Statistics 101: Section L - Laboratory 6
|
|
|
- Robyn Doyle
- 5 years ago
- Views:
Transcription
1 Statistics 101: Section L - Laboratory 6 In today s lab, we are going to look more at least squares regression, and interpretations of slopes and intercepts. Activity 1: From lab 1, we collected data on the Total Weight (in grams) of Fun Size bags M&Ms and on the number of M&Ms in each bag. Can we use the number of M&Ms in each bag to predict the Total Weight of the bag? Go to the course web page and open the JMP data file that contains the data on the M&Ms. Use Analyze Fit Y by X with Total Weight as the Y, Response and Number as the X, Factor. Fit a line to the data and have JMP plot the residuals. Turn in this output with the answers to the questions below. a) Describe the general relationship between the number of M&Ms in each bag and the total weight of the bag. Does it appear this relationship is strong or weak? b) Give the equation of the least squares regression line for predicting the total weight of the bag from the number of M&Ms in the bag. c) Find the predicted total weight of a bag of M&Ms if the bag contains 21 M&Ms. d) Calculate the residual for the bag with 21 M&Ms and a total weight of 20 grams. e) Study the residual plot. Describe what you see and how this might affect (or not affect) the least squares regression of the two variables. f) Give the value of the estimated y-intercept for this regression. Give the interpretation of the g) Look closely at the interpretation of the intercept value. What physical quantity does the intercept estimate? h) Give the value of the estimated slope for this regression. Give the interpretation of the i) Look closely at the interpretation of the slope value. What physical quantity does the slope estimate? j) How well do the intercept and slope values estimate these quantities? k) Obviously, there is variability present in our data. Not all bags with the same number of M&Ms will weigh the same due to differences in the weight of each M&M and differences in the weight of the bag. In our data, more possible sources of variability were introduced when the data were collected. Name at least one of these sources. Activity 2: Use Analyze Fit Y by X with Contents Weight as the Y, Response and Number as the X, Factor. Fit a line to the data and have JMP plot the residuals. Turn in this output with the answers to the questions below. a) Give the equation of the least squares regression line for predicting the contents weight of the bag from the number of M&Ms in the bag. b) Give the value of the estimated y-intercept for this regression. Give the interpretation of the c) Look closely at the interpretation of the intercept value. What should the true value of the intercept be? d) Give the value of the slope for this regression. Give the interpretation of the slope value in the context of the problem. 1
2 e) How does this value compare to the value of the slope in Activity 1? What should be the true relationship between the slope in Activity 1 and the slope in Activity 2? Activity 3: Use Analyze Fit Y by X with Total Weight as the Y, Response and Contents Weight as the X, Factor. Fit a line to the data and have JMP plot the residuals. Turn in this output with the answers to the questions below. a) Give the equation of the least squares regression line for predicting the total weight of the bag from the contents weight of M&Ms in the bag. b) Give the value of the estimated slope for this regression. Give the interpretation of the c) Look closely at the interpretation of the slope value. What should the true value of the slope be? d) Give the value of the estimated y-intercept for this regression. Give the interpretation of the e) How does this value compare to the value of the intercept in Activity 1? What should be the true relationship between the intercept in Activity 1 and the intercept in Activity 3? 2
3 Stat 101 L: Laboratory 6 Answer Sheet Names: Activity 1: a) Describe the general relationship between the number of M&Ms in each bag and the total weight of the bag. Does it appear this relationship is strong or weak? b) Give the equation of the least squares regression line for predicting the total weight of the bag from the number of M&Ms in the bag. c) Find the predicted total weight of a bag of M&Ms if the bag contains 21 M&Ms. d) Calculate the residual for the bag with 21 M&Ms and a total weight of 20 grams. e) Study the residual plot. Describe what you see and how this might affect (or not affect) the least squares regression of the two variables. f) Give the value of the estimated y-intercept for this regression. Give the interpretation of the g) Look closely at the interpretation of the intercept value. What physical quantity does the intercept estimate? 3
4 h) Give the value of the estimated slope for this regression. Give the interpretation of the i) Look closely at the interpretation of the slope value. What physical quantity does the slope estimate? j) How well do the intercept and slope values estimate these quantities? k) Obviously, there is variability present in our data. Not all bags with the same number of M&Ms will weigh the same due to differences in the weight of each M&M and differences in the weight of the bag. In our data, more possible sources of variability were introduced when the data were collected. Name at least one of these sources. Activity 2: a) Give the equation of the least squares regression line for predicting the contents weight of the bag from the number of M&Ms in the bag. b) Give the value of the estimated y-intercept for this regression. Give the interpretation of the c) Look closely at the interpretation of the intercept value. What should the true value of the intercept be? d) Give the value of the slope for this regression. Give the interpretation of the slope value in the context of the problem. 4
5 e) How does this value compare to the value of the slope in Activity 1? What should be the true relationship between the slope in Activity 1 and the slope in Activity 2? Activity 3: a) Give the equation of the least squares regression line for predicting the total weight of the bag from the contents weight of M&Ms in the bag. b) Give the value of the estimated slope for this regression. Give the interpretation of the c) Look closely at the interpretation of the slope value. What should the true value of the slope be? d) Give the value of the estimated y-intercept for this regression. Give the interpretation of the e) How does this value compare to the value of the intercept in Activity 1? What should be the true relationship between the intercept in Activity 1 and the intercept in Activity 3? 5
Linear regression model
Regression Model Assumptions (Solutions) STAT-UB.0003: Regression and Forecasting Models Linear regression model 1. Here is the least squares regression fit to the Zagat restaurant data: 10 15 20 25 10
Going from General to Specific
Going from General to Specific Regression of Interest rate on All 7 Variables Comments: R Square is good at 63.8% The residual plot on the right is not looking entirely random Unemployment variable has
Stat 328, Summer 2005
Stat 328, Summer 2005 Exam #2, 6/18/05 Name (print) UnivID I have neither given nor received any unauthorized aid in completing this exam. Signed Answer each question completely showing your work where
Algebra 1 Unit 3: Writing Equations
Lesson 8: Making Predictions and Creating Scatter Plots The table below represents the cost of a car over the recent years. Year Cost of a Car (in US dollars) 2000 22,500 2002 26,000 2004 32,000 2006 37,500
Business Statistics: A First Course
Business Statistics: A First Course Fifth Edition Chapter 12 Correlation and Simple Linear Regression Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc. Chap 12-1 Learning Objectives In this
Rand Final Pop 2. Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question.
Name: Class: Date: Rand Final Pop 2 Multiple Choice Identify the choice that best completes the statement or answers the question. Scenario 12-1 A high school guidance counselor wonders if it is possible
(i.e. the rate of change of y with respect to x)
Section 1.3 - Linear Functions and Math Models Example 1: Questions we d like to answer: 1. What is the slope of the line? 2. What is the equation of the line? 3. What is the y-intercept? 4. What is the
Stat3011: Solution of Midterm Exam One
1 Stat3011: Solution of Midterm Exam One Fall/2003, Tiefeng Jiang Name: Problem 1 (30 points). Choose one appropriate answer in each of the following questions. 1. (B ) The mean age of five people in a
σ e, which will be large when prediction errors are Linear regression model
Linear regression model we assume that two quantitative variables, x and y, are linearly related; that is, the population of (x, y) pairs are related by an ideal population regression line y = α + βx +
Intermediate Macroeconomics: Economics 301 Exam 1. October 4, 2012 B. Daniel
October 4, 2012 B. Daniel Intermediate Macroeconomics: Economics 301 Exam 1 Name Answer all of the following questions. Each is worth 25 points. Label all axes, initial values and all values after shocks.
MANAGEMENT ACCOUNTING 2. Module Code: ACCT08004
School of Business & Enterprise Paisley & Hamilton Campus Session 015-016 Trimester 1 MANAGEMENT ACCOUNTING Module Code: ACCT08004 Date: 1st January 016 Time: 1400-1600 Answer THREE questions Question
WEB APPENDIX 8A 7.1 ( 8.9)
WEB APPENDIX 8A CALCULATING BETA COEFFICIENTS The CAPM is an ex ante model, which means that all of the variables represent before-the-fact expected values. In particular, the beta coefficient used in
Finding the Equation from a Slope and y-intercept
Lesson 4.4 Objectives Write linear equations given a slope and y-intercept, a slope and a point, or a graph. Writing Linear Equations Michael turns on the high-temperature oven each morning when he comes
AP Stats: 3B ~ Least Squares Regression and Residuals. Objectives:
Objectives: INTERPRET the slope and y intercept of a least-squares regression line USE the least-squares regression line to predict y for a given x CALCULATE and INTERPRET residuals and their standard
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Sample Exam 3 MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Question 1-7: The managers of a brokerage firm are interested in finding out if the
HIGH-LOW METHOD. Key Terms and Concepts to Know
HIGH-LOW METHOD Key Terms and Concepts to Know Variable, Fixed and Mixed Costs Many costs are clearly variable, such as direct labor and direct materials, or clearly fixed, such as rent and salaries. Other
Introduction to Population Modeling
Introduction to Population Modeling In addition to estimating the size of a population, it is often beneficial to estimate how the population size changes over time. Ecologists often uses models to create
Review Exercise Set 13. Find the slope and the equation of the line in the following graph. If the slope is undefined, then indicate it as such.
Review Exercise Set 13 Exercise 1: Find the slope and the equation of the line in the following graph. If the slope is undefined, then indicate it as such. Exercise 2: Write a linear function that can
A STATISTICAL ANALYSIS OF GDP AND FINAL CONSUMPTION USING SIMPLE LINEAR REGRESSION. THE CASE OF ROMANIA
A STATISTICAL ANALYSIS OF GDP AND FINAL CONSUMPTION USING SIMPLE LINEAR REGRESSION. THE CASE OF ROMANIA 990 200 Bălăcescu Aniela Lecturer PhD, Constantin Brancusi University of Targu Jiu, Faculty of Economics
Professor Christina Romer SUGGESTED ANSWERS TO PROBLEM SET 5
Economics 2 Spring 2016 Professor Christina Romer Professor David Romer SUGGESTED ANSWERS TO PROBLEM SET 5 1. The left-hand diagram below shows the situation when there is a negotiated real wage,, that
Non-linearities in Simple Regression
Non-linearities in Simple Regression 1. Eample: Monthly Earnings and Years of Education In this tutorial, we will focus on an eample that eplores the relationship between total monthly earnings and years
The Least Squares Regression Line
The Least Squares Regression Line Section 5.3 Cathy Poliak, Ph.D. cathy@math.uh.edu Office hours: T Th 1:30 pm - 3:30 pm 620 PGH & 5:30 pm - 7:00 pm CASA Department of Mathematics University of Houston
Pre-Algebra Blizzard Bag Number 3
Name: Class: Date: ID: A Pre-Algebra Blizzard Bag Number 3 Multiple Choice Identify the choice that best completes the statement or answers the question. Express each ratio as a fraction in simplest form..
Estimating Support Labor for a Production Program
Estimating Support Labor for a Production Program ISPA / SCEA Joint Conference June 24-27, 2008 Jeff Platten PMP, CCE/A Systems Project Engineer Northrop Grumman Corporation Biography Jeff Platten is a
Tests for the Difference Between Two Linear Regression Intercepts
Chapter 853 Tests for the Difference Between Two Linear Regression Intercepts Introduction Linear regression is a commonly used procedure in statistical analysis. One of the main objectives in linear regression
Homework Assignment Section 3
Homework Assignment Section 3 Tengyuan Liang Business Statistics Booth School of Business Problem 1 A company sets different prices for a particular stereo system in eight different regions of the country.
ACC 121 PRINCIPLES OF MANAGERIAL ACCOUNTING
PRINCIPLES OF MANAGERIAL ACCOUNTING COURSE DESCRIPTION: Prerequisites: ACC 120 Corequisites: None This course includes a greater emphasis on managerial and cost accounting skills. Emphasis is on managerial
Estimating Beta. The standard procedure for estimating betas is to regress stock returns (R j ) against market returns (R m ): R j = a + b R m
Estimating Beta 122 The standard procedure for estimating betas is to regress stock returns (R j ) against market returns (R m ): R j = a + b R m where a is the intercept and b is the slope of the regression.
3. The distinction between variable costs and fixed costs is:
Practice Exam # 2 Dr. Bailey ACCT3310, Spring 2014, Chapters 4, 5, & 6 There are 25 questions, each worth 4 points. Please see my earlier advice on the appropriate use of this exam. Its purpose is to give
$0.00 $0.50 $1.00 $1.50 $2.00 $2.50 $3.00 $3.50 $4.00 Price
Orange Juice Sales and Prices In this module, you will be looking at sales and price data for orange juice in grocery stores. You have data from 83 stores on three brands (Tropicana, Minute Maid, and the
rise m x run The slope is a ratio of how y changes as x changes: Lines and Linear Modeling POINT-SLOPE form: y y1 m( x
Chapter 1 Notes 1 (c) Epstein, 013 Chapter 1 Notes (c) Epstein, 013 Chapter1: Lines and Linear Modeling POINT-SLOPE form: y y1 m( x x1) 1.1 The Cartesian Coordinate System A properly laeled set of axes
Copyright 2011 Pearson Education, Inc. Publishing as Addison-Wesley.
Appendix: Statistics in Action Part I Financial Time Series 1. These data show the effects of stock splits. If you investigate further, you ll find that most of these splits (such as in May 1970) are 3-for-1
Statistical Models of Stocks and Bonds. Zachary D Easterling: Department of Economics. The University of Akron
Statistical Models of Stocks and Bonds Zachary D Easterling: Department of Economics The University of Akron Abstract One of the key ideas in monetary economics is that the prices of investments tend to
Long Run vs. Short Run
Long Run vs. Short Run Long Run: A period long enough for nominal wages and other input prices to change in response to a change in the nation s price level. The Basic Model of Economic Fluctuations Two
Homework Assignment Section 3
Homework Assignment Section 3 Tengyuan Liang Business Statistics Booth School of Business Problem 1 A company sets different prices for a particular stereo system in eight different regions of the country.
Quantitative Methods
THE ASSOCIATION OF BUSINESS EXECUTIVES DIPLOMA PART 2 QM Quantitative Methods afternoon 26 May 2004 1 Time allowed: 3 hours. 2 Answer any FOUR questions. 3 All questions carry 25 marks. Marks for subdivisions
Homework Solutions - Lecture 2 Part 2
Homework Solutions - Lecture 2 Part 2 1. In 1995, Time Warner Inc. had a Beta of 1.61. Part of the reason for this high Beta was the debt left over from the leveraged buyout of Time by Warner in 1989,
Dummy Variables. 1. Example: Factors Affecting Monthly Earnings
Dummy Variables A dummy variable or binary variable is a variable that takes on a value of 0 or 1 as an indicator that the observation has some kind of characteristic. Common examples: Sex (female): FEMALE=1
Chapter 14. Descriptive Methods in Regression and Correlation. Copyright 2016, 2012, 2008 Pearson Education, Inc. Chapter 14, Slide 1
Chapter 14 Descriptive Methods in Regression and Correlation Copyright 2016, 2012, 2008 Pearson Education, Inc. Chapter 14, Slide 1 Section 14.1 Linear Equations with One Independent Variable Copyright
Multiple Regression. Review of Regression with One Predictor
Fall Semester, 2001 Statistics 621 Lecture 4 Robert Stine 1 Preliminaries Multiple Regression Grading on this and other assignments Assignment will get placed in folder of first member of Learning Team.
Final Exam Suggested Solutions
University of Washington Fall 003 Department of Economics Eric Zivot Economics 483 Final Exam Suggested Solutions This is a closed book and closed note exam. However, you are allowed one page of handwritten
A Brief Illustration of Regression Analysis in Economics John Bucci. Okun s Law
Okun s Law The following regression exercise measures the original relationship between unemployment and real output, as established first by the economist Arthur Okun in the 1960s. Brief History Arthur
Economics 102 Homework #7 Due: December 7 th at the beginning of class
Economics 102 Homework #7 Due: December 7 th at the beginning of class Complete all of the problems. Please do not write your answers on this sheet. Show all of your work. 1. The economy starts in long
SFSU FIN822 Project 1
SFSU FIN822 Project 1 This project can be done in a team of up to 3 people. Your project report must be accompanied by printouts of programming outputs. You could use any software to solve the problems.
Mrs Mat. Name: 2. Which is the following equation rewritten in slopeintercept. A) y = x + 1. B) y = 4x + 1. C) y = -4x + 1.
Slope, Intercepts, and Graphing Equations Exam Expressions and Equations 8.EE - Understand the connections between proportional relationships, lines, and linear equations. No Calculator! Make sure all
Factors affecting the share price of FMCG Companies
Factors affecting the share price of FMCG Companies Authors: Dharia Dilasha, Kakadia Sachita ABSTRACT To review the factors affecting the share prices of various FMCG companies like revenues, operating
Lesson 2.6 Creating and Graphing Linear Equations in Two Variables
Lesson 2.6 Creating and Graphing Linear Equations in Two Variables Concept: Graphing Linear Equations EQ: How do I create and graph a linear equation in two variables from a word problem? (Standard CED.2)
Potpourri confidence limits for σ, the standard deviation of a normal population
Potpourri... This session (only the first part of which is covered on Saturday AM... the rest of it and Session 6 are covered Saturday PM) is an amalgam of several topics. These are 1. confidence limits
Chapter 3 - Cost Estimation Techniques
Chapter 3 - Cost Estimation Techniques Tsui-Ping Chung 1 The objective of Chapter 3 is to present various methods for estimating important factors in an engineering economy study. 2 Estimating the future
MATH 217 Test 2 Version A
MATH 217 Test 2 Version A Name: KEY Sec Number: Answer all questions to the best of your ability. Note you should show as much work as is possible. For questions answered using Excel be sure to include
CHAPTER 4 DATA ANALYSIS Data Hypothesis
CHAPTER 4 DATA ANALYSIS 4.1. Data Hypothesis The hypothesis for each independent variable to express our expectations about the characteristic of each independent variable and the pay back performance
Quadratic Modeling Elementary Education 10 Business 10 Profits
Quadratic Modeling Elementary Education 10 Business 10 Profits This week we are asking elementary education majors to complete the same activity as business majors. Our first goal is to give elementary
Financial Applications Involving Exponential Functions
Section 6.5: Financial Applications Involving Exponential Functions When you invest money, your money earns interest, which means that after a period of time you will have more money than you started with.
1) Please EXPLAIN below your error in problem #1. What will you do to correct this error in the future?
Individualized Quiz Remedial Help Name: ALL QUESTIONS REQUIRING YOU TO WRITE IN ENGLISH MUST BE ANSWERED IN COMPLETE SENTENCES. If you answered question #1 incorrectly please answer the following. 1) Please
Web Extension: Continuous Distributions and Estimating Beta with a Calculator
19878_02W_p001-008.qxd 3/10/06 9:51 AM Page 1 C H A P T E R 2 Web Extension: Continuous Distributions and Estimating Beta with a Calculator This extension explains continuous probability distributions
Chapter 5 Project: Broiler Chicken Production. Name Name
Chapter 5 Project: Broiler Chicken Production Name Name 1. Background information The graph and data that form the basis of this project were taken from a very useful web site sponsored by the National
SUMMARY OUTPUT. Regression Statistics Multiple R R Square Adjusted R Standard E Observation 5
SUMMARY OUTPUT Regression Statistics Multiple R 0.658946 R Square 0.43421 Adjusted R 0.245613 Standard E 0.019307 Observation 5 ANOVA df SS MS F ignificance F Regression 1 0.000858 0.000858 2.302318 0.226463
ST 350 Lecture Worksheet #33 Reiland
ST 350 Lecture Worksheet #33 Reiland SOLUTIONS Name Lotteries: Good Idea or Scam? Lotteries have become important sources of revenue for many state governments. However, people have criticized lotteries
Washington University Fall Economics 487. Project Proposal due Monday 10/22 Final Project due Monday 12/3
Washington University Fall 2001 Department of Economics James Morley Economics 487 Project Proposal due Monday 10/22 Final Project due Monday 12/3 For this project, you will analyze the behaviour of 10
Line of Best Fit Our objective is to fit a line in the scatterplot that fits the data the best Line of best fit looks like:
Line of Best Fit Our ojective is to fit a line in the scatterplot that fits the data the est Line of est fit looks like: ŷ = + x Least Square Regression Line That s a hat on the y, meaning that it is a
Name Period. Linear Correlation
Linear Regression Models Directions: Use the information below to solve the problems in this packet. Packets are due at the end of the period and students who do not finish will be required to come in
SJAM MPM 1D Unit 5 Day 13
Homework 1. Identify the dependent variable. a) The distance a person walks depends on the time they walk. b) The recipe for 1 muffins requires cups of flour. c) Houses need 1 fire alarm per floor.. Identify
EXAMINATIONS OF THE HONG KONG STATISTICAL SOCIETY
EXAMINATIONS OF THE HONG KONG STATISTICAL SOCIETY HIGHER CERTIFICATE IN STATISTICS, 2016 MODULE 7 : Time series and index numbers Time allowed: One and a half hours Candidates should answer THREE questions.
11/28/2018. Overview. Multiple Linear Regression Analysis. Multiple regression. Multiple regression. Multiple regression. Multiple regression
Multiple Linear Regression Analysis BSAD 30 Dave Novak Fall 208 Source: Ragsdale, 208 Spreadsheet Modeling and Decision Analysis 8 th edition 207 Cengage Learning 2 Overview Last class we considered the
Math Week in Review #1. Perpendicular Lines - slopes are opposite (or negative) reciprocals of each other
Math 141 Spring 2006 c Heather Ramsey Page 1 Section 1.2 m = y x = y 2 y 1 x 2 x 1 Math 141 - Week in Review #1 Point-Slope Form: y y 1 = m(x x 1 ), where m is slope and (x 1,y 1 ) is any point on the
STA2601. Tutorial letter 105/2/2018. Applied Statistics II. Semester 2. Department of Statistics STA2601/105/2/2018 TRIAL EXAMINATION PAPER
STA2601/105/2/2018 Tutorial letter 105/2/2018 Applied Statistics II STA2601 Semester 2 Department of Statistics TRIAL EXAMINATION PAPER Define tomorrow. university of south africa Dear Student Congratulations
ECO 2013: Macroeconomics Valencia Community College
ECO 2013: Macroeconomics Valencia Community College Exam 3 Fall 2008 1. The most important determinant of consumer spending is: A. the level of household debt. B. consumer expectations. C. the stock of
Final Project. College Algebra. Upon successful completion of this course, the student will be able to:
COURSE OBJECTIVES Upon successful completion of this course, the student will be able to: 1. Perform operations on algebraic expressions 2. Perform operations on functions expressed in standard function
KOÇ UNIVERSITY ECON 202 Macroeconomics Fall Problem Set VI C = (Y T) I = 380 G = 400 T = 0.20Y Y = C + I + G.
KOÇ UNIVERSITY ECON 202 Macroeconomics Fall 2007 Problem Set VI 1. Consider the following model of an economy: C = 20 + 0.75(Y T) I = 380 G = 400 T = 0.20Y Y = C + I + G. (a) What is the value of the MPC
Name Name. To enter the data manually, go to the StatCrunch website (www.statcrunch.com) and log in (new users must register).
Chapter 5 Project: Broiler Chicken Production Name Name 1. Background information The graph and data that form the basis of this project were taken from a very useful web site sponsored by the National
proc genmod; model malform/total = alcohol / dist=bin link=identity obstats; title 'Table 2.7'; title2 'Identity Link';
BIOS 6244 Analysis of Categorical Data Assignment 5 s 1. Consider Exercise 4.4, p. 98. (i) Write the SAS code, including the DATA step, to fit the linear probability model and the logit model to the data
York University. Suggested Solutions
York University Atkinson Faculty of Liberal and professional Studies Department of Economics ECON1010C Term Test 2 July 20, 2005 Instructor: Sharif F. Khan Suggested Solutions PART A 1. B 2. A 3. D 4.
Biol 356 Lab 7. Mark-Recapture Population Estimates
Biol 356 Lab 7. Mark-Recapture Population Estimates For many animals, counting the exact numbers of individuals in a population is impractical. There may simply be too many to count, or individuals may
The line drawn for part (a) will depend on each student s subjective choice about the position of the line. For this reason, it has been omitted.
CHAPTER 2 Exercise Answers EXERCISE 2.3 (a) The line drawn for part (a) will depend on each student s subjective choice about the position of the line. For this reason, it has been omitted. (b) b2 1.514286
Please show work for all calculated answers. Show work in a neat and organized manner.
Math 083 Review for Final Exam Name Please show work for all calculated answers. Show work in a neat and organized manner. 1) Using the frequency table for a monthly budget, find all of the relative frequencies
Glossary of Budgeting and Planning Terms
Budgeting Basics and Beyond, Third Edition By Jae K. Shim and Joel G. Siegel Copyright 2009 by John Wiley & Sons, Inc.. Glossary of Budgeting and Planning Terms Active Financial Planning Software Budgeting
Sensex Realized Volatility Index (REALVOL)
Sensex Realized Volatility Index (REALVOL) Introduction Volatility modelling has traditionally relied on complex econometric procedures in order to accommodate the inherent latent character of volatility.
Chapter 3: Answers to Questions and Problems
Chapter 3: Answers to Questions and Problems 1. a. When P = $12, R = ($12)(1) = $12. When P = $10, R = ($10)(2) = $20. Thus, the price decrease results in an $8 increase in total revenue, so demand is
Finance Practice Midterm #1 Solutions
Finance 30210 Practice Midterm #1 Solutions 1) Suppose that you have the opportunity to invest $50,000 in a new restaurant in South Bend. (FYI: Dr. HG Parsa of Ohio State University has done a study that
CHAPTER 10 DETERMINING HOW COSTS BEHAVE. Difference in costs Difference in machine-hours $5,400 $4,000. = $0.35 per machine-hour
CHAPTER 10 DETERMINING HOW COSTS BEHAVE 10-16 (10 min.) Estimating a cost function. 1. Slope coefficient = Difference in costs Difference in machine-hours = = $5,400 $4,000 10,000 6, 000 $1, 400 4,000
PARAMETRIC AND NON-PARAMETRIC BOOTSTRAP: A SIMULATION STUDY FOR A LINEAR REGRESSION WITH RESIDUALS FROM A MIXTURE OF LAPLACE DISTRIBUTIONS
PARAMETRIC AND NON-PARAMETRIC BOOTSTRAP: A SIMULATION STUDY FOR A LINEAR REGRESSION WITH RESIDUALS FROM A MIXTURE OF LAPLACE DISTRIBUTIONS Melfi Alrasheedi School of Business, King Faisal University, Saudi
What Practitionors Nood to Know...
What Practitionors Nood to Know... by Mark Kritzman How can we predict uncertain outcomes? We could study the relations between the uncertain variable to be predicted and some known variable. Suppose,
7-3 Exponential Review I can apply exponential properties and use them I can model real-world situations using exponential functions Warm-Up 1. Find the next three terms in the sequence 2, 6, 18, 54,,,
MA162: Finite mathematics
MA162: Finite mathematics Paul Koester University of Kentucky September 4, 2013 Schedule: First Web Assign assignment due on Friday, September 6 by 6:00 pm. Second Web Assign assignment due on Tuesday,
Read the following situation to determine whether the inequality correctly models the company s information.
Algebra 1 EOC Review #7 m w2q0y1c7h \KquntBaE qsoohfhthwwabrhei vlxlvcc.e a naslzlu BrniVgwhgtosV er_e\sxeerivdeado. Additional Practice 1) Selected-Response Name Date Period Sandra sells necklaces at
False_ The average revenue of a firm can be increasing in the firm s output.
LECTURE 12: SPECIAL COST FUNCTIONS AND PROFIT MAXIMIZATION ANSWERS AND SOLUTIONS True/False Questions False_ If the isoquants of a production function exhibit diminishing MRTS, then the input choice that
Cost-Volume-Profit Analysis
Cost-Volume-Profit Analysis Chapter 21 PowerPoint Editor: Beth Kane, MBA, CPA Wild, Shaw, and Chiappetta Fundamental Accounting Principles 22nd Edition Copyright 2015 McGraw-Hill Education. All rights
3.3 rates and slope intercept form ink.notebook. October 23, page 103. page 104. page Rates and Slope Intercept Form
3.3 rates and slope intercept form ink.notebook page 103 page 104 page 102 3.3 Rates and Slope Intercept Form Lesson Objectives 3.3 Rates and Slope-Intercept Form Press the tabs to view details. Standards
Chapter 4. Consumer and Firm Behavior: The Work- Leisure Decision and Profit Maximization. Copyright 2014 Pearson Education, Inc.
Chapter 4 Consumer and Firm Behavior: The Work- Leisure Decision and Profit Maximization Copyright Chapter 4 Topics Behavior of the representative consumer Behavior of the representative firm 1-2 Representative
NEWCASTLE UNIVERSITY. School SEMESTER /2013 ACE2013. Statistics for Marketing and Management. Time allowed: 2 hours
NEWCASTLE UNIVERSITY School SEMESTER 2 2012/2013 Statistics for Marketing and Management Time allowed: 2 hours Candidates should attempt ALL questions. Marks for each question are indicated. However you
Models of Patterns. Lecture 3, SMMD 2005 Bob Stine
Models of Patterns Lecture 3, SMMD 2005 Bob Stine Review Speculative investing and portfolios Risk and variance Volatility adjusted return Volatility drag Dependence Covariance Review Example Stock and
Cross Hedging Agricultural Commodities
Cross Hedging Agricultural Commodities Kansas State University Agricultural Experiment Station and Cooperative Extension Service Manhattan, Kansas 1 Cross Hedging Agricultural Commodities Jennifer Graff
REGIONAL WORKSHOP ON TRAFFIC FORECASTING AND ECONOMIC PLANNING
International Civil Aviation Organization 27/8/10 WORKING PAPER REGIONAL WORKSHOP ON TRAFFIC FORECASTING AND ECONOMIC PLANNING Cairo 2 to 4 November 2010 Agenda Item 3 a): Forecasting Methodology (Presented
> attach(grocery) > boxplot(sales~discount, ylab="sales",xlab="discount")
Example of More than 2 Categories, and Analysis of Covariance Example > attach(grocery) > boxplot(sales~discount, ylab="sales",xlab="discount") Sales 160 200 240 > tapply(sales,discount,mean) 10.00% 15.00%
Multiple regression - a brief introduction
Multiple regression - a brief introduction Multiple regression is an extension to regular (simple) regression. Instead of one X, we now have several. Suppose, for example, that you are trying to predict
Professor Christina Romer SUGGESTED ANSWERS TO PROBLEM SET 5
Economics 2 Spring 2017 Professor Christina Romer Professor David Romer SUGGESTED ANSWERS TO PROBLEM SET 5 1. The tool we use to analyze the determination of the normal real interest rate and normal investment
Econometric Methods for Valuation Analysis
Econometric Methods for Valuation Analysis Margarita Genius Dept of Economics M. Genius (Univ. of Crete) Econometric Methods for Valuation Analysis Cagliari, 2017 1 / 26 Correlation Analysis Simple Regression
Forecasting Chapter 14
Forecasting Chapter 14 14-01 Forecasting Forecast: A prediction of future events used for planning purposes. It is a critical inputs to business plans, annual plans, and budgets Finance, human resources,
Market Approach A. Relationship to Appraisal Principles
Market Approach A. Relationship to Appraisal Principles 1. Supply and demand Prices are determined by negotiation between buyers and sellers o Buyers demand side o Sellers supply side At a specific time
Econometrics and Economic Data
Econometrics and Economic Data Chapter 1 What is a regression? By using the regression model, we can evaluate the magnitude of change in one variable due to a certain change in another variable. For example,