationships
More informationWorkshop Research Methods and Statistical Analysis
Workshop Research Methods and Statistical Analysis Session 2 Data Analysis Sandra Poeschl 08.04.2013 Page 1 Research process Research Question State of Research / Theoretical Background Design Data Collection
More informationTutorial 4: Power and Sample Size for the Two-sample t-test with Unequal Variances
Tutorial 4: Power and Sample Size for the Two-sample t-test with Unequal Variances Preface Power is the probability that a study will reject the null hypothesis. The estimated probability is a function
More informationConfidence Intervals for One-Way Repeated Measures Contrasts
Chapter 44 Confidence Intervals for One-Way Repeated easures Contrasts Introduction This module calculates the expected width of a confidence interval for a contrast (linear combination) of the means in
More informationGeneralized linear models
Generalized linear models Outline for today What is a generalized linear model Linear predictors and link functions Example: estimate a proportion Analysis of deviance Example: fit dose- response data
More informationInferences for Correlation
Inferences for Correlation Quantitative Methods II Plan for Today Recall: correlation coefficient Bivariate normal distributions Hypotheses testing for population correlation Confidence intervals for population
More informationModule 2. General Linear Model
D.G. Bonett (9/018) Module General Linear Model The relation between one response variable (y) and q 1 predictor variables (x 1, x,, x q ) for one randomly selected person can be represented by the following
More informationDifference in two or more average scores in different groups
ANOVAs Analysis of Variance (ANOVA) Difference in two or more average scores in different groups Each participant tested once Same outcome tested in each group Simplest is one-way ANOVA (one variable as
More informationHow do we compare the relative performance among competing models?
How do we compare the relative performance among competing models? 1 Comparing Data Mining Methods Frequent problem: we want to know which of the two learning techniques is better How to reliably say Model
More informationGeneral Linear Model (Chapter 4)
General Linear Model (Chapter 4) Outcome variable is considered continuous Simple linear regression Scatterplots OLS is BLUE under basic assumptions MSE estimates residual variance testing regression coefficients
More information23. Inference for regression
23. Inference for regression The Practice of Statistics in the Life Sciences Third Edition 2014 W. H. Freeman and Company Objectives (PSLS Chapter 23) Inference for regression The regression model Confidence
More informationFactorial designs. Experiments
Chapter 5: Factorial designs Petter Mostad mostad@chalmers.se Experiments Actively making changes and observing the result, to find causal relationships. Many types of experimental plans Measuring response
More informationSimple Linear Regression: One Qualitative IV
Simple Linear Regression: One Qualitative IV 1. Purpose As noted before regression is used both to explain and predict variation in DVs, and adding to the equation categorical variables extends regression
More informationBusiness Statistics. Lecture 10: Course Review
Business Statistics Lecture 10: Course Review 1 Descriptive Statistics for Continuous Data Numerical Summaries Location: mean, median Spread or variability: variance, standard deviation, range, percentiles,
More informationCorrelation and Regression Bangkok, 14-18, Sept. 2015
Analysing and Understanding Learning Assessment for Evidence-based Policy Making Correlation and Regression Bangkok, 14-18, Sept. 2015 Australian Council for Educational Research Correlation The strength
More informationInference for the Regression Coefficient
Inference for the Regression Coefficient Recall, b 0 and b 1 are the estimates of the slope β 1 and intercept β 0 of population regression line. We can shows that b 0 and b 1 are the unbiased estimates
More informationVisual interpretation with normal approximation
Visual interpretation with normal approximation H 0 is true: H 1 is true: p =0.06 25 33 Reject H 0 α =0.05 (Type I error rate) Fail to reject H 0 β =0.6468 (Type II error rate) 30 Accept H 1 Visual interpretation
More informationRon Heck, Fall Week 3: Notes Building a Two-Level Model
Ron Heck, Fall 2011 1 EDEP 768E: Seminar on Multilevel Modeling rev. 9/6/2011@11:27pm Week 3: Notes Building a Two-Level Model We will build a model to explain student math achievement using student-level
More informationST430 Exam 1 with Answers
ST430 Exam 1 with Answers Date: October 5, 2015 Name: Guideline: You may use one-page (front and back of a standard A4 paper) of notes. No laptop or textook are permitted but you may use a calculator.
More informationSPSS Output. ANOVA a b Residual Coefficients a Standardized Coefficients
SPSS Output Homework 1-1e ANOVA a Sum of Squares df Mean Square F Sig. 1 Regression 351.056 1 351.056 11.295.002 b Residual 932.412 30 31.080 Total 1283.469 31 a. Dependent Variable: Sexual Harassment
More informationSPSS Guide For MMI 409
SPSS Guide For MMI 409 by John Wong March 2012 Preface Hopefully, this document can provide some guidance to MMI 409 students on how to use SPSS to solve many of the problems covered in the D Agostino
More informationExtending the Robust Means Modeling Framework. Alyssa Counsell, Phil Chalmers, Matt Sigal, Rob Cribbie
Extending the Robust Means Modeling Framework Alyssa Counsell, Phil Chalmers, Matt Sigal, Rob Cribbie One-way Independent Subjects Design Model: Y ij = µ + τ j + ε ij, j = 1,, J Y ij = score of the ith
More informationSTATISTICS 4, S4 (4769) A2
(4769) A2 Objectives To provide students with the opportunity to explore ideas in more advanced statistics to a greater depth. Assessment Examination (72 marks) 1 hour 30 minutes There are four options
More informationBIOL 458 BIOMETRY Lab 9 - Correlation and Bivariate Regression
BIOL 458 BIOMETRY Lab 9 - Correlation and Bivariate Regression Introduction to Correlation and Regression The procedures discussed in the previous ANOVA labs are most useful in cases where we are interested
More informationAnalysis of 2x2 Cross-Over Designs using T-Tests
Chapter 234 Analysis of 2x2 Cross-Over Designs using T-Tests Introduction This procedure analyzes data from a two-treatment, two-period (2x2) cross-over design. The response is assumed to be a continuous
More informationSaya, selaku Ketua Paguyuban Lansia Gereja Katolik Kelahiran Santa. Perawan Maria Surabaya, menyatakan bahwa mahasiswa bernama Dewi Setiawati
LAMP IRAN SURA T KETERANGAN Saya, selaku Ketua Paguyuban Lansia Gereja Katolik Kelahiran Santa Perawan Maria Surabaya, menyatakan bahwa mahasiswa bernama Dewi Setiawati benar-benar telah melakukan pengambilan
More informationCorrelation and Linear Regression
Correlation and Linear Regression Correlation: Relationships between Variables So far, nearly all of our discussion of inferential statistics has focused on testing for differences between group means
More informationRegression ( Kemampuan Individu, Lingkungan kerja dan Motivasi)
Regression (, Lingkungan kerja dan ) Descriptive Statistics Mean Std. Deviation N 3.87.333 32 3.47.672 32 3.78.585 32 s Pearson Sig. (-tailed) N Kemampuan Lingkungan Individu Kerja.000.432.49.432.000.3.49.3.000..000.000.000..000.000.000.
More informationChapter 12 - Lecture 2 Inferences about regression coefficient
Chapter 12 - Lecture 2 Inferences about regression coefficient April 19th, 2010 Facts about slope Test Statistic Confidence interval Hypothesis testing Test using ANOVA Table Facts about slope In previous
More informationChapter 14 Simple Linear Regression (A)
Chapter 14 Simple Linear Regression (A) 1. Characteristics Managerial decisions often are based on the relationship between two or more variables. can be used to develop an equation showing how the variables
More informationN Utilization of Nursing Research in Advanced Practice, Summer 2008
University of Michigan Deep Blue deepblue.lib.umich.edu 2008-07 536 - Utilization of ursing Research in Advanced Practice, Summer 2008 Tzeng, Huey-Ming Tzeng, H. (2008, ctober 1). Utilization of ursing
More informationBusiness Statistics. Lecture 10: Correlation and Linear Regression
Business Statistics Lecture 10: Correlation and Linear Regression Scatterplot A scatterplot shows the relationship between two quantitative variables measured on the same individuals. It displays the Form
More informationInferences for Regression
Inferences for Regression An Example: Body Fat and Waist Size Looking at the relationship between % body fat and waist size (in inches). Here is a scatterplot of our data set: Remembering Regression In
More informationExperimental Design and Data Analysis for Biologists
Experimental Design and Data Analysis for Biologists Gerry P. Quinn Monash University Michael J. Keough University of Melbourne CAMBRIDGE UNIVERSITY PRESS Contents Preface page xv I I Introduction 1 1.1
More informationOutline. PubH 5450 Biostatistics I Prof. Carlin. Confidence Interval for the Mean. Part I. Reviews
Outline Outline PubH 5450 Biostatistics I Prof. Carlin Lecture 11 Confidence Interval for the Mean Known σ (population standard deviation): Part I Reviews σ x ± z 1 α/2 n Small n, normal population. Large
More informationSTA121: Applied Regression Analysis
STA121: Applied Regression Analysis Linear Regression Analysis - Chapters 3 and 4 in Dielman Artin Department of Statistical Science September 15, 2009 Outline 1 Simple Linear Regression Analysis 2 Using
More information