Simple Linear Regression: Prediction. Instructor: G. William Schwert
|
|
- Estella Leonard
- 6 years ago
- Views:
Transcription
1 APS 425 Fall 2015 Simple Linear Regression: Prediction Instructor: G. William Schwert Ciba-Geigy Ritalin Experiment Ritalin is tested to see if it helps with Central Auditory Processing Disorder (CAPD) Similar symptoms to ADD/ADHD Experiment: Randomly select 64 children All receive auditory test 32 (control group) receive no drug (or placebo?) 32 (treatment group) receive varying doses of Ritalin All children are tested a second time (c) Prof. G. William Schwert,
2 Ciba-Geigy Ritalin Experiment DOSAGE i = amount of Ritalin received by child i Measured as Mg of Ritalin per Kg of body weight IMPROVE i = child s 2 nd test score 1 st test score Dataset A425_ritalin.wf1 also contains: AGE of child in months Gender (FEMALE = 1, for girls) Predictions (c) Prof. G. William Schwert,
3 Predictions Predictive model: IMPROVE i = DOSAGE i = the estimate of E[IMPROVE i DOSAGE i, b 0, b 1 ] What is your estimate of the average IMPROVE score for all children who receive a dosage of 0.35 mg/kg? IMPROVE i = DOSAGE i = This question asks about the average or expected value for all children who get a DOSAGE of 0.35mg/kg. Predictions A given child has been administered a DOSAGE of 0.35mg/kg. What value do you predict for the child s IMPROVE score? ^ IMPROVE i = = This question asks you to predict the value for an individual child who gets a DOSAGE of 0.35mg/kg (c) Prof. G. William Schwert,
4 Predictions The prediction of the value for an individual child = the expected value for the population (of all children with DOSAGE = 0.35mg/kg) (see previous two slides) However, standard errors are different! Let s derive them next Std Error of Predictions Standard error for predicting an individual value The linear model is: Y i = + X i + e i Our prediction is: ^ Y i = b 0 + b 1 X i Three sources of error: Error in estimating Error in estimating Error in estimating e i (c) Prof. G. William Schwert,
5 Std Error of Predictions Standard error of Y i : SDP = [ s 2 + s 2 / n + s 2 (X i X) 2 / x i 2 ] ½ _ Error term Intercept Slope uncertainty uncertainty uncertainty where s 2 ^ = e 2 i / (n-2) is the residual variance and s 2 / x 2 i is the variance of b 1 Prediction Intervals A 100(1 )% confidence interval for Y i is: [Y i - t /2 SDP, Y i + t /2 SDP] where Pr{t n-2 > t /2 }= /2 In the Ritalin case with one child getting a DOSAGE of 0.35mg/kg, we have SDP = , so a 95% confidence interval for IMPROVE i is [ (12.056), (12.056)] = [ 19.61, 28.59] (c) Prof. G. William Schwert,
6 Using Eviews to Get Prediction Intervals Redefine the workfile range so that you can generate an out-ofsample prediction Using Eviews to Get Prediction Intervals Double-click dosage to open the spreadsheet Then click Edit+/- Move to observation 65 (which will say NA ) Type in.35 into the workspace bar to enter this value for the 65 th observation (c) Prof. G. William Schwert,
7 Using Eviews to Get Prediction Intervals Upper limit Point estimate Using the regression equation predicting IMPROVE as a function of DOSAGE, click FORECAST Then specify the forecast sample as the value of DOSAGE = 0.35 you just entered Lower limit Prediction Intervals for Improvement from DOSAGE Upper 95% PI Predicted IMPROVE Lower 95% PI DOSAGE = DOSAGE (c) Prof. G. William Schwert,
8 Std Error of Prediction for the Average Standard error for predicting the expected or average value A group of children have been administered a DOSAGE of 0.35mg/kg. What value do you predict for the average IMPROVE score of these children on the test? ^ IMPROVE i = = Std Error of Prediction for the Average Now let s consider the standard error The linear model is: Y i = + X i + e i We are predicting: E[Y 0 ] = + X 0 Our prediction is: Y i = b + b X i Two sources of uncertainty: Error in estimating Error in estimating (c) Prof. G. William Schwert,
9 Std Error of Prediction for the Average Standard error of E(Y i ): SEP = [s 2 / n + s 2 (X i X) 2 / x i 2 ] ½ _ Intercept uncertainty Slope uncertainty A 100(1 )% confidence interval for E(Y i ) is: [Y i - t /2 SEP, Y i + t /2 SEP] where Pr{t n-2 > t /2 }= /2 Std Error of Prediction for the Average In the Ritalin case with one child getting a DOSAGE of 0.35mg/kg, we have SEP = 1.585, so a 95% confidence interval for E(IMPROVE i DOSAGE = 0.35, b 0, b 1 ) is [ (1.585), (1.585)] = [1.319, 7.657] (c) Prof. G. William Schwert,
10 Prediction Intervals for Expected Improvement from DOSAGE Upper 95% PI Predicted IMPROVE Lower 95% PI Note: prediction interval for expected improvement are much narrower and curvature is more apparent DOSAGE Predictions and Eviews for DOSAGE = 0.35 (SEP) 2 = s 2 / n + (SE(b 1 )) 2 (X i X) 2 = / 64 + (5.723) 2 ( ) 2 = => SEP = (SEP) 2 + (SE of Regression) 2 = (SDP) 2 [ ] 1/2 = Note that SEP and SDP depend on X i = 0.35 _ (c) Prof. G. William Schwert,
11 Predictions and Eviews The range of DOSAGE values in the data is 0 to 0.71 Given this sample, the predictive model is: ^ IMPROVE i = = We have no support from the data whether this relation extends outside of the sample range (e.g., to dosages > 0.71 mg/kg) To predict outside the sample range is called extrapolation Extrapolation is ill-advised and subject to much criticism Predictions of IMPROVE from Eviews Generate predicted values, predict, and the standard deviation of the prediction, sdp (c) Prof. G. William Schwert,
12 Prediction Interval for IMPROVE from Eviews Generate upper and lower limits for 95% prediction interval, predup and preddown Prediction Interval for IMPROVE from Eviews Create a group of dosage, predict, predup, and preddown (c) Prof. G. William Schwert,
13 Prediction Interval for IMPROVE from Eviews Graph XY line One X against all Y s Make sure that the predictor variable, DOSAGE, is the first one in the set Prediction Interval for IMPROVE from Eviews Note that the 95% prediction interval for IMPROVE covers a wide range of outcomes for individual students No assurance that any one child will improve if given Ritalin (c) Prof. G. William Schwert,
14 Standard Error of the Regression Line for IMPROVE from Eviews We will calculate the standard error of the regression line (SEP) First, save the standard error of the regression, SER Next, derive SEP from SDP and SER Standard Errors Around the Regression Line for IMPROVE Generate upper and lower limits for 95% regression line, regrup and regrdown (c) Prof. G. William Schwert,
15 Standard Errors Around the Regression Line for IMPROVE Create a group of dosage, predict, regrup, and regrdown Standard Errors Around the Regression Line for IMPROVE Graph simple scatter Make sure that the predictor variable, DOSAGE, is the first one in the set (c) Prof. G. William Schwert,
16 Standard Errors Around the Regression Line for IMPROVE Note that the 95% confidence interval for the regression line is much narrower For the dosages used in this experiment the entire interval covers positive improvement Links Ritalin Data Return to APS 425 Home Page (c) Prof. G. William Schwert,
Simple Linear Regression
Simple Linear Regression Assoc. Prof Dr Sarimah Abdullah Unit of Biostatistics & Research Methodology School of Medical Sciences, Health Campus Universiti Sains Malaysia Regression Regression analysis
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 information1.4 - Linear Regression and MS Excel
1.4 - Linear Regression and MS Excel Regression is an analytic technique for determining the relationship between a dependent variable and an independent variable. When the two variables have a linear
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 informationCHAPTER 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 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 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 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 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 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 informationConditional Distributions and the Bivariate Normal Distribution. James H. Steiger
Conditional Distributions and the Bivariate Normal Distribution James H. Steiger Overview In this module, we have several goals: Introduce several technical terms Bivariate frequency distribution Marginal
More informationSCATTER PLOTS AND TREND LINES
1 SCATTER PLOTS AND TREND LINES LEARNING MAP INFORMATION STANDARDS 8.SP.1 Construct and interpret scatter s for measurement to investigate patterns of between two quantities. Describe patterns such as
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 informationIntro to SPSS. Using SPSS through WebFAS
Intro to SPSS Using SPSS through WebFAS http://www.yorku.ca/computing/students/labs/webfas/ Try it early (make sure it works from your computer) If you need help contact UIT Client Services Voice: 416-736-5800
More information14.1: Inference about the Model
14.1: Inference about the Model! When a scatterplot shows a linear relationship between an explanatory x and a response y, we can use the LSRL fitted to the data to predict a y for a given x. However,
More informationMultiple Regression. James H. Steiger. Department of Psychology and Human Development Vanderbilt University
Multiple Regression James H. Steiger Department of Psychology and Human Development Vanderbilt University James H. Steiger (Vanderbilt University) Multiple Regression 1 / 19 Multiple Regression 1 The Multiple
More informationMultiple Linear Regression Analysis
Revised July 2018 Multiple Linear Regression Analysis This set of notes shows how to use Stata in multiple regression analysis. It assumes that you have set Stata up on your computer (see the Getting Started
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 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. Example 1: Table 2.1 The Effect of Additives on Daphnia Heart Rate. Time (min)
Notes for Alphas Line graphs provide a way to map independent and dependent variables that are both quantitative. When both variables are quantitative, the segment that connects every two points on the
More informationChapter 14. Inference for Regression Inference about the Model 14.1 Testing the Relationship Signi!cance Test Practice
Chapter 14 Inference for Regression Our!nal topic of the year involves inference for the regression model. In Chapter 3 we learned how to!nd the Least Squares Regression Line for a set of bivariate data.
More informationSTATISTICS 201. Survey: Provide this Info. How familiar are you with these? Survey, continued IMPORTANT NOTE. Regression and ANOVA 9/29/2013
STATISTICS 201 Survey: Provide this Info Outline for today: Go over syllabus Provide requested information on survey (handed out in class) Brief introduction and hands-on activity Name Major/Program Year
More informationMMI 409 Spring 2009 Final Examination Gordon Bleil. 1. Is there a difference in depression as a function of group and drug?
MMI 409 Spring 2009 Final Examination Gordon Bleil Table of Contents Research Scenario and General Assumptions Questions for Dataset (Questions are hyperlinked to detailed answers) 1. Is there a difference
More informationMidterm STAT-UB.0003 Regression and Forecasting Models. I will not lie, cheat or steal to gain an academic advantage, or tolerate those who do.
Midterm STAT-UB.0003 Regression and Forecasting Models The exam is closed book and notes, with the following exception: you are allowed to bring one letter-sized page of notes into the exam (front and
More informationPsy201 Module 3 Study and Assignment Guide. Using Excel to Calculate Descriptive and Inferential Statistics
Psy201 Module 3 Study and Assignment Guide Using Excel to Calculate Descriptive and Inferential Statistics What is Excel? Excel is a spreadsheet program that allows one to enter numerical values or data
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 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 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 information1. The figure below shows the lengths in centimetres of fish found in the net of a small trawler.
Bivariate Data 1 IB MATHEMATICS SL Topic: Bivariate Data NAME: DATE: 1. The figure below shows the lengths in centimetres of fish found in the net of a small trawler. Number of fish 11 10 9 8 7 6 5 4 3
More informationQuadratic Functions I
Quadratic Functions I by Frank C. Wilson Activity Collection Featuring real-world contexts: Autism Awareness Autism Awareness Business Growth - USAA Changing Population Kentucky Changing Population - New
More informationIAPT: Regression. Regression analyses
Regression analyses IAPT: Regression Regression is the rather strange name given to a set of methods for predicting one variable from another. The data shown in Table 1 and come from a student project
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 informationCNV PCA Search Tutorial
CNV PCA Search Tutorial Release 8.1 Golden Helix, Inc. March 18, 2014 Contents 1. Data Preparation 2 A. Join Log Ratio Data with Phenotype Information.............................. 2 B. Activate only
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 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 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 informationResearch Designs and Potential Interpretation of Data: Introduction to Statistics. Let s Take it Step by Step... Confused by Statistics?
Research Designs and Potential Interpretation of Data: Introduction to Statistics Karen H. Hagglund, M.S. Medical Education St. John Hospital & Medical Center Karen.Hagglund@ascension.org Let s Take it
More informationPitfalls in Linear Regression Analysis
Pitfalls in Linear Regression Analysis Due to the widespread availability of spreadsheet and statistical software for disposal, many of us do not really have a good understanding of how to use regression
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 informationProblem Set 3 ECN Econometrics Professor Oscar Jorda. Name. ESSAY. Write your answer in the space provided.
Problem Set 3 ECN 140 - Econometrics Professor Oscar Jorda Name ESSAY. Write your answer in the space provided. 1) Sir Francis Galton, a cousin of James Darwin, examined the relationship between the height
More informationWELCOME! Lecture 11 Thommy Perlinger
Quantitative Methods II WELCOME! Lecture 11 Thommy Perlinger Regression based on violated assumptions If any of the assumptions are violated, potential inaccuracies may be present in the estimated regression
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 informationOrdinary Least Squares Regression
Ordinary Least Squares Regression March 2013 Nancy Burns (nburns@isr.umich.edu) - University of Michigan From description to cause Group Sample Size Mean Health Status Standard Error Hospital 7,774 3.21.014
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 informationSTAT 135 Introduction to Statistics via Modeling: Midterm II Thursday November 16th, Name:
STAT 135 Introduction to Statistics via Modeling: Midterm II Thursday November 16th, 2017 Name: 1 1 Short Answer a) For each of these five regression scenarios, name an appropriate visualization (along
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 informationHeritability. The concept
Heritability The concept What is the Point of Heritability? Is a trait due to nature or nurture? (Genes or environment?) You and I think this is a good point to address, but it is not addressed! What is
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 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 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 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 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 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 informationHERITABILITY INTRODUCTION. Objectives
36 HERITABILITY In collaboration with Mary Puterbaugh and Larry Lawson Objectives Understand the concept of heritability. Differentiate between broad-sense heritability and narrowsense heritability. Learn
More informationBOOTSTRAPPING CONFIDENCE LEVELS FOR HYPOTHESES ABOUT QUADRATIC (U-SHAPED) REGRESSION MODELS
BOOTSTRAPPING CONFIDENCE LEVELS FOR HYPOTHESES ABOUT QUADRATIC (U-SHAPED) REGRESSION MODELS 12 June 2012 Michael Wood University of Portsmouth Business School SBS Department, Richmond Building Portland
More informationStatistics and Probability
Statistics and a single count or measurement variable. S.ID.1: Represent data with plots on the real number line (dot plots, histograms, and box plots). S.ID.2: Use statistics appropriate to the shape
More informationQ: How do I get the protein concentration in mg/ml from the standard curve if the X-axis is in units of µg.
Photometry Frequently Asked Questions Q: How do I get the protein concentration in mg/ml from the standard curve if the X-axis is in units of µg. Protein standard curves are traditionally presented as
More information12.1 Inference for Linear Regression. Introduction
12.1 Inference for Linear Regression vocab examples Introduction Many people believe that students learn better if they sit closer to the front of the classroom. Does sitting closer cause higher achievement,
More informationApplication Note 201
201 Application note: Determination of the of Milk Application note no.: 201 By: E. de Jong Date: January 2001 Copyright Delta Instruments 2005 www.deltainstruments.com Table of contents Table of contents
More informationCorrelation and regression
PG Dip in High Intensity Psychological Interventions Correlation and regression Martin Bland Professor of Health Statistics University of York http://martinbland.co.uk/ Correlation Example: Muscle strength
More informationUse the above variables and any you might need to construct to specify the MODEL A/C comparisons you would use to ask the following questions.
Fall, 2002 Grad Stats Final Exam There are four questions on this exam, A through D, and each question has multiple sub-questions. Unless otherwise indicated, each sub-question is worth 3 points. Question
More informationQuestion 1(25= )
MSG500 Final 20-0-2 Examiner: Rebecka Jörnsten, 060-49949 Remember: To pass this course you also have to hand in a final project to the examiner. Open book, open notes but no calculators or computers allowed.
More informationA response variable is a variable that. An explanatory variable is a variable that.
Name:!!!! Date: Scatterplots The most common way to display the relation between two quantitative variable is a scatterplot. Statistical studies often try to show through scatterplots, that changing one
More informationMultiple Regression Analysis
Multiple Regression Analysis Basic Concept: Extend the simple regression model to include additional explanatory variables: Y = β 0 + β1x1 + β2x2 +... + βp-1xp + ε p = (number of independent variables
More informationRegression CHAPTER SIXTEEN NOTE TO INSTRUCTORS OUTLINE OF RESOURCES
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
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 informationClass 7 Everything is Related
Class 7 Everything is Related Correlational Designs l 1 Topics Types of Correlational Designs Understanding Correlation Reporting Correlational Statistics Quantitative Designs l 2 Types of Correlational
More informationProblem 1) Match the terms to their definitions. Every term is used exactly once. (In the real midterm, there are fewer terms).
Problem 1) Match the terms to their definitions. Every term is used exactly once. (In the real midterm, there are fewer terms). 1. Bayesian Information Criterion 2. Cross-Validation 3. Robust 4. Imputation
More informationHW 3.2: page 193 #35-51 odd, 55, odd, 69, 71-78
35. What s My Line? You use the same bar of soap to shower each morning. The bar weighs 80 grams when it is new. Its weight goes down by 6 grams per day on average. What is the equation of the regression
More informationTo open a CMA file > Download and Save file Start CMA Open file from within CMA
Example name Effect size Analysis type Level Tamiflu Symptom relief Mean difference (Hours to relief) Basic Basic Reference Cochrane Figure 4 Synopsis We have a series of studies that evaluated the effect
More informationSkala Stress. Putaran 1 Reliability. Case Processing Summary. N % Excluded a 0.0 Total
Skala Stress Putaran 1 Reliability Case Processing Summary N % Cases Valid Excluded a 0.0 Total a. Listwise deletion based on all variables in the procedure. Reliability Statistics Cronbach's Alpha N of
More information10. LINEAR REGRESSION AND CORRELATION
1 10. LINEAR REGRESSION AND CORRELATION The contingency table describes an association between two nominal (categorical) variables (e.g., use of supplemental oxygen and mountaineer survival ). We have
More informationANOVA in SPSS (Practical)
ANOVA in SPSS (Practical) Analysis of Variance practical In this practical we will investigate how we model the influence of a categorical predictor on a continuous response. Centre for Multilevel Modelling
More informationRegression. Page 1. Variables Entered/Removed b Variables. Variables Removed. Enter. Method. Psycho_Dum
Regression Model Variables Entered/Removed b Variables Entered Variables Removed Method Meds_Dum,. Enter Psycho_Dum a. All requested variables entered. b. Dependent Variable: Beck's Depression Score Model
More informationUnderstandable Statistics
Understandable Statistics correlated to the Advanced Placement Program Course Description for Statistics Prepared for Alabama CC2 6/2003 2003 Understandable Statistics 2003 correlated to the Advanced Placement
More informationConstructing a mixed model using the AIC
Constructing a mixed model using the AIC The Data: The Citalopram study (PI Dr. Zisook) Does Citalopram reduce the depression in schizophrenic patients with subsyndromal depression Two Groups: Citalopram
More informationLinear Regression in SAS
1 Suppose we wish to examine factors that predict patient s hemoglobin levels. Simulated data for six patients is used throughout this tutorial. data hgb_data; input id age race $ bmi hgb; cards; 21 25
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 informationOur goal in this section is to explain a few more concepts about experiments. Don t be concerned with the details.
Our goal in this section is to explain a few more concepts about experiments. Don t be concerned with the details. 1 We already mentioned an example with two explanatory variables or factors the case of
More informationBOOTSTRAPPING CONFIDENCE LEVELS FOR HYPOTHESES ABOUT REGRESSION MODELS
BOOTSTRAPPING CONFIDENCE LEVELS FOR HYPOTHESES ABOUT REGRESSION MODELS 17 December 2009 Michael Wood University of Portsmouth Business School SBS Department, Richmond Building Portland Street, Portsmouth
More informationMULTIPLE OLS REGRESSION RESEARCH QUESTION ONE:
1 MULTIPLE OLS REGRESSION RESEARCH QUESTION ONE: Predicting State Rates of Robbery per 100K We know that robbery rates vary significantly from state-to-state in the United States. In any given state, we
More informationTutorial #7A: Latent Class Growth Model (# seizures)
Tutorial #7A: Latent Class Growth Model (# seizures) 2.50 Class 3: Unstable (N = 6) Cluster modal 1 2 3 Mean proportional change from baseline 2.00 1.50 1.00 Class 1: No change (N = 36) 0.50 Class 2: Improved
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 informationSouth Australian Research and Development Institute. Positive lot sampling for E. coli O157
final report Project code: Prepared by: A.MFS.0158 Andreas Kiermeier Date submitted: June 2009 South Australian Research and Development Institute PUBLISHED BY Meat & Livestock Australia Limited Locked
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 informationUF#Stats#Club#STA#2023#Exam#1#Review#Packet# #Fall#2013#
UF#Stats#Club#STA##Exam##Review#Packet# #Fall## The following data consists of the scores the Gators basketball team scored during the 8 games played in the - season. 84 74 66 58 79 8 7 64 8 6 78 79 77
More informationMidterm Exam MMI 409 Spring 2009 Gordon Bleil
Midterm Exam MMI 409 Spring 2009 Gordon Bleil Table of contents: (Hyperlinked to problem sections) Problem 1 Hypothesis Tests Results Inferences Problem 2 Hypothesis Tests Results Inferences Problem 3
More informationPearson Education Limited Edinburgh Gate Harlow Essex CM20 2JE England and Associated Companies throughout the world
Pearson Education Limited Edinburgh Gate Harlow Essex CM20 2JE England and Associated Companies throughout the world Visit us on the World Wide Web at: www.pearsoned.co.uk Pearson Education Limited 2014
More informationHomework #3. SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.
Homework #3 Name Due Due on on February Tuesday, Due on February 17th, Sept Friday 28th 17th, Friday SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Fill
More informationPart 1. For each of the following questions fill-in the blanks. Each question is worth 2 points.
Part 1. For each of the following questions fill-in the blanks. Each question is worth 2 points. 1. The bell-shaped frequency curve is so common that if a population has this shape, the measurements are
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 informationThe Pretest! Pretest! Pretest! Assignment (Example 2)
The Pretest! Pretest! Pretest! Assignment (Example 2) May 19, 2003 1 Statement of Purpose and Description of Pretest Procedure When one designs a Math 10 exam one hopes to measure whether a student s ability
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 informationComparison of Different Methods of Detecting Publication Bias
Shaping the Future of Drug Development Comparison of Different Methods of Detecting Publication Bias PhUSE 2017, Edinburgh Janhavi Kale, Cytel Anwaya Nirpharake, Cytel Outline Systematic review and Meta-analysis
More informationREGRESSION MODELLING IN PREDICTING MILK PRODUCTION DEPENDING ON DAIRY BOVINE LIVESTOCK
REGRESSION MODELLING IN PREDICTING MILK PRODUCTION DEPENDING ON DAIRY BOVINE LIVESTOCK Agatha POPESCU University of Agricultural Sciences and Veterinary Medicine Bucharest, 59 Marasti, District 1, 11464,
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 informationComparing Methods for Once Daily Tobramycin Exposure Predictions in Children with Cystic Fibrosis
Comparing Methods for Once Daily Tobramycin Exposure Predictions in Children with Cystic Fibrosis Stefanie HENNIG, Franziska STILLER, Beverly TEO, Christine STAATZ, Brisbane Cystic fibrosis (CF) & Once
More informationLab 8: Multiple Linear Regression
Lab 8: Multiple Linear Regression 1 Grading the Professor Many college courses conclude by giving students the opportunity to evaluate the course and the instructor anonymously. However, the use of these
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 informationADVANCED ANOVA APPLICATIONS
TWO FACTOR ANOVA ADVANCED ANOVA APPLICATIONS Repeated-Measures designs Compare 2 measurements from each participant Related-Samples, or Repeated-Measures, t-test Compare more than 2 measurements from each
More information