Week TSX Index


 Allen Brooks
 4 years ago
 Views:
Transcription
1 1) The S & P/TSX Composite Index is based on common stock prices of a group of Canadian stocks. The weekly close level of the TSX for 6 weeks are shown: Week TSX Index a) Compute the three week moving averages and forecast the TSX Index forecast for week 7 b) Compute the three week weighted moving averages using 3, 2 and 1 for most recent, second most recent, and third most recent periods. Find the TSX Index forecast for week 7. c) Compute the exponentially smoothed forecasts for weeks 2 to 7 using α = 0.7. d) Calculate the MAD and MAPE for all three methods. Which provides the best forecast for the TSX Index? 2) The following data represents the quarterly piano sales of Sawyer Piano House for 3 consecutive years. Year Quarter Sales (000$) a) Compute the seasonal indices and normalized indices using overall average sales. b) Compute the seasonal indices and normalized indices using centred moving average sales. c) If the average quarterly forecast for year 4 is $10,000, use the seasonal indices (unnormalized) to calculate seasonally adjusted quarterly forecasts for year 4. 1
2 3) James Steven has been hired by the Victory Stores, a convenience store, to study how factors such as floor space area, number of parking spaces, and average family income of families in the city affect daily sales. A random sample of 15 stores is obtained and the data are as follows: Sales ($) Floor Area Parking Spaces Income ($ 000) SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 15 ANOVA df SS MS F Significance F Regression Residual Total Coefficients Standard Error t Stat Pvalue Lower 95% Upper 95% Intercept E Floor Area Parking Spaces Income ($ 000)
3 a) From the Excel output above determine the regression equation, adjusted coefficient of determination, correlation coefficient, standard error of the estimate. Interpret the meaning of each. b) Interpret the coefficients of each of the independent variables. c) Forecast a sales level for 500 (floor area), 5 (parking spaces) and 50 (Income, 000s). d) Complete the coefficients table by filling in the missing data for t stat (Floor Area) and Lower and Upper 95% confidence limits (Parking Spaces). e) Conduct an overall hypothesis test to determine if the regression equation is significant (useful) in explaining differences in Sales. Use a 5% significance level. f) Conduct individual hypothesis tests to determine which independent variables are significant or should be dropped in explaining differences in Sales. Use a 5% significance level. 4) Complete the following ANOVA table, assuming the sample size is 20. ANOVA df SS MS F Regression Residual (Error) Total 3
4 Definitions and concepts to know 1. Time Series Forecasting 2. Causal Forecasting 3. Qualitative Forecasting (a) Delphi Method (b) Jury of Executive Opinion (c) Sales Force Composite (d) Consumer Market Survey 4. MAD 5. MAPE 6. Trend 7. Seasonal 8. Cyclical 9. Random 10. Moving Average 11. Weighted Moving Average 12. Exponential Smoothing, Smoothing constant or parameter 13. Stationary, Nonstationary 14. ANOVA Table 15. Simple Regression 16. Multiple Regression 17. Correlation Coefficient 18. Coefficient of Determination 19. Standard Error of the Estimate 4
5 Answers/Solutions 1) a) b) c) Error Actual Week Actual 3 Week Moving Error Error TSX Index Average Forecast Totals MAD = 21.7 MAPE = 0.257% Error Actual Week Actual 3 Week Moving Error Error TSX Index Average Forecast Totals MAD = 21.0 MAPE = 0.243% Error Actual Week Actual Exponentially Smoothed Error Error TSX Index Forecast α = (assumed) Totals MAD = 13.8 MAPE = 0.162% d) Exponential Smoothing provides the best forecast due lowest MAD (13.8) and MAPE (0.162%). 5
6 2) a) b) Year Quarter Sales Overall Seasonal Seasonal Seasonal Index Average Sales Ratios Index (Normalized) Total Total Seasonal Ratios Year Quarter 1 Quarter 2 Quarter 3 Quarter Average Seasonal Indices Year Quarter Sales Centred Moving Seasonal Seasonal Seasonal Index Average Sales Ratios Index (Normalized) Total Total Seasonal Ratios Year Quarter 1 Quarter 2 Quarter 3 Quarter Average Seasonal Indices 6
7 c) For Overall Average Year 4 Q1 = Q2 = 4860 Q3 = 5400 Q4 = For Centred Average Year 4 Q1 = Q2 = 5230 Q3 = 4210 Q4 = In the case of centred average, better to allocate an annual estimate of with the normalized indices. Year 4 Q1 = Q2 = 5480 Q3 = 4400 Q4 = ) a) From Summary Output Regression equation ŷ = x x x 3 Coefficient of Determination = R 2 = Correlation Coefficient = Standard Error of Estimate = ± Meanings  see Practice Q1 and Causal Forecasting Model notes b) For each additional square foot of floor area, we expect sales to increase by $, all else being held constant. For each additional parking place, we expect sales to increase by $, all else being held constant. For each additional ( 000)$ average income, we expect sales to decrease by $ c) $1781 d) t stat (Floor Area) = 4.479; Lower 95% (Parking Spaces) 4.269, Upper 95% (Parking Spaces) e) H 0 : β 1 = β 2 = β 3 = 0 H 1 : Not all β s are 0 F Crit = So we reject H 0 and conclude that the linear relationship exists and at least one of the regression coefficients is not zero. 7
8 f) For Floor Area: For Parking Spaces: For Income: H 0 : β 1 = 0 H 0 : β 2 = 0 H 0 : β 3 = 0 H 0 : β 1 0 H 0 : β 2 0 H 0 : β 3 0 Critical t value = (or ) Floor Area and Parking Spaces, we reject the null hypothesis and keep these two variables in the model. We fail to reject the null hypothesis and discard Income as an independent variable. 4) ANOVA df SS MS F Regression Residual (Error) Total
Regression stepbystep using Microsoft Excel
Step 1: Regression stepbystep using Microsoft Excel Notes prepared by Pamela Peterson Drake, James Madison University Type the data into the spreadsheet The example used throughout this How to is a regression
More informationSimple Methods and Procedures Used in Forecasting
Simple Methods and Procedures Used in Forecasting The project prepared by : Sven Gingelmaier Michael Richter Under direction of the Maria JadamusHacura What Is Forecasting? Prediction of future events
More information2. What is the general linear model to be used to model linear trend? (Write out the model) = + + + or
Simple and Multiple Regression Analysis Example: Explore the relationships among Month, Adv.$ and Sales $: 1. Prepare a scatter plot of these data. The scatter plots for Adv.$ versus Sales, and Month versus
More informationFinal Exam Practice Problem Answers
Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal
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 1propZint 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 informationModule 6: Introduction to Time Series Forecasting
Using Statistical Data to Make Decisions Module 6: Introduction to Time Series Forecasting Titus Awokuse and Tom Ilvento, University of Delaware, College of Agriculture and Natural Resources, Food and
More informationA Primer on Forecasting Business Performance
A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.
More informationOutline: Demand Forecasting
Outline: Demand Forecasting Given the limited background from the surveys and that Chapter 7 in the book is complex, we will cover less material. The role of forecasting in the chain Characteristics of
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 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 informationSPSS Guide: Regression Analysis
SPSS Guide: Regression Analysis I put this together to give you a stepbystep guide for replicating what we did in the computer lab. It should help you run the tests we covered. The best way to get familiar
More informationNot Your Dad s Magic Eight Ball
Not Your Dad s Magic Eight Ball Prepared for the NCSL Fiscal Analysts Seminar, October 21, 2014 Jim Landers, Office of Fiscal and Management Analysis, Indiana Legislative Services Agency Actual Forecast
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 Linear Regression Inference
Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation
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 information" Y. Notation and Equations for Regression Lecture 11/4. Notation:
Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through
More informationMGT 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
More informationPremaster Statistics Tutorial 4 Full solutions
Premaster Statistics Tutorial 4 Full solutions Regression analysis Q1 (based on Doane & Seward, 4/E, 12.7) a. Interpret the slope of the fitted regression = 125,000 + 150. b. What is the prediction for
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 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 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 informationProduction Planning. Chapter 4 Forecasting. Overview. Overview. Chapter 04 Forecasting 1. 7 Steps to a Forecast. What is forecasting?
Chapter 4 Forecasting Production Planning MRP Purchasing Sales Forecast Aggregate Planning Master Production Schedule Production Scheduling Production What is forecasting? Types of forecasts 7 steps of
More informationChapter 3 Quantitative Demand Analysis
Managerial Economics & Business Strategy Chapter 3 uantitative Demand Analysis McGrawHill/Irwin Copyright 2010 by the McGrawHill Companies, Inc. All rights reserved. Overview I. The Elasticity Concept
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 informationTheory at a Glance (For IES, GATE, PSU)
1. Forecasting Theory at a Glance (For IES, GATE, PSU) Forecasting means estimation of type, quantity and quality of future works e.g. sales etc. It is a calculated economic analysis. 1. Basic elements
More informationEstimation of σ 2, the variance of ɛ
Estimation of σ 2, the variance of ɛ The variance of the errors σ 2 indicates how much observations deviate from the fitted surface. If σ 2 is small, parameters β 0, β 1,..., β k will be reliably estimated
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 informationForecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model
Tropical Agricultural Research Vol. 24 (): 23 (22) Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model V. Sivapathasundaram * and C. Bogahawatte Postgraduate Institute
More informationGeneralized Linear Models
Generalized Linear Models We have previously worked with regression models where the response variable is quantitative and normally distributed. Now we turn our attention to two types of models where the
More informationMultiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear.
Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear. In the main dialog box, input the dependent variable and several predictors.
More informationCh.3 Demand Forecasting.
Part 3 : Acquisition & Production Support. Ch.3 Demand Forecasting. Edited by Dr. Seung Hyun Lee (Ph.D., CPL) IEMS Research Center, Email : lkangsan@iems.co.kr Demand Forecasting. Definition. An estimate
More informationMultiple Linear Regression
Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is
More informationKSTAT MINIMANUAL. Decision Sciences 434 Kellogg Graduate School of Management
KSTAT MINIMANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To
More informationForecasting Analytics. Group members:  Arpita  Kapil  Kaushik  Ridhima  Ushhan
Forecasting Analytics Group members:  Arpita  Kapil  Kaushik  Ridhima  Ushhan Business Problem Forecast daily sales of dairy products (excluding milk) to make a good prediction of future demand, and
More information2) The three categories of forecasting models are time series, quantitative, and qualitative. 2)
Exam Name TRUE/FALSE. Write 'T' if the statement is true and 'F' if the statement is false. 1) Regression is always a superior forecasting method to exponential smoothing, so regression should be used
More informationData Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
More informationUsing R for Linear Regression
Using R for Linear Regression In the following handout words and symbols in bold are R functions and words and symbols in italics are entries supplied by the user; underlined words and symbols are optional
More informationAugust 2012 EXAMINATIONS Solution Part I
August 01 EXAMINATIONS Solution Part I (1) In a random sample of 600 eligible voters, the probability that less than 38% will be in favour of this policy is closest to (B) () In a large random sample,
More informationTHE UNIVERSITY OF CHICAGO, Booth School of Business Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay. Solutions to Homework Assignment #2
THE UNIVERSITY OF CHICAGO, Booth School of Business Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay Solutions to Homework Assignment #2 Assignment: 1. Consumer Sentiment of the University of Michigan.
More informationObjectives of Chapters 7,8
Objectives of Chapters 7,8 Planning Demand and Supply in a SC: (Ch7, 8, 9) Ch7 Describes methodologies that can be used to forecast future demand based on historical data. Ch8 Describes the aggregate planning
More informationSection A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA  Part I
Index Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1 EduPristine CMA  Part I Page 1 of 11 Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting
More informationDisease Management Study Documents LongerTerm Economic Benefits
Disease Management Study Documents LongerTerm Economic Benefits Presented by: David S. Krause DMAA's 5th Annual Disease Management Leadership Forum October 13, 2003 Chicago, IL Disease Management Study
More informationSection 1: Simple Linear Regression
Section 1: Simple Linear Regression Carlos M. Carvalho The University of Texas McCombs School of Business http://faculty.mccombs.utexas.edu/carlos.carvalho/teaching/ 1 Regression: General Introduction
More informationHypothesis testing  Steps
Hypothesis testing  Steps Steps to do a twotailed test of the hypothesis that β 1 0: 1. Set up the hypotheses: H 0 : β 1 = 0 H a : β 1 0. 2. Compute the test statistic: t = b 1 0 Std. error of b 1 =
More informationNominal and Real U.S. GDP 19602001
Problem Set #5Key Sonoma State University Dr. Cuellar Economics 318 Managerial Economics Use the data set for gross domestic product (gdp.xls) to answer the following questions. (1) Show graphically
More informationOneWay Analysis of Variance: A Guide to Testing Differences Between Multiple Groups
OneWay Analysis of Variance: A Guide to Testing Differences Between Multiple Groups In analysis of variance, the main research question is whether the sample means are from different populations. The
More informationIndian School of Business Forecasting Sales for Dairy Products
Indian School of Business Forecasting Sales for Dairy Products Contents EXECUTIVE SUMMARY... 3 Data Analysis... 3 Forecast Horizon:... 4 Forecasting Models:... 4 Fresh milk  AmulTaaza (500 ml)... 4 Dahi/
More informationStock Valuation: Gordon Growth Model. Week 2
Stock Valuation: Gordon Growth Model Week 2 Approaches to Valuation 1. Discounted Cash Flow Valuation The value of an asset is the sum of the discounted cash flows. 2. Contingent Claim Valuation A contingent
More informationTesting for Lack of Fit
Chapter 6 Testing for Lack of Fit How can we tell if a model fits the data? If the model is correct then ˆσ 2 should be an unbiased estimate of σ 2. If we have a model which is not complex enough to fit
More informationUSING THE ETS MAJOR FIELD TEST IN BUSINESS TO COMPARE ONLINE AND CLASSROOM STUDENT LEARNING
USING THE ETS MAJOR FIELD TEST IN BUSINESS TO COMPARE ONLINE AND CLASSROOM STUDENT LEARNING Andrew Tiger, Southeastern Oklahoma State University, atiger@se.edu Jimmy Speers, Southeastern Oklahoma State
More informationTimeSeries Forecasting and Index Numbers
CHAPTER 15 TimeSeries Forecasting and Index Numbers LEARNING OBJECTIVES This chapter discusses the general use of forecasting in business, several tools that are available for making business forecasts,
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 informationForecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs
PRODUCTION PLANNING AND CONTROL CHAPTER 2: FORECASTING Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs
More informationCantonMassillon PM2.5 Nonattainment Area Monitor Missing Data Analysis
CantonMassillon PM2.5 Nonattainment Area Monitor Missing Data Analysis The current CantonMassillon nonattainment area is located in norast Ohio and includes Stark County. The area has two monitors measuring
More information1.1. Simple Regression in Excel (Excel 2010).
.. Simple Regression in Excel (Excel 200). To get the Data Analysis tool, first click on File > Options > AddIns > Go > Select Data Analysis Toolpack & Toolpack VBA. Data Analysis is now available under
More informationNIKE Case Study Solutions
NIKE Case Study Solutions Professor Corwin This case study includes several problems related to the valuation of Nike. We will work through these problems throughout the course to demonstrate some of the
More informationDidacticiel  Études de cas
1 Topic Regression analysis with LazStats (OpenStat). LazStat 1 is a statistical software which is developed by Bill Miller, the father of OpenStat, a wellknow tool by statisticians since many years. These
More informationForecasting in supply chains
1 Forecasting in supply chains Role of demand forecasting Effective transportation system or supply chain design is predicated on the availability of accurate inputs to the modeling process. One of the
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 information5. Linear Regression
5. Linear Regression Outline.................................................................... 2 Simple linear regression 3 Linear model............................................................. 4
More informationHedge Effectiveness Testing
Hedge Effectiveness Testing Using Regression Analysis Ira G. Kawaller, Ph.D. Kawaller & Company, LLC Reva B. Steinberg BDO Seidman LLP When companies use derivative instruments to hedge economic exposures,
More informationDemand Forecasting When a product is produced for a market, the demand occurs in the future. The production planning cannot be accomplished unless
Demand Forecasting When a product is produced for a market, the demand occurs in the future. The production planning cannot be accomplished unless the volume of the demand known. The success of the business
More informationForecasting DISCUSSION QUESTIONS
4 C H A P T E R Forecasting DISCUSSION QUESTIONS 1. Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When
More informationFactors affecting online sales
Factors affecting online sales Table of contents Summary... 1 Research questions... 1 The dataset... 2 Descriptive statistics: The exploratory stage... 3 Confidence intervals... 4 Hypothesis tests... 4
More informationTIME SERIES ANALYSIS & FORECASTING
CHAPTER 19 TIME SERIES ANALYSIS & FORECASTING Basic Concepts 1. Time Series Analysis BASIC CONCEPTS AND FORMULA The term Time Series means a set of observations concurring any activity against different
More informationDEPARTMENT 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,
More informationMultiple Regression Analysis A Case Study
Multiple Regression Analysis A Case Study Case Study Method 1 The first step in a case study analysis involves research into the subject property and a determination of the key factors that impact that
More information11. Analysis of Casecontrol Studies Logistic Regression
Research methods II 113 11. Analysis of Casecontrol Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:
More information1 Simple Linear Regression I Least Squares Estimation
Simple Linear Regression I Least Squares Estimation Textbook Sections: 8. 8.3 Previously, we have worked with a random variable x that comes from a population that is normally distributed with mean µ and
More informationChapter 9: Univariate Time Series Analysis
Chapter 9: Univariate Time Series Analysis In the last chapter we discussed models with only lags of explanatory variables. These can be misleading if: 1. The dependent variable Y t depends on lags of
More informationOneWay Analysis of Variance (ANOVA) Example Problem
OneWay Analysis of Variance (ANOVA) Example Problem Introduction Analysis of Variance (ANOVA) is a hypothesistesting technique used to test the equality of two or more population (or treatment) means
More informationSlides Prepared by JOHN S. LOUCKS St. Edward s University
s Prepared by JOHN S. LOUCKS St. Edward s University 2002 SouthWestern/Thomson Learning 1 Chapter 18 Forecasting Time Series and Time Series Methods Components of a Time Series Smoothing Methods Trend
More informationEconometrics I: Econometric Methods
Econometrics I: Econometric Methods Jürgen Meinecke Research School of Economics, Australian National University 24 May, 2016 Housekeeping Assignment 2 is now history The ps tute this week will go through
More informationDemand forecasting & Aggregate planning in a Supply chain. Session Speaker Prof.P.S.Satish
Demand forecasting & Aggregate planning in a Supply chain Session Speaker Prof.P.S.Satish 1 Introduction PEMPEMM2506 Forecasting provides an estimate of future demand Factors that influence demand and
More informationDemand Management Where Practice Meets Theory
Demand Management Where Practice Meets Theory Elliott S. Mandelman 1 Agenda What is Demand Management? Components of Demand Management (Not just statistics) Best Practices Demand Management Performance
More informationBusiness Valuation Review
Business Valuation Review Regression Analysis in Valuation Engagements By: George B. Hawkins, ASA, CFA Introduction Business valuation is as much as art as it is science. Sage advice, however, quantitative
More informationijcrb.com INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS OCTOBER 2014 VOL 6, NO 6
T h e r e l a t i o n s h i p b e t w e e n m e a s u r e s o f c o r p o r a t e g o v e r n a n c e a n d b a n k l o a n s as c a p i t a l s t r u c t u r e 1  G h a s e m R a y a t i S h a v a z
More information16 : Demand Forecasting
16 : Demand Forecasting 1 Session Outline Demand Forecasting Subjective methods can be used only when past data is not available. When past data is available, it is advisable that firms should use statistical
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 informationInteraction between quantitative predictors
Interaction between quantitative predictors In a firstorder model like the ones we have discussed, the association between E(y) and a predictor x j does not depend on the value of the other predictors
More informationCausal Forecasting Models
CTL.SC1x Supply Chain & Logistics Fundamentals Causal Forecasting Models MIT Center for Transportation & Logistics Causal Models Used when demand is correlated with some known and measurable environmental
More informationGLM I An Introduction to Generalized Linear Models
GLM I An Introduction to Generalized Linear Models CAS Ratemaking and Product Management Seminar March 2009 Presented by: Tanya D. Havlicek, Actuarial Assistant 0 ANTITRUST Notice The Casualty Actuarial
More informationSIMPLE LINEAR CORRELATION. r can range from 1 to 1, and is independent of units of measurement. Correlation can be done on two dependent variables.
SIMPLE LINEAR CORRELATION Simple linear correlation is a measure of the degree to which two variables vary together, or a measure of the intensity of the association between two variables. Correlation
More informationSales Forecasting System for Chemicals Supplying Enterprises
Sales Forecasting System for Chemicals Supplying Enterprises Ma. Del Rocio Castillo E. 1, Ma. Magdalena Chain Palavicini 1, Roberto Del Rio Soto 1 & M. Javier Cruz Gómez 2 1 Facultad de Contaduría y Administración,
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 informationElements of statistics (MATH04871)
Elements of statistics (MATH04871) Prof. Dr. Dr. K. Van Steen University of Liège, Belgium December 10, 2012 Introduction to Statistics Basic Probability Revisited Sampling Exploratory Data Analysis 
More informationExponential Smoothing with Trend. As we move toward mediumrange forecasts, trend becomes more important.
Exponential Smoothing with Trend As we move toward mediumrange forecasts, trend becomes more important. Incorporating a trend component into exponentially smoothed forecasts is called double exponential
More informationThis chapter will demonstrate how to perform multiple linear regression with IBM SPSS
CHAPTER 7B Multiple Regression: Statistical Methods Using IBM SPSS This chapter will demonstrate how to perform multiple linear regression with IBM SPSS first using the standard method and then using the
More informationTIME SERIES ANALYSIS
TIME SERIES ANALYSIS Ramasubramanian V. I.A.S.R.I., Library Avenue, New Delhi 110 012 ram_stat@yahoo.co.in 1. Introduction A Time Series (TS) is a sequence of observations ordered in time. Mostly these
More informationOutline. Topic 4  Analysis of Variance Approach to Regression. Partitioning Sums of Squares. Total Sum of Squares. Partitioning sums of squares
Topic 4  Analysis of Variance Approach to Regression Outline Partitioning sums of squares Degrees of freedom Expected mean squares General linear test  Fall 2013 R 2 and the coefficient of correlation
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More information8. Time Series and Prediction
8. Time Series and Prediction Definition: A time series is given by a sequence of the values of a variable observed at sequential points in time. e.g. daily maximum temperature, end of day share prices,
More informationTesting for Granger causality between stock prices and economic growth
MPRA Munich Personal RePEc Archive Testing for Granger causality between stock prices and economic growth Pasquale Foresti 2006 Online at http://mpra.ub.unimuenchen.de/2962/ MPRA Paper No. 2962, posted
More informationForecasting Methods. What is forecasting? Why is forecasting important? How can we evaluate a future demand? How do we make mistakes?
Forecasting Methods What is forecasting? Why is forecasting important? How can we evaluate a future demand? How do we make mistakes? Prod  Forecasting Methods Contents. FRAMEWORK OF PLANNING DECISIONS....
More informationChicago Booth BUSINESS STATISTICS 41000 Final Exam Fall 2011
Chicago Booth BUSINESS STATISTICS 41000 Final Exam Fall 2011 Name: Section: I pledge my honor that I have not violated the Honor Code Signature: This exam has 34 pages. You have 3 hours to complete this
More informationWe extended the additive model in two variables to the interaction model by adding a third term to the equation.
Quadratic Models We extended the additive model in two variables to the interaction model by adding a third term to the equation. Similarly, we can extend the linear model in one variable to the quadratic
More informationMULTIPLE LINEAR REGRESSION ANALYSIS USING MICROSOFT EXCEL. by Michael L. Orlov Chemistry Department, Oregon State University (1996)
MULTIPLE LINEAR REGRESSION ANALYSIS USING MICROSOFT EXCEL by Michael L. Orlov Chemistry Department, Oregon State University (1996) INTRODUCTION In modern science, regression analysis is a necessary part
More informationTime Series Analysis
Time Series Analysis Identifying possible ARIMA models Andrés M. Alonso Carolina GarcíaMartos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and GarcíaMartos
More informationCHAPTER 11 FORECASTING AND DEMAND PLANNING
OM CHAPTER 11 FORECASTING AND DEMAND PLANNING DAVID A. COLLIER AND JAMES R. EVANS 1 Chapter 11 Learning Outcomes l e a r n i n g o u t c o m e s LO1 Describe the importance of forecasting to the value
More informationWeek 5: Multiple Linear Regression
BUS41100 Applied Regression Analysis Week 5: Multiple Linear Regression Parameter estimation and inference, forecasting, diagnostics, dummy variables Robert B. Gramacy The University of Chicago Booth School
More information