Directions: Answer the following questions on another sheet of paper
|
|
- Spencer Stanley
- 7 years ago
- Views:
Transcription
1 Module 3 Review Directions: Answer the following questions on another sheet of paper Questions 1-16 refer to the following situation: Is there a relationship between crime rate and the number of unemployment rate among young men? The data below is for several US cities, collected by the FBI's Uniform Crime Report and other government agencies. The first column is the unemployment rate (as a percentage) for men aged The second column is the crime rate (number of offenses reported to police per million population) city unemployment rate crime rate Allentown Busytown Charleston Daisyville Easyville What is the explanatory variable? 2. What is the response variable? 3. Make a scatterplot, then describe it. 4. Compute (with a calculator) the mean and standard deviation of the unemployment rate 5. Compute (with a calculator) the mean and standard deviation of the crime rate 6. Compute the correlation (r), then explain what the result means. 7. Compute the least-squares regression line 8. In the least-squares regression line, (from problem 7), which number represents the slope? Explain the meaning of this number, in the context of the problem. Include units in your answer. 9. In the least-squares regression line, (from problem 7), which number represents the y-intercept? Explain the meaning of this number, in the context of the problem. Include units in your answer. 10. Suppose the unemployment rate for young men of another city, Fairview, is What would you predict the crime rate to be, according to the least-squares regression line? 11. How confident are you of your prediction in question 10? Explain your answer. 12. Suppose Fairview's actual crime rate is 154 incidents per million people. What is the residual? Explain what a negative residual would mean. Explain what a positive residual would mean. 13. If you had to compute the SSE by hand, show what the first two rows of the table would look like.
2 14. Does the scatterplot, regression line, etc. indicate that the unemployment rate among young males *causes* crime? Why or why not? If it doesn't, what does the scatterplot tell you about the relationship between unemployment and crime? 15. What are some confounding variables in this situation? 16. Give an example of extrapolation in this situation. Explain why the extrapolation is not necessarily valid. 17. (multiple choice) Measurements on young children in Mumbai, India, found this least-squares regression line for predicting the height y from armspan x: y = x. All measurements are in centimeters. How much, on the average, does height increase for each additional centimeter of armspan? a) 0.15 cm b) 6.40 cm c) 2.00 cm d) 0.93 cm e) 7.33 cm Questions refer to the following scatterplot: Describe the scatterplot: 19. In the scatterplot from the previous question, suppose r = 0.7. If an outlier was added to the scatterplot, at the point (10, 10), would r increase, decrease or stay about the same? 20. A researcher wanted to find the relationship between a weight and height of middle-aged women. Suppose the mean height was 168 cm, the standard deviation of the height was 4.5 cm. Suppose the mean weight was 58 kg and the standard deviation of the weight was 5.1 kg. Suppose r = a) Find the equation of the best-fit line. Let x = height and y = weight b) If a woman was 174 cm tall, use the best-fit line from part (a) to predict her weight. c) How reliable is your prediction from part (b)? Explain using concepts about correlation.
3 21. Match the following graphs to the following correlations (one value of r will not be used) a) r = 0.97 b)r = c) r = 0.76 d) r = 0.04 e) r = (multiple choice) The points on a scatterplot lie very close to the line y = 4-3x. The correlation between x and y is close to... a) -3 b) -1 c) 0 d) 1 e) Below is a scatterplot. a)use the scatterplot below to estimate the slope of the least squares regression line b) Use the scatterplot below to estimate the y-intercept c) use parts (a) and (b) to write out the equation of least squares regression line
4 Answers 1. Unemployment rate. This is the variable being used to try to explain and/or predict the crime rate. 2. crime rate. This is the variable in response to the unemployment rate. 3. The direction is positive. There is a moderate amount of scatter. The form looks curvilinear, but there are only 5 points, so it s hard to establish any definite trends. 4. Mean unemployment rate: x = 14.1 percent SD of unemployment rate: s x = 5.34 percentage points 5. Mean of crime rate: y =155.2 incidents per million people SD of crime rate: s y = incidents per million people 6. r = (rounded off). This means the correlation is fairly strong and the direction is positive. Below ae the calculations: unemployment rate (x) crime rate (y) total s b r y s 5.34 x a y bx (5.92)(14.1) The least-squares regression line is (using symbols) yˆ x Or (using words) predicted crime rate = (unemployment rate) divide by 5.34, then by 39.32, then by 4
5 8. The slope is It means each time the unemployment increases by one percentage point, the crime rate will increase by 5.92 incidents per million people. 9. The y-intercept is This means if the unemployment rate was zero, then the predicted crime rate would be 71.1 incidents per million people. 10. yˆ x = (5.92)(13.6) = The predicted crime rate would be incidents per million people. 11. By looking at the scatterplot, there is a moderate to amount of scatter in the plot. The prediction would be somewhat accurate. However (this is just a side comment) the sample size of only 5 cities is not very large. With a larger sample we could have a more confident prediction. 12. Residual = y yˆ = = 1.8 incidents per million people. The residual is positive, which means: The actual data is 1.8 units higher than the predicted amount. A negative residual would mean the actual data is lower than the predicted amount. 13. x y y residual Residual squared No, because correlation does not imply causation. There could be other variables that cause both the unemployment rate and the crime rate to increase together. However, the scatterplot DOES tell us that there is some kind of relationship between unemployment rate and crime rate. 15. There are many possible answers. Here are some examples: level of education of the young men, drug use, disabilities. Anything that increases both the unemployment and the crime rate would be a confounding variable. 16. Any example where the unemployment rate is outside the range of the given data (less than 9% or more than 23%) would be extrapolation. This is not necessarily valid because there is no data to show the crime rate will continue to increase in the same way. 17. d 18. Direction is positive, strength is moderate to strong, form is linear 19. It would decrease because the outlier would create a greater amount of scatter and a weaker correlation. r is sensitive to outliers. s a) b r y s 4.5 x a y ax (168) yˆ x b) yˆ x = *174 = kg c) This is not too reliable because r is low e 2. d 3. a 4. c
6 22. d 23. a) slope is about -0.8 b) y-intercept is about 85 c) yˆ 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
More informationLecture 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
More informationExercise 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.
More informationAnswer: 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
More informationChapter 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) -
More informationWe are often interested in the relationship between two variables. Do people with more years of full-time education earn higher salaries?
Statistics: Correlation Richard Buxton. 2008. 1 Introduction We are often interested in the relationship between two variables. Do people with more years of full-time education earn higher salaries? Do
More informationChapter 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
More informationCorrelation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables 2
Lesson 4 Part 1 Relationships between two numerical variables 1 Correlation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables
More informationCorrelation 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
More informationHomework 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.
More informationSimple 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
More informationRelationships 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)
More informationExample: 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
More informationWhat does the number m in y = mx + b measure? To find out, suppose (x 1, y 1 ) and (x 2, y 2 ) are two points on the graph of y = mx + b.
PRIMARY CONTENT MODULE Algebra - Linear Equations & Inequalities T-37/H-37 What does the number m in y = mx + b measure? To find out, suppose (x 1, y 1 ) and (x 2, y 2 ) are two points on the graph of
More informationSection 3 Part 1. Relationships between two numerical variables
Section 3 Part 1 Relationships between two numerical variables 1 Relationship between two variables The summary statistics covered in the previous lessons are appropriate for describing a single variable.
More informationLecture 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
More informationSection 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
More informationMULTIPLE 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
More informationThe 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
More information1. 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
More informationUnivariate 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
More informationUnit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression
Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a
More informationThe correlation coefficient
The correlation coefficient Clinical Biostatistics The correlation coefficient Martin Bland Correlation coefficients are used to measure the of the relationship or association between two quantitative
More informationCOMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared. jn2@ecs.soton.ac.uk
COMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared jn2@ecs.soton.ac.uk Relationships between variables So far we have looked at ways of characterizing the distribution
More informationSession 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
More informationSection 1.5 Linear Models
Section 1.5 Linear Models Some real-life problems can be modeled using linear equations. Now that we know how to find the slope of a line, the equation of a line, and the point of intersection of two lines,
More informationCHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression
Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the
More informationEXPERIMENT 3 Analysis of a freely falling body Dependence of speed and position on time Objectives
EXPERIMENT 3 Analysis of a freely falling body Dependence of speed and position on time Objectives to verify how the distance of a freely-falling body varies with time to investigate whether the velocity
More informationMTH 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
More informationCURVE FITTING LEAST SQUARES APPROXIMATION
CURVE FITTING LEAST SQUARES APPROXIMATION Data analysis and curve fitting: Imagine that we are studying a physical system involving two quantities: x and y Also suppose that we expect a linear relationship
More informationMULTIPLE 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
More informationa) 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
More informationCorrelation key concepts:
CORRELATION Correlation key concepts: Types of correlation Methods of studying correlation a) Scatter diagram b) Karl pearson s coefficient of correlation c) Spearman s Rank correlation coefficient d)
More information1/27/2013. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Introduce moderated multiple regression Continuous predictor continuous predictor Continuous predictor categorical predictor Understand
More informationFREE FALL. Introduction. Reference Young and Freedman, University Physics, 12 th Edition: Chapter 2, section 2.5
Physics 161 FREE FALL Introduction This experiment is designed to study the motion of an object that is accelerated by the force of gravity. It also serves as an introduction to the data analysis capabilities
More information1.2 GRAPHS OF EQUATIONS. Copyright Cengage Learning. All rights reserved.
1.2 GRAPHS OF EQUATIONS Copyright Cengage Learning. All rights reserved. What You Should Learn Sketch graphs of equations. Find x- and y-intercepts of graphs of equations. Use symmetry to sketch graphs
More informationChapter 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
More informationScatter 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
More informationX X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1)
CORRELATION AND REGRESSION / 47 CHAPTER EIGHT CORRELATION AND REGRESSION Correlation and regression are statistical methods that are commonly used in the medical literature to compare two or more variables.
More informationDealing with Data in Excel 2010
Dealing with Data in Excel 2010 Excel provides the ability to do computations and graphing of data. Here we provide the basics and some advanced capabilities available in Excel that are useful for dealing
More informationSimple Linear Regression, Scatterplots, and Bivariate Correlation
1 Simple Linear Regression, Scatterplots, and Bivariate Correlation This section covers procedures for testing the association between two continuous variables using the SPSS Regression and Correlate analyses.
More informationScatter Plots with Error Bars
Chapter 165 Scatter Plots with Error Bars Introduction The procedure extends the capability of the basic scatter plot by allowing you to plot the variability in Y and X corresponding to each point. Each
More informationDescribing Relationships between Two Variables
Describing Relationships between Two Variables Up until now, we have dealt, for the most part, with just one variable at a time. This variable, when measured on many different subjects or objects, took
More informationThe Dummy s Guide to Data Analysis Using SPSS
The Dummy s Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April, 2001 Amy Gamble 4/30/01 All Rights Rerserved TABLE OF CONTENTS PAGE Helpful Hints for All Tests...1 Tests
More informationStatistics 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
More informationA. Test the hypothesis: The older you are, the more money you earn. Plot the data on the scatter plot below, choosing appropriate scales and labels.
MPM1D Scatterplot Assignment A. Test the hypothesis: The older you are, the more money you earn. Plot the data on the scatter plot below, choosing appropriate scales and labels. Age Earnings ($) 25 22000
More informationFoundations for Functions
Activity: TEKS: Overview: Materials: Grouping: Time: Crime Scene Investigation (A.2) Foundations for functions. The student uses the properties and attributes of functions. The student is expected to:
More information2. 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
More informationCoordinate Plane, Slope, and Lines Long-Term Memory Review Review 1
Review. What does slope of a line mean?. How do you find the slope of a line? 4. Plot and label the points A (3, ) and B (, ). a. From point B to point A, by how much does the y-value change? b. From point
More informationRegression 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
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Module 7 Test Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. You are given information about a straight line. Use two points to graph the equation.
More informationLinear 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
More informationthe Median-Medi Graphing bivariate data in a scatter plot
the Median-Medi Students use movie sales data to estimate and draw lines of best fit, bridging technology and mathematical understanding. david c. Wilson Graphing bivariate data in a scatter plot and drawing
More informationHomework #1 Solutions
Homework #1 Solutions Problems Section 1.1: 8, 10, 12, 14, 16 Section 1.2: 2, 8, 10, 12, 16, 24, 26 Extra Problems #1 and #2 1.1.8. Find f (5) if f (x) = 10x x 2. Solution: Setting x = 5, f (5) = 10(5)
More informationhp calculators HP 50g Trend Lines The STAT menu Trend Lines Practice predicting the future using trend lines
The STAT menu Trend Lines Practice predicting the future using trend lines The STAT menu The Statistics menu is accessed from the ORANGE shifted function of the 5 key by pressing Ù. When pressed, a CHOOSE
More informationtable 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
More informationMEASURES OF VARIATION
NORMAL DISTRIBTIONS MEASURES OF VARIATION In statistics, it is important to measure the spread of data. A simple way to measure spread is to find the range. But statisticians want to know if the data are
More informationCopyright 2007 by Laura Schultz. All rights reserved. Page 1 of 5
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
More informationCopyright 2013 by Laura Schultz. All rights reserved. Page 1 of 7
Using Your TI-83/84/89 Calculator: Linear Correlation and Regression Dr. Laura Schultz Statistics I This handout describes how to use your calculator for various linear correlation and regression applications.
More information2. 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
More informationSimple 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
More informationch12 practice test SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.
ch12 practice test 1) The null hypothesis that x and y are is H0: = 0. 1) 2) When a two-sided significance test about a population slope has a P-value below 0.05, the 95% confidence interval for A) does
More information. 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
More informationA Determination of g, the Acceleration Due to Gravity, from Newton's Laws of Motion
A Determination of g, the Acceleration Due to Gravity, from Newton's Laws of Motion Objective In the experiment you will determine the cart acceleration, a, and the friction force, f, experimentally for
More informationUsing 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
More informationPhysics Lab Report Guidelines
Physics Lab Report Guidelines Summary The following is an outline of the requirements for a physics lab report. A. Experimental Description 1. Provide a statement of the physical theory or principle observed
More informationAssociation 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
More informationModule 3: Correlation and Covariance
Using Statistical Data to Make Decisions Module 3: Correlation and Covariance Tom Ilvento Dr. Mugdim Pašiƒ University of Delaware Sarajevo Graduate School of Business O ften our interest in data analysis
More informationSimple Linear Regression
STAT 101 Dr. Kari Lock Morgan Simple Linear Regression SECTIONS 9.3 Confidence and prediction intervals (9.3) Conditions for inference (9.1) Want More Stats??? If you have enjoyed learning how to analyze
More informationElementary Statistics Sample Exam #3
Elementary Statistics Sample Exam #3 Instructions. No books or telephones. Only the supplied calculators are allowed. The exam is worth 100 points. 1. A chi square goodness of fit test is considered to
More informationTutorial for the TI-89 Titanium Calculator
SI Physics Tutorial for the TI-89 Titanium Calculator Using Scientific Notation on a TI-89 Titanium calculator From Home, press the Mode button, then scroll down to Exponential Format. Select Scientific.
More informationWEB APPENDIX. Calculating Beta Coefficients. b Beta Rise Run Y 7.1 1 8.92 X 10.0 0.0 16.0 10.0 1.6
WEB APPENDIX 8A Calculating Beta Coefficients The CAPM is an ex ante model, which means that all of the variables represent before-thefact, expected values. In particular, the beta coefficient used in
More information2013 MBA Jump Start Program. Statistics Module Part 3
2013 MBA Jump Start Program Module 1: Statistics Thomas Gilbert Part 3 Statistics Module Part 3 Hypothesis Testing (Inference) Regressions 2 1 Making an Investment Decision A researcher in your firm just
More informationChapter 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
More informationSimple 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
More informationcontaining Kendall correlations; and the OUTH = option will create a data set containing Hoeffding statistics.
Getting Correlations Using PROC CORR Correlation analysis provides a method to measure the strength of a linear relationship between two numeric variables. PROC CORR can be used to compute Pearson product-moment
More informationThe Correlation Coefficient
The Correlation Coefficient Lelys Bravo de Guenni April 22nd, 2015 Outline The Correlation coefficient Positive Correlation Negative Correlation Properties of the Correlation Coefficient Non-linear association
More informationSTAT 350 Practice Final Exam Solution (Spring 2015)
PART 1: Multiple Choice Questions: 1) A study was conducted to compare five different training programs for improving endurance. Forty subjects were randomly divided into five groups of eight subjects
More informationWorksheet A5: Slope Intercept Form
Name Date Worksheet A5: Slope Intercept Form Find the Slope of each line below 1 3 Y - - - - - - - - - - Graph the lines containing the point below, then find their slopes from counting on the graph!.
More informationStatistics 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
More informationCALCULATIONS & 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
More informationDescriptive Statistics
Descriptive Statistics Descriptive statistics consist of methods for organizing and summarizing data. It includes the construction of graphs, charts and tables, as well various descriptive measures such
More information17. 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.
More informationOnce saved, if the file was zipped you will need to unzip it. For the files that I will be posting you need to change the preferences.
1 Commands in JMP and Statcrunch Below are a set of commands in JMP and Statcrunch which facilitate a basic statistical analysis. The first part concerns commands in JMP, the second part is for analysis
More informationHSPA 10 CSI Investigation Height and Foot Length: An Exercise in Graphing
HSPA 10 CSI Investigation Height and Foot Length: An Exercise in Graphing In this activity, you will play the role of crime scene investigator. The remains of two individuals have recently been found trapped
More informationII. 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,
More informationChapter 9 Descriptive Statistics for Bivariate Data
9.1 Introduction 215 Chapter 9 Descriptive Statistics for Bivariate Data 9.1 Introduction We discussed univariate data description (methods used to eplore the distribution of the values of a single variable)
More informationCurve Fitting in Microsoft Excel By William Lee
Curve Fitting in Microsoft Excel By William Lee This document is here to guide you through the steps needed to do curve fitting in Microsoft Excel using the least-squares method. In mathematical equations
More informationCourse Objective This course is designed to give you a basic understanding of how to run regressions in SPSS.
SPSS Regressions Social Science Research Lab American University, Washington, D.C. Web. www.american.edu/provost/ctrl/pclabs.cfm Tel. x3862 Email. SSRL@American.edu Course Objective This course is designed
More informationGraphing Linear Equations
Graphing Linear Equations I. Graphing Linear Equations a. The graphs of first degree (linear) equations will always be straight lines. b. Graphs of lines can have Positive Slope Negative Slope Zero slope
More information1. 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
More informationName: 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
More informationc. 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?
More informationCorrelational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots
Correlational Research Stephen E. Brock, Ph.D., NCSP California State University, Sacramento 1 Correlational Research A quantitative methodology used to determine whether, and to what degree, a relationship
More informationStat 412/512 CASE INFLUENCE STATISTICS. Charlotte Wickham. stat512.cwick.co.nz. Feb 2 2015
Stat 412/512 CASE INFLUENCE STATISTICS Feb 2 2015 Charlotte Wickham stat512.cwick.co.nz Regression in your field See website. You may complete this assignment in pairs. Find a journal article in your field
More informationPart 2: Analysis of Relationship Between Two Variables
Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable
More informationModule 5: Multiple Regression Analysis
Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College
More informationALGEBRA I (Common Core) Thursday, January 28, 2016 1:15 to 4:15 p.m., only
ALGEBRA I (COMMON CORE) The University of the State of New York REGENTS HIGH SCHOOL EXAMINATION ALGEBRA I (Common Core) Thursday, January 28, 2016 1:15 to 4:15 p.m., only Student Name: School Name: The
More informationClass 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
More informationUSING A TI-83 OR TI-84 SERIES GRAPHING CALCULATOR IN AN INTRODUCTORY STATISTICS CLASS
USING A TI-83 OR TI-84 SERIES GRAPHING CALCULATOR IN AN INTRODUCTORY STATISTICS CLASS W. SCOTT STREET, IV DEPARTMENT OF STATISTICAL SCIENCES & OPERATIONS RESEARCH VIRGINIA COMMONWEALTH UNIVERSITY Table
More information