HOMEWORK 4 Due: next class 2/8

Size: px
Start display at page:

Download "HOMEWORK 4 Due: next class 2/8"

Transcription

1 HOMEWORK 4 Due: next class 2/8 1. Recall the class data we collected concerning body image (about right, overweight, underweight). Following the body image example in OLI, answer the following question by completing the parts below: Does it appear that body image tends to be influenced by gender? a. What is the explanatory variable? Gender b. What is the response variable? Body image c. Construct a two-way table summarizing the relationship between gender and body image. Use the explanatory variable for the rows and the response variable for the columns. 12pm About right Overweight Underweight Total Female Male pm About Right Overweight Underweight Total Female Male d. Determine the conditional row percents, and make a new two-way table that shows them. 12 pm About right Overweight Underweight Total Female 57.90% 42.10% 0% 100% Male 35.30% 58.80% 5.90% 100% 1 pm About Right Overweight Underweight Total Female 62.50% 25% 12.50% 100% Male 53.80% 30.80% 15.40% 100% 1

2 e. Draw a double bar chart that compares the conditional row percents. 12 pm Female Male 1 About right Overweight Underweight 1 pm Female Male 1 About Right Overweight Underweight f. Are men and women in our class just as likely to think their weight is about right? Among those students who do not think their weight is about right, is there a difference between the genders in their feelings about body image? 12pm : No, there is a big difference in the proportion of men and women in this class who think their weight is about right. About 22% more women think their weight is about right. There is a difference between the genders in their feeling about their body image for the overweight and underweight group too. More men think they are overweight. No females think they are underweight, and one male considers himself underweight. 1pm : There is not much of a difference between men and women in this class who think their weight is about right. The difference is only about 10% (63%--54%). About 10% more women think their body is about right. Similarly, there is not much difference between the genders for overweight and underweight. A little bit more men think they are over-, or underweight, but the differences are less than 5%. 2

3 2. For each of the situations described below, (i) describe the explanatory variable and indicate if it is quantitative or categorical, and (ii) describe the response variable and indicate if it is quantitative or categorical: a. A student was curious about which route would get her to school faster, so she collected data on how long the trip took for a freeway route and for a non-freeway route, taking each route ten times. Explanatory variable: route (freeway or non-freeway) C Response variable: time to get school Q b. A psychologist is studying the effect of electroshock therapy on a subject's ability to solve simple tasks. The number of tasks completed in a 10-minute period is recorded for subjects, half of whom received electroshock and half of whom did not receive electroshock. Explanatory variable: whether or not the subject received electroshock C Response variable: number of tasks completed in a 10-minute period Q c. You want to determine whether students' expected grades at the beginning of an introduction to statistics course are positively related to their final course grades. Explanatory variable: grade at the beginning of the course C (if you think about letter grades) or Q (if you think about scores) Response variable: grade at the end of the course C (if you think about letter grades) or Q (if you think about scores) d. A study is made to analyze how students SAT scores are related to whether they graduate from college. Explanatory variable: SAT scores Q Response variable: whether or not students graduate C 3. The government Office of Vital Statistics studies a sample of married couples, measuring the heights of each husband and each wife. Is there a clear choice of explanatory variable and response variable here? Explain. No. It would be just as natural to try to predict the husband s height from that of the wife as the other way around. 4. A clinical trial compares the effectiveness of two drugs, Lithium and Imipramine, in preventing a recurrence of depression among patients who were hospitalized with depression. Here are the results: 3

4 a. Construct a two-way table that classifies drug against the recurrence variable. Use the explanatory variable for the rows and the response variable for the columns. Recurrence No Recurrence Total Lithium Imipramine b. Determine the conditional row percents, and make a new two-way table that shows them. Recurrence No Recurrence Lithium 18/27 = 66.7% 9/27 = 33.3% Imipramine 8/29 = 27.6% 21/29 = 72.4% c. Draw a double bar chart that compares the conditional row percents. 4

5 Recurrence No Recurrence Lithium Imipramine d. Summarize what your analysis shows about the relationship between drug and the frequency of recurrence. Imipramine appears far more effective in preventing a recurrence of depression than Lithium, as two thirds of those treated with Lithium had a recurrence, while a little more than a quarter of those treated with Imipramine had one. e. Based on the number of subjects used in this study, are you reasonably convinced that one of the two drugs is better than the other at preventing a recurrence of depression? (Note: This question simply calls for a judgment call on your part, there is no correct answer at this point. We will need the methods of inference that will be covered later in this course in order to answer it. 5

Identify two variables. Classify them as explanatory or response and quantitative or explanatory.

Identify two variables. Classify them as explanatory or response and quantitative or explanatory. OLI Module 2 - Examining Relationships Objective Summarize and describe the distribution of a categorical variable in context. Generate and interpret several different graphical displays of the distribution

More information

Lecture 10: Chapter 5, Section 2 Relationships (Two Categorical Variables)

Lecture 10: Chapter 5, Section 2 Relationships (Two Categorical Variables) Lecture 10: Chapter 5, Section 2 Relationships (Two Categorical Variables) Two-Way Tables Summarizing and Displaying Comparing Proportions or Counts Confounding Variables Cengage Learning Elementary Statistics:

More information

Introduction. Lecture 1. What is Statistics?

Introduction. Lecture 1. What is Statistics? Lecture 1 Introduction What is Statistics? Statistics is the science of collecting, organizing and interpreting data. The goal of statistics is to gain information and understanding from data. A statistic

More information

Chapter 1: Exploring Data

Chapter 1: Exploring Data Chapter 1: Exploring Data Section 1.1 The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE Chapter 1 Exploring Data Introduction: Data Analysis: Making Sense of Data 1.1 1.2 Displaying

More information

Statistics Success Stories and Cautionary Tales

Statistics Success Stories and Cautionary Tales Course Goals STATISTICS 8 Professor Jessica Utts http://www.ics.uci.edu/~jutts/8 Help you understand and appreciate how statistics affects your daily life. Teach you tools for understanding statistics

More information

Module 4 Introduction

Module 4 Introduction Module 4 Introduction Recall the Big Picture: We begin a statistical investigation with a research question. The investigation proceeds with the following steps: Produce Data: Determine what to measure,

More information

Table 1: One Year Net Survival Rates for All Cancers Excluding Non-Melanoma Skin Cancer:

Table 1: One Year Net Survival Rates for All Cancers Excluding Non-Melanoma Skin Cancer: Task 1: Draw a bar chart of the following data. All the data must be on one graph. The data shows yearly survival rates for all types of cancers combined (except non-melanoma skin cancer). Hint: Each period

More information

Undertaking statistical analysis of

Undertaking statistical analysis of Descriptive statistics: Simply telling a story Laura Delaney introduces the principles of descriptive statistical analysis and presents an overview of the various ways in which data can be presented by

More information

Part 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. 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 information

1. To review research methods and the principles of experimental design that are typically used in an experiment.

1. To review research methods and the principles of experimental design that are typically used in an experiment. Your Name: Section: 36-201 INTRODUCTION TO STATISTICAL REASONING Computer Lab Exercise Lab #7 (there was no Lab #6) Treatment for Depression: A Randomized Controlled Clinical Trial Objectives: 1. To review

More information

Slide 1 - Introduction to Statistics Tutorial: An Overview Slide notes

Slide 1 - Introduction to Statistics Tutorial: An Overview Slide notes Slide 1 - Introduction to Statistics Tutorial: An Overview Introduction to Statistics Tutorial: An Overview. This tutorial is the first in a series of several tutorials that introduce probability and statistics.

More information

CHAPTER 3 DATA ANALYSIS: DESCRIBING DATA

CHAPTER 3 DATA ANALYSIS: DESCRIBING DATA Data Analysis: Describing Data CHAPTER 3 DATA ANALYSIS: DESCRIBING DATA In the analysis process, the researcher tries to evaluate the data collected both from written documents and from other sources such

More information

Chapter 1: Exploring Data

Chapter 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 information

(a) 50% of the shows have a rating greater than: impossible to tell

(a) 50% of the shows have a rating greater than: impossible to tell KEY 1. Here is a histogram of the Distribution of grades on a quiz. How many students took the quiz? 15 What percentage of students scored below a 60 on the quiz? (Assume left-hand endpoints are included

More information

Graphic Organizers. Compare/Contrast. 1. Different. 2. Different. Alike

Graphic Organizers. Compare/Contrast. 1. Different. 2. Different. Alike 1 Compare/Contrast When you compare and contrast people, places, objects, or ideas, you are looking for how they are alike and how they are different. One way to organize your information is to use a Venn

More information

CHAPTER 3 Describing Relationships

CHAPTER 3 Describing Relationships CHAPTER 3 Describing Relationships 3.1 Scatterplots and Correlation The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers Reading Quiz 3.1 True/False 1.

More information

What is Data? Part 2: Patterns & Associations. INFO-1301, Quantitative Reasoning 1 University of Colorado Boulder

What is Data? Part 2: Patterns & Associations. INFO-1301, Quantitative Reasoning 1 University of Colorado Boulder What is Data? Part 2: Patterns & Associations INFO-1301, Quantitative Reasoning 1 University of Colorado Boulder August 29, 2016 Prof. Michael Paul Prof. William Aspray Overview This lecture will look

More information

Practice First Midterm Exam

Practice First Midterm Exam Practice First Midterm Exam Statistics 200 (Pfenning) This is a closed book exam worth 150 points. You are allowed to use a calculator and a two-sided sheet of notes. There are 9 problems, with point values

More information

BIG FIVE: Review your notes about the Big Five theory and your self-rating before beginning.

BIG FIVE: Review your notes about the Big Five theory and your self-rating before beginning. Name: Date: Hour: 1 Personality Tests Please follow the directions to complete both major types of personality tests. You must take at least 4 personality tests to complete this assignment. Please go to

More information

Medical Statistics 1. Basic Concepts Farhad Pishgar. Defining the data. Alive after 6 months?

Medical Statistics 1. Basic Concepts Farhad Pishgar. Defining the data. Alive after 6 months? Medical Statistics 1 Basic Concepts Farhad Pishgar Defining the data Population and samples Except when a full census is taken, we collect data on a sample from a much larger group called the population.

More information

I. Identifying the question Define Research Hypothesis and Questions

I. Identifying the question Define Research Hypothesis and Questions Term Paper I. Identifying the question What is the question? (What are my hypotheses?) Is it possible to answer the question with statistics? Is the data obtainable? (birth weight, socio economic, drugs,

More information

2/3 x 1 x 1/4 = 2/12 = 1/6

2/3 x 1 x 1/4 = 2/12 = 1/6 1. Imagine that you are a genetic counselor, and a couple planning to start a family comes to you for assistance. Charles was married once before, and he and his first wife had a child with cystic fibrosis

More information

Lesson 9 Presentation and Display of Quantitative Data

Lesson 9 Presentation and Display of Quantitative Data Lesson 9 Presentation and Display of Quantitative Data Learning Objectives All students will identify and present data using appropriate graphs, charts and tables. All students should be able to justify

More information

STATISTICS & PROBABILITY

STATISTICS & 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 information

SCATTER PLOTS AND TREND LINES

SCATTER 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 information

New Years Resolutions

New Years Resolutions New Years Resolutions DECEMBER 2009 Experian and the marks used herein are service marks or registered trademarks of Experian Information Solutions, Inc. Other product and company names mentioned herein

More information

Chapter 4: Causation: Can We Say What Caused the Effect? Sections 4.1 & 4.2: Association and Confounding / Observations v.s.

Chapter 4: Causation: Can We Say What Caused the Effect? Sections 4.1 & 4.2: Association and Confounding / Observations v.s. Stat 300: Intro to Probability & Statistics Textbook: Introduction to Statistical Investigations Name: American River College Chapter 4: Causation: Can We Say What Caused the Effect? Sections 4.1 & 4.2:

More information

Goodness of Pattern and Pattern Uncertainty 1

Goodness of Pattern and Pattern Uncertainty 1 J'OURNAL OF VERBAL LEARNING AND VERBAL BEHAVIOR 2, 446-452 (1963) Goodness of Pattern and Pattern Uncertainty 1 A visual configuration, or pattern, has qualities over and above those which can be specified

More information

Chapter 11. Inference for Means. Inference For Means: 11.1" The t # Distribution 11.1" Inference for a Mean 11.2" Comparing Two Means

Chapter 11. Inference for Means. Inference For Means: 11.1 The t # Distribution 11.1 Inference for a Mean 11.2 Comparing Two Means Chapter 11 Inference for Means In Chapter 10, we learned the logic behind inferential procedures. In this chapter, we will apply that logic to inference involving means. We will learn how to build con!dence

More information

Lab 4 (M13) Objective: This lab will give you more practice exploring the shape of data, and in particular in breaking the data into two groups.

Lab 4 (M13) Objective: This lab will give you more practice exploring the shape of data, and in particular in breaking the data into two groups. Lab 4 (M13) Objective: This lab will give you more practice exploring the shape of data, and in particular in breaking the data into two groups. Activity 1 Examining Data From Class Background Download

More information

Name AP Statistics UNIT 1 Summer Work Section II: Notes Analyzing Categorical Data

Name AP Statistics UNIT 1 Summer Work Section II: Notes Analyzing Categorical Data Name AP Statistics UNIT 1 Summer Work Date Section II: Notes 1.1 - Analyzing Categorical Data Essential Understanding: How can I represent the data when it is treated as a categorical variable? I. Distribution

More information

Lauren DiBiase, MS, CIC Associate Director Public Health Epidemiologist Hospital Epidemiology UNC Hospitals

Lauren DiBiase, MS, CIC Associate Director Public Health Epidemiologist Hospital Epidemiology UNC Hospitals Lauren DiBiase, MS, CIC Associate Director Public Health Epidemiologist Hospital Epidemiology UNC Hospitals Statistics Numbers that describe the health of the population The science used to interpret these

More information

Lecture 4: Chapter 3, Section 4 Designing Studies (Focus on Experiments)

Lecture 4: Chapter 3, Section 4 Designing Studies (Focus on Experiments) ecture 4: Chapter 3, Section 4 Designing Studies (Focus on Experiments) Definitions Randomization Control Blind Experiment Pitfalls Specific Experimental Designs Cengage earning Elementary Statistics:

More information

3.2A Least-Squares Regression

3.2A 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 information

Math Workshop On-Line Tutorial Judi Manola Paul Catalano

Math Workshop On-Line Tutorial Judi Manola Paul Catalano Math Workshop On-Line Tutorial Judi Manola Paul Catalano 1 Session 1 Kinds of Numbers and Data, Fractions, Negative Numbers, Rounding, Averaging, Properties of Real Numbers, Exponents and Square Roots,

More information

111, section 8.6 Applications of the Normal Distribution

111, section 8.6 Applications of the Normal Distribution 111, section 8.6 Applications of the Normal Distribution notes by Tim Pilachowski A probability density function f(x) for a continuous random variable has two necessary characteristics. 1. f(x) 0 for all

More information

USING OBSERVATIONS AND INFERENCES IN SCIENCE

USING OBSERVATIONS AND INFERENCES IN SCIENCE USING OBSERVATIONS AND INFERENCES IN SCIENCE What is an observation? When you observe, you become aware of something using one of your senses. Your five senses are smell, taste, sight, touch, and sound.

More information

Chapter 13. Experiments and Observational Studies

Chapter 13. Experiments and Observational Studies Chapter 13 Experiments and Observational Studies 1 /36 Homework Read Chpt 13 Do p312 1, 7, 9, 11, 17, 20, 25, 27, 29, 33, 40, 41 2 /36 Observational Studies In an observational study, researchers do not

More information

Standard Deviation and Standard Error Tutorial. This is significantly important. Get your AP Equations and Formulas sheet

Standard Deviation and Standard Error Tutorial. This is significantly important. Get your AP Equations and Formulas sheet Standard Deviation and Standard Error Tutorial This is significantly important. Get your AP Equations and Formulas sheet The Basics Let s start with a review of the basics of statistics. Mean: What most

More information

Test Bank for Privitera, Statistics for the Behavioral Sciences

Test Bank for Privitera, Statistics for the Behavioral Sciences 1. The use of tables and graphs to summarize data is an example of A) inferential statistics B) interpretation C) descriptive statistics D) generalization 2. Statistical analysis allows researchers to

More information

10.1 Estimating with Confidence. Chapter 10 Introduction to Inference

10.1 Estimating with Confidence. Chapter 10 Introduction to Inference 10.1 Estimating with Confidence Chapter 10 Introduction to Inference Statistical Inference Statistical inference provides methods for drawing conclusions about a population from sample data. Two most common

More information

From Knowledge to Action.

From Knowledge to Action. The New York City Health Literacy Campaign: From Knowledge to Action. Model Lessons Nutrition ESL Levels 5 & 6 student s edition Developed by the Mayor s Office of Adult Education Version 2008-2009 Activity

More information

Body Mass Index. The table below can be used to assess an adult s status BMI Status.

Body Mass Index. The table below can be used to assess an adult s status BMI Status. Body Mass Index (BMI) is used as a screening tool to identify possible weight problems, however, BMI is not a diagnostic tool. To determine if excess weight is a health risk further assessments are needed

More information

Big Idea 1 The Practice of Science. Big Idea 2 The Characteristics of Scientific Knowledge

Big Idea 1 The Practice of Science. Big Idea 2 The Characteristics of Scientific Knowledge Big Idea 1 The Practice of Science Big Idea 2 The Characteristics of Scientific Knowledge SC.5.N.1.2 Explain the difference between an experiment and other types of scientific investigation SC.5.N.1.5

More information

Class 1. b. Sampling a total of 100 Californians, where individuals are randomly selected from each major ethnic group.

Class 1. b. Sampling a total of 100 Californians, where individuals are randomly selected from each major ethnic group. What you need to know: Class 1 Sampling Study design The goal and importance of sampling methods Bias Sampling frame Volunteer sample Convenience sample Systematic sample Volunteer response Non-response

More information

Sta 309 (Statistics And Probability for Engineers)

Sta 309 (Statistics And Probability for Engineers) Instructor: Prof. Mike Nasab Sta 309 (Statistics And Probability for Engineers) Chapter (1) 1. Statistics: The science of collecting, organizing, summarizing, analyzing numerical information called data

More information

CELLS Part 2. Meeting 11 Student s Booklet. Contents. January 27 UCI. 1 Cancer growth 2 Blood 3 Cellular Tiling 4 Powers of 10

CELLS Part 2. Meeting 11 Student s Booklet. Contents. January 27 UCI. 1 Cancer growth 2 Blood 3 Cellular Tiling 4 Powers of 10 Meeting 11 Student s Booklet CELLS Part 2 January 27 2016 @ UCI Contents 1 Cancer growth 2 Blood 3 Cellular Tiling 4 Powers of 10 UC IRVINE MATH CEO http://www.math.uci.edu/mathceo/ 1 Cancer growth 1 Cancer

More information

Section I: Multiple Choice Select the best answer for each question.

Section I: Multiple Choice Select the best answer for each question. Chapter 1 AP Statistics Practice Test (TPS- 4 p78) Section I: Multiple Choice Select the best answer for each question. 1. You record the age, marital status, and earned income of a sample of 1463 women.

More information

SAMPLE. Instead of giving myself reasons why I can t, I give myself reasons why I can.

SAMPLE. Instead of giving myself reasons why I can t, I give myself reasons why I can. Chapter 1 Your Size Matters Instead of giving myself reasons why I can t, I give myself reasons why I can. After completing this chapter, you will be able to: Explain the importance of being at a healthy

More information

Chapter 3: Examining Relationships

Chapter 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 information

STP226 Brief Class Notes Instructor: Ela Jackiewicz

STP226 Brief Class Notes Instructor: Ela Jackiewicz CHAPTER 2 Organizing Data Statistics=science of analyzing data. Information collected (data) is gathered in terms of variables (characteristics of a subject that can be assigned a numerical value or nonnumerical

More information

Name: NYS DIFFUSION LAB REVIEW Date: PACKET 1: Difusion Through a Membrane

Name: NYS DIFFUSION LAB REVIEW Date: PACKET 1: Difusion Through a Membrane Name: NYS DIFFUSION LAB REVIEW Date: PACKET 1: Difusion Through a Membrane 1. The diagram below represents a laboratory setup used to demonstrate the movement of molecules across a selectively permeable

More information

OCW Epidemiology and Biostatistics, 2010 David Tybor, MS, MPH and Kenneth Chui, PhD Tufts University School of Medicine October 27, 2010

OCW Epidemiology and Biostatistics, 2010 David Tybor, MS, MPH and Kenneth Chui, PhD Tufts University School of Medicine October 27, 2010 OCW Epidemiology and Biostatistics, 2010 David Tybor, MS, MPH and Kenneth Chui, PhD Tufts University School of Medicine October 27, 2010 SAMPLING AND CONFIDENCE INTERVALS Learning objectives for this session:

More information

Practice Questions 5b Modern Approaches

Practice Questions 5b Modern Approaches 5b Modern Approaches Name Due In Learner Comments Teacher Feedback What Went Well Even Better If A* 90% A 80% B 70% C 60% D 50% E 40% Q1. Give two assumptions of the cognitive approach. For each assumption,

More information

Popper If data follows a trend that is not linear, we cannot make a prediction about it. a. True b. False

Popper If data follows a trend that is not linear, we cannot make a prediction about it. a. True b. False Popper 12 1. If data follows a trend that is not linear, we cannot make a prediction about it. a. True b. False 5.5 Non-Linear Methods Many times a scatter-plot reveals a curved pattern instead of a linear

More information

Carrying out an Empirical Project

Carrying out an Empirical Project Carrying out an Empirical Project Empirical Analysis & Style Hint Special program: Pre-training 1 Carrying out an Empirical Project 1. Posing a Question 2. Literature Review 3. Data Collection 4. Econometric

More information

Topic 5 Day 2. Homework #2: Saint John's Wort

Topic 5 Day 2. Homework #2: Saint John's Wort Today's Agenda: 1. Hand back and go over Topic 4 Quizzes 2. Hand back and go over exit slips 3. Correct and collect Activities 5 7, 5 17 & 5 23 4. Activity 5 4 5. Activity 5 8. Activity 5 7. Topic 5 Preliminaries

More information

7. Bivariate Graphing

7. Bivariate Graphing 1 7. Bivariate Graphing Video Link: https://www.youtube.com/watch?v=shzvkwwyguk&index=7&list=pl2fqhgedk7yyl1w9tgio8w pyftdumgc_j Section 7.1: Converting a Quantitative Explanatory Variable to Categorical

More information

Science, Safety and Experimental Design. 1.1 What is Science?

Science, Safety and Experimental Design. 1.1 What is Science? Science, Safety and Experimental Design 1.1 What is Science? The Goals of Science 1. Deals only with the natural world The supernatural is outside the realm of science Science is one of the Ways of Knowing

More information

Unit 1 Exploring and Understanding Data

Unit 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 information

STATS8: Introduction to Biostatistics. Overview. Babak Shahbaba Department of Statistics, UCI

STATS8: Introduction to Biostatistics. Overview. Babak Shahbaba Department of Statistics, UCI STATS8: Introduction to Biostatistics Overview Babak Shahbaba Department of Statistics, UCI The role of statistical analysis in science This course discusses some biostatistical methods, which involve

More information

Algebra 2 P Experimental Design 11 5 Margin of Error

Algebra 2 P Experimental Design 11 5 Margin of Error Algebra 2 P53 11 4 Experimental Design 11 5 Margin of Error Nov 23 2:43 PM 11 4 Apr 27 10:17 AM 1 Controlled Experiment: Identical Conditions with the exception of one variable. Control Group: No Treatment

More information

Lecture 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 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 information

Matching Abacus to the outgoing curriculum Key Stage 1 KS1 MA2

Matching Abacus to the outgoing curriculum Key Stage 1 KS1 MA2 Matching Abacus to the outgoing curriculum Key Stage 1 KS1 MA2 1. Using and applying number Reference Year, Week, Day Problem solving KS1 Ma2.1a: approach problems involving number, and data presented

More information

AP STATISTICS 2009 SCORING GUIDELINES

AP STATISTICS 2009 SCORING GUIDELINES AP STATISTICS 2009 SCORING GUIDELINES Question 1 Intent of Question The primary goals of this question were to assess a student s ability to (1) construct an appropriate graphical display for comparing

More information

Statistics for Psychology

Statistics for Psychology Statistics for Psychology SIXTH EDITION CHAPTER 3 Some Key Ingredients for Inferential Statistics Some Key Ingredients for Inferential Statistics Psychologists conduct research to test a theoretical principle

More information

Population. Sample. AP Statistics Notes for Chapter 1 Section 1.0 Making Sense of Data. Statistics: Data Analysis:

Population. Sample. AP Statistics Notes for Chapter 1 Section 1.0 Making Sense of Data. Statistics: Data Analysis: Section 1.0 Making Sense of Data Statistics: Data Analysis: Individuals objects described by a set of data Variable any characteristic of an individual Categorical Variable places an individual into one

More information

Body Mass Index Screening Report for Pre-kindergarteners, Third Graders and Sixth Graders in Ottawa County Schools

Body Mass Index Screening Report for Pre-kindergarteners, Third Graders and Sixth Graders in Ottawa County Schools Body Mass Index Screening Report for Pre-kindergarteners, Third Graders and Sixth Graders in Ottawa County Schools September 2004 - June 2005 Author Uzo Chukwuma, MPH Ottawa County Health Department Acknowledgements

More information

Intervention- Heredity Web Quest

Intervention- Heredity Web Quest Name Date Period Intervention- Heredity Web Quest DNA from the Beginning Mendelian Genetics Go to http://www.dnaftb.org/dnaftb/1/concept/index.html Children resemble their parents Read the text and answer

More information

Analysis of Categorical Data from the Ashe Center Student Wellness Survey

Analysis of Categorical Data from the Ashe Center Student Wellness Survey Lab 6 Analysis of Categorical Data from the Ashe Center Student Wellness Survey Before starting this lab, you should be familiar with: the difference between categorical and quantitative variables, and

More information

1SCIENTIFIC METHOD PART A. THE SCIENTIFIC METHOD

1SCIENTIFIC METHOD PART A. THE SCIENTIFIC METHOD 1SCIENTIFIC METHOD LEARNING OUTCOMES Upon successful completion of this lab, you will be able to: Describe the steps of the scientific method Formulate research questions, hypotheses, and predictions Design

More information

LAB 4 Experimental Design

LAB 4 Experimental Design LAB 4 Experimental Design Generally speaking, the research design that is used and the properties of the variables combine to determine what statistical tests we use to analyze the data and draw our conclusions.

More information

STATISTICS INFORMED DECISIONS USING DATA

STATISTICS 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 information

Ch 1.1 & 1.2 Basic Definitions for Statistics

Ch 1.1 & 1.2 Basic Definitions for Statistics Ch 1.1 & 1.2 Basic Definitions for Statistics Objective A : Basic Definition A1. Definition What is Statistics? Statistics is the science of collecting, organizing, summarizing, and analyzing data to draw

More information

AQA A Level Psychology. Topic Companion. Memory. Joseph Sparks & Helen Lakin

AQA A Level Psychology. Topic Companion. Memory. Joseph Sparks & Helen Lakin AQA A Level Psychology Topic Companion Memory Joseph Sparks & Helen Lakin AQA A LEVEL Psychology topic companion: MEMORY Page 2 Contents Memory The multi-store model 3 Types of long-term memory 9 The working

More information

Statistics: Bar Graphs and Standard Error

Statistics: Bar Graphs and Standard Error www.mathbench.umd.edu Bar graphs and standard error May 2010 page 1 Statistics: Bar Graphs and Standard Error URL: http://mathbench.umd.edu/modules/prob-stat_bargraph/page01.htm Beyond the scatterplot

More information

Math Workshop On-Line Tutorial Judi Manola Paul Catalano. Slide 1. Slide 3

Math Workshop On-Line Tutorial Judi Manola Paul Catalano. Slide 1. Slide 3 Kinds of Numbers and Data First we re going to think about the kinds of numbers you will use in the problems you will encounter in your studies. Then we will expand a bit and think about kinds of data.

More information

EVERY DAY A GUIDE TO KNOW YOUR NUMBERS

EVERY DAY A GUIDE TO KNOW YOUR NUMBERS EVERY DAY A GUIDE TO KNOW YOUR NUMBERS WHAT IS BMI? Measuring your Body Mass Index (BMI) is a useful way to determine if you are at a healthy weight. Excess weight can increase your risk of heart disease,

More information

Chapter 2: The Organization and Graphic Presentation of Data Test Bank

Chapter 2: The Organization and Graphic Presentation of Data Test Bank Essentials of Social Statistics for a Diverse Society 3rd Edition Leon Guerrero Test Bank Full Download: https://testbanklive.com/download/essentials-of-social-statistics-for-a-diverse-society-3rd-edition-leon-guerrero-tes

More information

Lecture 4: Chapter 3, Section 4. (Focus on Experiments) Designing Studies. Looking Back: Review. Definitions

Lecture 4: Chapter 3, Section 4. (Focus on Experiments) Designing Studies. Looking Back: Review. Definitions ecture 4: Chapter 3, Section 4 Designing Studies (Focus on Experiments)!Definitions!Randomization!Control!Blind Experiment!Pitfalls!Specific Experimental Designs Cengage earning Elementary Statistics:

More information

MA 151: Using Minitab to Visualize and Explore Data The Low Fat vs. Low Carb Debate

MA 151: Using Minitab to Visualize and Explore Data The Low Fat vs. Low Carb Debate MA 151: Using Minitab to Visualize and Explore Data The Low Fat vs. Low Carb Debate September 5, 2018 1 Introduction to the Data We will be working with a synthetic data set meant to resemble the weight

More information

The exam is closed book, closed notes, closed computer, closed calculator, except the official midterm exam reference guide provided with the exam.

The exam is closed book, closed notes, closed computer, closed calculator, except the official midterm exam reference guide provided with the exam. Data 8 Spring 2018 Foundations of Data Science Midterm INSTRUCTIONS You have 45 minutes to complete the exam. The exam is closed book, closed notes, closed computer, closed calculator, except the official

More information

Chapter 1. Picturing Distributions with Graphs

Chapter 1. Picturing Distributions with Graphs Chapter 1 Picturing Distributions with Graphs Statistics Statistics is a science that involves the extraction of information from numerical data obtained during an experiment or from a sample. It involves

More information

REVIEW from Chapter 1 : Key Elements of a Statistical Problem

REVIEW from Chapter 1 : Key Elements of a Statistical Problem REVIEW from Chapter 1 : Key Elements of a Statistical Problem Describe the population Describe the variable/s of interest Describe the sample Describe the inference Describe sources of possible errors/bias

More information

CHAPTER 9: Producing Data: Experiments

CHAPTER 9: Producing Data: Experiments CHAPTER 9: Producing Data: Experiments The Basic Practice of Statistics 6 th Edition Moore / Notz / Fligner Lecture PowerPoint Slides Chapter 9 Concepts 2 Observation vs. Experiment Subjects, Factors,

More information

Chapter 5. Doing Tools: Increasing Your Pleasant Events

Chapter 5. Doing Tools: Increasing Your Pleasant Events 47 Chapter 5. Doing Tools: Increasing Your Pleasant Events The importance of engaging in pleasant events One popular theory about the causes of depression stresses the functional relationship between depression

More information

Chapter 7: Descriptive Statistics

Chapter 7: Descriptive Statistics Chapter Overview Chapter 7 provides an introduction to basic strategies for describing groups statistically. Statistical concepts around normal distributions are discussed. The statistical procedures of

More information

Tableau Public Viz Tool

Tableau Public Viz Tool Tableau Public Viz Tool The purpose of this document is to provide descriptions of the Split By variables for the 2016 VoiceGR Survey results displayed in the Tableau Public Viz Tool. Once you have entered

More information

Eid, S. (2010, October 04). Data Literacy 101. Presentation Presented at: Lehigh Valley Health Network, Allentown, PA.

Eid, S. (2010, October 04). Data Literacy 101. Presentation Presented at: Lehigh Valley Health Network, Allentown, PA. Lehigh Valley Health Network LVHN Scholarly Works Department of Community Health and Health Studies Data Literacy 101. Sherrine Eid MPH Lehigh Valley Health Network, Sherrine.Eid@lvhn.org Follow this and

More information

NUTRITION. Chapter 4 Lessons 5-6

NUTRITION. Chapter 4 Lessons 5-6 NUTRITION Chapter 4 Lessons 5-6 BODY IMAGE Body image can be influenced by the attitudes of family and friends and images from the media. body image The way you see your body Trying to change your weight

More information

Bivariate Graphing Rana Yousaf, Manpreet Mann, and Dan Hiney 24 Sept, 2017

Bivariate Graphing Rana Yousaf, Manpreet Mann, and Dan Hiney 24 Sept, 2017 Bivariate Graphing Rana Yousaf, Manpreet Mann, and Dan Hiney 24 Sept, 2017 load("c:/users/owner/desktop/math315/projects/data/addhealth_clean.rdata") library(ggplot2) library(mass) library(knitr) The following

More information

Training Course on Child Growth Assessment. WHO Child Growth Standards. Answer Sheets. Department of Nutrition for Health and Development

Training Course on Child Growth Assessment. WHO Child Growth Standards. Answer Sheets. Department of Nutrition for Health and Development Training Course on Child Growth Assessment WHO Child Growth Standards F Answer Sheets Department of Nutrition for Health and Development Answers for Module B: Measuring a Child s Growth 1 B: Measuring

More information

Lesson 11: Conditional Relative Frequencies and Association

Lesson 11: Conditional Relative Frequencies and Association Name date per Lesson 11: Conditional Relative Frequencies and Association Classwork After further discussion, the students involved in designing the superhero comic strip decided that before any decision

More information

Section 1: Exploring Data

Section 1: Exploring Data Section 1: Exploring Data The following maps the videos in this section to the Texas Essential Knowledge and Skills for Mathematics TAC 111.47(c). 1.01 Introduction to Statistics 1.02 Statistics and Parameters

More information

1 Version SP.A Investigate patterns of association in bivariate data

1 Version SP.A Investigate patterns of association in bivariate data Claim 1: Concepts and Procedures Students can explain and apply mathematical concepts and carry out mathematical procedures with precision and fluency. Content Domain: Statistics and Probability Target

More information

Comparing Different Studies

Comparing Different Studies 32 LESSON Comparing Different Studies Types of Studies UNDERSTAND By studying a small group within a larger group, you can make inferences about the larger group. The larger group, called the population,

More information

V. Gathering and Exploring Data

V. Gathering and Exploring Data V. Gathering and Exploring Data With the language of probability in our vocabulary, we re now ready to talk about sampling and analyzing data. Data Analysis We can divide statistical methods into roughly

More information

CHAPTER 1 SAMPLING AND DATA

CHAPTER 1 SAMPLING AND DATA CHAPTER 1 SAMPLING AND DATA 1 In the first chapter we are introduced to several very important statistical terms and concepts. Warning: Notice that in the previous sentence, there is no mention of formulas

More information

The Nature of Probability and Statistics

The Nature of Probability and Statistics Chapter 1 The Nature of Probability and Statistics Chapter 1 Overview Introduction 1-1 Descriptive and Inferential Statistics 1-2 Variables and Types of Data 1-3 Data Collection & Sampling Techniques 1-4

More information

12.1 Inference for Linear Regression. Introduction

12.1 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 information