3. If a forecast is too high when compared to an actual outcome, will that forecast error be positive or negative?

Size: px
Start display at page:

Download "3. If a forecast is too high when compared to an actual outcome, will that forecast error be positive or negative?"

Transcription

1 1. Does a moving average forecast become more or less responsive to changes in a data series when more data points are included in the average? 2. Does an exponential smoothing forecast become more or less responsive to changes in a data series when its alpha is increased? 3. If a forecast is too high when compared to an actual outcome, will that forecast error be positive or negative? 4. Are quantitative forecasting models generally used for shorter-term or longerterm decision making when compared to qualitative approaches? 5. What do quantitative forecasting techniques require, the absence of which makes them ineffective tools for predicting the future? 6. In time series analysis, any pattern that regularly repeats itself and is constant in length is referred to as what? 7. The use of which alpha would result in a smoother forecast, α = 0.2 or α = 0.5? 8. A forecasting technique that takes the previous forecast and adds some percentage of the previous forecast s error is called what? Chapter Four Forecasting Minute Answer Questions #1 to 12 PDF A1M040-01

2 9. Which forecasting technique is more qualitative, the Delphi method or linear regression? 10. Which calculation would reveal the bias in a forecasting technique, the mean error or mean absolute deviation? 11. To calculate a forecast s percent error, the forecast error is divided by what? 12. Assuming the same set of data is used in their calculation, which will always be the smaller (or perhaps equal) value, the correlation coefficient or the coefficient of determination? Chapter Four Forecasting Minute Answer Questions #1 to 12 PDF A1M040-01

3 Given forecast errors of 5, 10, 10, 0, 10, what is the ME? Given forecast errors of 4, 8, and 3, what is the MAD? What is the MSE? Given forecast errors of 4, 8, and 3, what is the tracking signal? Chapter Four Forecasting Quick Start #13-15 PDF A1Q043-01

4 Given forecast errors of 3, 2, 2, and 9, what is the tracking signal? Given forecast errors of 10, 2, 25, and 0 when actual values were 120, 145, 275, and 124, what is the mean absolute percent error? Chapter Four Forecasting Quick Start #16-17 PDF A1Q043-02

5 Given an actual demand of 34 this period, a predicted value of 45 this period, and an alpha of 0.2, what would be the simple exponential smoothing forecast for the next period? Chapter Four Forecasting Quick Start #18 PDF A1Q042-01

6 Given a forecast of 1,405 and an actual outcome of 1,670, what is the error in the forecast? What is the percent error? Given a forecast of 2,105 and an actual outcome of 1,980, what is the error in the forecast? What is the percent error? Chapter Four Forecasting Quick Start #19-20 PDF A1Q043-03

7 You are observing the sales department staff using exponential smoothing to forecast monthly sales. Their forecast for January s sales was 12,000 units. January s actual sales figure became available yesterday: 10,000 units. Today, the sales department announced their sales forecast of 11,300 units for February. What alpha are they using to forecast sales? Chapter Four Forecasting Ramp-up #21 PDF A1R042-01

8 The marketing department has just forecast that 10,000 units of item 778 will be ordered in the next fiscal year. Based on the marketing department s forecast and noting that the seasonal relative associated with the second fiscal quarter is 1.25, how many units of item 778 will be ordered during the second fiscal quarter? Chapter Four Forecasting Ramp-up #22 PDF A1R044-01

9 Here are the errors associated with a particular forecast over the past 5 months, in chronological order: 5, 10, 15, 0, 8. In which month was the forecast perfectly accurate? In which month was the forecast the least accurate? In which month or months was the forecast too high? Chapter Four Forecasting Ramp-up #23 PDF A1R043-01

10 A media company is investigating the relationship between movie ticket sales and book sales. The company has gathered information on 30 movies that were based on published books, with total movie ticket sales of $7,058 million. Threeyear sales of each movie s corresponding book totaled $3,136 million. When each movie s ticket sales is squared and these numbers summed together, the resulting value is $1,885,412 million, while squaring and summing together the corresponding 3-year book sales results in $551,300 million. Multiplying each movie s ticket sales by its 3-year book sales and summing these values equals $872,486 million. What are the correlation coefficient and the coefficient of determination for this hypothesized relationship between movie ticket sales and book sales? Chapter Four Forecasting Ramp-up #24 PDF A1R041-01

11 Tutoring Center needs to allocate tutors this week for office appointments, so it needs to forecast the number of students who will seek appointments. The director has gathered the following time series data recently: Period Student Appointments 4 weeks ago 95 3 weeks ago 80 2 weeks ago 65 Last week 50 Student Appointments a. What would naive forecasting suggest as the number of student appointments that can be expected this week? b. What is this week s forecast for student appointments using a 3-week moving average? What would the same forecast be using a 2-week moving average? Chapter Four Forecasting Scenario #25 PDF A1F042-01

12 c. What would be this week s forecast for student appointments using exponential smoothing with alpha of 0.2, if the forecast for 2 weeks ago was 90? d. If the director used these 4 weeks of data to create a linear regression, what does that linear regression formula suggest for this week s forecast of student appointments? What does the regression analysis suggest in general about student appointments at the Tutoring Center? Chapter Four Forecasting Scenario #25 PDF A1F042-01

13 Below are the seasonal relatives that describe the weekly fluctuation in the number of distinct users logging into a website daily, also known as the number of unique appearances per day: Day of the Week Seasonal Relative Monday 1.25 Tuesday 1.01 Wednesday 1.03 Thursday 1.09 Friday 0.94 Saturday 0.66 Sunday 1.01 of the Week Seasonal Relative a. Generally, what is the busiest day of the week for unique appearances on this website? Which is the least busy day of the week? b. Last week, 750 unique appearances were observed on Monday, followed by 650 on Tuesday. Deseasonalize these numbers for comparison to each other. What does this suggest about Monday and Tuesday of last week? Chapter Four Forecasting Scenario #26 PDF A1F044-01

14 c. A manager has forecast for the first full week of next month: an overall number of 3,500 unique appearances will be recorded throughout the 7 days of that week. Based on this estimate, which of the following is the most logical estimate of the number of unique appearances during the Thursday of that week? What is the most logical estimate of number of unique appearances during the Friday of that week? Chapter Four Forecasting Scenario #26 PDF A1F044-01

15 The South Florida Water Management District (SFWMD) must develop a linear regression model that can be used to estimate the fresh water needs of various communities. SFWMD has collected data on 50 communities, noting each community s population and total annual fresh water consumption. Using this data, you have calculated the following regression equation: Y = X where X is the population of the community and Y is the total annual fresh water consumption, in acre-feet. (An acre-foot is enough water to cover 1 acre, 1 foot deep.) a. Using this regression model for estimation, how much fresh water would a community of 1,500 people consume each year? A community of 45,000? b. According to this regression model, each new person who moves to a community increases its annual fresh water consumption by how much? c. According to this regression model, if a town were abandoned, such that no one was living there for the entire year, what would the town s fresh water consumption be? Is this possible? Chapter Four Forecasting Scenario #27 PDF A1F041-01

16 The manager of a building supply center suspects that the monthly sale of rolled insulation for installation in attics depends on the average air temperature during the month. The manager has a spreadsheet with 42 months of past data on this subject, in which the overall average monthly temperature was 49 degrees Fahrenheit and the overall average sales of rolled insulation was 428 rolls a month. On that spreadsheet, the manager has created a column in which each month s average air temperature is multiplied by each month s sales of rolled insulation, and then these 42 numbers are summed together to create the value 873,931. In another column of the spreadsheet, the manager has squared each month s average air temperature, and then summed the 42 squared temperatures to obtain a value of 105,080. a. Next month, the average air temperature is expected to be 35 degrees Fahrenheit. Use linear regression to predict next month s sales of rolled insulation. Chapter Four Forecasting Scenario #28 PDF A1F041-02

17 b. The sum of the 42 months of average air temperature readings is 2,058 and the total sales of rolled insulation over that same time period was 17,976 rolls. The sum of the squared monthly sales of rolled insulation was 7,710,080. What percent of the variation in the sale of rolled insulation is explained by its linear relationship to average temperature during these past 42 months? c. What is the value of the correlation coefficient between average air temperature and the sale of rolled insulation? What does this value suggest about the relationship between average air temperature and the sale of rolled insulation? Chapter Four Forecasting Scenario #28 PDF A1F041-02

18 A manager has been using a certain technique to forecast demand for project management software at her store. Actual demand and her corresponding predictions are shown below: Month Actual Demand Manager s Forecast March April May June July Month Actual Demand Manager s Forecast a. What was the manager s forecast error for April? b. What was the manager s forecast percent error for July? c. What are the mean error, the mean squared error, the mean absolute deviation, the mean absolute percent error, and the tracking signal for these 5 months of forecasting? Chapter Four Forecasting Scenario #29a-c PDF A1S043-01

19 A manager has been using a certain technique to forecast demand for project management software at her store. Actual demand and her corresponding predictions are shown below: Month Actual Demand Manager s Forecast March April May June July Month If the manager had used a 3-month moving average instead of her technique, what would have been her forecast for June? What would have been her percent error? If the manager had used simple exponential smoothing with α = 0.2 instead of her technique, what would the forecast for August be, assuming that simple exponential smoothing had produced a perfectly accurate forecast in March? and Manager s Forecast Chapter Four Forecasting Scenario #29d-e PDF A1S042-01

20 The service center at a large automobile dealership is trying to boost revenue by providing no-appointment-necessary oil changes to any type of vehicle that stops by the service center. To quickly estimate the number of these oil changes that the service center can be expected to complete in the upcoming week, the service manager has been using simple exponential smoothing with an alpha value of 0.1. The number of no-appointment-necessary oil changes that the service center completed over the past 4 weeks is listed here: 4 weeks ago: 25 3 weeks ago: 36 2 weeks ago: 33 Last week: 28 Last week, the service manager s forecast for the number of no-appointmentnecessary oil changes was a. What would be the service manager s forecast for this next week? b. What was the error in the service manager s forecast last week? c. Suppose the service manager decided to instead use a 3-week moving average. Now what would be the service manager s forecast for this next week? Chapter Four Forecasting Scenario #30 PDF A1F042-02

21 Block Commodities has gathered the following information concerning rock salt deliveries to its clients, which it believes are highly seasonal: Total Block Commodities Rock Salt Deliveries (tons) Year 1 Year 2 Year 3 Year 4 Average Monthly January February March April May June July August September October November December Total Annual a) Suppose Block Commodities calculated a set of seasonal relatives to express this monthly variation in rock salt deliveries, using this set of data. What would the value of the seasonal relative for the month of July? What would the value of the seasonal relative for the month of December? Chapter Four Forecasting Scenario #31 PDF A1F044-02

22 b) Block Commodities believes that this year will be a busy year for rock salt deliveries, forecasting a total of 1,200 tons to be delivered during the year. Using this annual forecast and Block s set of seasonal relatives, what would be a logical forecast for May of next year? What would be a logical forecast for October of next year? Chapter Four Forecasting Scenario #31 PDF A1F044-02

23 Dunkirk Consulting wishes to predict the amount of overhead expense that will be incurred by a consulting contract, to develop more accurate bids for future contracts. Dunkirk has the following data on 11 completed contracts, detailing how long the project took to complete and what the exact overhead expense was: Project C Project Code Name Project Duration (Days) Overhead Expense A A A A A B B B B B B ode Name Project Duration (Days) Overhead Expense, $ a. Using linear regression, develop a regression formula for predicting overhead expense, based on the duration of the project. Chapter Four Forecasting Scenario #32 PDF A1F041-03

24 b. What is the R 2 of this linear regression model? What does that mean? c. What does linear regression suggest about the overhead expense of a future project that is expected to be 110 days long? Chapter Four Forecasting Scenario #32 PDF A1F041-03

25 d. A manager at Dunkirk Consulting feels that the overhead expenses of supply chain consulting projects are generally different from the overhead expenses of marketing consulting projects, because supply chain projects generally require more travel, while marketing projects generally require more spending on external media services. (In the table above, codes for supply chain projects begin with an A while marketing projects begin with a B. ) Considering this observation by the manager, develop a better way to use linear regression to forecast overhead expense. Using this method, how much overhead expense would be predicted for a 110-day project if it were a supply chain project? How much overhead expense for a 110-day marketing project? Project Code Name Project Duration (Days) Overhead Expense A A A A A B B B B B B Chapter Four Forecasting Scenario #32 PDF A1F041-03

Forecasting. Chapter Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall

Forecasting. Chapter Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15 15-1 Chapter Topics Forecasting Components Time Series Methods Forecast Accuracy Time Series Forecasting Using Excel Time Series Forecasting Using QM for Windows Regression Methods

More information

Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall.

Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall. 13 Forecasting PowerPoint Slides by Jeff Heyl For Operations Management, 9e by Krajewski/Ritzman/Malhotra 2010 Pearson Education 13 1 Forecasting Forecasts are critical inputs to business plans, annual

More information

Product and Inventory Management (35E00300) Forecasting Models Trend analysis

Product and Inventory Management (35E00300) Forecasting Models Trend analysis Product and Inventory Management (35E00300) Forecasting Models Trend analysis Exponential Smoothing Data Storage Shed Sales Period Actual Value(Y t ) Ŷ t-1 α Y t-1 Ŷ t-1 Ŷ t January 10 = 10 0.1 February

More information

5, 0. Math 112 Fall 2017 Midterm 1 Review Problems Page Which one of the following points lies on the graph of the function f ( x) (A) (C) (B)

5, 0. Math 112 Fall 2017 Midterm 1 Review Problems Page Which one of the following points lies on the graph of the function f ( x) (A) (C) (B) Math Fall 7 Midterm Review Problems Page. Which one of the following points lies on the graph of the function f ( ) 5?, 5, (C) 5,,. Determine the domain of (C),,,, (E),, g. 5. Determine the domain of h

More information

Math 112 Spring 2018 Midterm 1 Review Problems Page 1

Math 112 Spring 2018 Midterm 1 Review Problems Page 1 Math Spring 8 Midterm Review Problems Page Note: Certain eam questions have been more challenging for students. Questions marked (***) are similar to those challenging eam questions.. Which one of the

More information

INTRODUCTION TO FORECASTING (PART 2) AMAT 167

INTRODUCTION TO FORECASTING (PART 2) AMAT 167 INTRODUCTION TO FORECASTING (PART 2) AMAT 167 Techniques for Trend EXAMPLE OF TRENDS In our discussion, we will focus on linear trend but here are examples of nonlinear trends: EXAMPLE OF TRENDS If you

More information

Chapter 5: Forecasting

Chapter 5: Forecasting 1 Textbook: pp. 165-202 Chapter 5: Forecasting Every day, managers make decisions without knowing what will happen in the future 2 Learning Objectives After completing this chapter, students will be able

More information

Forecasting. Copyright 2015 Pearson Education, Inc.

Forecasting. Copyright 2015 Pearson Education, Inc. 5 Forecasting To accompany Quantitative Analysis for Management, Twelfth Edition, by Render, Stair, Hanna and Hale Power Point slides created by Jeff Heyl Copyright 2015 Pearson Education, Inc. LEARNING

More information

Determine the trend for time series data

Determine the trend for time series data Extra Online Questions Determine the trend for time series data Covers AS 90641 (Statistics and Modelling 3.1) Scholarship Statistics and Modelling Chapter 1 Essent ial exam notes Time series 1. The value

More information

Antti Salonen PPU Le 2: Forecasting 1

Antti Salonen PPU Le 2: Forecasting 1 - 2017 1 Forecasting Forecasts are critical inputs to business plans, annual plans, and budgets Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan: output

More information

Chapter 8 - Forecasting

Chapter 8 - Forecasting Chapter 8 - Forecasting Operations Management by R. Dan Reid & Nada R. Sanders 4th Edition Wiley 2010 Wiley 2010 1 Learning Objectives Identify Principles of Forecasting Explain the steps in the forecasting

More information

Economics 390 Economic Forecasting

Economics 390 Economic Forecasting Economics 390 Economic Forecasting Prerequisite: Econ 410 or equivalent Course information is on website Office Hours Tuesdays & Thursdays 2:30 3:30 or by appointment Textbooks Forecasting for Economics

More information

Industrial Engineering Prof. Inderdeep Singh Department of Mechanical & Industrial Engineering Indian Institute of Technology, Roorkee

Industrial Engineering Prof. Inderdeep Singh Department of Mechanical & Industrial Engineering Indian Institute of Technology, Roorkee Industrial Engineering Prof. Inderdeep Singh Department of Mechanical & Industrial Engineering Indian Institute of Technology, Roorkee Module - 04 Lecture - 05 Sales Forecasting - II A very warm welcome

More information

PPU411 Antti Salonen. Forecasting. Forecasting PPU Forecasts are critical inputs to business plans, annual plans, and budgets

PPU411 Antti Salonen. Forecasting. Forecasting PPU Forecasts are critical inputs to business plans, annual plans, and budgets - 2017 1 Forecasting Forecasts are critical inputs to business plans, annual plans, and budgets Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan: output

More information

Antti Salonen KPP Le 3: Forecasting KPP227

Antti Salonen KPP Le 3: Forecasting KPP227 - 2015 1 Forecasting Forecasts are critical inputs to business plans, annual plans, and budgets Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan: output

More information

Every day, health care managers must make decisions about service delivery

Every day, health care managers must make decisions about service delivery Y CHAPTER TWO FORECASTING Every day, health care managers must make decisions about service delivery without knowing what will happen in the future. Forecasts enable them to anticipate the future and plan

More information

Chapter 1 0+7= 1+6= 2+5= 3+4= 4+3= 5+2= 6+1= 7+0= How would you write five plus two equals seven?

Chapter 1 0+7= 1+6= 2+5= 3+4= 4+3= 5+2= 6+1= 7+0= How would you write five plus two equals seven? Chapter 1 0+7= 1+6= 2+5= 3+4= 4+3= 5+2= 6+1= 7+0= If 3 cats plus 4 cats is 7 cats, what does 4 olives plus 3 olives equal? olives How would you write five plus two equals seven? Chapter 2 Tom has 4 apples

More information

A Plot of the Tracking Signals Calculated in Exhibit 3.9

A Plot of the Tracking Signals Calculated in Exhibit 3.9 CHAPTER 3 FORECASTING 1 Measurement of Error We can get a better feel for what the MAD and tracking signal mean by plotting the points on a graph. Though this is not completely legitimate from a sample-size

More information

Forecasting Chapter 3

Forecasting Chapter 3 Forecasting Chapter 3 Introduction Current factors and conditions Past experience in a similar situation 2 Accounting. New product/process cost estimates, profit projections, cash management. Finance.

More information

Operations Management

Operations Management 3-1 Forecasting Operations Management William J. Stevenson 8 th edition 3-2 Forecasting CHAPTER 3 Forecasting McGraw-Hill/Irwin Operations Management, Eighth Edition, by William J. Stevenson Copyright

More information

Forecasting: The First Step in Demand Planning

Forecasting: The First Step in Demand Planning Forecasting: The First Step in Demand Planning Jayant Rajgopal, Ph.D., P.E. University of Pittsburgh Pittsburgh, PA 15261 In a supply chain context, forecasting is the estimation of future demand General

More information

Motorcycle Sales January 9 February 7 March 10 April 8 May 7 June 12 July 10 August 11 September 12 October 10 November 14 December 16

Motorcycle Sales January 9 February 7 March 10 April 8 May 7 June 12 July 10 August 11 September 12 October 10 November 14 December 16 Problems 1 The Saki motorcycle dealer in the MinneapolisSt Paul area wants to make an accurate forecast of demand for the Saki Super TXII motorcycle during the next month Because the manufacturer is in

More information

My Calendar Notebook

My Calendar Notebook My Calendar Notebook 100 Days of School! Today s number + what number equals 100? + =100 Today is: Sunday Monday Tuesday Wednesday Thursday Friday Saturday The date is: The number before... The number

More information

Chapter 13: Forecasting

Chapter 13: Forecasting Chapter 13: Forecasting Assistant Prof. Abed Schokry Operations and Productions Management First Semester 2013-2014 Chapter 13: Learning Outcomes You should be able to: List the elements of a good forecast

More information

BESPOKEWeather Services Tuesday Morning Update: SLIGHTLY BEARISH

BESPOKEWeather Services Tuesday Morning Update: SLIGHTLY BEARISH Weather guidance overnight continued to tick demand expectations higher even after some very impressive afternoon guidance yesterday. We still see weather as extremely supportive for natural gas prices,

More information

Mathematics Practice Test 2

Mathematics Practice Test 2 Mathematics Practice Test 2 Complete 50 question practice test The questions in the Mathematics section require you to solve mathematical problems. Most of the questions are presented as word problems.

More information

SALES AND MARKETING Department MATHEMATICS. 2nd Semester. Bivariate statistics. Tutorials and exercises

SALES AND MARKETING Department MATHEMATICS. 2nd Semester. Bivariate statistics. Tutorials and exercises SALES AND MARKETING Department MATHEMATICS 2nd Semester Bivariate statistics Tutorials and exercises Online document: http://jff-dut-tc.weebly.com section DUT Maths S2. IUT de Saint-Etienne Département

More information

DAILY QUESTIONS 28 TH JUNE 18 REASONING - CALENDAR

DAILY QUESTIONS 28 TH JUNE 18 REASONING - CALENDAR DAILY QUESTIONS 28 TH JUNE 18 REASONING - CALENDAR LEAP AND NON-LEAP YEAR *A non-leap year has 365 days whereas a leap year has 366 days. (as February has 29 days). *Every year which is divisible by 4

More information

Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa. Forecasting demand 02/06/03 page 1 of 34

Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa. Forecasting demand 02/06/03 page 1 of 34 demand -5-4 -3-2 -1 0 1 2 3 Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa Forecasting demand 02/06/03 page 1 of 34 Forecasting is very difficult. especially about the

More information

Preliminary Material Data Sheet

Preliminary Material Data Sheet Level 1/Level 2 Certificate Foundation Level June 2015 Use of Mathematics 43503F/PM Core unit Preliminary Material Data Sheet To be opened and issued to candidates between Monday 27 April 2015 and Monday

More information

A week in the life of. Time days of the week. copy

A week in the life of. Time days of the week. copy Time days of the week You will need: black pens pencils scissors copy What to do: You are going to make your own A Week in the Life of Me book. Think of something special you do on each day of the week.

More information

Seasonal Hazard Outlook

Seasonal Hazard Outlook Winter 2016-2017 Current as of: October 21 Scheduled Update: December 614-799-6500 emawatch@dps.ohio.gov Overview Executive Summary Seasonal Forecast Heating Fuel Supply Winter Driving Preparedness Scheduled

More information

Forecasting. Dr. Richard Jerz rjerz.com

Forecasting. Dr. Richard Jerz rjerz.com Forecasting Dr. Richard Jerz 1 1 Learning Objectives Describe why forecasts are used and list the elements of a good forecast. Outline the steps in the forecasting process. Describe at least three qualitative

More information

2018 Annual Review of Availability Assessment Hours

2018 Annual Review of Availability Assessment Hours 2018 Annual Review of Availability Assessment Hours Amber Motley Manager, Short Term Forecasting Clyde Loutan Principal, Renewable Energy Integration Karl Meeusen Senior Advisor, Infrastructure & Regulatory

More information

Operations Management

Operations Management Operations Management Chapter 4 Forecasting PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 7e Operations Management, 9e 2008 Prentice Hall, Inc. 4 1 Outline Global

More information

6.1 Adding and Subtracting Polynomials

6.1 Adding and Subtracting Polynomials 6.1 Adding and Subtracting Polynomials Essential Question: How do you add or subtract two polynomials, and what type of expression is the result Resource Locker Explore Identifying and Analyzing Monomials

More information

6.1 Adding and Subtracting Polynomials

6.1 Adding and Subtracting Polynomials Name Class Date 6.1 Adding and Subtracting Polynomials Essential Question: How do you add or subtract two polynomials, and what type of expression is the result? Resource Locker Explore Identifying and

More information

Forecasting. Operations Analysis and Improvement Spring

Forecasting. Operations Analysis and Improvement Spring Forecasting Operations Analysis and Improvement 2015 Spring Dr. Tai-Yue Wang Industrial and Information Management Department National Cheng Kung University 1-2 Outline Introduction to Forecasting Subjective

More information

BESPOKEWeather Services Monday Morning Update: NEUTRAL

BESPOKEWeather Services Monday Morning Update: NEUTRAL Weather guidance over the weekend trended right in line with our expectations on Friday, as we saw warm forecasts continue through the short-term but significant cooling in both the medium and long-term.

More information

Forecasting. BUS 735: Business Decision Making and Research. exercises. Assess what we have learned

Forecasting. BUS 735: Business Decision Making and Research. exercises. Assess what we have learned Forecasting BUS 735: Business Decision Making and Research 1 1.1 Goals and Agenda Goals and Agenda Learning Objective Learn how to identify regularities in time series data Learn popular univariate time

More information

Introduction to Forecasting

Introduction to Forecasting Introduction to Forecasting Introduction to Forecasting Predicting the future Not an exact science but instead consists of a set of statistical tools and techniques that are supported by human judgment

More information

Decision 411: Class 3

Decision 411: Class 3 Decision 411: Class 3 Discussion of HW#1 Introduction to seasonal models Seasonal decomposition Seasonal adjustment on a spreadsheet Forecasting with seasonal adjustment Forecasting inflation Poor man

More information

TopGolf Inc: Demand Forecasting

TopGolf Inc: Demand Forecasting Senior Design Final Report TopGolf Inc: Demand Forecasting Prepared by: Kaitlyn Farmer, Janie Flowers, Zach Taher May 8th, 2014 Table of Contents Management Summary... 1 Background and Description of Project...

More information

Decision 411: Class 3

Decision 411: Class 3 Decision 411: Class 3 Discussion of HW#1 Introduction to seasonal models Seasonal decomposition Seasonal adjustment on a spreadsheet Forecasting with seasonal adjustment Forecasting inflation Poor man

More information

BESPOKEWeather Services Friday Morning Update: SLIGHTLY BEARISH

BESPOKEWeather Services Friday Morning Update: SLIGHTLY BEARISH Forecasts overnight cooled dramatically, with European cooling the medium-range the most. A cool shot from the Midwest into the Ohio River Valley and East from the 15 th through the 18 th looks to pull

More information

CIMA Professional

CIMA Professional CIMA Professional 201819 Manchester Interactive Timetable Version 3.1 Information last updated 12/10/18 Please note: Information and dates in this timetable are subject to change. A better way of learning

More information

PREPARED DIRECT TESTIMONY OF GREGORY TEPLOW SOUTHERN CALIFORNIA GAS COMPANY AND SAN DIEGO GAS & ELECTRIC COMPANY

PREPARED DIRECT TESTIMONY OF GREGORY TEPLOW SOUTHERN CALIFORNIA GAS COMPANY AND SAN DIEGO GAS & ELECTRIC COMPANY Application No: A.1-0- Exhibit No.: Witness: Gregory Teplow Application of Southern California Gas Company (U 0 G) and San Diego Gas & Electric Company (U 0 G) for Authority to Revise their Natural Gas

More information

CIMA Professional

CIMA Professional CIMA Professional 201819 Birmingham Interactive Timetable Version 3.1 Information last updated 12/10/18 Please note: Information and dates in this timetable are subject to change. A better way of learning

More information

Lecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University

Lecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University Lecture 15 20 Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University Modeling for Time Series Forecasting Forecasting is a necessary input to planning, whether in business,

More information

YEAR 10 GENERAL MATHEMATICS 2017 STRAND: BIVARIATE DATA PART II CHAPTER 12 RESIDUAL ANALYSIS, LINEARITY AND TIME SERIES

YEAR 10 GENERAL MATHEMATICS 2017 STRAND: BIVARIATE DATA PART II CHAPTER 12 RESIDUAL ANALYSIS, LINEARITY AND TIME SERIES YEAR 10 GENERAL MATHEMATICS 2017 STRAND: BIVARIATE DATA PART II CHAPTER 12 RESIDUAL ANALYSIS, LINEARITY AND TIME SERIES This topic includes: Transformation of data to linearity to establish relationships

More information

Lecture 4 Forecasting

Lecture 4 Forecasting King Saud University College of Computer & Information Sciences IS 466 Decision Support Systems Lecture 4 Forecasting Dr. Mourad YKHLEF The slides content is derived and adopted from many references Outline

More information

The Dayton Power and Light Company Load Profiling Methodology Revised 7/1/2017

The Dayton Power and Light Company Load Profiling Methodology Revised 7/1/2017 The Dayton Power and Light Company Load Profiling Methodology Revised 7/1/2017 Overview of Methodology Dayton Power and Light (DP&L) load profiles will be used to estimate hourly loads for customers without

More information

FINAL REPORT EVALUATION REVIEW OF TVA'S LOAD FORECAST RISK

FINAL REPORT EVALUATION REVIEW OF TVA'S LOAD FORECAST RISK Memorandum from the Office of the Inspector General Robert Irvin, WT 9C-K FINAL REPORT EVALUATION 2012-14507 REVIEW OF TVA'S LOAD FORECAST RISK As part of a series of reviews to evaluate the Tennessee

More information

Defining Normal Weather for Energy and Peak Normalization

Defining Normal Weather for Energy and Peak Normalization Itron White Paper Energy Forecasting Defining Normal Weather for Energy and Peak Normalization J. Stuart McMenamin, Ph.D Managing Director, Itron Forecasting 2008, Itron Inc. All rights reserved. 1 Introduction

More information

College Algebra. Word Problems

College Algebra. Word Problems College Algebra Word Problems Example 2 (Section P6) The table shows the numbers N (in millions) of subscribers to a cellular telecommunication service in the United States from 2001 through 2010, where

More information

Assistant Prof. Abed Schokry. Operations and Productions Management. First Semester

Assistant Prof. Abed Schokry. Operations and Productions Management. First Semester Chapter 3 Forecasting Assistant Prof. Abed Schokry Operations and Productions Management First Semester 2010 2011 Chapter 3: Learning Outcomes You should be able to: List the elements of a good forecast

More information

DEPARTMENT OF QUANTITATIVE METHODS & INFORMATION SYSTEMS

DEPARTMENT OF QUANTITATIVE METHODS & INFORMATION SYSTEMS DEPARTMENT OF QUANTITATIVE METHODS & INFORMATION SYSTEMS Moving Averages and Smoothing Methods ECON 504 Chapter 7 Fall 2013 Dr. Mohammad Zainal 2 This chapter will describe three simple approaches to forecasting

More information

CIMA Professional 2018

CIMA Professional 2018 CIMA Professional 2018 Interactive Timetable Version 16.25 Information last updated 06/08/18 Please note: Information and dates in this timetable are subject to change. A better way of learning that s

More information

CIMA Professional 2018

CIMA Professional 2018 CIMA Professional 2018 Newcastle Interactive Timetable Version 10.20 Information last updated 12/06/18 Please note: Information and dates in this timetable are subject to change. A better way of learning

More information

CP:

CP: Adeng Pustikaningsih, M.Si. Dosen Jurusan Pendidikan Akuntansi Fakultas Ekonomi Universitas Negeri Yogyakarta CP: 08 222 180 1695 Email : adengpustikaningsih@uny.ac.id Operations Management Forecasting

More information

A MACRO-DRIVEN FORECASTING SYSTEM FOR EVALUATING FORECAST MODEL PERFORMANCE

A MACRO-DRIVEN FORECASTING SYSTEM FOR EVALUATING FORECAST MODEL PERFORMANCE A MACRO-DRIVEN ING SYSTEM FOR EVALUATING MODEL PERFORMANCE Bryan Sellers Ross Laboratories INTRODUCTION A major problem of forecasting aside from obtaining accurate forecasts is choosing among a wide range

More information

2019 Settlement Calendar for ASX Cash Market Products. ASX Settlement

2019 Settlement Calendar for ASX Cash Market Products. ASX Settlement 2019 Settlement Calendar for ASX Cash Market Products ASX Settlement Settlement Calendar for ASX Cash Market Products 1 ASX Settlement Pty Limited (ASX Settlement) operates a trade date plus two Business

More information

DAILY MARKET REPORT 24 JANUARY 2018

DAILY MARKET REPORT 24 JANUARY 2018 DAILY MARKET REPORT 24 JANUARY 2018 Unigrain (Pty) Ltd Tel. No. : +27 11 692 4400 Fax. No. : +27 11 412 1183 Economic Indicators Quote at 12h00 previous day Current quote Change % Change Rand/Dollar 12.06

More information

Chapter 7 Forecasting Demand

Chapter 7 Forecasting Demand Chapter 7 Forecasting Demand Aims of the Chapter After reading this chapter you should be able to do the following: discuss the role of forecasting in inventory management; review different approaches

More information

Demand Forecasting. for. Microsoft Dynamics 365 for Operations. User Guide. Release 7.1. April 2018

Demand Forecasting. for. Microsoft Dynamics 365 for Operations. User Guide. Release 7.1. April 2018 Demand Forecasting for Microsoft Dynamics 365 for Operations User Guide Release 7.1 April 2018 2018 Farsight Solutions Limited All Rights Reserved. Portions copyright Business Forecast Systems, Inc. This

More information

Accounts at a Glance (As at the end of JULY 2013)

Accounts at a Glance (As at the end of JULY 2013) Accounts at a Glance (As at the end of JULY 2013) 1 Sl. No Budget estimates 2013-14 Actuals upto July 2013 (Rupees in crore) % of Actuals to Budget Estimates Current 2013-14 Corresponding period of the

More information

1. Revision on Time Description Reflect and Review Teasers Answers Recall conversion of 12-hour clock time to 24-hour clock time and vice versa.

1. Revision on Time Description Reflect and Review Teasers Answers Recall conversion of 12-hour clock time to 24-hour clock time and vice versa. 12 1. Revision on Time Recall conversion of 12-hour clock time to 24-hour clock time and vice versa. Recall number of, weeks, months, ordinary years and leap years. To convert 6:45 p.m. to 24-hour clock

More information

Series. Student. Time and Money. My name

Series. Student. Time and Money. My name Series Student Time and Money My name Copyright 2009 3P Learning. All rights reserved. First edition printed 2009 in Australia. A catalogue record for this book is available from 3P Learning Ltd. ISN 978-1-921860-14-0

More information

Lease Statistics (The First Half of FY2018)

Lease Statistics (The First Half of FY2018) Lease Statistics (The First Half of FY2018) October 29, 2018 The lease transaction volume in the first half of FY2018 (from April 2018 to September 2018) is 2,362.8 billion yen, increased by 3.9% compared

More information

LOADS, CUSTOMERS AND REVENUE

LOADS, CUSTOMERS AND REVENUE EB-00-0 Exhibit K Tab Schedule Page of 0 0 LOADS, CUSTOMERS AND REVENUE The purpose of this evidence is to present the Company s load, customer and distribution revenue forecast for the test year. The

More information

Regression Analysis II

Regression Analysis II Regression Analysis II Measures of Goodness of fit Two measures of Goodness of fit Measure of the absolute fit of the sample points to the sample regression line Standard error of the estimate An index

More information

Decision 411: Class 3

Decision 411: Class 3 Decision 411: Class 3 Discussion of HW#1 Introduction to seasonal models Seasonal decomposition Seasonal adjustment on a spreadsheet Forecasting with seasonal adjustment Forecasting inflation Log transformation

More information

Forecasting Using Time Series Models

Forecasting Using Time Series Models Forecasting Using Time Series Models Dr. J Katyayani 1, M Jahnavi 2 Pothugunta Krishna Prasad 3 1 Professor, Department of MBA, SPMVV, Tirupati, India 2 Assistant Professor, Koshys Institute of Management

More information

Multivariate Regression Model Results

Multivariate Regression Model Results Updated: August, 0 Page of Multivariate Regression Model Results 4 5 6 7 8 This exhibit provides the results of the load model forecast discussed in Schedule. Included is the forecast of short term system

More information

A B C 1 Robert's Drugs 2 3 Week (t ) Sales t. Forec t

A B C 1 Robert's Drugs 2 3 Week (t ) Sales t. Forec t Chapter 7 Forecasting Quantitative Approaches to Forecasting The Components of a Time Series Measures of Forecast Accuracy Using Smoothing Methods in Forecasting Using Seasonal Components in Forecasting

More information

Huron School District Core Curriculum Guide Grade Level: 4th Content Area: Math

Huron School District Core Curriculum Guide Grade Level: 4th Content Area: Math Unit Title: Understand Whole Numbers and Operations Month(s): August, September, October 4N3.1; 4N1.1; 4A3.1; 4A1.3 4A1.2; 4A2.1; 4A2.2; 4A4.1 4A1.1 To read, write, and indentify the place value of whole

More information

EVALUATION OF ALGORITHM PERFORMANCE 2012/13 GAS YEAR SCALING FACTOR AND WEATHER CORRECTION FACTOR

EVALUATION OF ALGORITHM PERFORMANCE 2012/13 GAS YEAR SCALING FACTOR AND WEATHER CORRECTION FACTOR EVALUATION OF ALGORITHM PERFORMANCE /3 GAS YEAR SCALING FACTOR AND WEATHER CORRECTION FACTOR. Background The annual gas year algorithm performance evaluation normally considers three sources of information

More information

CustomWeather Statistical Forecasting (MOS)

CustomWeather Statistical Forecasting (MOS) CustomWeather Statistical Forecasting (MOS) Improve ROI with Breakthrough High-Resolution Forecasting Technology Geoff Flint Founder & CEO CustomWeather, Inc. INTRODUCTION Economists believe that 70% of

More information

3. (1.2.13, 19, 31) Find the given limit. If necessary, state that the limit does not exist.

3. (1.2.13, 19, 31) Find the given limit. If necessary, state that the limit does not exist. Departmental Review for Survey of Calculus Revised Fall 2013 Directions: All work should be shown and all answers should be exact and simplified (unless stated otherwise) to receive full credit on the

More information

The SAB Medium Term Sales Forecasting System : From Data to Planning Information. Kenneth Carden SAB : Beer Division Planning

The SAB Medium Term Sales Forecasting System : From Data to Planning Information. Kenneth Carden SAB : Beer Division Planning The SAB Medium Term Sales Forecasting System : From Data to Planning Information Kenneth Carden SAB : Beer Division Planning Planning in Beer Division F Operational planning = what, when, where & how F

More information

one two three four five six seven eight nine ten eleven twelve thirteen fourteen fifteen zero oneteen twoteen fiveteen tenteen

one two three four five six seven eight nine ten eleven twelve thirteen fourteen fifteen zero oneteen twoteen fiveteen tenteen Stacking races game Numbers, ordinal numbers, dates, days of the week, months, times Instructions for teachers Cut up one pack of cards. Divide the class into teams of two to four students and give them

More information

Lecture 1: Introduction to Forecasting

Lecture 1: Introduction to Forecasting NATCOR: Forecasting & Predictive Analytics Lecture 1: Introduction to Forecasting Professor John Boylan Lancaster Centre for Forecasting Department of Management Science Leading research centre in applied

More information

Bike Week Crash Analysis

Bike Week Crash Analysis Bike Week Crash Analysis David Salzer Patrick Santoso University of New Hampshire 7/15/2014 1 What is Bike Week? Official name is Laconia Motorcycle Week First or second week of June 2013 attendance: 330,000

More information

Components for Accurate Forecasting & Continuous Forecast Improvement

Components for Accurate Forecasting & Continuous Forecast Improvement Components for Accurate Forecasting & Continuous Forecast Improvement An ISIS Solutions White Paper November 2009 Page 1 Achieving forecast accuracy for business applications one year in advance requires

More information

JANUARY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY

JANUARY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY Vocabulary (01) The Calendar (012) In context: Look at the calendar. Then, answer the questions. JANUARY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY 1 New 2 3 4 5 6 Year s Day 7 8 9 10 11

More information

Rationale for choosing a less accurate forecast

Rationale for choosing a less accurate forecast Journal of Targeting, Measurement and Analysis for Marketing Rationale for choosing a less accurate forecast RECEIVED (IN REVISED FORM): 31 AUGUST, 2000 MichaelD.Geurts Marriott School of Management, Brigham

More information

SHORT TERM LOAD FORECASTING

SHORT TERM LOAD FORECASTING Indian Institute of Technology Kanpur (IITK) and Indian Energy Exchange (IEX) are delighted to announce Training Program on "Power Procurement Strategy and Power Exchanges" 28-30 July, 2014 SHORT TERM

More information

PHYS 480/580 Introduction to Plasma Physics Fall 2017

PHYS 480/580 Introduction to Plasma Physics Fall 2017 PHYS 480/580 Introduction to Plasma Physics Fall 2017 Instructor: Prof. Stephen Bradshaw (302 Herman Brown Hall, ext. 4045) Email: stephen.bradshaw {at} rice.edu Class Website: Owl Space Lectures: Tuesday

More information

Midterm 2 - Solutions

Midterm 2 - Solutions Ecn 102 - Analysis of Economic Data University of California - Davis February 24, 2010 Instructor: John Parman Midterm 2 - Solutions You have until 10:20am to complete this exam. Please remember to put

More information

Analyzing the effect of Weather on Uber Ridership

Analyzing the effect of Weather on Uber Ridership ABSTRACT MWSUG 2016 Paper AA22 Analyzing the effect of Weather on Uber Ridership Snigdha Gutha, Oklahoma State University Anusha Mamillapalli, Oklahoma State University Uber has changed the face of taxi

More information

BESPOKEWeather Services Monday Afternoon Update: SLIGHTLY BULLISH

BESPOKEWeather Services Monday Afternoon Update: SLIGHTLY BULLISH Monday Afternoon Update: SLIGHTLY BULLISH Report Summary: The September natural gas contract declined a bit less than a percent today, recovering through the afternoon after heavy selling this morning.

More information

2018 Planner.

2018 Planner. 2018 Planner www.spiritwakes.com This planner belongs to Planner creators and contributors Laura Bowen, Fiona Richards, Trevellyn Dyer Illustrated by Abi Gordon Created under Sister Business a branch

More information

Demand and Supply Integration:

Demand and Supply Integration: Demand and Supply Integration: The Key to World-Class Demand Forecasting Mark A. Moon FT Press Contents Preface xxi Chapter 1 Demand/Supply Integration 1 the Idea Behind DSI 2 How DSI Is Different from

More information

STAT 115: Introductory Methods for Time Series Analysis and Forecasting. Concepts and Techniques

STAT 115: Introductory Methods for Time Series Analysis and Forecasting. Concepts and Techniques STAT 115: Introductory Methods for Time Series Analysis and Forecasting Concepts and Techniques School of Statistics University of the Philippines Diliman 1 FORECASTING Forecasting is an activity that

More information

Materials for assessing adult numeracy

Materials for assessing adult numeracy Materials for assessing adult numeracy Number Task Write this number in figures. Two hundred and seventy two thousand four hundred and twenty nine. In which of these numbers is the 7 worth seventy? Write

More information

Name (print, please) ID

Name (print, please) ID Name (print, please) ID Operations Management I 7- Winter 00 Odette School of Business University of Windsor Midterm Exam I Solution Wednesday, ebruary, 0:00 :0 pm Last Name A-S: Odette B0 Last Name T-Z:

More information

Student Book SERIES. Time and Money. Name

Student Book SERIES. Time and Money. Name Student Book Name ontents Series Topic Time (pp. 24) l months of the year l calendars and dates l seasons l ordering events l duration and language of time l hours, minutes and seconds l o clock l half

More information

CHAPTER 1: Decomposition Methods

CHAPTER 1: Decomposition Methods CHAPTER 1: Decomposition Methods Prof. Alan Wan 1 / 48 Table of contents 1. Data Types and Causal vs.time Series Models 2 / 48 Types of Data Time series data: a sequence of observations measured over time,

More information

The Purpose of Hypothesis Testing

The Purpose of Hypothesis Testing Section 8 1A:! An Introduction to Hypothesis Testing The Purpose of Hypothesis Testing See s Candy states that a box of it s candy weighs 16 oz. They do not mean that every single box weights exactly 16

More information