# Experimental Designs leading to multiple regression analysis

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Experimental Designs leading to multiple regression analysis 1. (Randomized) designed experiments. 2. Randomized block experiments. 3. Observational studies: probability based sample surveys 4. Observational studies: sample of convenience.

2 Randomized designed experiments want to study the effect of variables x 1, x 2,..., x p on a response variable Y. Experimenter chooses n sets of values of x 1, x 2,..., x p and measures the response Y on n experimental units. Experimental Units are assigned at random to levels (that is to the particular combinations of x values). This is a much better method than other methods for deciding which experimental units get which x values.

3 Designed Experiments Example: Experimental Unit is a batch of plaster n = 18 batches made. x 1 is the sand content and x 2 is the fibre content. We tried 3 settings of x 1, 3 of x 2 and tried each of the 3 3 = 9 combinations twice.

4 Randomized Block Designs Want to study the effect of variables x 1, x 2,..., x p on a response variable Y of an experimental unit. BUT Y is probably influenced by variable B which the experimenter cannot control.

5 Example of Randomized Block Designs x 1 is log(dose) of some drug. B = sex of patient (patient is the experimental unit). experimenter can assign patient to level of x 1 but NOT to the level of B. B is called a blocking factor. Blocking can serve useful purpose: increase precision of estimates of effects.

6 Another Example Y is lung capacity B 1 is cigarettes smoked per day B 2 is age B 3 is sex x 1 is daily vitamin C intake x 2 is daily Echinacea dose Key point is that x 1 and x 2 are under control of the experimenter but the other factors are not.

7 Observational Studies values of Y and variables x 1, x 2,..., x p are determined by sampling from a population. covariates x 1, x 2,..., x p are not controlled by the experimenter.

8 Example As in the previous example but suppose vitamin C and echinacea intakes are not controlled, just measured. Vital Distinction Cause and effect relations are convincingly deduced only for controlled variables. Interpretation of regression coefficients is difficult in observational studies.

9 Cause and Effect Inference in an observational study is largely descriptive. BUT researchers in social science often want to know if changes in variable X cause changes in Y. The interpretation is that if X could be manipulated then Y would be changed. To demonstrate that changing X causes changes in Y we hold all other important variables constant and try experimental units at various settings of X. Variables we don t know about or can t control are equalized between the different levels of X by randomly assigning units to the different values of X. An observational study is one where X cannot be controlled and other variables cannot be held constant.

10 Hypothetical Example Think about a case where men have generally higher values of both X and Y and women have generally lower values but that among men there is no relation between X and Y Here is a possible plot, the triangles being men.

11

12 Discussion If you didn t know about the influence of sex you would see a positive correlation between X and Y. But if you compute separate correlations for the two groups you see the variables are unrelated. Remember, if you manipulate X in the picture you are either doing so for a women (and X and Y are unrelated for women) or for a man (and again X and Y are unrelated). In either case Y will be unaffected because you would not be affecting the sex of a person.

13 Discussion Continued Doing multiple regression is very much like this. Imagine you have a response variable Y, a variable X whose influence on Y is of primary interest and some other variables which probably influence Y and may influence X as well. You would like to look at the relation between X and Y in groups of cases where all the other covariate values are the same; this is not generally possible. Instead, we estimate the average value of Y for each possible combination of the variable X and the other variables. We ask if this mean depends on X. We say we are adjusting for the other covariates. The method works pretty well if we have identified all the possible confounding variables so that we can adjust for them all.

14 SENIC example Example to come: regress risk of infection on many variables. One is Nurses per patient. Estimated coefficient is positive. More nurses means more infection? Fire nurses? Trouble: no such deduction is rigorously possible. Need to be sure there is no 3rd variable correlated with both X and Y which causes variation in both and for which you haven t adjusted. Designed experiments deal with problem by randomization. The slope in a regression model corresponding to X measures the change expected in Y when X is changed by 1 unit and all the other variables in the regression are held constant. Regression method is used to adjust for the other covariates. Researchers say things like Adjusted for Length of service and publication rate sex has no impact on salary of professors. But not many say that particular thing.

15 Observational Studies: samples of convenience Multiple regression logic depends on model assumptions. Sometimes justified by fact data is sample from population with suitable structure. Sometimes used when data is just gathered in some convenient way. Inference extends from sample to population from which it is sample. Sampling biases produce invalid regression results.

16 Compare and contrast Randomized controlled experiments provide most compelling proof of cause and effect. But experiments not entirely like real world better medical care, for instance, in a clinical trial than is normal. So effect sizes in experiment may not match effects in real world. Observational studies nearly always leave room for doubt about cause and effect. Econometricians use technique called instrumental variables. Technique requires assumptions. Demonstration of cause and effect in such studies always uses assumptions. Many different observational studies showing same point in different contexts are more compelling.

### 1. Role of statistical models

1. Role of statistical models Statistical models 2 Statistical model........................................................... 3 Statistical model...........................................................

### STATISTICS 8 CHAPTERS 1 TO 6, SAMPLE MULTIPLE CHOICE QUESTIONS

STATISTICS 8 CHAPTERS 1 TO 6, SAMPLE MULTIPLE CHOICE QUESTIONS Correct answers are in bold italics.. This scenario applies to Questions 1 and 2: A study was done to compare the lung capacity of coal miners

### Chapter 1: Role of statistical models

Chapter 1: Role of statistical models Statistical models 2 Statistical model (1)........................................................ 3 Statistical model (2)........................................................

### Instrumental Variables Regression. Instrumental Variables (IV) estimation is used when the model has endogenous s.

Instrumental Variables Regression Instrumental Variables (IV) estimation is used when the model has endogenous s. IV can thus be used to address the following important threats to internal validity: Omitted

### CALCULATIONS & STATISTICS

CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents

### Class 19: Two Way Tables, Conditional Distributions, Chi-Square (Text: Sections 2.5; 9.1)

Spring 204 Class 9: Two Way Tables, Conditional Distributions, Chi-Square (Text: Sections 2.5; 9.) Big Picture: More than Two Samples In Chapter 7: We looked at quantitative variables and compared the

### How to Use the Research Design Algorithm

How to Use the Research Design Algorithm Below is a diamond by diamond guide for using the Research Design Algorithm developed by the Academy of Nutrition and Dietetics, 2010. Included are Tips on what

### MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Sample Practice problems - chapter 12-1 and 2 proportions for inference - Z Distributions Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide

### 14.74 Lecture 7: The effect of school buildings on schooling: A naturalexperiment

14.74 Lecture 7: The effect of school buildings on schooling: A naturalexperiment Esther Duflo February 25-March 1 1 The question we try to answer Does the availability of more schools cause an increase

### Bivariate Analysis. Correlation. Correlation. Pearson's Correlation Coefficient. Variable 1. Variable 2

Bivariate Analysis Variable 2 LEVELS >2 LEVELS COTIUOUS Correlation Used when you measure two continuous variables. Variable 2 2 LEVELS X 2 >2 LEVELS X 2 COTIUOUS t-test X 2 X 2 AOVA (F-test) t-test AOVA

### Elementary Statistics

Elementary Statistics Chapter 1 Dr. Ghamsary Page 1 Elementary Statistics M. Ghamsary, Ph.D. Chap 01 1 Elementary Statistics Chapter 1 Dr. Ghamsary Page 2 Statistics: Statistics is the science of collecting,

### Testing Scientific Explanations (In words slides page 7)

Testing Scientific Explanations (In words slides page 7) Most people are curious about the causes for certain things. For example, people wonder whether exercise improves memory and, if so, why? Or, we

### Chapter 2 Quantitative, Qualitative, and Mixed Research

1 Chapter 2 Quantitative, Qualitative, and Mixed Research This chapter is our introduction to the three research methodology paradigms. A paradigm is a perspective based on a set of assumptions, concepts,

### A Few Basics of Probability

A Few Basics of Probability Philosophy 57 Spring, 2004 1 Introduction This handout distinguishes between inductive and deductive logic, and then introduces probability, a concept essential to the study

### p1^ = 0.18 p2^ = 0.12 A) 0.150 B) 0.387 C) 0.300 D) 0.188 3) n 1 = 570 n 2 = 1992 x 1 = 143 x 2 = 550 A) 0.270 B) 0.541 C) 0.520 D) 0.

Practice for chapter 9 and 10 Disclaimer: the actual exam does not mirror this. This is meant for practicing questions only. The actual exam in not multiple choice. Find the number of successes x suggested

### Georgia Department of Education Common Core Georgia Performance Standards Framework Teacher Edition Coordinate Algebra Unit 4

Equal Salaries for Equal Work? Mathematical Goals Represent data on a scatter plot Describe how two variables are related Informally assess the fit of a function by plotting and analyzing residuals Fit

### The Importance of Statistics Education

The Importance of Statistics Education Professor Jessica Utts Department of Statistics University of California, Irvine http://www.ics.uci.edu/~jutts jutts@uci.edu Outline of Talk What is Statistics? Four

### THE SECOND DERIVATIVE

THE SECOND DERIVATIVE Summary 1. Curvature Concavity and convexity... 2 2. Determining the nature of a static point using the second derivative... 6 3. Absolute Optima... 8 The previous section allowed

### Sample Size and Power in Clinical Trials

Sample Size and Power in Clinical Trials Version 1.0 May 011 1. Power of a Test. Factors affecting Power 3. Required Sample Size RELATED ISSUES 1. Effect Size. Test Statistics 3. Variation 4. Significance

### What is a P-value? Ronald A. Thisted, PhD Departments of Statistics and Health Studies The University of Chicago

What is a P-value? Ronald A. Thisted, PhD Departments of Statistics and Health Studies The University of Chicago 8 June 1998, Corrections 14 February 2010 Abstract Results favoring one treatment over another

### They try to discover what kinds of cells the BNV infects, how it kills those cells, and what happens to the surrounding cells.

A Vaccine s Journey 2 A Journey From the time an idea blooms in someone s mind, to the time a nurse says give me your arm, the development of a vaccine takes10 or more years. The stages of development

### Metacognition and Mathematical Problem Solving: Helping Students to Ask The Right Questions

Metacognition and Mathematical Problem Solving: Helping Students to Ask The Right Questions Shirley Gartmann and Melissa Freiberg The acquisition of problem solving, reasoning and critical thinking skills

### UCLA STAT 13 Statistical Methods - Final Exam Review Solutions Chapter 7 Sampling Distributions of Estimates

UCLA STAT 13 Statistical Methods - Final Exam Review Solutions Chapter 7 Sampling Distributions of Estimates 1. (a) (i) µ µ (ii) σ σ n is exactly Normally distributed. (c) (i) is approximately Normally

### MATH 10: Elementary Statistics and Probability Chapter 9: Hypothesis Testing with One Sample

MATH 10: Elementary Statistics and Probability Chapter 9: Hypothesis Testing with One Sample Tony Pourmohamad Department of Mathematics De Anza College Spring 2015 Objectives By the end of this set of

### THE MULTINOMIAL DISTRIBUTION. Throwing Dice and the Multinomial Distribution

THE MULTINOMIAL DISTRIBUTION Discrete distribution -- The Outcomes Are Discrete. A generalization of the binomial distribution from only 2 outcomes to k outcomes. Typical Multinomial Outcomes: red A area1

### Module 7: Hypothesis Testing I Statistics (OA3102)

Module 7: Hypothesis Testing I Statistics (OA3102) Professor Ron Fricker Naval Postgraduate School Monterey, California Reading assignment: WM&S chapter 10.1-10.5 Revision: 2-12 1 Goals for this Module

### REGRESSION LINES IN STATA

REGRESSION LINES IN STATA THOMAS ELLIOTT 1. Introduction to Regression Regression analysis is about eploring linear relationships between a dependent variable and one or more independent variables. Regression

### Answer: C. The strength of a correlation does not change if units change by a linear transformation such as: Fahrenheit = 32 + (5/9) * Centigrade

Statistics Quiz Correlation and Regression -- ANSWERS 1. Temperature and air pollution are known to be correlated. We collect data from two laboratories, in Boston and Montreal. Boston makes their measurements

### The Cross-Sectional Study:

The Cross-Sectional Study: Investigating Prevalence and Association Ronald A. Thisted Departments of Health Studies and Statistics The University of Chicago CRTP Track I Seminar, Autumn, 2006 Lecture Objectives

### Mind on Statistics. Chapter 4

Mind on Statistics Chapter 4 Sections 4.1 Questions 1 to 4: The table below shows the counts by gender and highest degree attained for 498 respondents in the General Social Survey. Highest Degree Gender

### Simple Regression Theory II 2010 Samuel L. Baker

SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the

### Chapter Seven. Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS

Chapter Seven Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS Section : An introduction to multiple regression WHAT IS MULTIPLE REGRESSION? Multiple

### Chapter 10 - Practice Problems 1

Chapter 10 - Practice Problems 1 1. A researcher is interested in determining if one could predict the score on a statistics exam from the amount of time spent studying for the exam. In this study, the

### DEVELOPING HYPOTHESIS AND

Shalini Prasad Ajith Rao Eeshoo Rehani DEVELOPING 500 METHODS SEPTEMBER 18 TH 2001 DEVELOPING HYPOTHESIS AND Introduction Processes involved before formulating the hypotheses. Definition Nature of Hypothesis

### Section 14 Simple Linear Regression: Introduction to Least Squares Regression

Slide 1 Section 14 Simple Linear Regression: Introduction to Least Squares Regression There are several different measures of statistical association used for understanding the quantitative relationship

### Self-Check and Review Chapter 1 Sections 1.1-1.2

Self-Check and Review Chapter 1 Sections 1.1-1.2 Practice True/False 1. The entire collection of individuals or objects about which information is desired is called a sample. 2. A study is an observational

### Repeated Measures Design. Mark Conaway October 11, 1999

Repeated Measures Design Mark Conaway October 11, 1999 Common design: Repeated measures designs Take measurements on same subject over time or under different conditions. Same basic idea as a randomized

### Philosophical argument

Michael Lacewing Philosophical argument At the heart of philosophy is philosophical argument. Arguments are different from assertions. Assertions are simply stated; arguments always involve giving reasons.

### Challenges in Longitudinal Data Analysis: Baseline Adjustment, Missing Data, and Drop-out

Challenges in Longitudinal Data Analysis: Baseline Adjustment, Missing Data, and Drop-out Sandra Taylor, Ph.D. IDDRC BBRD Core 23 April 2014 Objectives Baseline Adjustment Introduce approaches Guidance

### Outline. Correlation & Regression, III. Review. Relationship between r and regression

Outline Correlation & Regression, III 9.07 4/6/004 Relationship between correlation and regression, along with notes on the correlation coefficient Effect size, and the meaning of r Other kinds of correlation

### 1. The Scientific Method

1 1. The Scientific Method The distinctive characteristic of science is its methodology. Science is not just a body of knowledge, but knowledge assembled by the application of the scientific methodology.

### Financial Risk Management Exam Sample Questions/Answers

Financial Risk Management Exam Sample Questions/Answers Prepared by Daniel HERLEMONT 1 2 3 4 5 6 Chapter 3 Fundamentals of Statistics FRM-99, Question 4 Random walk assumes that returns from one time period

### In an experimental study there are two types of variables: Independent variable (I will abbreviate this as the IV)

1 Experimental Design Part I Richard S. Balkin, Ph. D, LPC-S, NCC 2 Overview Experimental design is the blueprint for quantitative research and serves as the foundation of what makes quantitative research

### 1. Suppose that a score on a final exam depends upon attendance and unobserved factors that affect exam performance (such as student ability).

Examples of Questions on Regression Analysis: 1. Suppose that a score on a final exam depends upon attendance and unobserved factors that affect exam performance (such as student ability). Then,. When

### Greatest Common Factor and Least Common Multiple

Greatest Common Factor and Least Common Multiple Intro In order to understand the concepts of Greatest Common Factor (GCF) and Least Common Multiple (LCM), we need to define two key terms: Multiple: Multiples

### II. DISTRIBUTIONS distribution normal distribution. standard scores

Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,

### Chapter 10. Key Ideas Correlation, Correlation Coefficient (r),

Chapter 0 Key Ideas Correlation, Correlation Coefficient (r), Section 0-: Overview We have already explored the basics of describing single variable data sets. However, when two quantitative variables

### Sample Exam #1 Elementary Statistics

Sample Exam #1 Elementary Statistics Instructions. No books, notes, or calculators are allowed. 1. Some variables that were recorded while studying diets of sharks are given below. Which of the variables

### Research Methods & Experimental Design

Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and

### Statistics 151 Practice Midterm 1 Mike Kowalski

Statistics 151 Practice Midterm 1 Mike Kowalski Statistics 151 Practice Midterm 1 Multiple Choice (50 minutes) Instructions: 1. This is a closed book exam. 2. You may use the STAT 151 formula sheets and

### Exam 2 Study Guide and Review Problems

Exam 2 Study Guide and Review Problems Exam 2 covers chapters 4, 5, and 6. You are allowed to bring one 3x5 note card, front and back, and your graphing calculator. Study tips: Do the review problems below.

### They can be divided into two types. What are the two categories? Which expressions above go in which category? Category 2 =

What do these linking expressions have in common? 1. Apparently, 2. Clearly, 3. Controversially, 4. Evidently, 5. Hypothetically, 6. Invariably, 7. Objectively (speaking), 8. Obviously, 9. Potentially,

### c. Construct a boxplot for the data. Write a one sentence interpretation of your graph.

MBA/MIB 5315 Sample Test Problems Page 1 of 1 1. An English survey of 3000 medical records showed that smokers are more inclined to get depressed than non-smokers. Does this imply that smoking causes depression?

### Reflections on Probability vs Nonprobability Sampling

Official Statistics in Honour of Daniel Thorburn, pp. 29 35 Reflections on Probability vs Nonprobability Sampling Jan Wretman 1 A few fundamental things are briefly discussed. First: What is called probability

### IPDET Module 6: Descriptive, Normative, and Impact Evaluation Designs

IPDET Module 6: Descriptive, Normative, and Impact Evaluation Designs Intervention or Policy Evaluation Questions Design Questions Elements Types Key Points Introduction What Is Evaluation Design? Connecting

### Characteristics of Binomial Distributions

Lesson2 Characteristics of Binomial Distributions In the last lesson, you constructed several binomial distributions, observed their shapes, and estimated their means and standard deviations. In Investigation

### Fairfield Public Schools

Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity

### Association Between Variables

Contents 11 Association Between Variables 767 11.1 Introduction............................ 767 11.1.1 Measure of Association................. 768 11.1.2 Chapter Summary.................... 769 11.2 Chi

### People have thought about, and defined, probability in different ways. important to note the consequences of the definition:

PROBABILITY AND LIKELIHOOD, A BRIEF INTRODUCTION IN SUPPORT OF A COURSE ON MOLECULAR EVOLUTION (BIOL 3046) Probability The subject of PROBABILITY is a branch of mathematics dedicated to building models

### Data Analysis: Analyzing Data - Inferential Statistics

WHAT IT IS Return to Table of ontents WHEN TO USE IT Inferential statistics deal with drawing conclusions and, in some cases, making predictions about the properties of a population based on information

### A Basic Introduction to Missing Data

John Fox Sociology 740 Winter 2014 Outline Why Missing Data Arise Why Missing Data Arise Global or unit non-response. In a survey, certain respondents may be unreachable or may refuse to participate. Item

### UNDERSTANDING ANALYSIS OF COVARIANCE (ANCOVA)

UNDERSTANDING ANALYSIS OF COVARIANCE () In general, research is conducted for the purpose of explaining the effects of the independent variable on the dependent variable, and the purpose of research design

### Statistics E100 Fall 2013 Practice Midterm I - A Solutions

STATISTICS E100 FALL 2013 PRACTICE MIDTERM I - A SOLUTIONS PAGE 1 OF 5 Statistics E100 Fall 2013 Practice Midterm I - A Solutions 1. (16 points total) Below is the histogram for the number of medals won

### Session 7 Bivariate Data and Analysis

Session 7 Bivariate Data and Analysis Key Terms for This Session Previously Introduced mean standard deviation New in This Session association bivariate analysis contingency table co-variation least squares

### Causality and Treatment Effects

Causality and Treatment Effects Prof. Jacob M. Montgomery Quantitative Political Methodology (L32 363) October 21, 2013 Lecture 13 (QPM 2013) Causality and Treatment Effects October 21, 2013 1 / 19 Overview

### 5. Linear regression and correlation

Statistics for Engineers 5-1 5. Linear regression and correlation If we measure a response variable at various values of a controlled variable, linear regression is the process of fitting a straight line

### AP Stats- Mrs. Daniel Chapter 4 MC Practice

AP Stats- Mrs. Daniel Chapter 4 MC Practice Name: 1. Archaeologists plan to examine a sample of 2-meter-square plots near an ancient Greek city for artifacts visible in the ground. They choose separate

### Introduction to Fixed Effects Methods

Introduction to Fixed Effects Methods 1 1.1 The Promise of Fixed Effects for Nonexperimental Research... 1 1.2 The Paired-Comparisons t-test as a Fixed Effects Method... 2 1.3 Costs and Benefits of Fixed

### 4. Introduction to Statistics

Statistics for Engineers 4-1 4. Introduction to Statistics Descriptive Statistics Types of data A variate or random variable is a quantity or attribute whose value may vary from one unit of investigation

### Why Sample? Why not study everyone? Debate about Census vs. sampling

Sampling Why Sample? Why not study everyone? Debate about Census vs. sampling Problems in Sampling? What problems do you know about? What issues are you aware of? What questions do you have? Key Sampling

### Prospective, retrospective, and cross-sectional studies

Prospective, retrospective, and cross-sectional studies Patrick Breheny April 3 Patrick Breheny Introduction to Biostatistics (171:161) 1/17 Study designs that can be analyzed with χ 2 -tests One reason

### Three Essential Properties of a Well-Designed Experiment

Experimental Design Three Essential Properties of a Well-Designed Experiment Experimenter must: 1. Systematically vary (manipulate) at least one independent variable 2. Assign participants to experimental

### Experimental Design. 1. Randomized assignment 2. Pre-test ( O1) 3. Independent Variable ( X ) 4. Post-Test ( O2 )

Experimental Design 1. Randomized assignment 2. Pre-test ( O1) 3. Independent Variable ( X ) 4. Post-Test ( O2 ) Randomized Trial Group R O1» X O2 Randomized Control Group R»O1 O2 True Experimental Designs

### Scientific Methods II: Correlational Research

Scientific Methods II: Correlational Research EXAMPLES "MARRIAGE SLOWS CANCER DEATHS Evidence that married people have a better chance of surviving cancer than do singles means that the unmarried might

### Section 6-5 Sample Spaces and Probability

492 6 SEQUENCES, SERIES, AND PROBABILITY 52. How many committees of 4 people are possible from a group of 9 people if (A) There are no restrictions? (B) Both Juan and Mary must be on the committee? (C)

### Hints for Success on the AP Statistics Exam. (Compiled by Zack Bigner)

Hints for Success on the AP Statistics Exam. (Compiled by Zack Bigner) The Exam The AP Stat exam has 2 sections that take 90 minutes each. The first section is 40 multiple choice questions, and the second

### Statistics Notes Revision in Maths Week

Statistics Notes Revision in Maths Week 1 Section - Producing Data 1.1 Introduction Statistics is the science that studies the collection and interpretation of numerical data. Statistics divides the study

### SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES

SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR

### Data Types. 1. Continuous 2. Discrete quantitative 3. Ordinal 4. Nominal. Figure 1

Data Types By Tanya Hoskin, a statistician in the Mayo Clinic Department of Health Sciences Research who provides consultations through the Mayo Clinic CTSA BERD Resource. Don t let the title scare you.

### 14.74 Lecture 4: Health Esther Duflo February 17, 2004

14.74 Lecture 4: Health Esther Duflo February 17, 2004 1 The burden of disease The burden of disease is large, and very unequally distributed: See table 2 in page 1 of the handout: In 1990, 50 million

### Exhibit memory of previously-learned materials by recalling facts, terms, basic concepts, and answers. Key Words

The Six Levels of Questioning Level 1 Knowledge Exhibit memory of previously-learned materials by recalling facts, terms, basic concepts, and answers. who what why when where which omit choose find how

### How to choose an analysis to handle missing data in longitudinal observational studies

How to choose an analysis to handle missing data in longitudinal observational studies ICH, 25 th February 2015 Ian White MRC Biostatistics Unit, Cambridge, UK Plan Why are missing data a problem? Methods:

### Mind on Statistics. Chapter 10

Mind on Statistics Chapter 10 Section 10.1 Questions 1 to 4: Some statistical procedures move from population to sample; some move from sample to population. For each of the following procedures, determine

### Attitudes on Marijuana Survey: Young Adults Ages 18 to 25 in the New York Area who Attend College

Attitudes on Marijuana Survey: Young Adults Ages 18 to 25 in the New York Area who Attend College Conducted for: Presented on: February 16, 2015 Conducted by: www.qmarketresearch.com Page 1 Contents Background...

Using Your TI-83/84 Calculator: Linear Correlation and Regression Elementary Statistics Dr. Laura Schultz This handout describes how to use your calculator for various linear correlation and regression

### Types of Error in Surveys

2 Types of Error in Surveys Surveys are designed to produce statistics about a target population. The process by which this is done rests on inferring the characteristics of the target population from

### Regents Exam Questions A2.S.8: Correlation Coefficient

A2.S.8: Correlation Coefficient: Interpret within the linear regression model the value of the correlation coefficient as a measure of the strength of the relationship 1 Which statement regarding correlation

### Guidance for Industry

Guidance for Industry Q2B Validation of Analytical Procedures: Methodology November 1996 ICH Guidance for Industry Q2B Validation of Analytical Procedures: Methodology Additional copies are available from:

### Inclusion and Exclusion Criteria

Inclusion and Exclusion Criteria Inclusion criteria = attributes of subjects that are essential for their selection to participate. Inclusion criteria function remove the influence of specific confounding

### Analyzing Intervention Effects: Multilevel & Other Approaches. Simplest Intervention Design. Better Design: Have Pretest

Analyzing Intervention Effects: Multilevel & Other Approaches Joop Hox Methodology & Statistics, Utrecht Simplest Intervention Design R X Y E Random assignment Experimental + Control group Analysis: t

### FEV1 (litres) Figure 1: Models for gas consumption and lung capacity

Simple Linear Regression: Reliability of predictions Richard Buxton. 2008. 1 Introduction We often use regression models to make predictions. In Figure 1 (a), we ve fitted a model relating a household

### Missing data in randomized controlled trials (RCTs) can

EVALUATION TECHNICAL ASSISTANCE BRIEF for OAH & ACYF Teenage Pregnancy Prevention Grantees May 2013 Brief 3 Coping with Missing Data in Randomized Controlled Trials Missing data in randomized controlled

### Income protection insurance

Income protection insurance for independent information About us We are an independent watchdog set up by the Government to: regulate firms that provide financial services; and help you make informed decisions

### Experiments and Observational Studies

Experiments and Observational Studies Recall basic definitions: Population Sample Response variable Explanatory variable 1 2 3 4 Articles in the New York Times http://www.nytimes.com/2009/01/27/health/research/27long.html?

### 2. METHODS OF DATA COLLECTION. Types of Data. Some examples from Wainer, Palmer and Bradlow (Chance):

2. METHODS OF DATA COLLECTION Proper data collection is important. Even sophisticated statistical analyses can t compensate for data with bias, ambiguity or errors. Some examples from Wainer, Palmer and

### Construct a scatterplot for the given data. 2) x Answer:

Review for Test 5 STA 2023 spr 2014 Name Given the linear correlation coefficient r and the sample size n, determine the critical values of r and use your finding to state whether or not the given r represents

### SPSS: Descriptive and Inferential Statistics. For Windows

For Windows August 2012 Table of Contents Section 1: Summarizing Data...3 1.1 Descriptive Statistics...3 Section 2: Inferential Statistics... 10 2.1 Chi-Square Test... 10 2.2 T tests... 11 2.3 Correlation...

### Pre-experimental Designs for Description. Y520 Strategies for Educational Inquiry

Pre-experimental Designs for Description Y520 Strategies for Educational Inquiry Pre-experimental designs-1 Research Methodology Is concerned with how the design is implemented and how the research is