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Consider your character and integrity

Consider your character and integrity

Let’s assume we find ourselves in the following scenario:

In your spare time at work, you have developed a new spreadsheet program on your work computer in your office. It is even more powerful, yet easier to use than anything on the market. You share your new program with a friend who encourages you to market it on your own because you could probably make an incredible profit in a very short amount of time. This is a very attractive option, yet you developed it using company equipment and during time that you were at work. What do you do?

Keep in mind the 8 Steps are:

Step One; Gather the facts
Step Two; Define the ethical issues
Step Three; Identify the affected parties (stakeholders)
Step Four; Identify the consequences
Step Five; Identify the obligations
Step Six; Consider your character and integrity
Step Seven; Think creatively about potential actions
Step Eight; Check your gut!
Ok, for the scenario provided above we would begin by gathering the facts. We created something that could be very lucrative, but did so during company time with company equipment without permission. The potential ethical issue is that we “stole” time from our employer and used their equipment for our own benefit. The affected parties would be ourselves (we created the program), our company, other companies (as this new software could save time and help increase corporate profitability), and possibly future consumers who would use the software as well. The obligations we have could be to provide for our family with more stability if we can sell the software, an obligation to consumers and other companies that may benefit from using my software, and an obligation to our current employer to use their time wisely and not take advantage of work equipment for our own personal gain. In considering our character and integrity, we would evaluate our morals and what we “feel” is right and wrong. Next, we think about our choices, make a decision and check our gut. Let’s ask again, what would you do?

Prompt
In a properly IWG formatted essay of 600 words, including two IWG references (one may be your text), you will choose an actual or possible ethical dilemma or situation that is relevant for the chosen career path that you are studying for. You will analyze this possible ethical dilemma with the Eight Steps to Sound Ethical Decision Making,and decide what to do.

Please be sure to complete each of the following steps in your 600 word essay:

  1. Describe an ethical situation you or someone in your field of study or career path might face. You may use your own past experience, however, please do not use one shown in our Week 1 discussion videos. Then in a single sentence, state what you think you should do.
  2. Next, analyze the situation you have chosen with the Eight Steps.
  3. Discuss the course of action or solution that the process of analyzing the situation from the Eight Steps has led you to in this particular situation.
  4. Conclude by comparing the outcomes recommended by the Eight Steps with your initial response stated in the first step. How are they similar? How are they different?

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Planning project:

Planning project:

choose nine developmentally appropriate activities for young children. Your capability to effectively plan these activities demonstrates your mastery of the course learning outcomes and your ability to use your knowledge to plan effective activities for young children. Early childhood educators play an important role in the future success of children, and your ability to create effective curriculum experiences is a fundamental part of that.

To prepare for this assignment,

Please refer to the Week 5 Guidance for further tips and examples that will support your success with this discussion.
Review and download the ECE 203 Activity Template.
There are nine required sections total: Science/Sensory, Language and Literacy, Creativity, Fine motor (please choose an indoor activity), Gross motor (please choose an outdoor activity), Self-Concept, Emotional Skills/ Regulation, Social Skills, and Math.
Read the required resources for this week and consider reviewing the recommended resources as well.
Remember that any applicable resource used throughout this course can support the requirement for four scholarly resources for this assignment.

If you did not begin the development of your ePortfolio in ECE 101, read Portfolium Student Guide to help you set up your ePortfolio.
Choose an area of focus:
Center-Based Preschool (3, 4, or 5 years old)
Center-Based Infant/Toddler (young infants, mobile infants, or toddlers)
Early Childhood (4–8 years old)
In your assignment, create a nine-page Word document that addresses the following:
For the Center-Based Preschool Option
Complete each section of the ECE 203 Activity Template.
To complete the sections for a Center-Based Preschool:
Indicate the age group (3s, 4s or 5s).
List the intended goals.
List all of the materials that will be needed for each activity.
Explain in detail the process/teaching strategies that will be used for each activity.
Specify how each activity is developmentally appropriate for that age group.
For the Center-Based Infant/Toddler Option

Complete each section of the Activity Template.
To complete the sections for a Center-Based Infant/Toddler:
Indicate the age group (3s, 4s or 5s). Of the nine activities, three should be appropriate for young infants, three for mobile infants and three for toddlers.
List the intended goals.
List all of the materials that will be needed for each activity.
Explain in detail the process/teaching strategies that will be used for each activity.
Specify how each activity is developmentally appropriate for that age group.
For the Early Childhood (4–8 Years Old) Option

Complete each section of the Activity Template
To complete the sections for Early Childhood:
Indicate the age group (4, 5, 6, 7, 8).
List the intended goals.
List all of the materials that will be needed for each activity.
Explain in detail the process/teaching strategies that will be used for each activity.
Specify how each activity is developmentally appropriate for that age group.
For this assignment, you must submit

A link to your electronic portfolio in Portfolium. To do this you will copy and paste the Web address into the comments feature in Waypoint.
A Word document including your completed assignment, as well as the link to your ePortfolio.
Click on the Assignment Submission button. The Waypoint “Student Dashboard” will appear.
Browse for your assignment.
Click Upload.
Confirm that your assignment was successfully submitted by viewing the appropriate week’s assignment tab in Waypoint, or clicking on Check Assignment Status within the Meet Your Instructor unit in the left navigation panel.
The Developmentally Appropriate Activity Planning project:

Must be nine double-spaced pages in length (not including title and reference pages) and formatted according to APA style as outlined in Ashford Writing Center (Links to an external site.)’s APA Style (Links to an external site.)
Must include a separate title page with the following:
Title of paper
Student’s name
Course name and number
Instructor’s name
Date submitted
Must use at least three scholarly sources in addition to the course text.
To assist you in completing the library research required for this assignment, view this Help! Need Article (Links to an external site.) tutorial, which can help you find a good starting place for your research.
The Scholarly, Peer Reviewed, and Other Credible Sources (Links to an external site.) table offers additional guidance on appropriate source types. If you have questions about whether a specific source is appropriate for this assignment, contact your instructor. Your instructor has the final say about the appropriateness of a specific source for a particular assignment.
Must document any information used from sources in APA style as outlined in the Ashford Writing Center’s Citing Within Your Paper (Links to an external site.)
Must include a separate references page that is formatted according to APA style as outlined in the Ashford Writing Center. See the Formatting Your References List (Links to an external site.) resource in the Ashford Writing Center for specifications.

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Weeks 5 and 6

Weeks 5 and 6

my satisfaction! I do NOT have time to argue with writers via email. If you can do the work and do it right, then take this job. If you can’t then stay out of my inbox! This is a business and I only pay for good work. Thanks in advance!

ASSIGNMENT DIRECTIONS:

Answer the questions under the assessment tab. (Follow attached example)

Online Learning Modules and Assessments: Weeks 5 and 6
Perhaps this will help you to unpack differentiated instruction. Be sure to complete the Reading Assignments that are included in the tutorial.

Differentiated Instruction: Maximizing the Learning of All Students

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Article Analysis Assignment

Article Analysis Assignment

culture from a news website that was published within the past two months: The article can be about a type of music, literary genre (i.e. popular or avant-garde books), movie, fashion style or way of making a living (such as hunter-gatherer societies).

Select two of the following subdivisions that are presented in Chapter 1 of your textbook: Cultural anthropology (how is culture learned), archaeology (materials within the culture), or anthropological linguistics (how did people speak).

The article analysis should be one to two pages long, with 1.5 line spacing. The purpose of the analysis is to explain the article through the different points of view of these two different fields. Use the following outline to write your article analysis:

I. Introduction

Include: title of article, name of author, source and date of publication, very brief summary of the article, why the topic is important to society, the purpose of your paper (that is to explain an article through two subdivisions of anthropology)

II. How can the article be explained by subdivision # 1?

III. How can the article be explained by subdivision # 2?

IV. Conclusion

Which of the two theories you chose appears to be the “best” (the most suitable) to analyze the topic presented in the article and why?

Also briefly explain how this news article can relate to our present society and humanity as a whole.

References

List all of the sources that you used to write this article analysis. At the very least, this should include the textbook and the source of your article.

Note: For the article, do not only copy and paste a link. You also need the author, full name of the source (i.e. The New York Times), the date of publication, and the date you accessed the article online.

Important Note: The information you need for the two page article analysis assignment starts on page 7 of your textbook. This will provide you with an overview of the following different fields of anthropology. For the assignment you have to explain the article about culture through the points of view of two of the following fields:

  • Cultural anthropology (Basically how is behavior learned through the media, our parents, other artists, etc.)
  • Archaeology (This will focus on material culture such as artifacts, technology, and structures. Another way to ask this is: How will people in one thousand years look at the remains of the culture described in the article?)
  • Linguistic anthropology (This focuses on the language that people use. Is there a certain vocabulary specific to a subculture? How did people’s way of expressing themselves change over the years? Do authors and musicians for example still speak like beat poets of the 1950’s or like Shakespeare? Language evolves.)

Chose two of the above and write at least two paragraphs about them between the introduction and conclusion. You will basically analyze one article through two “points of view” after taking another look at the outline.

The post Article Analysis Assignment appeared first on superioressaypapers.

How will people in one thousand years look at the remains of the culture described in the article?

How will people in one thousand years look at the remains of the culture described in the article?

culture from a news website that was published within the past two months: The article can be about a type of music, literary genre (i.e. popular or avant-garde books), movie, fashion style or way of making a living (such as hunter-gatherer societies).

Select two of the following subdivisions that are presented in Chapter 1 of your textbook: Cultural anthropology (how is culture learned), archaeology (materials within the culture), or anthropological linguistics (how did people speak).

The article analysis should be one to two pages long, with 1.5 line spacing. The purpose of the analysis is to explain the article through the different points of view of these two different fields. Use the following outline to write your article analysis:

I. Introduction

Include: title of article, name of author, source and date of publication, very brief summary of the article, why the topic is important to society, the purpose of your paper (that is to explain an article through two subdivisions of anthropology)

II. How can the article be explained by subdivision # 1?

III. How can the article be explained by subdivision # 2?

IV. Conclusion

Which of the two theories you chose appears to be the “best” (the most suitable) to analyze the topic presented in the article and why?

Also briefly explain how this news article can relate to our present society and humanity as a whole.

References

List all of the sources that you used to write this article analysis. At the very least, this should include the textbook and the source of your article.

Note: For the article, do not only copy and paste a link. You also need the author, full name of the source (i.e. The New York Times), the date of publication, and the date you accessed the article online.

Important Note: The information you need for the two page article analysis assignment starts on page 7 of your textbook. This will provide you with an overview of the following different fields of anthropology. For the assignment you have to explain the article about culture through the points of view of two of the following fields:

  • Cultural anthropology (Basically how is behavior learned through the media, our parents, other artists, etc.)
  • Archaeology (This will focus on material culture such as artifacts, technology, and structures. Another way to ask this is: How will people in one thousand years look at the remains of the culture described in the article?)
  • Linguistic anthropology (This focuses on the language that people use. Is there a certain vocabulary specific to a subculture? How did people’s way of expressing themselves change over the years? Do authors and musicians for example still speak like beat poets of the 1950’s or like Shakespeare? Language evolves.)

Chose two of the above and write at least two paragraphs about them between the introduction and conclusion. You will basically analyze one article through two “points of view” after taking another look at the outline.

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criminal justice

criminal justice

Your final paper should be the cumulative result of the efforts made throughout the semester to design a research project. Use the prior assignments and feedback to develop your final paper. Your Final Paper should be 10-12 pages long, and it should include all of the following elements:

Title page
Abstract
Introduction
Literature Review
Hypothetical Methods Section
Conclusion section
Why this research would be useful
How might this research affect policy or practice in the field of criminology/criminal justice
Your paper should contain at least 10 citations from high quality external sources. A high quality source is one coming from peer-reviewed journal articles, scholarly books, and governmental records or reports.

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Please review the assignment attached

Please review the assignment attached

along with the grading rubric.

Must be Non-plagiarisezd, cited and referecnes..

I am looking for 100% work..

Added file to show what the previous week work was to show the business i choose in which this paper would have to be based on… Facebook…

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How Do I Start Bidding For Work?

How Do I Start Bidding For Work?

Powerpoint Presentation For Chapter 7 Forecasting

powerpoint presentation for this work below:

Statistics, Data Analysis, and Decision Modeling

FOURTH EDITION

James R. Evans

9780558689766

Chapter 7 Forecasting

Introduction

QUALITATIVE AND JUDGMENTAL METHODS

Historical Analogy

The Delphi Method

Indicators and Indexes for Forecasting

STATISTICAL FORECASTING MODELS

FORECASTING MODELS FOR STATIONARY TIME SERIES

Moving Average Models

Error Metrics and Forecast Accuracy

Exponential Smoothing Models

FORECASTING MODELS FOR TIME SERIES WITH TREND AND SEASONALITY

Models for Linear Trends

Models for Seasonality

Models for Trend and Seasonality

CHOOSING AND OPTIMIZING FORECASTING MODELS USING CB PREDICTOR

REGRESSION MODELS FOR FORECASTING

Autoregressive Forecasting Models

Incorporating Seasonality in Regression Models

Regression Forecasting with Causal Variables

THE PRACTICE OF FORECASTING

BASIC CONCEPTS REVIEW QUESTIONS

SKILL-BUILDING EXERCISES

SKILL-BUILDING EXERCISES

PROBLEMS AND APPLICATIONS

CASE: ENERGY FORECASTING

APPENDIX: ADVANCED FORECASTING MODELS—THEORY AND COMPUTATION

Double Moving Average

Double Exponential Smoothing

Additive Seasonality

Multiplicative Seasonality

Holt–Winters Additive Model

Holt– –Winters Multiplicative Model

INTRODUCTION

One of the major problems that managers face is forecasting future events in order to make good decisions. For example, forecasts of interest rates, energy prices, and other economic indicators are needed for financial planning; sales forecasts are needed to plan production and workforce capacity; and forecasts of trends in demographics, consumer behavior, and technological innovation are needed for long-term strategic planning. The government also invests significant resources on predicting short-run U.S. business performance using the Index of Leading Indicators. This index focuses on the performance of individual businesses, which often is highly correlated with the performance of the overall economy, and is used to forecast economic trends for the nation as a whole. In this chapter, we introduce some common methods and approaches to forecasting, including both qualitative and quantitative techniques.

Managers may choose from a wide range of forecasting techniques. Selecting the appropriate method depends on the characteristics of the forecasting problem, such as the time horizon of the variable being forecast, as well as available information on which the forecast will be based. Three major categories of forecasting approaches are qualitative and judgmental techniques, statistical time-series models, and explanatory/causal methods.

Qualitative and judgmental techniques rely on experience and intuition; they are necessary when historical data are not available or when the decision maker needs to forecast far into the future. For example, a forecast of when the next generation of a microprocessor will be available and what capabilities it might have will depend greatly on the opinions and expertise of individuals who understand the technology.

Statistical time-series models find greater applicability for short-range forecasting problems. A time series is a stream of historical data, such as weekly sales. Time-series models assume that whatever forces have influenced sales in the recent past will continue into the near future; thus, forecasts are developed by extrapolating these data into the future.

Explanatory/causal models seek to identify factors that explain statistically the patterns observed in the variable being forecast, usually with regression analysis. While time-series models use only time as the independent variable, explanatory/causal models generally include other factors. For example, forecasting the price of oil might incorporate independent variables such as the demand for oil (measured in barrels), the proportion of oil stock generated by OPEC countries, and tax rates. Although we can never prove that changes in these variables actually cause changes in the price of oil, we often have evidence that a strong influence exists.

Surveys of forecasting practices have shown that both judgmental and quantitative methods are used for forecasting sales of product lines or product families, as well as for broad company and industry forecasts. Simple time-series models are used for short- and medium-range forecasts, whereas regression analysis is the most popular method for long-range forecasting. However, many companies rely on judgmental methods far more than quantitative methods, and almost half judgmentally adjust quantitative forecasts.

In this chapter, we focus on these three approaches to forecasting. Specifically, we will discuss the following:

Historical analogy and the Delphi method as approaches to judgmental forecasting

Moving average and exponential smoothing models for time-series forecasting, with a discussion of evaluating the quality of forecasts

A brief discussion of advanced time-series models and the use of Crystal Ball (CB) Predictor for optimizing forecasts

The use of regression models for explanatory/causal forecasting

Some insights into practical issues associated with forecasting

Qualitative and Judgmental Methods

Qualitative, or judgmental, forecasting methods are valuable in situations for which no historical data are available or for those that specifically require human expertise and knowledge. One example might be identifying future opportunities and threats as part of a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis within a strategic planning exercise. Another use of judgmental methods is to incorporate nonquantitative information, such as the impact of government regulations or competitor behavior, in a quantitative forecast. Judgmental techniques range from such simple methods as a manager’s opinion or a group-based jury of executive opinion to more structured approaches such as historical analogy and the Delphi method.

Historical Analogy

One judgmental approach is historical analogy, in which a forecast is obtained through a comparative analysis with a previous situation. For example, if a new product is being introduced, the response of similar previous products to marketing campaigns can be used as a basis to predict how the new marketing campaign might fare. Of course, temporal changes or other unique factors might not be fully considered in such an approach. However, a great deal of insight can often be gained through an analysis of past experiences. For example, in early 1998, the price of oil was about $22 a barrel. However, in mid-1998, the price of a barrel of oil dropped to around $11. The reasons for this price drop included an oversupply of oil from new production in the Caspian Sea region, high production in non-OPEC regions, and lower-than-normal demand. In similar circumstances in the past, OPEC would meet and take action to raise the price of oil. Thus, from historical analogy, we might forecast a rise in the price of oil. OPEC members did in fact meet in mid-1998 and agreed to cut their production, but nobody believed that they would actually cooperate effectively, and the price continued to drop for a time. Subsequently, in 2000, the price of oil rose dramatically, falling again in late 2001. Analogies often provide good forecasts, but you need to be careful to recognize new or different circumstances. Another analogy is international conflict relative to the price of oil. Should war break out, the price would be expected to rise, analogous to what it has done in the past.

The Delphi Method

A popular judgmental forecasting approach, called the Delphi method, uses a panel of experts, whose identities are typically kept confidential from one another, to respond to a sequence of questionnaires. After each round of responses, individual opinions, edited to ensure anonymity, are shared, allowing each to see what the other experts think. Seeing other experts’ opinions helps to reinforce those in agreement and to influence those who did not agree to possibly consider other factors. In the next round, the experts revise their estimates, and the process is repeated, usually for no more than two or three rounds. The Delphi method promotes unbiased exchanges of ideas and discussion and usually results in some convergence of opinion. It is one of the better approaches to forecasting long-range trends and impacts.

Indicators and Indexes for Forecasting

Bottom of Form

Indicators and indexes generally play an important role in developing judgmental forecasts. Indicators are measures that are believed to influence the behavior of a variable we wish to forecast. By monitoring changes in indicators, we expect to gain insight about the future behavior of the variable to help forecast the future. For example, one variable that is important to the nation’s economy is the Gross Domestic Product (GDP), which is a measure of the value of all goods and services produced in the United States. Despite its shortcomings (for instance, unpaid work such as housekeeping and child care is not measured; production of poor-quality output inflates the measure, as does work expended on corrective action), it is a practical and useful measure of economic performance. Like most time series, the GDP rises and falls in a cyclical fashion. Predicting future trends in the GDP is often done by analyzing leading indicators—series that tend to rise and fall some predictable length of time prior to the peaks and valleys of the GDP. One example of a leading indicator is the formation of business enterprises; as the rate of new businesses grows, one would expect the GDP to increase in the future. Other examples of leading indicators are the percent change in the money supply (M1) and net change in business loans. Other indicators, called lagging indicators, tend to have peaks and valleys that follow those of the GDP. Some lagging indicators are the Consumer Price Index, prime rate, business investment expenditures, or inventories on hand. The GDP can be used to predict future trends in these indicators.

Indicators are often combined quantitatively into an index. The direction of movement of all the selected indicators are weighted and combined, providing an index of overall expectation. For example, financial analysts use the Dow Jones Industrial Average as an index of general stock market performance. Indexes do not provide a complete forecast, but rather a better picture of direction of change, and thus play an important role in judgmental forecasting.

The Department of Commerce began an Index of Leading Indicators to help predict future economic performance. Components of the index include the following:

•average weekly hours, manufacturing

•average weekly initial claims, unemployment insurance

•new orders, consumer goods and materials

•vendor performance—slower deliveries

•new orders, nondefense capital goods

•building permits, private housing

•stock prices, 500 common stocks (Standard & Poor)

•money supply

•interest rate spread

•index of consumer

•average weekly hours, manufacturing

•average weekly initial claims, unemployment insurance

•new orders, consumer goods and materials

•vendor performance—slower deliveries

•new orders, nondefense capital goods

•building permits, private housing

•stock prices, 500 common stocks (Standard & Poor)

•money supply

•interest rate spread

•index of consumer expectations (University of Michigan)

Business Conditions Digest included more than 100 time series in seven economic areas. This publication was discontinued in March 1990, but information related to the Index of Leading Indicators was continued in Survey of Current Business. In December 1995, the U.S. Department of Commerce sold this data source to The Conference Board, which now markets the information under the title Business Cycle Indicators; information can be obtained at its Web site (www.conference-board.org). The site includes excellent current information about the calculation of the index, as well as its current components.

Statistical Forecasting Models

Many forecasts are based on analysis of historical time-series data and are predicated on the assumption that the future is an extrapolation of the past. We will assume that a time series consists of T periods of data, At, = 1, 2, …, T. A naive approach is to eyeball a trend—a gradual shift in the value of the time series—by visually examining a plot of the data. For instance, Figure 7.1 shows a chart of total energy production from the data in the Excel file Energy Production & Consumption. We see that energy production was rising quite rapidly during the 1960s; however, the slope appears to have decreased after 1970. It appears that production is increasing by about 500,000 each year and that this can provide a reasonable forecast provided that the trend continues.

Figure 7.1 Total Energy Production Time Series

Figure 7.2 Federal Funds Rate Time Series

Time series may also exhibit short-term seasonal effects (over a year, month, week, or even a day) as well as longer-term cyclical effects or nonlinear trends. At a neighborhood grocery store, for instance, short-term seasonal patterns may occur over a week, with the heaviest volume of customers on weekends, and even during the course of a day. Cycles relate to much longer-term behavior, such as periods of inflation and recession or bull and bear stock market behavior. Figure 7.2 shows a chart of the data in the Excel file Federal Funds Rate. We see some evidence of long-term cycles in the time series.

Of course, unscientific approaches such as the “eyeball method” may be a bit unsettling to a manager making important decisions. Subtle effects and interactions of seasonal and cyclical factors may not be evident from simple visual extrapolation of data. Statistical methods, which involve more formal analyses of time series, are invaluable in developing good forecasts. A variety of statistically based forecasting methods for time series are commonly used. Among the most popular are moving average methods, exponential smoothing, and regression analysis. These can be implemented very easily on a spreadsheet using basic functions available in Microsoft Excel and its Data Analysis tools; these are summarized in Table 7.1. Moving average and exponential smoothing models work best for stationary time series. For time series that involve trends and/or seasonal factors, other techniques have been developed. These include double moving average and exponential smoothing models, seasonal additive and multiplicative models, and Holt–Winters additive and multiplicative models . We will review each of these types of models. This book provides an Excel add-in, CB Predictor, that applies these methods and incorporates some intelligent technology. We will describe CB Predictor later in this chapter.

Table 7.1 Excel Support for Forecasting

Excel Functions Description

TREND (known_y’s, known_x’s, new_x’s, constant)

Returns values along a linear trend line

LINEST(known_y’s, known_x’s, new_x’s, constant, stats)

Returns an array that describes a straight line that best fits the data

FORECAST(x, known_y’s, known_x’s)

Calculates a future value along a linear trend

Analysis Toolpak

Description

Moving average Projects forecast values based on the

       average value of the variable over a specific number of preceding periods

Exponential smoothing Predicts a value based on the forecast for the

                                            prior period, adjusted for the error in that prior forecast

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Decision Modeling

Decision Modeling

Historical Analogy

The Delphi Method

Indicators and Indexes for Forecasting

STATISTICAL FORECASTING MODELS

FORECASTING MODELS FOR STATIONARY TIME SERIES

Moving Average Models

Error Metrics and Forecast Accuracy

Exponential Smoothing Models

FORECASTING MODELS FOR TIME SERIES WITH TREND AND SEASONALITY

Models for Linear Trends

Models for Seasonality

Models for Trend and Seasonality

CHOOSING AND OPTIMIZING FORECASTING MODELS USING CB PREDICTOR

REGRESSION MODELS FOR FORECASTING

Autoregressive Forecasting Models

Incorporating Seasonality in Regression Models

Regression Forecasting with Causal Variables

THE PRACTICE OF FORECASTING

BASIC CONCEPTS REVIEW QUESTIONS

SKILL-BUILDING EXERCISES

SKILL-BUILDING EXERCISES

PROBLEMS AND APPLICATIONS

CASE: ENERGY FORECASTING

APPENDIX: ADVANCED FORECASTING MODELS—THEORY AND COMPUTATION

Double Moving Average

Double Exponential Smoothing

Additive Seasonality

Multiplicative Seasonality

Holt–Winters Additive Model

Holt– –Winters Multiplicative Model

INTRODUCTION

One of the major problems that managers face is forecasting future events in order to make good decisions. For example, forecasts of interest rates, energy prices, and other economic indicators are needed for financial planning; sales forecasts are needed to plan production and workforce capacity; and forecasts of trends in demographics, consumer behavior, and technological innovation are needed for long-term strategic planning. The government also invests significant resources on predicting short-run U.S. business performance using the Index of Leading Indicators. This index focuses on the performance of individual businesses, which often is highly correlated with the performance of the overall economy, and is used to forecast economic trends for the nation as a whole. In this chapter, we introduce some common methods and approaches to forecasting, including both qualitative and quantitative techniques.

Managers may choose from a wide range of forecasting techniques. Selecting the appropriate method depends on the characteristics of the forecasting problem, such as the time horizon of the variable being forecast, as well as available information on which the forecast will be based. Three major categories of forecasting approaches are qualitative and judgmental techniques, statistical time-series models, and explanatory/causal methods.

Qualitative and judgmental techniques rely on experience and intuition; they are necessary when historical data are not available or when the decision maker needs to forecast far into the future. For example, a forecast of when the next generation of a microprocessor will be available and what capabilities it might have will depend greatly on the opinions and expertise of individuals who understand the technology.

Statistical time-series models find greater applicability for short-range forecasting problems. A time series is a stream of historical data, such as weekly sales. Time-series models assume that whatever forces have influenced sales in the recent past will continue into the near future; thus, forecasts are developed by extrapolating these data into the future.

Explanatory/causal models seek to identify factors that explain statistically the patterns observed in the variable being forecast, usually with regression analysis. While time-series models use only time as the independent variable, explanatory/causal models generally include other factors. For example, forecasting the price of oil might incorporate independent variables such as the demand for oil (measured in barrels), the proportion of oil stock generated by OPEC countries, and tax rates. Although we can never prove that changes in these variables actually cause changes in the price of oil, we often have evidence that a strong influence exists.

Surveys of forecasting practices have shown that both judgmental and quantitative methods are used for forecasting sales of product lines or product families, as well as for broad company and industry forecasts. Simple time-series models are used for short- and medium-range forecasts, whereas regression analysis is the most popular method for long-range forecasting. However, many companies rely on judgmental methods far more than quantitative methods, and almost half judgmentally adjust quantitative forecasts.

In this chapter, we focus on these three approaches to forecasting. Specifically, we will discuss the following:

Historical analogy and the Delphi method as approaches to judgmental forecasting

Moving average and exponential smoothing models for time-series forecasting, with a discussion of evaluating the quality of forecasts

A brief discussion of advanced time-series models and the use of Crystal Ball (CB) Predictor for optimizing forecasts

The use of regression models for explanatory/causal forecasting

Some insights into practical issues associated with forecasting

Qualitative and Judgmental Methods

Qualitative, or judgmental, forecasting methods are valuable in situations for which no historical data are available or for those that specifically require human expertise and knowledge. One example might be identifying future opportunities and threats as part of a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis within a strategic planning exercise. Another use of judgmental methods is to incorporate nonquantitative information, such as the impact of government regulations or competitor behavior, in a quantitative forecast. Judgmental techniques range from such simple methods as a manager’s opinion or a group-based jury of executive opinion to more structured approaches such as historical analogy and the Delphi method.

Historical Analogy

One judgmental approach is historical analogy, in which a forecast is obtained through a comparative analysis with a previous situation. For example, if a new product is being introduced, the response of similar previous products to marketing campaigns can be used as a basis to predict how the new marketing campaign might fare. Of course, temporal changes or other unique factors might not be fully considered in such an approach. However, a great deal of insight can often be gained through an analysis of past experiences. For example, in early 1998, the price of oil was about $22 a barrel. However, in mid-1998, the price of a barrel of oil dropped to around $11. The reasons for this price drop included an oversupply of oil from new production in the Caspian Sea region, high production in non-OPEC regions, and lower-than-normal demand. In similar circumstances in the past, OPEC would meet and take action to raise the price of oil. Thus, from historical analogy, we might forecast a rise in the price of oil. OPEC members did in fact meet in mid-1998 and agreed to cut their production, but nobody believed that they would actually cooperate effectively, and the price continued to drop for a time. Subsequently, in 2000, the price of oil rose dramatically, falling again in late 2001. Analogies often provide good forecasts, but you need to be careful to recognize new or different circumstances. Another analogy is international conflict relative to the price of oil. Should war break out, the price would be expected to rise, analogous to what it has done in the past.

The Delphi Method

A popular judgmental forecasting approach, called the Delphi method, uses a panel of experts, whose identities are typically kept confidential from one another, to respond to a sequence of questionnaires. After each round of responses, individual opinions, edited to ensure anonymity, are shared, allowing each to see what the other experts think. Seeing other experts’ opinions helps to reinforce those in agreement and to influence those who did not agree to possibly consider other factors. In the next round, the experts revise their estimates, and the process is repeated, usually for no more than two or three rounds. The Delphi method promotes unbiased exchanges of ideas and discussion and usually results in some convergence of opinion. It is one of the better approaches to forecasting long-range trends and impacts.

Indicators and Indexes for Forecasting

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Indicators and indexes generally play an important role in developing judgmental forecasts. Indicators are measures that are believed to influence the behavior of a variable we wish to forecast. By monitoring changes in indicators, we expect to gain insight about the future behavior of the variable to help forecast the future. For example, one variable that is important to the nation’s economy is the Gross Domestic Product (GDP), which is a measure of the value of all goods and services produced in the United States. Despite its shortcomings (for instance, unpaid work such as housekeeping and child care is not measured; production of poor-quality output inflates the measure, as does work expended on corrective action), it is a practical and useful measure of economic performance. Like most time series, the GDP rises and falls in a cyclical fashion. Predicting future trends in the GDP is often done by analyzing leading indicators—series that tend to rise and fall some predictable length of time prior to the peaks and valleys of the GDP. One example of a leading indicator is the formation of business enterprises; as the rate of new businesses grows, one would expect the GDP to increase in the future. Other examples of leading indicators are the percent change in the money supply (M1) and net change in business loans. Other indicators, called lagging indicators, tend to have peaks and valleys that follow those of the GDP. Some lagging indicators are the Consumer Price Index, prime rate, business investment expenditures, or inventories on hand. The GDP can be used to predict future trends in these indicators.

Indicators are often combined quantitatively into an index. The direction of movement of all the selected indicators are weighted and combined, providing an index of overall expectation. For example, financial analysts use the Dow Jones Industrial Average as an index of general stock market performance. Indexes do not provide a complete forecast, but rather a better picture of direction of change, and thus play an important role in judgmental forecasting.

The Department of Commerce began an Index of Leading Indicators to help predict future economic performance. Components of the index include the following:

•average weekly hours, manufacturing

•average weekly initial claims, unemployment insurance

•new orders, consumer goods and materials

•vendor performance—slower deliveries

•new orders, nondefense capital goods

•building permits, private housing

•stock prices, 500 common stocks (Standard & Poor)

•money supply

•interest rate spread

•index of consumer

•average weekly hours, manufacturing

•average weekly initial claims, unemployment insurance

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