AP Statistics. Chapter 4 Review
|
|
|
- Bonnie Owen
- 8 years ago
- Views:
Transcription
1 Name AP Statistics Chapter 4 Review 1. In a study of the link between high blood pressure and cardiovascular disease, a group of white males aged 35 to 64 was followed for 5 years. At the beginning of the study, each man had his blood pressure measured and it was classified as either "low" systolic blood pressure (less than 140 mm Hg) or "high" blood pressure (140 mm Hg or higher). The following table gives the number of men in each blood pressure category and the number of deaths from cardiovascular disease during the 5-year period. Blood pressure Deaths Total Low High Based on these data, which of the following statements is correct? A. These data are consistent with the idea that there is a link between high blood pressure and death from cardiovascular disease. B. The mortality rate (proportion of deaths) for men with high blood pressure is 5 times that of men with low blood pressure. C. These data probably understate the link between high blood pressure and death from cardiovascular disease, because men will tend to understate their true blood pressure. D. Although there were more deaths in the high blood pressure group, this is expected, because there were 1500 more men in that group. E. All of the above. Use the following information for questions 2 through 6. A review of voter registration records in a small town yielded the following table of the number of males and females registered as Democrat, Republican, or some other affiliation. Male Female Democrat Republican Other The proportion of males that are registered as Democrats is A. 300 B. 30 C D E Your percentage from question number 12 is part of A. The marginal distribution of political party registration. B. The marginal distribution of gender. C. The conditional distribution of gender among Democrats. D. The conditional distribution of political party registration among males. E. The conditional distribution of males within gender.
2 4. The proportion of registered Democrats that are male is A. 300 B. 33 C D E Your percentage from question number 14 is part of A. The marginal distribution of political party registration. B. The marginal distribution of gender. C. The conditional distribution of gender among Democrats. D. The conditional distribution of political party registration among males. E. The conditional distribution of males within gender. 6. Which of the following graphs accurately represents the distribution for political party registration for each gender? A. D. B. E. C.
3 Use the following information for questions Below is a two-way table summarizing the number of cylinders in selected car models manufactured in six different countries in the 1990 s. Number of cylinders Total France Germany Italy Japan Sweden U.S.A Total The percentage of all cars listed in the table with 4-cylinder engines is A. 19%. B. 21%. C. 50%. D. 80%. E. 91%. 8. The percent of cars with 4-cylinder engines that are made in Germany is A. 10.5%. B. 21%. C. 50%. D. 80%. E. 91%. 9. Which of the following is a marginal distribution? A. The percentage of all four-cylinder cars manufactured in Germany. B. The number of four-cylinder cars manufactured in Germany. C. The percentage of all cars manufactured in each country. D. The percentage of cars manufactured in Germany for each number of cylinders. E. The numbers 4, 5, 6, From this table, we might conclude that A. there is a strong association between country of origin and number of cylinders. B. about 18% of the cars sold in the United States were manufactured in Japan. C. these data could be more effectively presented with a box plot. D. the only eight cylinder cars in this data set were manufactured in Germany. E. All the cars on Italian roads have four cylinders. 11. A random sample of 100 students in grades 10 through 12 were sampled and asked their year in school and whether they were involved in interscholastic sports, intramural sports, or no sports. The results are summarized in the segmented bar graph on the next page.
4 Based on this graph, which of the following statements is true? A. More seniors are involved in interscholastic sports than sophomores. B. There is no association between year in school and whether students are involved in sports. C. There were more seniors in the sample than juniors. D. Juniors have the highest percentage participation in intramurals. E. Less than half the seniors are involved in either interscholastic or intramural sports. 12. Suppose we measure a response variable Y for several values of an explanatory variable X. A scatterplot of log Y versus log X looks approximately like a negatively-sloping straight line. We may conclude that A. the rate of growth of Y is positive, but slowing down over time. B. an exponential growth model would approximately describe the relationship between Y and X. C. a power model would approximately describe the relationship between Y and X. D. the relationship between Y and X is a positively-sloping straight line. E. the residual plot of the regression of log Y on log X would have a U-shaped pattern suggesting a non-linear relationship. 13. Using least-squares regression on data from 1990 through 2009, I determine that the (base 10) logarithm of the population of a country is approximately described by the equation. Which of the following is the predicted population of the country in the year 2010? A. 6.6 B. 735 C. 2,000,000 D. 3,981,072 E. 33,000, Which of the following would provide evidence that a power law model describes the relationship between a response variable y and an explanatory variable x? A. A normal probability plot of the residuals of the regression of log y versus log x looks approximately linear. B. A normal probability plot of the residuals of the regression of log y versus x looks approximately linear. C. A scatterplot of log y versus x looks approximately linear. D. A scatterplot of y versus log x looks approximately linear. E. A scatterplot of log y versus log x looks approximately linear.
5 15. Suppose the relationship between a response variable y and an explanatory variable x is modeled well by the equation A. A plot of y against x. B. A plot of y against log x. C. A plot of log y against x. D. A plot of 10 y against x. E. A plot of log y against log x.. Which of the following plots is most likely to be roughly linear? Use the following information for questions 16 and 17. Like most animals, small marine crustaceans are not able to digest all the food they eat. Moreover, the percentage of food eaten that is assimilated (that is, digested) decreases as the amount of food eaten increases. A scatterplot of this relationship for a certain species of crustacean (at right) indicates that it is non-linear. However, a scatterplot of ln Assimilation versus ln Food Intake is strongly linear. Below is a computer regression analysis of the transformed data (note that natural logarithms are used). Predictor Coef SE Coef T P Constant ln Food Intake S = R-Sq = 84.7% R-Sq(adj) = 82.5% 16. Which of the following best describe the model that is given by this computer printout? A. A power model with equation B. An exponential model with equation C. A power model with equation D. An exponential model with equation E. A power model with equation
6 17. If, as described above, the scatterplot of ln Assimilation versus ln Food Intake is strongly linear, which of the following best describes the residual plot for the regression of these two variables? A. A roughly straight line. B. A U-shaped pattern, with positive residuals for low and high values of food intake and negative residuals in between. C. A U-shaped pattern, with negative residuals for low and high values of food intake and positive residuals in between. D. A curved pattern similar to the scatterplot of the variables Food Intake and Assimilation before the logarithmic transformation. E. A random scattering of points on either side of the line whose equation is residuals = 0. Use of the Internet worldwide increased steadily from 1990 to A scatterplot of this growth (at right) shows a strongly non-linear pattern. However, a scatterplot of ln Internet Users versus Year is much closer to linear. Below is a computer regression analysis of the transformed data (note that natural logarithms are used). Predictor Coef SE Coef T P Constant Year S = R-Sq = 98.2% R-Sq(adj) = 98.0% 18. Use Scenario Which of the following best describe the model that is given by this computer printout? A. A power model with equation B. A power model with equation C. A power model with equation D. An exponential model with equation E. An exponential model with equation
7 Chapter 4 Review Answer Section MULTIPLE CHOICE 1. ANS: A PTS: 1 TOP: Compare two categorical variables (not in two-way table. 2. ANS: D PTS: 1 TOP: Conditional distribution--calculation 3. ANS: D PTS: 1 TOP: Conditional distribution--identification 4. ANS: C PTS: 1 TOP: Conditional distribution--calculation 5. ANS: C PTS: 1 TOP: Conditional distribution--identification 6. ANS: E PTS: 1 TOP: Segmented bar graphs 7. ANS: C PTS: 1 TOP: Marginal distribution-calculation 8. ANS: B PTS: 1 TOP: Conditional distribution--calculation 9. ANS: C PTS: 1 TOP: Marginal distribution-identification 10. ANS: A PTS: 1 TOP: Interpret two-way table 11. ANS: D PTS: 1 TOP: Interpret segmented bar graph 12. ANS: C PTS: 1 TOP: Interpreting log-log scatterplot 13. ANS: D PTS: 1 TOP: Prediction from log Y vs. X regression 14. ANS: E PTS: 1 TOP: Power functions and transformations 15. ANS: C PTS: 1 TOP: Exponential functions and transformations 16. ANS: A PTS: 1 TOP: Model/Equation for log-log transformation 17. ANS: E PTS: 1 TOP: Residuals for good linear fit 18. ANS: D PTS: 1 TOP: Model/Equation for semi-log transformation
Name: Date: Use the following to answer questions 2-3:
Name: Date: 1. A study is conducted on students taking a statistics class. Several variables are recorded in the survey. Identify each variable as categorical or quantitative. A) Type of car the student
Regression Analysis: A Complete Example
Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc, Inc./Getty
Lecture 11: Chapter 5, Section 3 Relationships between Two Quantitative Variables; Correlation
Lecture 11: Chapter 5, Section 3 Relationships between Two Quantitative Variables; Correlation Display and Summarize Correlation for Direction and Strength Properties of Correlation Regression Line Cengage
Straightening Data in a Scatterplot Selecting a Good Re-Expression Model
Straightening Data in a Scatterplot Selecting a Good Re-Expression What Is All This Stuff? Here s what is included: Page 3: Graphs of the three main patterns of data points that the student is likely to
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Review MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) All but one of these statements contain a mistake. Which could be true? A) There is a correlation
a) Find the five point summary for the home runs of the National League teams. b) What is the mean number of home runs by the American League teams?
1. Phone surveys are sometimes used to rate TV shows. Such a survey records several variables listed below. Which ones of them are categorical and which are quantitative? - the number of people watching
Transforming Bivariate Data
Math Objectives Students will recognize that bivariate data can be transformed to reduce the curvature in the graph of a relationship between two variables. Students will use scatterplots, residual plots,
MGT 267 PROJECT. Forecasting the United States Retail Sales of the Pharmacies and Drug Stores. Done by: Shunwei Wang & Mohammad Zainal
MGT 267 PROJECT Forecasting the United States Retail Sales of the Pharmacies and Drug Stores Done by: Shunwei Wang & Mohammad Zainal Dec. 2002 The retail sale (Million) ABSTRACT The present study aims
MTH 140 Statistics Videos
MTH 140 Statistics Videos Chapter 1 Picturing Distributions with Graphs Individuals and Variables Categorical Variables: Pie Charts and Bar Graphs Categorical Variables: Pie Charts and Bar Graphs Quantitative
c 2015, Jeffrey S. Simonoff 1
Modeling Lowe s sales Forecasting sales is obviously of crucial importance to businesses. Revenue streams are random, of course, but in some industries general economic factors would be expected to have
2. Simple Linear Regression
Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according
17. SIMPLE LINEAR REGRESSION II
17. SIMPLE LINEAR REGRESSION II The Model In linear regression analysis, we assume that the relationship between X and Y is linear. This does not mean, however, that Y can be perfectly predicted from X.
Linear Regression. Chapter 5. Prediction via Regression Line Number of new birds and Percent returning. Least Squares
Linear Regression Chapter 5 Regression Objective: To quantify the linear relationship between an explanatory variable (x) and response variable (y). We can then predict the average response for all subjects
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
Chapter 7: Simple linear regression Learning Objectives
Chapter 7: Simple linear regression Learning Objectives Reading: Section 7.1 of OpenIntro Statistics Video: Correlation vs. causation, YouTube (2:19) Video: Intro to Linear Regression, YouTube (5:18) -
Simple Predictive Analytics Curtis Seare
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
South Carolina College- and Career-Ready (SCCCR) Probability and Statistics
South Carolina College- and Career-Ready (SCCCR) Probability and Statistics South Carolina College- and Career-Ready Mathematical Process Standards The South Carolina College- and Career-Ready (SCCCR)
Univariate Regression
Univariate Regression Correlation and Regression The regression line summarizes the linear relationship between 2 variables Correlation coefficient, r, measures strength of relationship: the closer r is
Example: Boats and Manatees
Figure 9-6 Example: Boats and Manatees Slide 1 Given the sample data in Table 9-1, find the value of the linear correlation coefficient r, then refer to Table A-6 to determine whether there is a significant
1. The parameters to be estimated in the simple linear regression model Y=α+βx+ε ε~n(0,σ) are: a) α, β, σ b) α, β, ε c) a, b, s d) ε, 0, σ
STA 3024 Practice Problems Exam 2 NOTE: These are just Practice Problems. This is NOT meant to look just like the test, and it is NOT the only thing that you should study. Make sure you know all the material
1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
4. Simple regression. QBUS6840 Predictive Analytics. https://www.otexts.org/fpp/4
4. Simple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/4 Outline The simple linear model Least squares estimation Forecasting with regression Non-linear functional forms Regression
Categorical Data Analysis
Richard L. Scheaffer University of Florida The reference material and many examples for this section are based on Chapter 8, Analyzing Association Between Categorical Variables, from Statistical Methods
AP STATISTICS TEST #2 - REVIEW - Ch. 14 &15 Period:
AP STATISTICS Name TEST #2 - REVIEW - Ch. 14 &15 Period: 1) The city council has 6 men and 3 women. If we randomly choose two of them to co-chair a committee, what is the probability these chairpersons
Using Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data
Using Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data Introduction In several upcoming labs, a primary goal will be to determine the mathematical relationship between two variable
Lesson 3.2.1 Using Lines to Make Predictions
STATWAY INSTRUCTOR NOTES i INSTRUCTOR SPECIFIC MATERIAL IS INDENTED AND APPEARS IN GREY ESTIMATED TIME 50 minutes MATERIALS REQUIRED Overhead or electronic display of scatterplots in lesson BRIEF DESCRIPTION
Correlation and Regression
Correlation and Regression Scatterplots Correlation Explanatory and response variables Simple linear regression General Principles of Data Analysis First plot the data, then add numerical summaries Look
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
The Big Picture. Correlation. Scatter Plots. Data
The Big Picture Correlation Bret Hanlon and Bret Larget Department of Statistics Universit of Wisconsin Madison December 6, We have just completed a length series of lectures on ANOVA where we considered
AP STATISTICS (Warm-Up Exercises)
AP STATISTICS (Warm-Up Exercises) 1. Describe the distribution of ages in a city: 2. Graph a box plot on your calculator for the following test scores: {90, 80, 96, 54, 80, 95, 100, 75, 87, 62, 65, 85,
11. Analysis of Case-control Studies Logistic Regression
Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:
Getting Correct Results from PROC REG
Getting Correct Results from PROC REG Nathaniel Derby, Statis Pro Data Analytics, Seattle, WA ABSTRACT PROC REG, SAS s implementation of linear regression, is often used to fit a line without checking
Diagrams and Graphs of Statistical Data
Diagrams and Graphs of Statistical Data One of the most effective and interesting alternative way in which a statistical data may be presented is through diagrams and graphs. There are several ways in
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
The importance of graphing the data: Anscombe s regression examples
The importance of graphing the data: Anscombe s regression examples Bruce Weaver Northern Health Research Conference Nipissing University, North Bay May 30-31, 2008 B. Weaver, NHRC 2008 1 The Objective
10. Analysis of Longitudinal Studies Repeat-measures analysis
Research Methods II 99 10. Analysis of Longitudinal Studies Repeat-measures analysis This chapter builds on the concepts and methods described in Chapters 7 and 8 of Mother and Child Health: Research methods.
Regression III: Advanced Methods
Lecture 4: Transformations Regression III: Advanced Methods William G. Jacoby Michigan State University Goals of the lecture The Ladder of Roots and Powers Changing the shape of distributions Transforming
Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010
Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different
Experiment #1, Analyze Data using Excel, Calculator and Graphs.
Physics 182 - Fall 2014 - Experiment #1 1 Experiment #1, Analyze Data using Excel, Calculator and Graphs. 1 Purpose (5 Points, Including Title. Points apply to your lab report.) Before we start measuring
Draft 1, Attempted 2014 FR Solutions, AP Statistics Exam
Free response questions, 2014, first draft! Note: Some notes: Please make critiques, suggest improvements, and ask questions. This is just one AP stats teacher s initial attempts at solving these. I, as
Exercise 1.12 (Pg. 22-23)
Individuals: The objects that are described by a set of data. They may be people, animals, things, etc. (Also referred to as Cases or Records) Variables: The characteristics recorded about each individual.
The Logistic Function
MATH 120 Elementary Functions The Logistic Function Examples & Exercises In the past weeks, we have considered the use of linear, exponential, power and polynomial functions as mathematical models in many
The Volatility Index Stefan Iacono University System of Maryland Foundation
1 The Volatility Index Stefan Iacono University System of Maryland Foundation 28 May, 2014 Mr. Joe Rinaldi 2 The Volatility Index Introduction The CBOE s VIX, often called the market fear gauge, measures
Linear Regression. use http://www.stat.columbia.edu/~martin/w1111/data/body_fat. 30 35 40 45 waist
Linear Regression In this tutorial we will explore fitting linear regression models using STATA. We will also cover ways of re-expressing variables in a data set if the conditions for linear regression
Computer exercise 4 Poisson Regression
Chalmers-University of Gothenburg Department of Mathematical Sciences Probability, Statistics and Risk MVE300 Computer exercise 4 Poisson Regression When dealing with two or more variables, the functional
Practice#1(chapter1,2) Name
Practice#1(chapter1,2) Name Solve the problem. 1) The average age of the students in a statistics class is 22 years. Does this statement describe descriptive or inferential statistics? A) inferential statistics
Relationships Between Two Variables: Scatterplots and Correlation
Relationships Between Two Variables: Scatterplots and Correlation Example: Consider the population of cars manufactured in the U.S. What is the relationship (1) between engine size and horsepower? (2)
2. Professor, Department of Risk Management and Insurance, National Chengchi. University, Taipei, Taiwan, R.O.C. 11605; jerry2@nccu.edu.
Authors: Jack C. Yue 1 and Hong-Chih Huang 2 1. Professor, Department of Statistics, National Chengchi University, Taipei, Taiwan, R.O.C. 11605; csyue@nccu.edu.tw 2. Professor, Department of Risk Management
Problem of the Month Through the Grapevine
The Problems of the Month (POM) are used in a variety of ways to promote problem solving and to foster the first standard of mathematical practice from the Common Core State Standards: Make sense of problems
TRINITY COLLEGE. Faculty of Engineering, Mathematics and Science. School of Computer Science & Statistics
UNIVERSITY OF DUBLIN TRINITY COLLEGE Faculty of Engineering, Mathematics and Science School of Computer Science & Statistics BA (Mod) Enter Course Title Trinity Term 2013 Junior/Senior Sophister ST7002
Simple linear regression
Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between
Scatter Plot, Correlation, and Regression on the TI-83/84
Scatter Plot, Correlation, and Regression on the TI-83/84 Summary: When you have a set of (x,y) data points and want to find the best equation to describe them, you are performing a regression. This page
Problem Solving and Data Analysis
Chapter 20 Problem Solving and Data Analysis The Problem Solving and Data Analysis section of the SAT Math Test assesses your ability to use your math understanding and skills to solve problems set in
SAS Software to Fit the Generalized Linear Model
SAS Software to Fit the Generalized Linear Model Gordon Johnston, SAS Institute Inc., Cary, NC Abstract In recent years, the class of generalized linear models has gained popularity as a statistical modeling
UNIVERSITY OF TORONTO SCARBOROUGH Department of Computer and Mathematical Sciences Midterm Test March 2014
UNIVERSITY OF TORONTO SCARBOROUGH Department of Computer and Mathematical Sciences Midterm Test March 2014 STAB22H3 Statistics I Duration: 1 hour and 45 minutes Last Name: First Name: Student number: Aids
Data Mining Part 5. Prediction
Data Mining Part 5. Prediction 5.7 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Linear Regression Other Regression Models References Introduction Introduction Numerical prediction is
AC 2010-1373: FERMENTATION LABORATORY EXERCISE HELPS FIRST YEAR STUDENTS UNDERSTAND LOG-TRANSFORMED VARIABLES
AC 2010-1373: FERMENTATION LABORATORY EXERCISE HELPS FIRST YEAR STUDENTS UNDERSTAND LOG-TRANSFORMED VARIABLES Polly Piergiovanni, Lafayette College J. Ronald Martin, Lafayette College American Society
Mind on Statistics. Chapter 15
Mind on Statistics Chapter 15 Section 15.1 1. A student survey was done to study the relationship between class standing (freshman, sophomore, junior, or senior) and major subject (English, Biology, French,
2. Here is a small part of a data set that describes the fuel economy (in miles per gallon) of 2006 model motor vehicles.
Math 1530-017 Exam 1 February 19, 2009 Name Student Number E There are five possible responses to each of the following multiple choice questions. There is only on BEST answer. Be sure to read all possible
. 58 58 60 62 64 66 68 70 72 74 76 78 Father s height (inches)
PEARSON S FATHER-SON DATA The following scatter diagram shows the heights of 1,0 fathers and their full-grown sons, in England, circa 1900 There is one dot for each father-son pair Heights of fathers and
COMMON CORE STATE STANDARDS FOR
COMMON CORE STATE STANDARDS FOR Mathematics (CCSSM) High School Statistics and Probability Mathematics High School Statistics and Probability Decisions or predictions are often based on data numbers in
DEPARTMENT OF PSYCHOLOGY UNIVERSITY OF LANCASTER MSC IN PSYCHOLOGICAL RESEARCH METHODS ANALYSING AND INTERPRETING DATA 2 PART 1 WEEK 9
DEPARTMENT OF PSYCHOLOGY UNIVERSITY OF LANCASTER MSC IN PSYCHOLOGICAL RESEARCH METHODS ANALYSING AND INTERPRETING DATA 2 PART 1 WEEK 9 Analysis of covariance and multiple regression So far in this course,
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Exam Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) The government of a town needs to determine if the city's residents will support the
The Comparisons. Grade Levels Comparisons. Focal PSSM K-8. Points PSSM CCSS 9-12 PSSM CCSS. Color Coding Legend. Not Identified in the Grade Band
Comparison of NCTM to Dr. Jim Bohan, Ed.D Intelligent Education, LLC Intel.educ@gmail.com The Comparisons Grade Levels Comparisons Focal K-8 Points 9-12 pre-k through 12 Instructional programs from prekindergarten
What is the purpose of this document? What is in the document? How do I send Feedback?
This document is designed to help North Carolina educators teach the Common Core (Standard Course of Study). NCDPI staff are continually updating and improving these tools to better serve teachers. Statistics
MULTIPLE REGRESSION EXAMPLE
MULTIPLE REGRESSION EXAMPLE For a sample of n = 166 college students, the following variables were measured: Y = height X 1 = mother s height ( momheight ) X 2 = father s height ( dadheight ) X 3 = 1 if
Constructing a Basefile for Simulating Kunming s Medical Insurance Scheme of Urban Employees
INTERNATIONAL JOURNAL OF MICROSIMULATION (2011) 4(3) 3-16 Constructing a Basefile for Simulating Kunming s Medical Insurance Scheme of Urban Employees Xiong Linping Department of Health Services Management,
table to see that the probability is 0.8413. (b) What is the probability that x is between 16 and 60? The z-scores for 16 and 60 are: 60 38 = 1.
Review Problems for Exam 3 Math 1040 1 1. Find the probability that a standard normal random variable is less than 2.37. Looking up 2.37 on the normal table, we see that the probability is 0.9911. 2. Find
Summarizing and Displaying Categorical Data
Summarizing and Displaying Categorical Data Categorical data can be summarized in a frequency distribution which counts the number of cases, or frequency, that fall into each category, or a relative frequency
Curve Fitting, Loglog Plots, and Semilog Plots 1
Curve Fitting, Loglog Plots, and Semilog Plots 1 In this MATLAB exercise, you will learn how to plot data and how to fit lines to your data. Suppose you are measuring the height h of a seedling as it grows.
Lean Six Sigma Analyze Phase Introduction. TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY
TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY Before we begin: Turn on the sound on your computer. There is audio to accompany this presentation. Audio will accompany most of the online
Chapter 23. Inferences for Regression
Chapter 23. Inferences for Regression Topics covered in this chapter: Simple Linear Regression Simple Linear Regression Example 23.1: Crying and IQ The Problem: Infants who cry easily may be more easily
Tutorial on Using Excel Solver to Analyze Spin-Lattice Relaxation Time Data
Tutorial on Using Excel Solver to Analyze Spin-Lattice Relaxation Time Data In the measurement of the Spin-Lattice Relaxation time T 1, a 180 o pulse is followed after a delay time of t with a 90 o pulse,
Examining a Fitted Logistic Model
STAT 536 Lecture 16 1 Examining a Fitted Logistic Model Deviance Test for Lack of Fit The data below describes the male birth fraction male births/total births over the years 1931 to 1990. A simple logistic
Betting with the Kelly Criterion
Betting with the Kelly Criterion Jane June 2, 2010 Contents 1 Introduction 2 2 Kelly Criterion 2 3 The Stock Market 3 4 Simulations 5 5 Conclusion 8 1 Page 2 of 9 1 Introduction Gambling in all forms,
Motor and Household Insurance: Pricing to Maximise Profit in a Competitive Market
Motor and Household Insurance: Pricing to Maximise Profit in a Competitive Market by Tom Wright, Partner, English Wright & Brockman 1. Introduction This paper describes one way in which statistical modelling
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
I. Basic concepts: Buoyancy and Elasticity II. Estimating Tax Elasticity III. From Mechanical Projection to Forecast
Elements of Revenue Forecasting II: the Elasticity Approach and Projections of Revenue Components Fiscal Analysis and Forecasting Workshop Bangkok, Thailand June 16 27, 2014 Joshua Greene Consultant IMF-TAOLAM
Math 58. Rumbos Fall 2008 1. Solutions to Review Problems for Exam 2
Math 58. Rumbos Fall 2008 1 Solutions to Review Problems for Exam 2 1. For each of the following scenarios, determine whether the binomial distribution is the appropriate distribution for the random variable
Chapter 13 Introduction to Linear Regression and Correlation Analysis
Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing
Results from the 2014 AP Statistics Exam. Jessica Utts, University of California, Irvine Chief Reader, AP Statistics jutts@uci.edu
Results from the 2014 AP Statistics Exam Jessica Utts, University of California, Irvine Chief Reader, AP Statistics jutts@uci.edu The six free-response questions Question #1: Extracurricular activities
Chapter 5 Estimating Demand Functions
Chapter 5 Estimating Demand Functions 1 Why do you need statistics and regression analysis? Ability to read market research papers Analyze your own data in a simple way Assist you in pricing and marketing
Statistics 2014 Scoring Guidelines
AP Statistics 2014 Scoring Guidelines College Board, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks of the College Board. AP Central is the official online home
Lecture 13/Chapter 10 Relationships between Measurement (Quantitative) Variables
Lecture 13/Chapter 10 Relationships between Measurement (Quantitative) Variables Scatterplot; Roles of Variables 3 Features of Relationship Correlation Regression Definition Scatterplot displays relationship
High School Statistics and Probability Common Core Sample Test Version 2
High School Statistics and Probability Common Core Sample Test Version 2 Our High School Statistics and Probability sample test covers the twenty most common questions that we see targeted for this level.
Johns Hopkins University Bloomberg School of Public Health
Johns Hopkins University Bloomberg School of Public Health Report on Johns Hopkins University School of Medicine Faculty Salary Analysis, 2003-2004 With Additional Comments November 29, 2005 Objectives:
UNIT PLAN: EXPONENTIAL AND LOGARITHMIC FUNCTIONS
UNIT PLAN: EXPONENTIAL AND LOGARITHMIC FUNCTIONS Summary: This unit plan covers the basics of exponential and logarithmic functions in about 6 days of class. It is intended for an Algebra II class. The
How To Understand The Scientific Theory Of Evolution
Introduction to Statistics for the Life Sciences Fall 2014 Volunteer Definition A biased sample systematically overestimates or underestimates a characteristic of the population Paid subjects for drug
Part Three. Cost Behavior Analysis
Part Three Cost Behavior Analysis Cost Behavior Cost behavior is the manner in which a cost changes as some related activity changes An understanding of cost behavior is necessary to plan and control costs
How To Predict Call Option Prices Using Regression Models
Predicting Call Option Prices Using Regression Models Munira Shahir July 24, 2014 Abstract One method to consists of predict call option prices is the Black-Scholes equation. However, that is utilizing
Joint models for classification and comparison of mortality in different countries.
Joint models for classification and comparison of mortality in different countries. Viani D. Biatat 1 and Iain D. Currie 1 1 Department of Actuarial Mathematics and Statistics, and the Maxwell Institute
Unit 9 Describing Relationships in Scatter Plots and Line Graphs
Unit 9 Describing Relationships in Scatter Plots and Line Graphs Objectives: To construct and interpret a scatter plot or line graph for two quantitative variables To recognize linear relationships, non-linear
Solution Let us regress percentage of games versus total payroll.
Assignment 3, MATH 2560, Due November 16th Question 1: all graphs and calculations have to be done using the computer The following table gives the 1999 payroll (rounded to the nearest million dolars)
Homework 8 Solutions
Math 17, Section 2 Spring 2011 Homework 8 Solutions Assignment Chapter 7: 7.36, 7.40 Chapter 8: 8.14, 8.16, 8.28, 8.36 (a-d), 8.38, 8.62 Chapter 9: 9.4, 9.14 Chapter 7 7.36] a) A scatterplot is given below.
Priority Areas of Australian Clinical Health R&D
Priority Areas of Australian Clinical Health R&D Nick Pappas* CSES Working Paper No. 16 ISSN: 1322 5138 ISBN: 1-86272-552-7 December 1999 *Nick Pappas is a Henderson Research Fellow at the Centre for Strategic
Implied Volatility Skews in the Foreign Exchange Market. Empirical Evidence from JPY and GBP: 1997-2002
Implied Volatility Skews in the Foreign Exchange Market Empirical Evidence from JPY and GBP: 1997-2002 The Leonard N. Stern School of Business Glucksman Institute for Research in Securities Markets Faculty
LOGISTIC REGRESSION ANALYSIS
LOGISTIC REGRESSION ANALYSIS C. Mitchell Dayton Department of Measurement, Statistics & Evaluation Room 1230D Benjamin Building University of Maryland September 1992 1. Introduction and Model Logistic
PITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU
PITFALLS IN TIME SERIES ANALYSIS Cliff Hurvich Stern School, NYU The t -Test If x 1,..., x n are independent and identically distributed with mean 0, and n is not too small, then t = x 0 s n has a standard