NORTH SOUTH UNIVERSITY TUTORIAL 2
|
|
- Lydia Harmon
- 5 years ago
- Views:
Transcription
1 NORTH SOUTH UNIVERSITY TUTORIAL 2 AHMED HOSSAIN,PhD Data Management and Analysis AHMED HOSSAIN,PhD - Data Management and Analysis 1
2 Correlation Analysis INTRODUCTION In correlation analysis, we estimate a sample correlation coefficient, more specifically the Pearson Product Moment correlation coefficient. The sample correlation coefficient, denoted r, ranges between -1 and +1. r quantifies the direction and strength of the linear relationship between the two variables. The sign of the r indicates the direction of the association. The magnitude of the r indicates the strength of the association. For example, a correlation of r = 0.9 suggests a strong, positive association between two variables, whereas a correlation of r = -0.2 suggest a weak, negative association. r close to zero suggests no linear association between two continuous variables. Limitations: There may be a non-linear association between two continuous variables, but computation of a r does not detect this. AHMED HOSSAIN,PhD - Data Management and Analysis 2
3 Correlation Analysis SCATTER DIAGRAM We wish to estimate the association between gestational age and infant birth weight. In this example, birth weight is the dependent variable and gestational age is the independent variable. Thus Y =birth weight and X=gestational age. Note that the independent variable is on the horizontal axis (or X-axis), and the dependent variable is on the vertical axis (or Y-axis). AHMED HOSSAIN,PhD - Data Management and Analysis 3
4 Correlation Analysis SCATTER DIAGRAM AHMED HOSSAIN,PhD - Data Management and Analysis 4
5 Simple Linear Regression INTRODUCTION In simple linear regression we are concerned about the relationship between two variables, X and Y. There are two components to such a relationship 1 The strength of the relationship. 2 The direction of the relationship. We shall also be interested in making inferences about the relationship. We will be assuming here that the relationship between X and Y is linear (or has been linearized through transformation). AHMED HOSSAIN,PhD - Data Management and Analysis 5
6 Regression INTRODUCTION Technique used for the modeling and analysis of numerical data. Exploits the relationship between two or more variables so that we can gain information about one of them through knowing values of the other. Regression can be used for prediction, estimation, hypothesis testing, and modeling causal relationships. AHMED HOSSAIN,PhD - Data Management and Analysis 6
7 Simple Linear Regression ASSUMPTIONS Suppose that we have a dataset (y 1, x 1 ), (y 2, x 2 ),, (y n, x n). Our interest is in using our model to predict values of Y for any given value of X = x. If we know the values of β 0 and β 1 then the fitted value for the observation y i would be β 0 + β 1 x i. The error in the fitted value can be measured by the vertical distance ɛ i = y i β 0 β 1 x i We would like to make these errors as small as possible. AHMED HOSSAIN,PhD - Data Management and Analysis 7
8 Simple Linear Regression EXAMPLE AHMED HOSSAIN,PhD - Data Management and Analysis 8
9 Simple Linear Regression EXAMPLE AHMED HOSSAIN,PhD - Data Management and Analysis 9
10 INTRODUCTION Extension of the simple linear regression model to two or more independent variables y = β 0 + β 1 x 1 + β 2 x β nx n + ɛ For example, Expression = Baseline + Age + Tissue + Sex + Error. Partial Regression Coefficients: β i effect on the dependent variable when increasing the ith independent variable by 1 unit, holding all other predictors constant. AHMED HOSSAIN,PhD - Data Management and Analysis 10
11 CATEGORICAL INDEPENDENT VARIABLES AHMED HOSSAIN,PhD - Data Management and Analysis 11
12 CATEGORICAL INDEPENDENT VARIABLES AHMED HOSSAIN,PhD - Data Management and Analysis 12
13 RESULTS FROM R Call: lm(formula = y X1 + X2) Residuals: Coefficients: Min 1Q Median 3Q Max Estimate Std. Error t value Pr(> t ) (Intercept) ** X X Signif. codes: 0 *** ** 0.01 * Residual standard error: on 37 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 2 and 37 DF, p-value: AHMED HOSSAIN,PhD - Data Management and Analysis 13
14 HYPOTHESIS TESTS: INDIVIDUAL REGRESSION COEFFICIENTS AHMED HOSSAIN,PhD - Data Management and Analysis 14
15 HYPOTHESIS TESTING: MODEL UTILITY TEST AHMED HOSSAIN,PhD - Data Management and Analysis 15
16 THE COEFFICIENT OF DETERMINATION The total sum of squares is a measure of the variability in y 1,, y n without taking the covariate into account. The error sum of squares is the amount of variability left after fitting a linear regression for the covariate. The model sum of squares is the amount of variability explained by the model. The proportion of the variability explained by the model is R 2 = SSR SST = 1 SSE SST In simple regression R 2 is the square of the sample correlation between x 1,, x n and y 1,, y n. AHMED HOSSAIN,PhD - Data Management and Analysis 16
17 BIRTHWEIGHT IS CONTINIOUS AND CATEGORICAL INDEPENDENT VARIABLES AHMED HOSSAIN,PhD - Data Management and Analysis 17
18 RESULTS AHMED HOSSAIN,PhD - Data Management and Analysis 18
19 INTERACTION INTERACTION Interaction effects represent the combined effects of variables on the criterion or dependent measure. When an interaction effect is present, the impact of one variable depends on the level of the other variable. EXAMPLE 1 Interaction between adding sugar to coffee and stirring the coffee. Neither of the two individual variables has much effect on sweetness but a combination of the two does. EXAMPLE 2 Interaction between smoking and inhaling asbestos fibres: Both raise lung carcinoma risk, but exposure to asbestos multiplies the cancer risk in smokers and non-smokers. Here, the joint effect of inhaling asbestos and smoking is higher than the sum of both effects. AHMED HOSSAIN,PhD - Data Management and Analysis 19
20 IDENTIFYING INTERACTION CATEGORICAL PREDICTORS If the researcher is interested in whether the treatment is equally effective for females and males. That is, is there a difference in treatment depending on gender group? This is a question of interaction. Interaction results whose lines do not cross. CONTINUOUS PREDICTORS : Single slope test. AHMED HOSSAIN,PhD - Data Management and Analysis 20
Content. Basic Statistics and Data Analysis for Health Researchers from Foreign Countries. Research question. Example Newly diagnosed Type 2 Diabetes
Content Quantifying association between continuous variables. Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma siersma@sund.ku.dk The Research Unit for General
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 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 informationSimple 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 informationEXECUTIVE SUMMARY DATA AND PROBLEM
EXECUTIVE SUMMARY Every morning, almost half of Americans start the day with a bowl of cereal, but choosing the right healthy breakfast is not always easy. Consumer Reports is therefore calculated by an
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 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 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 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 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 informationGPA vs. Hours of Sleep: A Simple Linear Regression Jacob Ushkurnis 12/16/2016
GPA vs. Hours of Sleep: A Simple Linear Regression Jacob Ushkurnis 12/16/2016 Introduction As a college student, life can sometimes get extremely busy and stressful when there is a lot of work to do. More
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 informationNormal Q Q. Residuals vs Fitted. Standardized residuals. Theoretical Quantiles. Fitted values. Scale Location 26. Residuals vs Leverage
Residuals 400 0 400 800 Residuals vs Fitted 26 42 29 Standardized residuals 2 0 1 2 3 Normal Q Q 26 42 29 360 400 440 2 1 0 1 2 Fitted values Theoretical Quantiles Standardized residuals 0.0 0.5 1.0 1.5
More informationMath 215, Lab 7: 5/23/2007
Math 215, Lab 7: 5/23/2007 (1) Parametric versus Nonparamteric Bootstrap. Parametric Bootstrap: (Davison and Hinkley, 1997) The data below are 12 times between failures of airconditioning equipment in
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 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 informationData Analysis in the Health Sciences. Final Exam 2010 EPIB 621
Data Analysis in the Health Sciences Final Exam 2010 EPIB 621 Student s Name: Student s Number: INSTRUCTIONS This examination consists of 8 questions on 17 pages, including this one. Tables of the normal
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 informationMidterm Exam ANSWERS Categorical Data Analysis, CHL5407H
Midterm Exam ANSWERS Categorical Data Analysis, CHL5407H 1. Data from a survey of women s attitudes towards mammography are provided in Table 1. Women were classified by their experience with mammography
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 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 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 informationPoisson regression. Dae-Jin Lee Basque Center for Applied Mathematics.
Dae-Jin Lee dlee@bcamath.org Basque Center for Applied Mathematics http://idaejin.github.io/bcam-courses/ D.-J. Lee (BCAM) Intro to GLM s with R GitHub: idaejin 1/40 Modeling count data Introduction Response
More informationChapter 14: More Powerful Statistical Methods
Chapter 14: More Powerful Statistical Methods Most questions will be on correlation and regression analysis, but I would like you to know just basically what cluster analysis, factor analysis, and conjoint
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 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 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 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 informationM15_BERE8380_12_SE_C15.6.qxd 2/21/11 8:21 PM Page Influence Analysis 1
M15_BERE8380_12_SE_C15.6.qxd 2/21/11 8:21 PM Page 1 15.6 Influence Analysis FIGURE 15.16 Minitab worksheet containing computed values for the Studentized deleted residuals, the hat matrix elements, and
More informationSTAT 503X Case Study 1: Restaurant Tipping
STAT 503X Case Study 1: Restaurant Tipping 1 Description Food server s tips in restaurants may be influenced by many factors including the nature of the restaurant, size of the party, table locations in
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 informationMEA DISCUSSION PAPERS
Inference Problems under a Special Form of Heteroskedasticity Helmut Farbmacher, Heinrich Kögel 03-2015 MEA DISCUSSION PAPERS mea Amalienstr. 33_D-80799 Munich_Phone+49 89 38602-355_Fax +49 89 38602-390_www.mea.mpisoc.mpg.de
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 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 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 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 informationRegression models, R solution day7
Regression models, R solution day7 Exercise 1 In this exercise, we shall look at the differences in vitamin D status for women in 4 European countries Read and prepare the data: vit
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 information1. Objective: analyzing CD4 counts data using GEE marginal model and random effects model. Demonstrate the analysis using SAS and STATA.
LDA lab Feb, 6 th, 2002 1 1. Objective: analyzing CD4 counts data using GEE marginal model and random effects model. Demonstrate the analysis using SAS and STATA. 2. Scientific question: estimate the average
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 informationStatistical Methods and Reasoning for the Clinical Sciences
Statistical Methods and Reasoning for the Clinical Sciences Evidence-Based Practice Eiki B. Satake, PhD Contents Preface Introduction to Evidence-Based Statistics: Philosophical Foundation and Preliminaries
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 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 informationDaniel Boduszek University of Huddersfield
Daniel Boduszek University of Huddersfield d.boduszek@hud.ac.uk Introduction to Correlation SPSS procedure for Pearson r Interpretation of SPSS output Presenting results Partial Correlation Correlation
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 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 informationBAM Monitor Performance. Seasonal and Geographic Variation in NC
BAM Monitor Performance Seasonal and Geographic Variation in NC 2009-10 2 Presenter Wayne Cornelius 3 Introduction Unsuccessful ARM tests in 2007 and 2009, using a configuration of R&P TEOM monitor. Acquired
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 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 informationSTATISTICAL METHODS FOR DIAGNOSTIC TESTING: AN ILLUSTRATION USING A NEW METHOD FOR CANCER DETECTION XIN SUN. PhD, Kansas State University, 2012
STATISTICAL METHODS FOR DIAGNOSTIC TESTING: AN ILLUSTRATION USING A NEW METHOD FOR CANCER DETECTION by XIN SUN PhD, Kansas State University, 2012 A THESIS Submitted in partial fulfillment of the requirements
More informationOriginal Article Downloaded from jhs.mazums.ac.ir at 22: on Friday October 5th 2018 [ DOI: /acadpub.jhs ]
Iranian journal of health sciences 213;1(3):58-7 http://jhs.mazums.ac.ir Original Article Downloaded from jhs.mazums.ac.ir at 22:2 +33 on Friday October 5th 218 [ DOI: 1.18869/acadpub.jhs.1.3.58 ] A New
More informationProblem #1 Neurological signs and symptoms of ciguatera poisoning as the start of treatment and 2.5 hours after treatment with mannitol.
Ho (null hypothesis) Ha (alternative hypothesis) Problem #1 Neurological signs and symptoms of ciguatera poisoning as the start of treatment and 2.5 hours after treatment with mannitol. Hypothesis: Ho:
More informationExamining Relationships Least-squares regression. Sections 2.3
Examining Relationships Least-squares regression Sections 2.3 The regression line A regression line describes a one-way linear relationship between variables. An explanatory variable, x, explains variability
More informationChapter 13 Estimating the Modified Odds Ratio
Chapter 13 Estimating the Modified Odds Ratio Modified odds ratio vis-à-vis modified mean difference To a large extent, this chapter replicates the content of Chapter 10 (Estimating the modified mean difference),
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 informationCHILD HEALTH AND DEVELOPMENT STUDY
CHILD HEALTH AND DEVELOPMENT STUDY 9. Diagnostics In this section various diagnostic tools will be used to evaluate the adequacy of the regression model with the five independent variables developed in
More informationIntroduction to regression
Introduction to regression Regression describes how one variable (response) depends on another variable (explanatory variable). Response variable: variable of interest, measures the outcome of a study
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 information6. Unusual and Influential Data
Sociology 740 John ox Lecture Notes 6. Unusual and Influential Data Copyright 2014 by John ox Unusual and Influential Data 1 1. Introduction I Linear statistical models make strong assumptions about the
More informationm 11 m.1 > m 12 m.2 risk for smokers risk for nonsmokers
SOCY5061 RELATIVE RISKS, RELATIVE ODDS, LOGISTIC REGRESSION RELATIVE RISKS: Suppose we are interested in the association between lung cancer and smoking. Consider the following table for the whole population:
More informationMultiple Linear Regression
Multiple Linear Regression CSU Chico, Math 314 2018-12-05 Multiple Linear Regression 2018-12-05 1 / 41 outline Recap Multiple Linear Regression assumptions lite example interpretation adjusted R 2 simple
More informationFinal Exam Version A
Final Exam Version A Open Book and Notes your 4-digit code: Staple the question sheets to your answers Write your name only once on the back of this sheet. Problem 1: (10 points) A popular method to isolate
More informationSTP 231 Example FINAL
STP 231 Example FINAL Instructor: Ela Jackiewicz Honor Statement: I have neither given nor received information regarding this exam, and I will not do so until all exams have been graded and returned.
More informationLesson 1: Distributions and Their Shapes
Lesson 1 Name Date Lesson 1: Distributions and Their Shapes 1. Sam said that a typical flight delay for the sixty BigAir flights was approximately one hour. Do you agree? Why or why not? 2. Sam said that
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 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 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 informationAP Statistics Practice Test Ch. 3 and Previous
AP Statistics Practice Test Ch. 3 and Previous Name Date Use the following to answer questions 1 and 2: A researcher measures the height (in feet) and volume of usable lumber (in cubic feet) of 32 cherry
More informationSection 3 Correlation and Regression - Teachers Notes
The data are from the paper: Exploring Relationships in Body Dimensions Grete Heinz and Louis J. Peterson San José State University Roger W. Johnson and Carter J. Kerk South Dakota School of Mines and
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 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 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 informationt-test Tutorial Aliza McConnahey & Josh Petkash
t-test Tutorial Aliza McConnahey & Josh Petkash t-testing? A t-test is used to test against the mean of a population when the population standard deviation is not known. In order to conduct a t-test, a
More informationStatistics Assignment 11 - Solutions
Statistics 44.3 Assignment 11 - Solutions 1. Samples were taken of individuals with each blood type to see if the average white blood cell count differed among types. Eleven individuals in each group were
More informationA Handbook of Statistical Analyses Using R. Brian S. Everitt and Torsten Hothorn
A Handbook of Statistical Analyses Using R Brian S. Everitt and Torsten Hothorn CHAPTER 12 Meta-Analysis: Nicotine Gum and Smoking Cessation and the Efficacy of BCG Vaccine in the Treatment of Tuberculosis
More informationThe purpose of our project was to determine the percentage of the Drug. Enforcement Administration s (DEA) budget that is being spent on drug
Statistics Report DEA Budget Danielle Maginnis, Justin Sousa, Tom Zinckgraf The purpose of our project was to determine the percentage of the Drug Enforcement Administration s (DEA) budget that is being
More informationStatistics as a Tool. A set of tools for collecting, organizing, presenting and analyzing numerical facts or observations.
Statistics as a Tool A set of tools for collecting, organizing, presenting and analyzing numerical facts or observations. Descriptive Statistics Numerical facts or observations that are organized describe
More informationObjective: To describe a new approach to neighborhood effects studies based on residential mobility and demonstrate this approach in the context of
Objective: To describe a new approach to neighborhood effects studies based on residential mobility and demonstrate this approach in the context of neighborhood deprivation and preterm birth. Key Points:
More informationApplication of Local Control Strategy in analyses of the effects of Radon on Lung Cancer Mortality for 2,881 US Counties
Application of Local Control Strategy in analyses of the effects of Radon on Lung Cancer Mortality for 2,881 US Counties Bob Obenchain, Risk Benefit Statistics, August 2015 Our motivation for using a Cut-Point
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 informationChapter 9. Factorial ANOVA with Two Between-Group Factors 10/22/ Factorial ANOVA with Two Between-Group Factors
Chapter 9 Factorial ANOVA with Two Between-Group Factors 10/22/2001 1 Factorial ANOVA with Two Between-Group Factors Recall that in one-way ANOVA we study the relation between one criterion variable and
More informationOn Regression Analysis Using Bivariate Extreme Ranked Set Sampling
On Regression Analysis Using Bivariate Extreme Ranked Set Sampling Atsu S. S. Dorvlo atsu@squ.edu.om Walid Abu-Dayyeh walidan@squ.edu.om Obaid Alsaidy obaidalsaidy@gmail.com Abstract- Many forms of ranked
More informationPsych 5741/5751: Data Analysis University of Boulder Gary McClelland & Charles Judd. Exam #2, Spring 1992
Exam #2, Spring 1992 Question 1 A group of researchers from a neurobehavioral institute are interested in the relationships that have been found between the amount of cerebral blood flow (CB FLOW) to the
More informationINTERPRET SCATTERPLOTS
Chapter2 MODELING A BUSINESS 2.1: Interpret Scatterplots 2.2: Linear Regression 2.3: Supply and Demand 2.4: Fixed and Variable Expenses 2.5: Graphs of Expense and Revenue Functions 2.6: Breakeven Analysis
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 informationStatistical techniques to evaluate the agreement degree of medicine measurements
Statistical techniques to evaluate the agreement degree of medicine measurements Luís M. Grilo 1, Helena L. Grilo 2, António de Oliveira 3 1 lgrilo@ipt.pt, Mathematics Department, Polytechnic Institute
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 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 informationClincial Biostatistics. Regression
Regression analyses Clincial Biostatistics 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
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 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 informationSTAT445 Midterm Project1
STAT445 Midterm Project1 Executive Summary This report works on the dataset of Part of This Nutritious Breakfast! In this dataset, 77 different breakfast cereals were collected. The dataset also explores
More informationLecture Outline. Biost 517 Applied Biostatistics I. Purpose of Descriptive Statistics. Purpose of Descriptive Statistics
Biost 517 Applied Biostatistics I Scott S. Emerson, M.D., Ph.D. Professor of Biostatistics University of Washington Lecture 3: Overview of Descriptive Statistics October 3, 2005 Lecture Outline Purpose
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 informationHZAU MULTIVARIATE HOMEWORK #2 MULTIPLE AND STEPWISE LINEAR REGRESSION
HZAU MULTIVARIATE HOMEWORK #2 MULTIPLE AND STEPWISE LINEAR REGRESSION Using the malt quality dataset on the class s Web page: 1. Determine the simple linear correlation of extract with the remaining variables.
More informationToday: Binomial response variable with an explanatory variable on an ordinal (rank) scale.
Model Based Statistics in Biology. Part V. The Generalized Linear Model. Single Explanatory Variable on an Ordinal Scale ReCap. Part I (Chapters 1,2,3,4), Part II (Ch 5, 6, 7) ReCap Part III (Ch 9, 10,
More informationDr. Kelly Bradley Final Exam Summer {2 points} Name
{2 points} Name You MUST work alone no tutors; no help from classmates. Email me or see me with questions. You will receive a score of 0 if this rule is violated. This exam is being scored out of 00 points.
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 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 information