Regression CHAPTER SIXTEEN NOTE TO INSTRUCTORS OUTLINE OF RESOURCES
|
|
- Kathleen Gallagher
- 5 years ago
- Views:
Transcription
1 CHAPTER SIXTEEN Regression NOTE TO INSTRUCTORS This chapter includes a number of complex concepts that may seem intimidating to students. Encourage students to focus on the big picture through some of the discussion questions and classroom activities. You can ease students concerns about multiple regression by describing it as similar to simple linear regression except that researchers examine multiple variables rather than only one. OUTLINE OF RESOURCES III. III. III. IV. Simple Linear Regression Discussion Question 16-1 (p. 152) Discussion Question 16-2 (p. 152) Classroom Activity 16-1: Make It Your Own (p. 153) Discussion Question 16-3 (p. 153) Discussion Question 16-4 (p. 153) Discussion Question 16-5 (p. 154) Classroom Activity 16-2: Finding the Regression Line (p. 154) Interpretation and Prediction Classroom Activity 16-3: Make It Your Own (p. 154) Discussion Question 16-6 (p. 155) Discussion Question 16-7 (p. 156) Multiple Regression Discussion Question 16-8 (p. 156) Discussion Question 16-9 (p. 157) Additional Readings (p. 158) Online Resources (p. 158) Next Steps: Structural Equation Modeling (SEM) Discussion Question 16-9 (p. 157) Classroom Activity 16-4: Careers in Prediction (p. 157) Classroom Activity 16-5: SEM in Context (p. 158) 151
2 152 CHAPTER 16 REGRESSION IV. Handouts Handout 16-1: Finding the Regression Line (p. 159) Handout 16-2: Careers in Prediction (p. 160) Handout 16-3: Examing SEM in Context (p. 161) CHAPTER GUIDE I. Simple Linear Regression 1. Simple linear regression is a statistical tool that enables us to predict an individual s score on the dependent variable from his or her score on one independent variable. 2. Regression allows us to make quantitative predictions that more precisely explain relations among variables. > Discussion Question 16-1 What is simple linear regression, and why is it useful? Simple linear regression is a tool that allows us to make predictions. Simple linear regression is useful as an extension of correlation that allows us to quantify the relationship among variables with greater precision and accuracy. 3. Because simple linear regression helps us to find the equation for a line, we must have data that are linearly related to use it. 4. We can use z scores when making these predictions. Specifically, the formula is z Y^ = (r XY )(z X ). The first z score is for the dependent variable and the second z score is for the independent variable. The ^ symbol signals that the z score is predicted rather than being the actual score. 5. The tendency for scores that are particularly high or low to drift toward the mean over time is known as regression to the mean. 6. Usually, we want to predict a raw score from a raw score. We will first need to convert a raw score on one variable to a z score. We can then predict a z score for the second variable. Finally, we convert the z score from the second variable to a raw score. > Discussion Question 16-2 How would we predict a raw score from a raw score? In order to predict a raw score from a raw score, we must first transform one raw score into a z score. Then we multiply that z score by the correlation coefficient to get the predicted z score for the second variable. Finally, we transpose that z score back into a raw score and make our prediction.
3 CHAPTER 16 REGRESSION 153 Classroom Activity 16-1 Make It Your Own Use your students weight and height as measures for this exercise, or use height and age if you think using weight would be a sensitive issue. Have your students anonymously submit their weight and height or height and age. Load these data into SPSS and run the analysis as a correlation and simple regression. 7. The intercept is the predicted value for Y when X is equal to 0, which is the point at which the line crosses or intercepts the y-axis. 8. The slope is the amount that Y is predicted to increase for an increase of 1 in X. > Discussion Question 16-3 What is the difference between the intercept and the slope? Why do we calculate them in simple linear regression? The difference between the intercept and the slope is that the intercept is the predicted value for Y when X is equal to 0 and the slope is the amount that Y is predicted to increase for an increase of 1 unit in X. We calculate them in simple linear regression because it allows us to develop a raw-score regression equation for predicting the raw score for Y. 9. Both the intercept and the slope are needed to calculate the equation of a line: Y^ = a + b(x). 10. To calculate the intercept, we calculate the z score for X when X = 0 by using the formula: z X = (X M X )/SD X. We then use the z-score regression equation to calculate the predicted z score for Y by using the formula: z Y^ = (r XY )(z X ). We then convert this z score to the predicted raw score for Y using the formula: Y^ = z Y^ (SD Y ) + M Y. 11. To calculate the slope, we repeat the previous steps that we used to calculate the intercept but use an X of 1 rather than 0. We then determine the change in Y^ as X increased from 0 to 1. It is important to include the appropriate sign based on whether there is an increase or decrease in Y^. > Discussion Question 16-4 How do we calculate the slope of the regression line? How is it different from calculating the intercept? We calculate the slope of the regression line by first calculating a z score for X when X = 1 by using the formula: z X = (X M X )/SD X. We then use the z-score regression equation to calculate the predicted z score for Y by using the formula: z Y^ = (r XY )(z X ). We then convert this z score to the predicted raw score for Y using the formula: Y^ = z Y^ (SD Y ) + M Y.
4 154 CHAPTER 16 REGRESSION Calculating the slope of the regression line is different from calculating the intercept of the regression line because for calculating the slope we substitute an X of 0 with an X of With both the intercept and slope calculated, we can now use our formula to predict the raw score for Y. 13. If we find at least three other predicted values for Y, we can use these values to draw a regression line. This is also known as the line of best fit. 14. A negative slope means that the regression line starts in the upper left of the graph and ends in the lower right. A positive slope means that the regression line starts in the lower left of the graph and ends in the upper right. > Discussion Question 16-5 How can you tell whether a slope is positive or negative? You can tell whether a slope is positive or negative by first drawing a regression line through the dots on a graph corresponding to pairs of scores for X and Y^; A negative slope means that the line looks like it s going downhill as we move from left to right, while a positive slope means that the line looks like it s going uphill as we move from left to right. Classroom Activity 16-2 Finding the Regression Line Have students use data created from Classroom Activity 16-3, Creating Correlations, from the previous chapter. Have students use the data collected to determine the regression line. Handout 16-1, found at the end of this chapter, can be used to aid in this process. II. 15. The standardized regression coefficient (also known as beta weight), a standardized version of the slope in a regression equation, is the predicted change in the dependent variable in terms of standard deviations for a 1 standard deviation increase in the independent variable. 16. The standardized regression coefficient is symbolized by β and pronouned beta or called beta weight. It is calculated using the formula β = (b)( SS X / SS Y ). Interpretation and Prediction 1. The number that best describes how far away, on average, the data points are from the line of best fit is called the standard error of the estimate. In other words, it is a statistic indicating the typical distance between a regression line and the actual data points.
5 CHAPTER 16 REGRESSION 155 Classroom Activity 16-3 Make It Your Own In this activity, use SAT scores and overall GPAs to demonstrate simple regression. Again, anonymously collect the data from the students. Have the students frame the research question for a correlation for a simple regression. After running the analysis, have the students discuss the results. It is likely that your data may suffer from a restricted range but that is a good point for class discussion because real data are messy. 2. The proportionate reduction in error is a statistic that quantifies how much more accurate our predictions are when we use the regression line instead of the mean as a prediction tool. > Discussion Question 16-6 Why do you think that we would use the mean as a basis of comparison with the regression line? Why would we use the mean instead of some other number from our sample? We use the mean as a basis of comparison with the regression line because, with limited information, the mean is a fair predictor. By using the mean, we can calculate the coefficient of determination and measure how accurate our predictions are in using the regression line compared to the mean. We use the mean instead of some other number from a sample because the mean is involved in calculating the regression equation and, as a result, we can quantify the improvement in prediction that results from using the regression line over the mean. 3. If we were to subtract the mean score of the sample from each person s score, square that value, and sum all of the values, we would obtain the sum of squared errors, or the sum of squares total (SS total ). This is the error that results if we were to predict the mean as the score for each person. 4. We want our regression equation to be a substantial improvement over just using the mean as our prediction. 5. To determine how much better our regression equation predicts over the mean, we plug each X value into the regression equation. 6. To find the sum of squared errors, or SS error, we subtract each predicted score from the mean, square the errors, and sum them. 7. To find the amount of error we ve reduced, we subtract the sum of squared errors from the sum of squares total. This number is divided by the sum of squares total to obtain a proportion. 8. The proportionate reduction in error is symbolized as r 2 and is calculated using the formula: r 2 = (SS total SS error )/SS total.
6 156 CHAPTER 16 REGRESSION > Discussion Question 16-7 What is the difference between the SS total and the SS error? What is the purpose of calculating them? The difference between the SS total and the SS error is that the SS total represents the error in prediction from the mean compared to the SS error, which represents error from predicting Y with our regression equation. The purpose of calculating them is to quantify the amount of error that we ve reduced in using the regression equation instead of the mean. 9. We could also calculate r 2 by squaring the correlation coefficient. III. Multiple Regression 1. An orthogonal variable is an independent variable that makes a separate and distinct contribution in the prediction of a dependent variable, as compared to another independent variable. 2. Multiple regression is a statistical technique that includes two or more predictor variables in a prediction equation. 3. Multiple regression is more widely used than simple linear regression because most dependent variables are best explained by using more than one independent variable. > Discussion Question 16-8 Why is multiple regression an improvement over simple linear regression? Multiple regression is an improvement over simple linear regression because it provides greater prediction by incorporating two or more predictor variables into the regression equation. 4. Compared to using averages, multiple regression represents a significant advance in our ability to predict human behavior. 5. When calculating the proportionate reduction in error for multiple regression, its symbol is R 2 rather than r 2 to indicate that the error is based on more than one independent variable. 6. In stepwise multiple regression, computer software determines the order in which independent variables are included in the equation. 7. Stepwise multiple regression is frequently used because it is the default in many software programs and is useful in the absence of a clear, predictive theory. 8. Another approach is to use hierarchical multiple regression whereby the researcher adds independent variables into the equation in an order determined by theory. 9. In order to use hierarchical multiple regression, we need to have a specific predictive theory that we are testing.
7 CHAPTER 16 REGRESSION 157 > Discussion Question 16-9 What is the difference between stepwise and hierarchical multiple regression? When would you want to use one technique rather than the other? The difference between stepwise and hierarchical multiple regression is that, in stepwise regression, the computer software program determines the order of variable entry, while in hierarchical regression analysis, the researcher determines order of variable entry in light of theory. A stepwise regression can be used in the absence of theory, such as in model building, while hierarchical regression can be used to test a specific theory. Classroom Activity 16-4 Careers in Prediction The chapter refers to many opportunities for using prediction within certain careers. In this activity, students will expand on this topic using Handout The goal of this activity is for students to observe the relevance and usefulness of regression in their daily experience. IV. Next Steps: Structural Equation Modeling (SEM) 1. Structural equation modeling (SEM) is one of several statistical techniques (and one of the most sophisticated statistical approaches) that quantifies how well sample data fit a theoretical model that hypothesizes a set of relations among multiple variables. 2. When using SEM, statisticians will refer to a statistical (or theoretical) model, which is a hypothesized network of relations, often portrayed graphically, among multiple variables. 3. When creating a model that hypothesizes the relation among factors being tested, we create paths that describe the connection between two variables in a statistical mode. We can conduct a path analysis to examine a hypothesized model by conducting a series of regression analyses that quantify the paths at each succeeding step in the model. 4. In SEM, we refer to variables that we observe and are measured as manifest variables. 5. In contrast, latent variables are ideas that we want to research but cannot directly measure. We will try to indirectly observe such variables using appropriate measurement tools. 6. When encountering a model such as SEM, it is important to first figure out what variables the researcher is studying. Next, look at the numbers to see what variables are related and the signs of the numbers to see the direction of the relation.
8 158 CHAPTER 16 REGRESSION Classroom Activity 16-5 SEM in Context In this activity, students will try to understand how path analysis is used in context. To do this, students will download or be given copies of the article: Kim, Y. M. & Neff, J. A. (2010). Direct and indirect effects of parental influence upon adolescent alcohol use: A structural equation modeling analysis. Journal of Child & Adolescent Substance Abuse, 19(3), Students will use Handout 16-3 in their analysis of the article. Additional Readings Harrell, F. E. (2001). Regression Modeling Strategies. New York: Springer. Beyond discussing regression, this book also explores when and how to use this statistic. It is geared toward graduate students and researchers. Cohen, J., and Cohen, P. (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Mahwah, NJ: Lawrence Erlbaum Publishers. This book is data oriented and presents an excellent nonmathematical approach to data analysis. It aims toward at least a graduate level course in statistics, but is also an invaluable reference for those wanting more depth in this area. Online Resources The following site provides you with simulations or demonstrations for almost all topics found in the textbook, as well as additional information about each topic: The regression by eye is a good support for students as they learn to visually grasp regression. The following is the award-winning Web Experimental Psychology Lab site, home of the Magic experiment: ch/sowi/ulf/lab/webexppsylab.html. There are a number of fun experiments that your students can explore, including ranking probability terms and learning via tutorial dialogues.
9 CHAPTER 16 REGRESSION 159 HANDOUT 16-1: FINDING THE REGRESSION LINE Directions: For this exercise, use the data obtained from Classroom Exercise 15-3, Creating Correlations, from Chapter 15 to answer the questions below. 1. What is the intercept for this data? 2. What is the slope for this data? 3. What is the equation of the regression line for this data? 4. Using the regression line, predict Y using any relevant number for X (except 0 or 1).
10 160 CHAPTER 16 REGRESSION HANDOUT 16-2: CAREERS IN PREDICTION Directions: Answer the questions below to explore the relevance of prediction in everyday experience by examining how prediction is used in certain jobs. 1. Brainstorm a list of careers that could use prediction and write them below. Use the authors suggestions in the textbook chapter to help you with this, but be sure to develop additional ideas. You may also want to use online or newspaper job listings to help you decide how prediction could be useful in these careers. 2. For each job listed above, discuss why you think prediction could be useful and how prediction would be used in this context.
11 CHAPTER 16 REGRESSION 161 HANDOUT 16-3: EXAMINING SEM IN CONTEXT Directions: For this exercise, you will need the article: Kim, Y. M. & Neff, J. A. (2010). Direct and indirect effects of parental influence upon adolescent alcohol use: A structural equation modeling analysis. Journal of Child & Adolescent Substance Abuse, 19(3), Read the article, and answer the questions below to help you understand how SEM is used in psychological research. 1. Summarize the research in the space below. What were the researchers hypotheses? What were their methods? What were their conclusions? 2. Draw the authors model in the space below and include their findings from SEM. 3. Interpret the findings from SEM. What do their findings actually mean?
12
CHAPTER TWO REGRESSION
CHAPTER TWO REGRESSION 2.0 Introduction The second chapter, Regression analysis is an extension of correlation. The aim of the discussion of exercises is to enhance students capability to assess the effect
More informationStatistics for Psychology
Statistics for Psychology SIXTH EDITION CHAPTER 12 Prediction Prediction a major practical application of statistical methods: making predictions make informed (and precise) guesses about such things as
More informationChapter 3 CORRELATION AND REGRESSION
CORRELATION AND REGRESSION TOPIC SLIDE Linear Regression Defined 2 Regression Equation 3 The Slope or b 4 The Y-Intercept or a 5 What Value of the Y-Variable Should be Predicted When r = 0? 7 The Regression
More information3 CONCEPTUAL FOUNDATIONS OF STATISTICS
3 CONCEPTUAL FOUNDATIONS OF STATISTICS In this chapter, we examine the conceptual foundations of statistics. The goal is to give you an appreciation and conceptual understanding of some basic statistical
More informationCRITERIA FOR USE. A GRAPHICAL EXPLANATION OF BI-VARIATE (2 VARIABLE) REGRESSION ANALYSISSys
Multiple Regression Analysis 1 CRITERIA FOR USE Multiple regression analysis is used to test the effects of n independent (predictor) variables on a single dependent (criterion) variable. Regression tests
More informationResults & Statistics: Description and Correlation. I. Scales of Measurement A Review
Results & Statistics: Description and Correlation The description and presentation of results involves a number of topics. These include scales of measurement, descriptive statistics used to summarize
More informationBusiness Statistics Probability
Business Statistics The following was provided by Dr. Suzanne Delaney, and is a comprehensive review of Business Statistics. The workshop instructor will provide relevant examples during the Skills Assessment
More informationCHAPTER ONE CORRELATION
CHAPTER ONE CORRELATION 1.0 Introduction The first chapter focuses on the nature of statistical data of correlation. The aim of the series of exercises is to ensure the students are able to use SPSS to
More informationMULTIPLE LINEAR REGRESSION 24.1 INTRODUCTION AND OBJECTIVES OBJECTIVES
24 MULTIPLE LINEAR REGRESSION 24.1 INTRODUCTION AND OBJECTIVES In the previous chapter, simple linear regression was used when you have one independent variable and one dependent variable. This chapter
More information(a) 50% of the shows have a rating greater than: impossible to tell
q 1. Here is a histogram of the Distribution of grades on a quiz. How many students took the quiz? What percentage of students scored below a 60 on the quiz? (Assume left-hand endpoints are included in
More informationCHAPTER 3 DATA ANALYSIS: DESCRIBING DATA
Data Analysis: Describing Data CHAPTER 3 DATA ANALYSIS: DESCRIBING DATA In the analysis process, the researcher tries to evaluate the data collected both from written documents and from other sources such
More informationA Penny for Your Thoughts: Scientific Measurements and Introduction to Excel
A Penny for Your Thoughts: Scientific Measurements and Introduction to Excel Pre-lab Assignment: Introduction Reading: 1. Chapter sections 1.4 through 1.6 in your course text. 2. This lab handout. Questions:
More informationUnit 1 Exploring and Understanding Data
Unit 1 Exploring and Understanding Data Area Principle Bar Chart Boxplot Conditional Distribution Dotplot Empirical Rule Five Number Summary Frequency Distribution Frequency Polygon Histogram Interquartile
More informationExperiment 1: Scientific Measurements and Introduction to Excel
Experiment 1: Scientific Measurements and Introduction to Excel Reading: Chapter 1 of your textbook and this lab handout. Learning Goals for Experiment 1: To use a scientific notebook as a primary record
More informationExperiment 1: Scientific Measurements and Introduction to Excel
Experiment 1: Scientific Measurements and Introduction to Excel Reading: Chapter 1 of your textbook and this lab handout. Learning Goals for Experiment 1: To use a scientific notebook as a primary record
More informationMEASUREMENT OF SKILLED PERFORMANCE
MEASUREMENT OF SKILLED PERFORMANCE Name: Score: Part I: Introduction The most common tasks used for evaluating performance in motor behavior may be placed into three categories: time, response magnitude,
More informationFORM C Dr. Sanocki, PSY 3204 EXAM 1 NAME
PSYCH STATS OLD EXAMS, provided for self-learning. LEARN HOW TO ANSWER the QUESTIONS; memorization of answers won t help. All answers are in the textbook or lecture. Instructors can provide some clarification
More informationRegression Including the Interaction Between Quantitative Variables
Regression Including the Interaction Between Quantitative Variables The purpose of the study was to examine the inter-relationships among social skills, the complexity of the social situation, and performance
More informationChapter 1: Exploring Data
Chapter 1: Exploring Data Key Vocabulary:! individual! variable! frequency table! relative frequency table! distribution! pie chart! bar graph! two-way table! marginal distributions! conditional distributions!
More informationChapter 3: Examining Relationships
Name Date Per Key Vocabulary: response variable explanatory variable independent variable dependent variable scatterplot positive association negative association linear correlation r-value regression
More informationDescribe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo
Business Statistics The following was provided by Dr. Suzanne Delaney, and is a comprehensive review of Business Statistics. The workshop instructor will provide relevant examples during the Skills Assessment
More information12/30/2017. PSY 5102: Advanced Statistics for Psychological and Behavioral Research 2
PSY 5102: Advanced Statistics for Psychological and Behavioral Research 2 Selecting a statistical test Relationships among major statistical methods General Linear Model and multiple regression Special
More informationAddendum: Multiple Regression Analysis (DRAFT 8/2/07)
Addendum: Multiple Regression Analysis (DRAFT 8/2/07) When conducting a rapid ethnographic assessment, program staff may: Want to assess the relative degree to which a number of possible predictive variables
More informationSimple Linear Regression the model, estimation and testing
Simple Linear Regression the model, estimation and testing Lecture No. 05 Example 1 A production manager has compared the dexterity test scores of five assembly-line employees with their hourly productivity.
More informationStatistical Methods Exam I Review
Statistical Methods Exam I Review Professor: Dr. Kathleen Suchora SI Leader: Camila M. DISCLAIMER: I have created this review sheet to supplement your studies for your first exam. I am a student here at
More informationStill important ideas
Readings: OpenStax - Chapters 1 13 & Appendix D & E (online) Plous Chapters 17 & 18 - Chapter 17: Social Influences - Chapter 18: Group Judgments and Decisions Still important ideas Contrast the measurement
More informationFind the slope of the line that goes through the given points. 1) (-9, -68) and (8, 51) 1)
Math 125 Semester Review Problems Name Find the slope of the line that goes through the given points. 1) (-9, -68) and (8, 51) 1) Solve the inequality. Graph the solution set, and state the solution set
More informationReadings: Textbook readings: OpenStax - Chapters 1 13 (emphasis on Chapter 12) Online readings: Appendix D, E & F
Readings: Textbook readings: OpenStax - Chapters 1 13 (emphasis on Chapter 12) Online readings: Appendix D, E & F Plous Chapters 17 & 18 Chapter 17: Social Influences Chapter 18: Group Judgments and Decisions
More informationAppendix B Statistical Methods
Appendix B Statistical Methods Figure B. Graphing data. (a) The raw data are tallied into a frequency distribution. (b) The same data are portrayed in a bar graph called a histogram. (c) A frequency polygon
More informationSPSS output for 420 midterm study
Ψ Psy Midterm Part In lab (5 points total) Your professor decides that he wants to find out how much impact amount of study time has on the first midterm. He randomly assigns students to study for hours,
More informationSTATISTICS & PROBABILITY
STATISTICS & PROBABILITY LAWRENCE HIGH SCHOOL STATISTICS & PROBABILITY CURRICULUM MAP 2015-2016 Quarter 1 Unit 1 Collecting Data and Drawing Conclusions Unit 2 Summarizing Data Quarter 2 Unit 3 Randomness
More informationSTATISTICS INFORMED DECISIONS USING DATA
STATISTICS INFORMED DECISIONS USING DATA Fifth Edition Chapter 4 Describing the Relation between Two Variables 4.1 Scatter Diagrams and Correlation Learning Objectives 1. Draw and interpret scatter diagrams
More information11/18/2013. Correlational Research. Correlational Designs. Why Use a Correlational Design? CORRELATIONAL RESEARCH STUDIES
Correlational Research Correlational Designs Correlational research is used to describe the relationship between two or more naturally occurring variables. Is age related to political conservativism? Are
More informationbivariate analysis: The statistical analysis of the relationship between two variables.
bivariate analysis: The statistical analysis of the relationship between two variables. cell frequency: The number of cases in a cell of a cross-tabulation (contingency table). chi-square (χ 2 ) test for
More information2.75: 84% 2.5: 80% 2.25: 78% 2: 74% 1.75: 70% 1.5: 66% 1.25: 64% 1.0: 60% 0.5: 50% 0.25: 25% 0: 0%
Capstone Test (will consist of FOUR quizzes and the FINAL test grade will be an average of the four quizzes). Capstone #1: Review of Chapters 1-3 Capstone #2: Review of Chapter 4 Capstone #3: Review of
More informationBangor University Laboratory Exercise 1, June 2008
Laboratory Exercise, June 2008 Classroom Exercise A forest land owner measures the outside bark diameters at.30 m above ground (called diameter at breast height or dbh) and total tree height from ground
More informationCorrelation and Regression
Dublin Institute of Technology ARROW@DIT Books/Book Chapters School of Management 2012-10 Correlation and Regression Donal O'Brien Dublin Institute of Technology, donal.obrien@dit.ie Pamela Sharkey Scott
More informationWelcome to OSA Training Statistics Part II
Welcome to OSA Training Statistics Part II Course Summary Using data about a population to draw graphs Frequency distribution and variability within populations Bell Curves: What are they and where do
More informationLab 4 (M13) Objective: This lab will give you more practice exploring the shape of data, and in particular in breaking the data into two groups.
Lab 4 (M13) Objective: This lab will give you more practice exploring the shape of data, and in particular in breaking the data into two groups. Activity 1 Examining Data From Class Background Download
More informationStudy Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
More informationSTATISTICS AND RESEARCH DESIGN
Statistics 1 STATISTICS AND RESEARCH DESIGN These are subjects that are frequently confused. Both subjects often evoke student anxiety and avoidance. To further complicate matters, both areas appear have
More informationSection 3.2 Least-Squares Regression
Section 3.2 Least-Squares Regression Linear relationships between two quantitative variables are pretty common and easy to understand. Correlation measures the direction and strength of these relationships.
More informationChapter 2. Behavioral Variability and Research
Chapter 2 Behavioral Variability and Research Chapter Outline Variability and the Research Process Variance: An Index of Variability Systematic and Error Variance Effect Size: Assessing the Strength of
More informationSample Math 71B Final Exam #1. Answer Key
Sample Math 71B Final Exam #1 Answer Key 1. (2 points) Graph the equation. Be sure to plot the points on the graph at. 2. Solve for. 3. Given that, find and simplify. 4. Suppose and a. (1 point) Find.
More informationPart III Taking Chances for Fun and Profit
Part III Taking Chances for Fun and Profit Chapter 8 Are Your Curves Normal? Probability and Why it Counts What You Will Learn in Chapter 8 How probability relates to statistics Characteristics of the
More informationStudy Guide #2: MULTIPLE REGRESSION in education
Study Guide #2: MULTIPLE REGRESSION in education What is Multiple Regression? When using Multiple Regression in education, researchers use the term independent variables to identify those variables that
More informationNEW YORK CITY COLLEGE OF TECHNOLOGY The City University of New York
NEW YORK CITY COLLEGE OF TECHNOLOGY The City University of New York DEPARTMENT: Mathematics COURSE: MAT 0630/ MA 630 TITLE: DESCRIPTION: TEXT: Elementary Algebra with Basic Mathematics Review Fundamentals
More information12/31/2016. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Introduce moderated multiple regression Continuous predictor continuous predictor Continuous predictor categorical predictor Understand
More informationLecture 6B: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression
Lecture 6B: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression! Equation of Regression Line; Residuals! Effect of Explanatory/Response Roles! Unusual Observations! Sample
More informationSummary & Conclusion. Lecture 10 Survey Research & Design in Psychology James Neill, 2016 Creative Commons Attribution 4.0
Summary & Conclusion Lecture 10 Survey Research & Design in Psychology James Neill, 2016 Creative Commons Attribution 4.0 Overview 1. Survey research and design 1. Survey research 2. Survey design 2. Univariate
More informationSpeed Accuracy Trade-Off
Speed Accuracy Trade-Off Purpose To demonstrate the speed accuracy trade-off illustrated by Fitts law. Background The speed accuracy trade-off is one of the fundamental limitations of human movement control.
More informationThings you need to know about the Normal Distribution. How to use your statistical calculator to calculate The mean The SD of a set of data points.
Things you need to know about the Normal Distribution How to use your statistical calculator to calculate The mean The SD of a set of data points. The formula for the Variance (SD 2 ) The formula for the
More informationSUMMER 2011 RE-EXAM PSYF11STAT - STATISTIK
SUMMER 011 RE-EXAM PSYF11STAT - STATISTIK Full Name: Årskortnummer: Date: This exam is made up of three parts: Part 1 includes 30 multiple choice questions; Part includes 10 matching questions; and Part
More information7 Grip aperture and target shape
7 Grip aperture and target shape Based on: Verheij R, Brenner E, Smeets JBJ. The influence of target object shape on maximum grip aperture in human grasping movements. Exp Brain Res, In revision 103 Introduction
More information2 Assumptions of simple linear regression
Simple Linear Regression: Reliability of predictions Richard Buxton. 2008. 1 Introduction We often use regression models to make predictions. In Figure?? (a), we ve fitted a model relating a household
More information(a) 50% of the shows have a rating greater than: impossible to tell
KEY 1. Here is a histogram of the Distribution of grades on a quiz. How many students took the quiz? 15 What percentage of students scored below a 60 on the quiz? (Assume left-hand endpoints are included
More informationThe Jumping Dog Quadratic Activity
Standards: The Jumping Dog Quadratic Activity A2.4.1 Identify the family of function best suited for modeling a given real-world situation. A2.4.3 Using the adapted general symbolic form, draw reasonable
More informationSurvey research (Lecture 1) Summary & Conclusion. Lecture 10 Survey Research & Design in Psychology James Neill, 2015 Creative Commons Attribution 4.
Summary & Conclusion Lecture 10 Survey Research & Design in Psychology James Neill, 2015 Creative Commons Attribution 4.0 Overview 1. Survey research 2. Survey design 3. Descriptives & graphing 4. Correlation
More informationSurvey research (Lecture 1)
Summary & Conclusion Lecture 10 Survey Research & Design in Psychology James Neill, 2015 Creative Commons Attribution 4.0 Overview 1. Survey research 2. Survey design 3. Descriptives & graphing 4. Correlation
More informationPublisher: Pearson Education, Inc. publishing as Prentice Hall
Section I. Correlation with the Mathematics 2009 SOL and Curriculum Framework Rating 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19 6.20 Section II. Additional Criteria:
More informationMath 075 Activities and Worksheets Book 2:
Math 075 Activities and Worksheets Book 2: Linear Regression Name: 1 Scatterplots Intro to Correlation Represent two numerical variables on a scatterplot and informally describe how the data points are
More informationAnalysis and Interpretation of Data Part 1
Analysis and Interpretation of Data Part 1 DATA ANALYSIS: PRELIMINARY STEPS 1. Editing Field Edit Completeness Legibility Comprehensibility Consistency Uniformity Central Office Edit 2. Coding Specifying
More informationSPSS output for 420 midterm study
Ψ Psy Midterm Part In lab (5 points total) Your professor decides that he wants to find out how much impact amount of study time has on the first midterm. He randomly assigns students to study for hours,
More informationCHAPTER 3 RESEARCH METHODOLOGY
CHAPTER 3 RESEARCH METHODOLOGY 3.1 Introduction 3.1 Methodology 3.1.1 Research Design 3.1. Research Framework Design 3.1.3 Research Instrument 3.1.4 Validity of Questionnaire 3.1.5 Statistical Measurement
More informationLecture 12: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression
Lecture 12: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression Equation of Regression Line; Residuals Effect of Explanatory/Response Roles Unusual Observations Sample
More informationChapter 3: Describing Relationships
Chapter 3: Describing Relationships Objectives: Students will: Construct and interpret a scatterplot for a set of bivariate data. Compute and interpret the correlation, r, between two variables. Demonstrate
More informationBiology 345: Biometry Fall 2005 SONOMA STATE UNIVERSITY Lab Exercise 5 Residuals and multiple regression Introduction
Biology 345: Biometry Fall 2005 SONOMA STATE UNIVERSITY Lab Exercise 5 Residuals and multiple regression Introduction In this exercise, we will gain experience assessing scatterplots in regression and
More information9 research designs likely for PSYC 2100
9 research designs likely for PSYC 2100 1) 1 factor, 2 levels, 1 group (one group gets both treatment levels) related samples t-test (compare means of 2 levels only) 2) 1 factor, 2 levels, 2 groups (one
More informationMTH 225: Introductory Statistics
Marshall University College of Science Mathematics Department MTH 225: Introductory Statistics Course catalog description Basic probability, descriptive statistics, fundamental statistical inference procedures
More information11/24/2017. Do not imply a cause-and-effect relationship
Correlational research is used to describe the relationship between two or more naturally occurring variables. Is age related to political conservativism? Are highly extraverted people less afraid of rejection
More informationAn Empirical Study on Causal Relationships between Perceived Enjoyment and Perceived Ease of Use
An Empirical Study on Causal Relationships between Perceived Enjoyment and Perceived Ease of Use Heshan Sun Syracuse University hesun@syr.edu Ping Zhang Syracuse University pzhang@syr.edu ABSTRACT Causality
More informationManuscript Presentation: Writing up APIM Results
Manuscript Presentation: Writing up APIM Results Example Articles Distinguishable Dyads Chung, M. L., Moser, D. K., Lennie, T. A., & Rayens, M. (2009). The effects of depressive symptoms and anxiety on
More information5 To Invest or not to Invest? That is the Question.
5 To Invest or not to Invest? That is the Question. Before starting this lab, you should be familiar with these terms: response y (or dependent) and explanatory x (or independent) variables; slope and
More informationReliability and Validity of the Divided
Aging, Neuropsychology, and Cognition, 12:89 98 Copyright 2005 Taylor & Francis, Inc. ISSN: 1382-5585/05 DOI: 10.1080/13825580590925143 Reliability and Validity of the Divided Aging, 121Taylor NANC 52900
More informationStat 13, Lab 11-12, Correlation and Regression Analysis
Stat 13, Lab 11-12, Correlation and Regression Analysis Part I: Before Class Objective: This lab will give you practice exploring the relationship between two variables by using correlation, linear regression
More informationDescribe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo
Please note the page numbers listed for the Lind book may vary by a page or two depending on which version of the textbook you have. Readings: Lind 1 11 (with emphasis on chapters 10, 11) Please note chapter
More informationPsychology Research Process
Psychology Research Process Logical Processes Induction Observation/Association/Using Correlation Trying to assess, through observation of a large group/sample, what is associated with what? Examples:
More informationStats 95. Statistical analysis without compelling presentation is annoying at best and catastrophic at worst. From raw numbers to meaningful pictures
Stats 95 Statistical analysis without compelling presentation is annoying at best and catastrophic at worst. From raw numbers to meaningful pictures Stats 95 Why Stats? 200 countries over 200 years http://www.youtube.com/watch?v=jbksrlysojo
More informationStill important ideas
Readings: OpenStax - Chapters 1 11 + 13 & Appendix D & E (online) Plous - Chapters 2, 3, and 4 Chapter 2: Cognitive Dissonance, Chapter 3: Memory and Hindsight Bias, Chapter 4: Context Dependence Still
More informationExample of Interpreting and Applying a Multiple Regression Model
Example of Interpreting and Applying a Multiple Regression We'll use the same data set as for the bivariate correlation example -- the criterion is 1 st year graduate grade point average and the predictors
More information3.2 Least- Squares Regression
3.2 Least- Squares Regression Linear (straight- line) relationships between two quantitative variables are pretty common and easy to understand. Correlation measures the direction and strength of these
More informationSPRING GROVE AREA SCHOOL DISTRICT. Course Description. Instructional Strategies, Learning Practices, Activities, and Experiences.
SPRING GROVE AREA SCHOOL DISTRICT PLANNED COURSE OVERVIEW Course Title: Basic Introductory Statistics Grade Level(s): 11-12 Units of Credit: 1 Classification: Elective Length of Course: 30 cycles Periods
More informationTEACHING REGRESSION WITH SIMULATION. John H. Walker. Statistics Department California Polytechnic State University San Luis Obispo, CA 93407, U.S.A.
Proceedings of the 004 Winter Simulation Conference R G Ingalls, M D Rossetti, J S Smith, and B A Peters, eds TEACHING REGRESSION WITH SIMULATION John H Walker Statistics Department California Polytechnic
More informationWDHS Curriculum Map Probability and Statistics. What is Statistics and how does it relate to you?
WDHS Curriculum Map Probability and Statistics Time Interval/ Unit 1: Introduction to Statistics 1.1-1.3 2 weeks S-IC-1: Understand statistics as a process for making inferences about population parameters
More informationStandard Scores. Richard S. Balkin, Ph.D., LPC-S, NCC
Standard Scores Richard S. Balkin, Ph.D., LPC-S, NCC 1 Normal Distributions While Best and Kahn (2003) indicated that the normal curve does not actually exist, measures of populations tend to demonstrate
More informationMEASURES OF ASSOCIATION AND REGRESSION
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Posc/Uapp 816 MEASURES OF ASSOCIATION AND REGRESSION I. AGENDA: A. Measures of association B. Two variable regression C. Reading: 1. Start Agresti
More informationSW 9300 Applied Regression Analysis and Generalized Linear Models 3 Credits. Master Syllabus
SW 9300 Applied Regression Analysis and Generalized Linear Models 3 Credits Master Syllabus I. COURSE DOMAIN AND BOUNDARIES This is the second course in the research methods sequence for WSU doctoral students.
More informationThe Lens Model and Linear Models of Judgment
John Miyamoto Email: jmiyamot@uw.edu October 3, 2017 File = D:\P466\hnd02-1.p466.a17.docm 1 http://faculty.washington.edu/jmiyamot/p466/p466-set.htm Psych 466: Judgment and Decision Making Autumn 2017
More information3.2A Least-Squares Regression
3.2A Least-Squares Regression Linear (straight-line) relationships between two quantitative variables are pretty common and easy to understand. Our instinct when looking at a scatterplot of data is to
More informationRegression Equation. November 29, S10.3_3 Regression. Key Concept. Chapter 10 Correlation and Regression. Definitions
MAT 155 Statistical Analysis Dr. Claude Moore Cape Fear Community College Chapter 10 Correlation and Regression 10 1 Review and Preview 10 2 Correlation 10 3 Regression 10 4 Variation and Prediction Intervals
More informationP. 274: 1-5, 1-14, P. 286: 1-8, 1-13, P , 1-39
0 0 7 th Grade Day WELCOME TO 7 TH GRADE 1 Test 3.1 Introduction Integers/ Absolute Value P. 192-198 2 3.2 Adding Integers P. 203-210 (P. 225-227 openers) Bar Models 3 3.3 P. 216-222 Subtracting Integers
More informationisc ove ring i Statistics sing SPSS
isc ove ring i Statistics sing SPSS S E C O N D! E D I T I O N (and sex, drugs and rock V roll) A N D Y F I E L D Publications London o Thousand Oaks New Delhi CONTENTS Preface How To Use This Book Acknowledgements
More informationNORTH SOUTH UNIVERSITY TUTORIAL 2
NORTH SOUTH UNIVERSITY TUTORIAL 2 AHMED HOSSAIN,PhD Data Management and Analysis AHMED HOSSAIN,PhD - Data Management and Analysis 1 Correlation Analysis INTRODUCTION In correlation analysis, we estimate
More informationChapter 1: Explaining Behavior
Chapter 1: Explaining Behavior GOAL OF SCIENCE is to generate explanations for various puzzling natural phenomenon. - Generate general laws of behavior (psychology) RESEARCH: principle method for acquiring
More informationMAT Intermediate Algebra - Final Exam Review Textbook: Beginning & Intermediate Algebra, 5th Ed., by Martin-Gay
MAT1033 - Intermediate Algebra - Final Exam Review Textbook: Beginning & Intermediate Algebra, 5th Ed., by Martin-Gay Section 2.3 Solve the equation. 1) 9x - (3x - 1) = 2 1) 2) 1 3 x + 2 = 1 6 x + 4 3
More informationSimple Linear Regression One Categorical Independent Variable with Several Categories
Simple Linear Regression One Categorical Independent Variable with Several Categories Does ethnicity influence total GCSE score? We ve learned that variables with just two categories are called binary
More informationContents. Introduction x Acknowledgements
Contents Introduction x Acknowledgements xiii CHAPTER 1 Number skills 1 Are you ready? 2 Order of operations 3 Exercise 1A 4 Integers 6 Exercise 1B 7 Investigation Golf scores 9 Estimation and rounding
More informationThe Logic of Data Analysis Using Statistical Techniques M. E. Swisher, 2016
The Logic of Data Analysis Using Statistical Techniques M. E. Swisher, 2016 This course does not cover how to perform statistical tests on SPSS or any other computer program. There are several courses
More information