Analysis of Variance: Part 1

Size: px
Start display at page:

Download "Analysis of Variance: Part 1"

Transcription

1 Analysis of Variance: Part 1 Oneway ANOVA

2 When there are more than two means Each time two means are compared the probability (Type I error) =α.

3 When there are more than two means Each time two means are compared the probability (Type I error) =α. X 1 X 2 P(Type I error)=.05

4 When there are more than two means Each time two means are compared the probability (Type I error) =α. X 1 X 2 X 3 P(Type I error)=

5 When there are more than two means Each time two means are compared the probability (Type I error) =α. X 1 X 2 X 3 P(Type I error)= =.15

6 When there are more than two means Each time two means are compared the probability (Type I error) =α. X 1 X 2 X 3 X 4 P(Type I error)=

7 When there are more than two means Each time two means are compared the probability (Type I error) =α. X 1 X 2 X 3 X 4 P(Type I error)=

8 When there are more than two means Each time two means are compared the probability (Type I error) =α. X 1 X 2 X 3 X 4 P(Type I error)= =.30

9 When there are more than two means Each time two means are compared the probability (Type I error) =α. X 1 X 2 X 3 X 4 X 5 P(Type I error)= =.50

10 Protection level Analysis of Variance protects from inflating Type I errors by making the experiment-wise Probability (Type 1 Error) < α.

11 Partitioning the Variance Total Variance

12 Partitioning the Variance Total Variance Between Groups Within Groups

13 Between Groups Variance The variance in the data that can be attributed to the independent variable. The variance among the means.

14 Within Groups Variance Variance due to all other sources. Subject factors Error variance Residual variance Variances among data and group means in each group.

15 F-Ratio F= Between Groups Variance Within Groups Variance

16 Assumptions Treatments are Independent Dependent Variable is measured on at least an ordinal scale Dependent Variable is normally distributed

17 When to use Between Groups ANOVA Different Subjects are in each treatment. There are 2 means or more to compare. (Can use for 2 groups: t is easier)

18 How to set up the ANOVA Summary Table Source

19 How to set up the ANOVA Summary Table Source Total

20 How to set up the ANOVA Summary Table Source Between Within Total

21 How to set up the ANOVA Summary Table Source Between Sums of Squares SS B Within SS W Total SS T

22 How to set up the ANOVA Summary Table Source Sums of Squares df Between SS B df B Within SS W df W Total SS T df T

23 How to set up the ANOVA Summary Table Source Sums of Squares df Mean Squares Between SS B df B MS B Within SS W df W MS W Total SS T df T

24 How to set up the ANOVA Summary Table Source Sums of Squares df Mean Squares F Between SS B df B MS B F Within SS W df W MS W Total SS T df T

25 Calculating the F Statistic Calculate Sums of Squares Calculate df Calculate Mean Squares Calculate F

26 How to Calculate the Sums of Squares Between Source Sums of Squares Between SS B ( X ) n 2 2 T N Within SS W Total SS T

27 How to Calculate the Sums of Squares Between Source Sums of Squares Sum of scores in each group Between SS B ( X ) n 2 2 T N Within SS W Total SS T

28 How to Calculate the Sums of Squares Between Source Between Within Sums of Squares SS B SS W T=Total= Sum of all scores. ( X ) n 2 2 T N Total SS T

29 How to Calculate the Sums of Squares Between Source Sums of Squares Between SS B ( X ) n 2 2 T N Within SS W n=number of scores/group Total SS T

30 How to Calculate the Sums of Squares Between Source Sums of Squares Between SS B ( X ) n 2 2 T N Within N= Total number of scores SS W Total SS T

31 How to Calculate the Sums of Squares Total Source Sums of Squares Between SS B Within SS W Total SS T X 2 T N 2

32 How to Calculate the Sums of Squares Total Source Between Sums of Squares SS B Within SS W Sum of all scores squared first Total SS T X 2 T N 2

33 How to Calculate the Sums of Squares Within Source Sums of Squares Between SS B ( X ) n 2 2 T N Within SS W SS T -SS B Total SS T X 2 T N 2

34 How to Calculate the Degrees of Freedom Source Sums of Squares df Between SS B df B = k-1 Within SS W df W Total SS T df T

35 How to Calculate the Degrees of Freedom Source Sums of Squares df k is the number of groups Between SS B df B = k-1 Within SS W df W Total SS T df T

36 How to Calculate the Degrees of Freedom Source Sums of Squares df Between SS B df B = k-1 Within SS W df W = k(n-1) Total SS T df T

37 How to Calculate the Degrees of Freedom Source Sums of Squares df Between SS B df B = k-1 Within SS W df W = k(n-1) Total SS T df T = N - 1

38 How to Calculate the Mean Squares Source Sums of Squares df Mean Squares Between SS B df B MS B Within SS W df W MS W Total SS T df T

39 How to set up the ANOVA Summary Table Source Sums of Squares df Mean Squares F Between SS B df B MS B = F MS W Within SS W df W Total SS T df T

40 Determining the Critical F Alpha =.05 Find Column for df Between Find Row for df Within Compare Critical F to Obtained F

41 Statistical Decision Making If Critical F > Obtained F failed to reject null hypothesis If Critical F < Obtained F reject the null hypothesis

42 Interpreting the Results Graph Means Use a multiple Comparison test to determine which means are significantly different

43 Example: The Effects of Mood on Originality Scott Halam s Senior Thesis, 1997 H 1 : Positive mood will facilitate creativity more than negative mood or neutral mood. H 1: X positive > X Neutral =X Negative

44 A Portion 1 Data from Scott s Study Originality Scores Only female participants. 90

45 How to set up the ANOVA Summary Table:Example Source Mood Between Within Sums of Squares SS B SS W Total SS T

46 How to Calculate the Sums of Squares Within: Find the parts Source Sum scores for each Sums of group. Squares Total of all scores. Mood Between Within Total SS B Number in Number SS SS W T -SS each group B All scores squared, SS T ( n X 2 T 2 2 X N 2 ) T N Total then summed.

47 A Portion 1 Data from Scott s Study X n = = = = ( n X ) 2 T N

48 A Portion 1 Data from Scott s Study X n = T = N = = = = T 2 N ( )

49 Finishing SS B Source Sums of Squares Between Mood SS B = = Within SS W Total SS T

50 How to Calculate the Sums of Squares Total: Example Source Sums of Squares Between SS B Within SS W Total SS T X 2 T N 2

51 Finding SS w X T N

The One-Way Repeated-Measures ANOVA. (For Within-Subjects Designs)

The One-Way Repeated-Measures ANOVA. (For Within-Subjects Designs) The One-Way Repeated-Measures ANOVA (For Within-Subjects Designs) Logic of the Repeated-Measures ANOVA The repeated-measures ANOVA extends the analysis of variance to research situations using repeated-measures

More information

Comparing Several Means: ANOVA

Comparing 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 information

Study Guide #3: OneWay ANALYSIS OF VARIANCE (ANOVA)

Study Guide #3: OneWay ANALYSIS OF VARIANCE (ANOVA) Study Guide #3: OneWay ANALYSIS OF VARIANCE (ANOVA) About the ANOVA Test In educational research, we are most often involved finding out whether there are differences between groups. For example, is there

More information

Analysis of Variance (ANOVA)

Analysis of Variance (ANOVA) Analysis of Variance (ANOVA) Two types of ANOVA tests: Independent measures and Repeated measures Comparing 2 means: X 1 = 20 t - test X 2 = 30 How can we Compare 3 means?: X 1 = 20 X 2 = 30 X 3 = 35 ANOVA

More information

Advanced Experimental Design

Advanced Experimental Design Advanced Experimental Design Topic 8 Chapter : Repeated Measures Analysis of Variance Overview Basic idea, different forms of repeated measures Partialling out between subjects effects Simple repeated

More information

1. What does the alternate hypothesis ask for a one-way between-subjects analysis of variance?

1. What does the alternate hypothesis ask for a one-way between-subjects analysis of variance? 1. What does the alternate hypothesis ask for a one-way between-subjects analysis of variance? 2. What is the difference between between-group variability and within-group variability? 3. What does between-group

More information

The t-test: A z-score for a sample mean tells us where in the distribution the particular mean lies

The t-test: A z-score for a sample mean tells us where in the distribution the particular mean lies The t-test: So Far: Sampling distribution benefit is that even if the original population is not normal, a sampling distribution based on this population will be normal (for sample size > 30). Benefit

More information

One-Way ANOVA. Some examples of when ANOVA would be appropriate include:

One-Way ANOVA. Some examples of when ANOVA would be appropriate include: One-Way ANOVA 1. Purpose Analysis of variance (ANOVA) is used when one wishes to determine whether two or more groups (e.g., classes A, B, and C) differ on some outcome of interest (e.g., an achievement

More information

Difference in two or more average scores in different groups

Difference 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 information

An Old Research Question

An Old Research Question ANOVA An Old Research Question The impact of TV on high-school grade Watch or not watch Two groups The impact of TV hours on high-school grade Exactly how much TV watching would make difference Multiple

More information

Note: k = the # of conditions n = # of data points in a condition N = total # of data points

Note: k = the # of conditions n = # of data points in a condition N = total # of data points The ANOVA for2 Dependent Groups -- Analysis of 2-Within (or Matched)-Group Data with a Quantitative Response Variable Application: This statistic has two applications that can appear very different, but

More information

Sampling distribution of t. 2. Sampling distribution of t. 3. Example: Gas mileage investigation. II. Inferential Statistics (8) t =

Sampling distribution of t. 2. Sampling distribution of t. 3. Example: Gas mileage investigation. II. Inferential Statistics (8) t = 2. The distribution of t values that would be obtained if a value of t were calculated for each sample mean for all possible random of a given size from a population _ t ratio: (X - µ hyp ) t s x The result

More information

One-Way Analysis of Variance. With regression, we related two quantitative, typically continuous variables.

One-Way Analysis of Variance. With regression, we related two quantitative, typically continuous variables. One-Way Analysis of Variance With regression, we related two quantitative, typically continuous variables. Often we wish to relate a quantitative response variable with a qualitative (or simply discrete)

More information

" M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2

 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 information

The One-Way Independent-Samples ANOVA. (For Between-Subjects Designs)

The One-Way Independent-Samples ANOVA. (For Between-Subjects Designs) The One-Way Independent-Samples ANOVA (For Between-Subjects Designs) Computations for the ANOVA In computing the terms required for the F-statistic, we won t explicitly compute any sample variances or

More information

PSYC 331 STATISTICS FOR PSYCHOLOGISTS

PSYC 331 STATISTICS FOR PSYCHOLOGISTS PSYC 331 STATISTICS FOR PSYCHOLOGISTS Session 4 A PARAMETRIC STATISTICAL TEST FOR MORE THAN TWO POPULATIONS Lecturer: Dr. Paul Narh Doku, Dept of Psychology, UG Contact Information: pndoku@ug.edu.gh College

More information

Ch 2: Simple Linear Regression

Ch 2: Simple Linear Regression Ch 2: Simple Linear Regression 1. Simple Linear Regression Model A simple regression model with a single regressor x is y = β 0 + β 1 x + ɛ, where we assume that the error ɛ is independent random component

More information

Independent Samples ANOVA

Independent Samples ANOVA Independent Samples ANOVA In this example students were randomly assigned to one of three mnemonics (techniques for improving memory) rehearsal (the control group; simply repeat the words), visual imagery

More information

One-way ANOVA. Experimental Design. One-way ANOVA

One-way ANOVA. Experimental Design. One-way ANOVA Method to compare more than two samples simultaneously without inflating Type I Error rate (α) Simplicity Few assumptions Adequate for highly complex hypothesis testing 09/30/12 1 Outline of this class

More information

A discussion on multiple regression models

A 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 information

Review for Final. Chapter 1 Type of studies: anecdotal, observational, experimental Random sampling

Review 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 information

Chapter 7 Comparison of two independent samples

Chapter 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 information

COMPARING SEVERAL MEANS: ANOVA

COMPARING SEVERAL MEANS: ANOVA LAST UPDATED: November 15, 2012 COMPARING SEVERAL MEANS: ANOVA Objectives 2 Basic principles of ANOVA Equations underlying one-way ANOVA Doing a one-way ANOVA in R Following up an ANOVA: Planned contrasts/comparisons

More information

An inferential procedure to use sample data to understand a population Procedures

An inferential procedure to use sample data to understand a population Procedures Hypothesis Test An inferential procedure to use sample data to understand a population Procedures Hypotheses, the alpha value, the critical region (z-scores), statistics, conclusion Two types of errors

More information

Using SPSS for One Way Analysis of Variance

Using SPSS for One Way Analysis of Variance Using SPSS for One Way Analysis of Variance This tutorial will show you how to use SPSS version 12 to perform a one-way, between- subjects analysis of variance and related post-hoc tests. This tutorial

More information

Factorial Independent Samples ANOVA

Factorial Independent Samples ANOVA Factorial Independent Samples ANOVA Liljenquist, Zhong and Galinsky (2010) found that people were more charitable when they were in a clean smelling room than in a neutral smelling room. Based on that

More information

Multiple t Tests. Introduction to Analysis of Variance. Experiments with More than 2 Conditions

Multiple t Tests. Introduction to Analysis of Variance. Experiments with More than 2 Conditions Introduction to Analysis of Variance 1 Experiments with More than 2 Conditions Often the research that psychologists perform has more conditions than just the control and experimental conditions You might

More information

10/31/2012. One-Way ANOVA F-test

10/31/2012. One-Way ANOVA F-test PSY 511: Advanced Statistics for Psychological and Behavioral Research 1 1. Situation/hypotheses 2. Test statistic 3.Distribution 4. Assumptions One-Way ANOVA F-test One factor J>2 independent samples

More information

Sociology 6Z03 Review II

Sociology 6Z03 Review II Sociology 6Z03 Review II John Fox McMaster University Fall 2016 John Fox (McMaster University) Sociology 6Z03 Review II Fall 2016 1 / 35 Outline: Review II Probability Part I Sampling Distributions Probability

More information

Econ 3790: Business and Economic Statistics. Instructor: Yogesh Uppal

Econ 3790: Business and Economic Statistics. Instructor: Yogesh Uppal Econ 3790: Business and Economic Statistics Instructor: Yogesh Uppal Email: yuppal@ysu.edu Chapter 13, Part A: Analysis of Variance and Experimental Design Introduction to Analysis of Variance Analysis

More information

Tests about a population mean

Tests about a population mean October 2 nd, 2017 Overview Week 1 Week 2 Week 4 Week 7 Week 10 Week 12 Chapter 1: Descriptive statistics Chapter 6: Statistics and Sampling Distributions Chapter 7: Point Estimation Chapter 8: Confidence

More information

Analysis of Variance. Read Chapter 14 and Sections to review one-way ANOVA.

Analysis of Variance. Read Chapter 14 and Sections to review one-way ANOVA. Analysis of Variance Read Chapter 14 and Sections 15.1-15.2 to review one-way ANOVA. Design of an experiment the process of planning an experiment to insure that an appropriate analysis is possible. Some

More information

Introduction to Business Statistics QM 220 Chapter 12

Introduction to Business Statistics QM 220 Chapter 12 Department of Quantitative Methods & Information Systems Introduction to Business Statistics QM 220 Chapter 12 Dr. Mohammad Zainal 12.1 The F distribution We already covered this topic in Ch. 10 QM-220,

More information

HYPOTHESIS TESTING. Hypothesis Testing

HYPOTHESIS TESTING. Hypothesis Testing MBA 605 Business Analytics Don Conant, PhD. HYPOTHESIS TESTING Hypothesis testing involves making inferences about the nature of the population on the basis of observations of a sample drawn from the population.

More information

Department of Economics. Business Statistics. Chapter 12 Chi-square test of independence & Analysis of Variance ECON 509. Dr.

Department of Economics. Business Statistics. Chapter 12 Chi-square test of independence & Analysis of Variance ECON 509. Dr. Department of Economics Business Statistics Chapter 1 Chi-square test of independence & Analysis of Variance ECON 509 Dr. Mohammad Zainal Chapter Goals After completing this chapter, you should be able

More information

ANOVA CIVL 7012/8012

ANOVA CIVL 7012/8012 ANOVA CIVL 7012/8012 ANOVA ANOVA = Analysis of Variance A statistical method used to compare means among various datasets (2 or more samples) Can provide summary of any regression analysis in a table called

More information

The t-statistic. Student s t Test

The t-statistic. Student s t Test The t-statistic 1 Student s t Test When the population standard deviation is not known, you cannot use a z score hypothesis test Use Student s t test instead Student s t, or t test is, conceptually, very

More information

CIVL /8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8

CIVL /8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8 CIVL - 7904/8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8 Chi-square Test How to determine the interval from a continuous distribution I = Range 1 + 3.322(logN) I-> Range of the class interval

More information

CHAPTER 10 ONE-WAY ANALYSIS OF VARIANCE. It would be very unusual for all the research one might conduct to be restricted to

CHAPTER 10 ONE-WAY ANALYSIS OF VARIANCE. It would be very unusual for all the research one might conduct to be restricted to CHAPTER 10 ONE-WAY ANALYSIS OF VARIANCE It would be very unusual for all the research one might conduct to be restricted to comparisons of only two samples. Respondents and various groups are seldom divided

More information

Correlation. A statistics method to measure the relationship between two variables. Three characteristics

Correlation. A statistics method to measure the relationship between two variables. Three characteristics Correlation Correlation A statistics method to measure the relationship between two variables Three characteristics Direction of the relationship Form of the relationship Strength/Consistency Direction

More information

Hypothesis Testing. Hypothesis: conjecture, proposition or statement based on published literature, data, or a theory that may or may not be true

Hypothesis Testing. Hypothesis: conjecture, proposition or statement based on published literature, data, or a theory that may or may not be true Hypothesis esting Hypothesis: conjecture, proposition or statement based on published literature, data, or a theory that may or may not be true Statistical Hypothesis: conjecture about a population parameter

More information

One-Way ANOVA Source Table J - 1 SS B / J - 1 MS B /MS W. Pairwise Post-Hoc Comparisons of Means

One-Way ANOVA Source Table J - 1 SS B / J - 1 MS B /MS W. Pairwise Post-Hoc Comparisons of Means One-Way ANOVA Source Table ANOVA MODEL: ij = µ* + α j + ε ij H 0 : µ 1 = µ =... = µ j or H 0 : Σα j = 0 Source Sum of Squares df Mean Squares F Between Groups n j ( j - * ) J - 1 SS B / J - 1 MS B /MS

More information

One-Way Analysis of Variance (ANOVA) Paul K. Strode, Ph.D.

One-Way Analysis of Variance (ANOVA) Paul K. Strode, Ph.D. One-Way Analysis of Variance (ANOVA) Paul K. Strode, Ph.D. Purpose While the T-test is useful to compare the means of two samples, many biology experiments involve the parallel measurement of three or

More information

Summary of Chapter 7 (Sections ) and Chapter 8 (Section 8.1)

Summary of Chapter 7 (Sections ) and Chapter 8 (Section 8.1) Summary of Chapter 7 (Sections 7.2-7.5) and Chapter 8 (Section 8.1) Chapter 7. Tests of Statistical Hypotheses 7.2. Tests about One Mean (1) Test about One Mean Case 1: σ is known. Assume that X N(µ, σ

More information

OHSU OGI Class ECE-580-DOE :Design of Experiments Steve Brainerd

OHSU OGI Class ECE-580-DOE :Design of Experiments Steve Brainerd Why We Use Analysis of Variance to Compare Group Means and How it Works The question of how to compare the population means of more than two groups is an important one to researchers. Let us suppose that

More information

4/22/2010. Test 3 Review ANOVA

4/22/2010. Test 3 Review ANOVA Test 3 Review ANOVA 1 School recruiter wants to examine if there are difference between students at different class ranks in their reported intensity of school spirit. What is the factor? How many levels

More information

CHAPTER 17 CHI-SQUARE AND OTHER NONPARAMETRIC TESTS FROM: PAGANO, R. R. (2007)

CHAPTER 17 CHI-SQUARE AND OTHER NONPARAMETRIC TESTS FROM: PAGANO, R. R. (2007) FROM: PAGANO, R. R. (007) I. INTRODUCTION: DISTINCTION BETWEEN PARAMETRIC AND NON-PARAMETRIC TESTS Statistical inference tests are often classified as to whether they are parametric or nonparametric Parameter

More information

HYPOTHESIS TESTING: THE CHI-SQUARE STATISTIC

HYPOTHESIS TESTING: THE CHI-SQUARE STATISTIC 1 HYPOTHESIS TESTING: THE CHI-SQUARE STATISTIC 7 steps of Hypothesis Testing 1. State the hypotheses 2. Identify level of significant 3. Identify the critical values 4. Calculate test statistics 5. Compare

More information

Statistics and Quantitative Analysis U4320

Statistics and Quantitative Analysis U4320 Statistics and Quantitative Analysis U3 Lecture 13: Explaining Variation Prof. Sharyn O Halloran Explaining Variation: Adjusted R (cont) Definition of Adjusted R So we'd like a measure like R, but one

More information

AMS7: WEEK 7. CLASS 1. More on Hypothesis Testing Monday May 11th, 2015

AMS7: WEEK 7. CLASS 1. More on Hypothesis Testing Monday May 11th, 2015 AMS7: WEEK 7. CLASS 1 More on Hypothesis Testing Monday May 11th, 2015 Testing a Claim about a Standard Deviation or a Variance We want to test claims about or 2 Example: Newborn babies from mothers taking

More information

REVIEW 8/2/2017 陈芳华东师大英语系

REVIEW 8/2/2017 陈芳华东师大英语系 REVIEW Hypothesis testing starts with a null hypothesis and a null distribution. We compare what we have to the null distribution, if the result is too extreme to belong to the null distribution (p

More information

Can you tell the relationship between students SAT scores and their college grades?

Can you tell the relationship between students SAT scores and their college grades? Correlation One Challenge Can you tell the relationship between students SAT scores and their college grades? A: The higher SAT scores are, the better GPA may be. B: The higher SAT scores are, the lower

More information

Analysis of Variance: Repeated measures

Analysis of Variance: Repeated measures Repeated-Measures ANOVA: Analysis of Variance: Repeated measures Each subject participates in all conditions in the experiment (which is why it is called repeated measures). A repeated-measures ANOVA is

More information

Review of Multiple Regression

Review of Multiple Regression Ronald H. Heck 1 Let s begin with a little review of multiple regression this week. Linear models [e.g., correlation, t-tests, analysis of variance (ANOVA), multiple regression, path analysis, multivariate

More information

Unit 27 One-Way Analysis of Variance

Unit 27 One-Way Analysis of Variance Unit 27 One-Way Analysis of Variance Objectives: To perform the hypothesis test in a one-way analysis of variance for comparing more than two population means Recall that a two sample t test is applied

More information

Multiple comparisons - subsequent inferences for two-way ANOVA

Multiple comparisons - subsequent inferences for two-way ANOVA 1 Multiple comparisons - subsequent inferences for two-way ANOVA the kinds of inferences to be made after the F tests of a two-way ANOVA depend on the results if none of the F tests lead to rejection of

More information

Lecture (chapter 10): Hypothesis testing III: The analysis of variance

Lecture (chapter 10): Hypothesis testing III: The analysis of variance Lecture (chapter 10): Hypothesis testing III: The analysis of variance Ernesto F. L. Amaral March 19 21, 2018 Advanced Methods of Social Research (SOCI 420) Source: Healey, Joseph F. 2015. Statistics:

More information

Table 1: Fish Biomass data set on 26 streams

Table 1: Fish Biomass data set on 26 streams Math 221: Multiple Regression S. K. Hyde Chapter 27 (Moore, 5th Ed.) The following data set contains observations on the fish biomass of 26 streams. The potential regressors from which we wish to explain

More information

Analysis of Covariance. The following example illustrates a case where the covariate is affected by the treatments.

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 information

21.0 Two-Factor Designs

21.0 Two-Factor Designs 21.0 Two-Factor Designs Answer Questions 1 RCBD Concrete Example Two-Way ANOVA Popcorn Example 21.4 RCBD 2 The Randomized Complete Block Design is also known as the two-way ANOVA without interaction. A

More information

Mathematical Notation Math Introduction to Applied Statistics

Mathematical Notation Math Introduction to Applied Statistics Mathematical Notation Math 113 - Introduction to Applied Statistics Name : Use Word or WordPerfect to recreate the following documents. Each article is worth 10 points and should be emailed to the instructor

More information

Econometrics. 4) Statistical inference

Econometrics. 4) Statistical inference 30C00200 Econometrics 4) Statistical inference Timo Kuosmanen Professor, Ph.D. http://nomepre.net/index.php/timokuosmanen Today s topics Confidence intervals of parameter estimates Student s t-distribution

More information

Chapter 8 Student Lecture Notes 8-1. Department of Economics. Business Statistics. Chapter 12 Chi-square test of independence & Analysis of Variance

Chapter 8 Student Lecture Notes 8-1. Department of Economics. Business Statistics. Chapter 12 Chi-square test of independence & Analysis of Variance Chapter 8 Student Lecture Notes 8-1 Department of Economics Business Statistics Chapter 1 Chi-square test of independence & Analysis of Variance ECON 509 Dr. Mohammad Zainal Chapter Goals After completing

More information

8/28/2017. Repeated-Measures ANOVA. 1. Situation/hypotheses. 2. Test statistic. 3.Distribution. 4. Assumptions

8/28/2017. Repeated-Measures ANOVA. 1. Situation/hypotheses. 2. Test statistic. 3.Distribution. 4. Assumptions PSY 5101: Advanced Statistics for Psychological and Behavioral Research 1 Rationale of Repeated Measures ANOVA One-way and two-way Benefits Partitioning Variance Statistical Problems with Repeated- Measures

More information

Parametric versus Nonparametric Statistics-when to use them and which is more powerful? Dr Mahmoud Alhussami

Parametric versus Nonparametric Statistics-when to use them and which is more powerful? Dr Mahmoud Alhussami Parametric versus Nonparametric Statistics-when to use them and which is more powerful? Dr Mahmoud Alhussami Parametric Assumptions The observations must be independent. Dependent variable should be continuous

More information

DESAIN EKSPERIMEN Analysis of Variances (ANOVA) Semester Genap 2017/2018 Jurusan Teknik Industri Universitas Brawijaya

DESAIN EKSPERIMEN Analysis of Variances (ANOVA) Semester Genap 2017/2018 Jurusan Teknik Industri Universitas Brawijaya DESAIN EKSPERIMEN Analysis of Variances (ANOVA) Semester Jurusan Teknik Industri Universitas Brawijaya Outline Introduction The Analysis of Variance Models for the Data Post-ANOVA Comparison of Means Sample

More information

In a one-way ANOVA, the total sums of squares among observations is partitioned into two components: Sums of squares represent:

In a one-way ANOVA, the total sums of squares among observations is partitioned into two components: Sums of squares represent: Activity #10: AxS ANOVA (Repeated subjects design) Resources: optimism.sav So far in MATH 300 and 301, we have studied the following hypothesis testing procedures: 1) Binomial test, sign-test, Fisher s

More information

Nominal Data. Parametric Statistics. Nonparametric Statistics. Parametric vs Nonparametric Tests. Greg C Elvers

Nominal Data. Parametric Statistics. Nonparametric Statistics. Parametric vs Nonparametric Tests. Greg C Elvers Nominal Data Greg C Elvers 1 Parametric Statistics The inferential statistics that we have discussed, such as t and ANOVA, are parametric statistics A parametric statistic is a statistic that makes certain

More information

Sleep data, two drugs Ch13.xls

Sleep data, two drugs Ch13.xls Model Based Statistics in Biology. Part IV. The General Linear Mixed Model.. Chapter 13.3 Fixed*Random Effects (Paired t-test) ReCap. Part I (Chapters 1,2,3,4), Part II (Ch 5, 6, 7) ReCap Part III (Ch

More information

Variance Estimates and the F Ratio. ERSH 8310 Lecture 3 September 2, 2009

Variance Estimates and the F Ratio. ERSH 8310 Lecture 3 September 2, 2009 Variance Estimates and the F Ratio ERSH 8310 Lecture 3 September 2, 2009 Today s Class Completing the analysis (the ANOVA table) Evaluating the F ratio Errors in hypothesis testing A complete numerical

More information

Correlation Analysis

Correlation Analysis Simple Regression Correlation Analysis Correlation analysis is used to measure strength of the association (linear relationship) between two variables Correlation is only concerned with strength of the

More information

Factorial designs. Experiments

Factorial 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 information

One-Way Analysis of Variance: A Guide to Testing Differences Between Multiple Groups

One-Way Analysis of Variance: A Guide to Testing Differences Between Multiple Groups One-Way Analysis of Variance: A Guide to Testing Differences Between Multiple Groups In analysis of variance, the main research question is whether the sample means are from different populations. The

More information

ANOVA continued. Chapter 11

ANOVA continued. Chapter 11 ANOVA continued Chapter 11 Zettergren (003) School adjustment in adolescence for previously rejected, average, and popular children. Effect of peer reputation on academic performance and school adjustment

More information

psyc3010 lecture 2 factorial between-ps ANOVA I: omnibus tests

psyc3010 lecture 2 factorial between-ps ANOVA I: omnibus tests psyc3010 lecture 2 factorial between-ps ANOVA I: omnibus tests last lecture: introduction to factorial designs next lecture: factorial between-ps ANOVA II: (effect sizes and follow-up tests) 1 general

More information

16.3 One-Way ANOVA: The Procedure

16.3 One-Way ANOVA: The Procedure 16.3 One-Way ANOVA: The Procedure Tom Lewis Fall Term 2009 Tom Lewis () 16.3 One-Way ANOVA: The Procedure Fall Term 2009 1 / 10 Outline 1 The background 2 Computing formulas 3 The ANOVA Identity 4 Tom

More information

Analysis of Variance (ANOVA)

Analysis of Variance (ANOVA) Analysis of Variance (ANOVA) Used for comparing or more means an extension of the t test Independent Variable (factor) = categorical (qualita5ve) predictor should have at least levels, but can have many

More information

Acknowledge error Smaller samples, less spread

Acknowledge error Smaller samples, less spread Hypothesis Testing with t Tests Al Arlo Clark-Foos kf Using Samples to Estimate Population Parameters Acknowledge error Smaller samples, less spread s = Σ ( X M N 1 ) 2 The t Statistic Indicates the distance

More information

8/23/2018. One-Way ANOVA F-test. 1. Situation/hypotheses. 2. Test statistic. 3.Distribution. 4. Assumptions

8/23/2018. One-Way ANOVA F-test. 1. Situation/hypotheses. 2. Test statistic. 3.Distribution. 4. Assumptions PSY 5101: Advanced Statistics for Psychological and Behavioral Research 1 1. Situation/hypotheses 2. Test statistic One-Way ANOVA F-test One factor J>2 independent samples H o :µ 1 µ 2 µ J F 3.Distribution

More information

4/6/16. Non-parametric Test. Overview. Stephen Opiyo. Distinguish Parametric and Nonparametric Test Procedures

4/6/16. Non-parametric Test. Overview. Stephen Opiyo. Distinguish Parametric and Nonparametric Test Procedures Non-parametric Test Stephen Opiyo Overview Distinguish Parametric and Nonparametric Test Procedures Explain commonly used Nonparametric Test Procedures Perform Hypothesis Tests Using Nonparametric Procedures

More information

One-way between-subjects ANOVA. Comparing three or more independent means

One-way between-subjects ANOVA. Comparing three or more independent means One-way between-subjects ANOVA Comparing three or more independent means ANOVA: A Framework Understand the basic principles of ANOVA Why it is done? What it tells us? Theory of one-way between-subjects

More information

Statistical Analysis for QBIC Genetics Adapted by Ellen G. Dow 2017

Statistical Analysis for QBIC Genetics Adapted by Ellen G. Dow 2017 Statistical Analysis for QBIC Genetics Adapted by Ellen G. Dow 2017 I. χ 2 or chi-square test Objectives: Compare how close an experimentally derived value agrees with an expected value. One method to

More information

Regression Analysis. Table Relationship between muscle contractile force (mj) and stimulus intensity (mv).

Regression Analysis. Table Relationship between muscle contractile force (mj) and stimulus intensity (mv). Regression Analysis Two variables may be related in such a way that the magnitude of one, the dependent variable, is assumed to be a function of the magnitude of the second, the independent variable; however,

More information

Quantitative Methods for Economics, Finance and Management (A86050 F86050)

Quantitative Methods for Economics, Finance and Management (A86050 F86050) Quantitative Methods for Economics, Finance and Management (A86050 F86050) Matteo Manera matteo.manera@unimib.it Marzio Galeotti marzio.galeotti@unimi.it 1 This material is taken and adapted from Guy Judge

More information

Introduction to the Analysis of Variance (ANOVA) Computing One-Way Independent Measures (Between Subjects) ANOVAs

Introduction to the Analysis of Variance (ANOVA) Computing One-Way Independent Measures (Between Subjects) ANOVAs Introduction to the Analysis of Variance (ANOVA) Computing One-Way Independent Measures (Between Subjects) ANOVAs The Analysis of Variance (ANOVA) The analysis of variance (ANOVA) is a statistical technique

More information

Multiple Regression. More Hypothesis Testing. More Hypothesis Testing The big question: What we really want to know: What we actually know: We know:

Multiple 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 information

ANOVA: Comparing More Than Two Means

ANOVA: Comparing More Than Two Means ANOVA: Comparing More Than Two Means Chapter 11 Cathy Poliak, Ph.D. cathy@math.uh.edu Office Fleming 11c Department of Mathematics University of Houston Lecture 25-3339 Cathy Poliak, Ph.D. cathy@math.uh.edu

More information

2 and F Distributions. Barrow, Statistics for Economics, Accounting and Business Studies, 4 th edition Pearson Education Limited 2006

2 and F Distributions. Barrow, Statistics for Economics, Accounting and Business Studies, 4 th edition Pearson Education Limited 2006 and F Distributions Lecture 9 Distribution The distribution is used to: construct confidence intervals for a variance compare a set of actual frequencies with expected frequencies test for association

More information

QUEEN MARY, UNIVERSITY OF LONDON

QUEEN MARY, UNIVERSITY OF LONDON QUEEN MARY, UNIVERSITY OF LONDON MTH634 Statistical Modelling II Solutions to Exercise Sheet 4 Octobe07. We can write (y i. y.. ) (yi. y i.y.. +y.. ) yi. y.. S T. ( Ti T i G n Ti G n y i. +y.. ) G n T

More information

One-way Analysis of Variance. Major Points. T-test. Ψ320 Ainsworth

One-way Analysis of Variance. Major Points. T-test. Ψ320 Ainsworth One-way Analysis of Variance Ψ30 Ainsworth Major Points Problem with t-tests and multiple groups The logic behind ANOVA Calculations Multiple comparisons Assumptions of analysis of variance Effect Size

More information

Factorial Treatment Structure: Part I. Lukas Meier, Seminar für Statistik

Factorial Treatment Structure: Part I. Lukas Meier, Seminar für Statistik Factorial Treatment Structure: Part I Lukas Meier, Seminar für Statistik Factorial Treatment Structure So far (in CRD), the treatments had no structure. So called factorial treatment structure exists if

More information

Hypothesis Testing hypothesis testing approach

Hypothesis Testing hypothesis testing approach Hypothesis Testing In this case, we d be trying to form an inference about that neighborhood: Do people there shop more often those people who are members of the larger population To ascertain this, we

More information

Review. One-way ANOVA, I. What s coming up. Multiple comparisons

Review. One-way ANOVA, I. What s coming up. Multiple comparisons Review One-way ANOVA, I 9.07 /15/00 Earlier in this class, we talked about twosample z- and t-tests for the difference between two conditions of an independent variable Does a trial drug work better than

More information

Chi-Square. Heibatollah Baghi, and Mastee Badii

Chi-Square. Heibatollah Baghi, and Mastee Badii 1 Chi-Square Heibatollah Baghi, and Mastee Badii Different Scales, Different Measures of Association Scale of Both Variables Nominal Scale Measures of Association Pearson Chi-Square: χ 2 Ordinal Scale

More information

10/4/2013. Hypothesis Testing & z-test. Hypothesis Testing. Hypothesis Testing

10/4/2013. Hypothesis Testing & z-test. Hypothesis Testing. Hypothesis Testing & z-test Lecture Set 11 We have a coin and are trying to determine if it is biased or unbiased What should we assume? Why? Flip coin n = 100 times E(Heads) = 50 Why? Assume we count 53 Heads... What could

More information

Stat 529 (Winter 2011) Experimental Design for the Two-Sample Problem. Motivation: Designing a new silver coins experiment

Stat 529 (Winter 2011) Experimental Design for the Two-Sample Problem. Motivation: Designing a new silver coins experiment Stat 529 (Winter 2011) Experimental Design for the Two-Sample Problem Reading: 2.4 2.6. Motivation: Designing a new silver coins experiment Sample size calculations Margin of error for the pooled two sample

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur nalysis of Variance and Design of Experiment-I MODULE V LECTURE - 9 FCTORIL EXPERIMENTS Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Sums of squares Suppose

More information

Chapter 9: How can I tell if scores differ between three or more groups? One-way independent measures ANOVA.

Chapter 9: How can I tell if scores differ between three or more groups? One-way independent measures ANOVA. Chapter 9: How can I tell if scores differ between three or more groups? One-way independent measures ANOVA. Full answers to study questions 1. Familywise error as a result of conducting multiple t tests.

More information

Ch 3: Multiple Linear Regression

Ch 3: Multiple Linear Regression Ch 3: Multiple Linear Regression 1. Multiple Linear Regression Model Multiple regression model has more than one regressor. For example, we have one response variable and two regressor variables: 1. delivery

More information

Review of Statistics 101

Review of Statistics 101 Review of Statistics 101 We review some important themes from the course 1. Introduction Statistics- Set of methods for collecting/analyzing data (the art and science of learning from data). Provides methods

More information