This document provides an overview of time-series forecasting and index numbers. It discusses different time-series forecasting models including moving averages, exponential smoothing, linear trend, quadratic trend, and exponential trend models. It also covers identifying trend, seasonal, and irregular components in a time series. Smoothing methods like moving averages and exponential smoothing are presented as ways to identify trends in data. The document concludes by discussing linear, nonlinear, and exponential trend forecasting models for generating forecasts from time-series data.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
This document discusses forecasting time-series data using various models. It covers identifying components in time series, computing index numbers, smoothing-based and trend-based forecasting models, measuring forecast accuracy, and addressing autocorrelation. The key steps are developing models, identifying trends and seasonal components, computing forecasts, and comparing forecasts to actual data to evaluate model fit.
This document discusses various forecasting techniques used at Disney World for attendance forecasting. Disney generates daily, weekly, monthly, annual, and 5-year forecasts which are used for labor management, operations, and scheduling. Forecasting models take into account factors like economic conditions, airline prices, school schedules, and previous attendance data. Qualitative methods include expert panels, while quantitative methods analyze historical data using techniques like moving averages, exponential smoothing, and regression analysis. Accuracy varies from 0-3% for annual forecasts to 5% for 5-year forecasts.
This document provides an overview of key performance indicators and metrics related to the US construction market. It includes summaries of housing starts, home sales, and median home prices for March 2015. It also discusses the NAHB/Wells Fargo Housing Market Index rising in April, indicating builder confidence is increasing as the spring buying season begins. Various economic indices are presented, including the Dodge Momentum Index, Architecture Billings Index, and housing starts by region. In conclusion, the document examines the cyclical nature of the US nonresidential construction market and provides forecasts for residential and commercial construction through 2019.
This chapter discusses techniques for time-series forecasting and index numbers. It begins by explaining the importance of forecasting for governments, businesses and other organizations. It then outlines common qualitative and quantitative forecasting approaches, with a focus on time-series methods that use historical data patterns to predict future values. The chapter describes how to decompose a time series into trend, seasonal, cyclical and irregular components. It also explains techniques for smoothing time-series data, including moving averages and exponential smoothing. Finally, it covers methods for time-series forecasting based on trend lines, including linear, quadratic, exponential and other models.
This document discusses time-series forecasting and index numbers. It begins by outlining the chapter goals, which are to develop basic forecasting models, identify time-series components, use smoothing and trend-based forecasting models, forecast seasonal data, and compute index numbers. The document then explains key concepts like time-series plots and components, moving averages, exponential smoothing, trend-based forecasting using linear, quadratic and exponential models, and model selection criteria. Examples are provided throughout to illustrate time-series smoothing and forecasting techniques.
This chapter introduces basic concepts in business statistics including how statistics are used in business, the different types of data and their sources, and how to use Microsoft Excel and Minitab software programs. It discusses the difference between descriptive and inferential statistics, and covers variables, populations, samples, parameters, and statistics. Finally, it reviews levels of measurement and provides an overview of key terms used in Minitab and Microsoft Excel.
This chapter introduces basic concepts in business statistics including how statistics are used in business, types of data and their sources, and popular software programs like Microsoft Excel and Minitab. It discusses descriptive versus inferential statistics and reviews key terminology such as population, sample, parameters, and statistics. The chapter also covers different types of variables, levels of measurement, and considerations for properly using statistical software programs.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
This document discusses forecasting time-series data using various models. It covers identifying components in time series, computing index numbers, smoothing-based and trend-based forecasting models, measuring forecast accuracy, and addressing autocorrelation. The key steps are developing models, identifying trends and seasonal components, computing forecasts, and comparing forecasts to actual data to evaluate model fit.
This document discusses various forecasting techniques used at Disney World for attendance forecasting. Disney generates daily, weekly, monthly, annual, and 5-year forecasts which are used for labor management, operations, and scheduling. Forecasting models take into account factors like economic conditions, airline prices, school schedules, and previous attendance data. Qualitative methods include expert panels, while quantitative methods analyze historical data using techniques like moving averages, exponential smoothing, and regression analysis. Accuracy varies from 0-3% for annual forecasts to 5% for 5-year forecasts.
This document provides an overview of key performance indicators and metrics related to the US construction market. It includes summaries of housing starts, home sales, and median home prices for March 2015. It also discusses the NAHB/Wells Fargo Housing Market Index rising in April, indicating builder confidence is increasing as the spring buying season begins. Various economic indices are presented, including the Dodge Momentum Index, Architecture Billings Index, and housing starts by region. In conclusion, the document examines the cyclical nature of the US nonresidential construction market and provides forecasts for residential and commercial construction through 2019.
This chapter discusses techniques for time-series forecasting and index numbers. It begins by explaining the importance of forecasting for governments, businesses and other organizations. It then outlines common qualitative and quantitative forecasting approaches, with a focus on time-series methods that use historical data patterns to predict future values. The chapter describes how to decompose a time series into trend, seasonal, cyclical and irregular components. It also explains techniques for smoothing time-series data, including moving averages and exponential smoothing. Finally, it covers methods for time-series forecasting based on trend lines, including linear, quadratic, exponential and other models.
This document discusses time-series forecasting and index numbers. It begins by outlining the chapter goals, which are to develop basic forecasting models, identify time-series components, use smoothing and trend-based forecasting models, forecast seasonal data, and compute index numbers. The document then explains key concepts like time-series plots and components, moving averages, exponential smoothing, trend-based forecasting using linear, quadratic and exponential models, and model selection criteria. Examples are provided throughout to illustrate time-series smoothing and forecasting techniques.
This chapter introduces basic concepts in business statistics including how statistics are used in business, the different types of data and their sources, and how to use Microsoft Excel and Minitab software programs. It discusses the difference between descriptive and inferential statistics, and covers variables, populations, samples, parameters, and statistics. Finally, it reviews levels of measurement and provides an overview of key terms used in Minitab and Microsoft Excel.
This chapter introduces basic concepts in business statistics including how statistics are used in business, types of data and their sources, and popular software programs like Microsoft Excel and Minitab. It discusses descriptive versus inferential statistics and reviews key terminology such as population, sample, parameters, and statistics. The chapter also covers different types of variables, levels of measurement, and considerations for properly using statistical software programs.
This chapter discusses time-series forecasting and index numbers. It aims to develop basic forecasting models using smoothing methods like moving averages and exponential smoothing. It also covers trend-based forecasting using linear and nonlinear regression models. Time-series data contains trend, seasonal, cyclical, and irregular components that must be accounted for. Forecasting future values involves identifying patterns in historical data and extending those patterns into the future.
This document discusses forecasting methods for predicting future demand. It covers qualitative methods like jury of executive opinion and quantitative methods like naive forecasting, moving averages, and exponential smoothing. Exponential smoothing assigns weights to past demand that decrease exponentially, with the most recent demand weighted most heavily. The smoothing constant determines how quickly the weights decrease. Forecasting allows for better planning of human resources, capacity, and supply chain management.
The document discusses various forecasting techniques used to predict future values based on historical data patterns. It describes time series models like moving averages, exponential smoothing and trend projections that rely solely on past values to forecast. It also covers decomposition of time series data into trend, seasonality, cycles and random components. The document provides examples of scatter plots to visualize relationships in time series data and defines accuracy measures like MAD, MSE and MAPE to evaluate forecast errors. Overall it provides an overview of quantitative forecasting methods and how to implement them.
CA in Dwarka is a team of professional Chartered Accountant which are providing best services like Company Registration, Income Tax Return, Sales Tax Consultants, Bank Audit and other services specially in Dwarka and Delhi NCR.
Statistical Process ControlAgendaIntroductionNumber of.docxsusanschei
Statistical Process Control
Agenda
Introduction
Number of Ford Focus sold
Number of Ford Focus recalled
Summary and Recommendations
References
2
Introduction
Statistical Process Control
Method used for quality control
Ford Focus transmission problems
Statistical Process Control is a process that is used for quality control of a item. It monitors and control any process to ensure operations are at the full potential. In this case, Ford has been having many transmission issues with the Focus since 2012.
3
Number of Ford Focus sold
I Chart represents 1k units per month for last 40 months
Second chart: Yearly sales
Sales decreased by 89,133
Sales for the Focus have been on a rollercoaster ride since December 2015. When you observe the yearly sales you can see this vehicle has been on a rapid decline in sales.
4
I Control Chart
Sample Means1234567891011121314151617181920212223242526272829303132333435363738394011131919161719141211991010131413171516131210131171216131611997544721UCL1234567891011121314151617181920212223242526272829303132333435363738394015.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.452776989084956Mean1234567891011121314151617181920212223242526272829303132333435363738394011.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.475LCL123456789101112131415161718192021222324252627282930313233343536373839407.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.497223010915044
Time Point
Thousand Per Month Value
Equipped cars with new shifting transmission (bad concept)
First year 3,469 reported.
This chapter discusses time series analysis and forecasting. The key components are:
1. A time series contains data recorded over time and can be analyzed to identify trends and patterns that may continue in the future.
2. The components of a time series are secular trends, cyclical variation, seasonal variation, and irregular variation.
3. Moving averages and weighted moving averages can be used to smooth time series data and identify trends. Linear and nonlinear trend lines can also model trends in the data.
4. Seasonal indices identify seasonal patterns that repeat each year and can be used to deseasonalize time series data. Autocorrelation tests whether residuals are independent or correlated over time.
The document discusses various quantitative forecasting techniques including time series methods like moving averages and exponential smoothing. It provides examples of how to calculate 3-period moving averages and exponential smoothing forecasts using sample sales data. Exponential smoothing places more weight on recent observations compared to moving averages. The smoothing constant determines how quickly older data is discounted.
This chapter discusses numerical descriptive measures used to describe the central tendency, variation, and shape of data. It covers calculating the mean, median, mode, variance, standard deviation, and coefficient of variation for data. The geometric mean is introduced as a measure of the average rate of change over time. Outliers are identified using z-scores. Methods for summarizing and comparing data using these descriptive statistics are presented.
Financial analysis refers to business assessment in terms of stability, viability, profitability, and other important financial and non-financial factors. It is done through several different techniques, ratios, and charts, with the purpose of transforming static numbers from or in financial statements, to an added value for decision-makers. Usually, the analyzed information and the analysis results are presented frequently as a report or as a dashboard.
A dashboard (or data visualization) is used to present all indicators at once to help owners, investors, or managers make efficient decisions by identifying specific actions that should be taken to reach future targets or goals.
Charts and graphs are used to make information clearer and easier to understand. They play a critical role in helping people to visualise large amounts of information, make better decisions and communicate their results to others.
This modelling guide explains how to make quick charts and how they can be useful in analysing data.
In this guide we analyse the trend of operating revenues, operating costs and operating profits / (losses) over the timeline of a project.
Finc 3304 business finance work the web project part 2 (arnit1
This document provides instructions for the Work the Web project part 2. Students are asked to analyze the financial performance of a company over multiple years by collecting historical financial data, performing trend and ratio analyses, and calculating DuPont analysis ratios. Specifically, students will analyze trends in profitability ratios, compare the company's ratios to competitors' across various categories, and assess the company's return on assets and return on equity based on DuPont analysis components versus industry levels.
This document discusses various forecasting models and techniques. It begins by describing qualitative models that incorporate subjective factors like the Delphi method, jury of executive opinion, sales force composite, and consumer market surveys. It then covers time-series models like moving averages, exponential smoothing, trend projections, and decomposition that predict the future based on past data. Specific techniques are defined, like simple and weighted moving averages, and exponential smoothing. Examples are provided to illustrate how to apply these techniques to forecast data. Measures of forecast accuracy like mean absolute deviation are also introduced.
This chapter discusses choosing appropriate statistical techniques for analyzing numerical and categorical data. For numerical variables, it identifies questions about describing characteristics, drawing conclusions about the mean/standard deviation, determining differences between groups, identifying influencing factors, predicting values, and determining stability over time. For each, it lists relevant techniques. For categorical variables, it addresses similar questions and outlines techniques like hypothesis testing, regression, and control charts. The goal is to match the right analysis to the data type and research purpose.
This document outlines key concepts related to forecasting, including:
- The three time horizons for forecasting: short, medium, and long range.
- Qualitative and quantitative forecasting methods such as jury of executive opinion, Delphi method, moving averages, and exponential smoothing.
- Components of time series data including trend, seasonality, cyclicality, and randomness.
- Steps in a forecasting system and challenges with producing accurate forecasts.
- How Disney uses forecasting across its global operations to inform decisions.
The moving average formula of demand forecasting is explained herein with the help of an example in an easily understandable way. The ppt contains the meaning and formula of moving average along with an example.
Format for Case Analysis The Strategic AuditThere is no one bes.docxbudbarber38650
Format for Case Analysis: The Strategic Audit
There is no one best way to analyze or present a case report. Each instructor has personal preferences for format and approach. Nevertheless, in Appendix 12.B we suggest an approach for both written and oral reports that provides a systematic method for successfully attacking a case. This approach is based on the strategic audit, which is presented at the end of Chapter 1 in Appendix 1.A. We find that this approach provides structure and is very helpful for the typical student who may be a relative novice in case analysis. Regardless of the format chosen, be careful to include a complete analysis of key environmental variables—especially of trends in the industry and of the competition. Look at international developments as well.
If you choose to use the strategic audit as a guide to the analysis of complex strategy cases, you may want to use the strategic audit worksheet in Figure 12–1. Print a copy of the worksheet to use to take notes as you analyze a case. See Appendix 12.C for an example of a completed student-written analysis of a 1993 Maytag Corporation case done in an outline form using the strategic audit format. This is one example of what a case analysis in outline form may look like.
Case discussion focuses on critical analysis and logical development of thought. A solution is satisfactory if it resolves important problems and is likely to be implemented successfully. How the corporation actually dealt with the case problems has no real bearing on the analysis because management might have analyzed its problems incorrectly or implemented a series of flawed solutions.
FIGURE 12–1 Strategic Audit Worksheet
(Wheelen 341-342)
APPENDIX 12.B Suggested Case Analysis Methodology Using the Strategic Audit
First Reading of the Case
■Develop a general overview of the company and its external environment.
■Begin a list of the possible strategic factors facing the company at this time.
■List the research information you may need on the economy, industry, and competitors.
Over the past six years, increases in yearly revenues have consistently reached 12%. Byte Products Inc., headquartered in the U.S. Midwest, is regarded as one of the largest-volume suppliers of specialized components and is easily the industry leader.
Second Reading of the Case
■Read the case a second time, using the strategic audit as a framework for in-depth analysis. (See Appendix 1.A on pages 32-39.) You may want to make a copy of the strategic audit worksheet (Figure 12–1) to use to keep track of your comments as you read the case.
■The questions in the strategic audit parallel the strategic decision-making process shown in Figure 1–5 (pages 26-27).
■The audit provides you with a conceptual framework to examine the company’s mission, objectives, strategies, and policies, as well as problems, symptoms, facts, opinions, and issues.
■Perform a financial analysis of the company, using ratio analysis (see Table 12–1), and do t.
8 9 forecasting of financial statementsJohn McSherry
1) Lectures 8 and 9 cover forecasting techniques and credit risk analysis. Readings are provided on analyst forecasts and credit risk assessment models.
2) There are two general approaches to forecasting - non-econometric qualitative methods typically used by analysts, and econometric quantitative methods. Top-down and bottom-up are common non-econometric techniques.
3) Financial ratios tend to revert to historical norms over time. An analysis of a company's ratios should consider the typical behavior of those ratios and anchor forecasts accordingly.
This chapter discusses time-series forecasting and index numbers. It aims to develop basic forecasting models using smoothing methods like moving averages and exponential smoothing. It also covers trend-based forecasting using linear and nonlinear regression models. Time-series data contains trend, seasonal, cyclical, and irregular components that must be accounted for. Forecasting future values involves identifying patterns in historical data and extending those patterns into the future.
This document discusses forecasting methods for predicting future demand. It covers qualitative methods like jury of executive opinion and quantitative methods like naive forecasting, moving averages, and exponential smoothing. Exponential smoothing assigns weights to past demand that decrease exponentially, with the most recent demand weighted most heavily. The smoothing constant determines how quickly the weights decrease. Forecasting allows for better planning of human resources, capacity, and supply chain management.
The document discusses various forecasting techniques used to predict future values based on historical data patterns. It describes time series models like moving averages, exponential smoothing and trend projections that rely solely on past values to forecast. It also covers decomposition of time series data into trend, seasonality, cycles and random components. The document provides examples of scatter plots to visualize relationships in time series data and defines accuracy measures like MAD, MSE and MAPE to evaluate forecast errors. Overall it provides an overview of quantitative forecasting methods and how to implement them.
CA in Dwarka is a team of professional Chartered Accountant which are providing best services like Company Registration, Income Tax Return, Sales Tax Consultants, Bank Audit and other services specially in Dwarka and Delhi NCR.
Statistical Process ControlAgendaIntroductionNumber of.docxsusanschei
Statistical Process Control
Agenda
Introduction
Number of Ford Focus sold
Number of Ford Focus recalled
Summary and Recommendations
References
2
Introduction
Statistical Process Control
Method used for quality control
Ford Focus transmission problems
Statistical Process Control is a process that is used for quality control of a item. It monitors and control any process to ensure operations are at the full potential. In this case, Ford has been having many transmission issues with the Focus since 2012.
3
Number of Ford Focus sold
I Chart represents 1k units per month for last 40 months
Second chart: Yearly sales
Sales decreased by 89,133
Sales for the Focus have been on a rollercoaster ride since December 2015. When you observe the yearly sales you can see this vehicle has been on a rapid decline in sales.
4
I Control Chart
Sample Means1234567891011121314151617181920212223242526272829303132333435363738394011131919161719141211991010131413171516131210131171216131611997544721UCL1234567891011121314151617181920212223242526272829303132333435363738394015.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.452776989084956Mean1234567891011121314151617181920212223242526272829303132333435363738394011.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.475LCL123456789101112131415161718192021222324252627282930313233343536373839407.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.497223010915044
Time Point
Thousand Per Month Value
Equipped cars with new shifting transmission (bad concept)
First year 3,469 reported.
This chapter discusses time series analysis and forecasting. The key components are:
1. A time series contains data recorded over time and can be analyzed to identify trends and patterns that may continue in the future.
2. The components of a time series are secular trends, cyclical variation, seasonal variation, and irregular variation.
3. Moving averages and weighted moving averages can be used to smooth time series data and identify trends. Linear and nonlinear trend lines can also model trends in the data.
4. Seasonal indices identify seasonal patterns that repeat each year and can be used to deseasonalize time series data. Autocorrelation tests whether residuals are independent or correlated over time.
The document discusses various quantitative forecasting techniques including time series methods like moving averages and exponential smoothing. It provides examples of how to calculate 3-period moving averages and exponential smoothing forecasts using sample sales data. Exponential smoothing places more weight on recent observations compared to moving averages. The smoothing constant determines how quickly older data is discounted.
This chapter discusses numerical descriptive measures used to describe the central tendency, variation, and shape of data. It covers calculating the mean, median, mode, variance, standard deviation, and coefficient of variation for data. The geometric mean is introduced as a measure of the average rate of change over time. Outliers are identified using z-scores. Methods for summarizing and comparing data using these descriptive statistics are presented.
Financial analysis refers to business assessment in terms of stability, viability, profitability, and other important financial and non-financial factors. It is done through several different techniques, ratios, and charts, with the purpose of transforming static numbers from or in financial statements, to an added value for decision-makers. Usually, the analyzed information and the analysis results are presented frequently as a report or as a dashboard.
A dashboard (or data visualization) is used to present all indicators at once to help owners, investors, or managers make efficient decisions by identifying specific actions that should be taken to reach future targets or goals.
Charts and graphs are used to make information clearer and easier to understand. They play a critical role in helping people to visualise large amounts of information, make better decisions and communicate their results to others.
This modelling guide explains how to make quick charts and how they can be useful in analysing data.
In this guide we analyse the trend of operating revenues, operating costs and operating profits / (losses) over the timeline of a project.
Finc 3304 business finance work the web project part 2 (arnit1
This document provides instructions for the Work the Web project part 2. Students are asked to analyze the financial performance of a company over multiple years by collecting historical financial data, performing trend and ratio analyses, and calculating DuPont analysis ratios. Specifically, students will analyze trends in profitability ratios, compare the company's ratios to competitors' across various categories, and assess the company's return on assets and return on equity based on DuPont analysis components versus industry levels.
This document discusses various forecasting models and techniques. It begins by describing qualitative models that incorporate subjective factors like the Delphi method, jury of executive opinion, sales force composite, and consumer market surveys. It then covers time-series models like moving averages, exponential smoothing, trend projections, and decomposition that predict the future based on past data. Specific techniques are defined, like simple and weighted moving averages, and exponential smoothing. Examples are provided to illustrate how to apply these techniques to forecast data. Measures of forecast accuracy like mean absolute deviation are also introduced.
This chapter discusses choosing appropriate statistical techniques for analyzing numerical and categorical data. For numerical variables, it identifies questions about describing characteristics, drawing conclusions about the mean/standard deviation, determining differences between groups, identifying influencing factors, predicting values, and determining stability over time. For each, it lists relevant techniques. For categorical variables, it addresses similar questions and outlines techniques like hypothesis testing, regression, and control charts. The goal is to match the right analysis to the data type and research purpose.
This document outlines key concepts related to forecasting, including:
- The three time horizons for forecasting: short, medium, and long range.
- Qualitative and quantitative forecasting methods such as jury of executive opinion, Delphi method, moving averages, and exponential smoothing.
- Components of time series data including trend, seasonality, cyclicality, and randomness.
- Steps in a forecasting system and challenges with producing accurate forecasts.
- How Disney uses forecasting across its global operations to inform decisions.
The moving average formula of demand forecasting is explained herein with the help of an example in an easily understandable way. The ppt contains the meaning and formula of moving average along with an example.
Format for Case Analysis The Strategic AuditThere is no one bes.docxbudbarber38650
Format for Case Analysis: The Strategic Audit
There is no one best way to analyze or present a case report. Each instructor has personal preferences for format and approach. Nevertheless, in Appendix 12.B we suggest an approach for both written and oral reports that provides a systematic method for successfully attacking a case. This approach is based on the strategic audit, which is presented at the end of Chapter 1 in Appendix 1.A. We find that this approach provides structure and is very helpful for the typical student who may be a relative novice in case analysis. Regardless of the format chosen, be careful to include a complete analysis of key environmental variables—especially of trends in the industry and of the competition. Look at international developments as well.
If you choose to use the strategic audit as a guide to the analysis of complex strategy cases, you may want to use the strategic audit worksheet in Figure 12–1. Print a copy of the worksheet to use to take notes as you analyze a case. See Appendix 12.C for an example of a completed student-written analysis of a 1993 Maytag Corporation case done in an outline form using the strategic audit format. This is one example of what a case analysis in outline form may look like.
Case discussion focuses on critical analysis and logical development of thought. A solution is satisfactory if it resolves important problems and is likely to be implemented successfully. How the corporation actually dealt with the case problems has no real bearing on the analysis because management might have analyzed its problems incorrectly or implemented a series of flawed solutions.
FIGURE 12–1 Strategic Audit Worksheet
(Wheelen 341-342)
APPENDIX 12.B Suggested Case Analysis Methodology Using the Strategic Audit
First Reading of the Case
■Develop a general overview of the company and its external environment.
■Begin a list of the possible strategic factors facing the company at this time.
■List the research information you may need on the economy, industry, and competitors.
Over the past six years, increases in yearly revenues have consistently reached 12%. Byte Products Inc., headquartered in the U.S. Midwest, is regarded as one of the largest-volume suppliers of specialized components and is easily the industry leader.
Second Reading of the Case
■Read the case a second time, using the strategic audit as a framework for in-depth analysis. (See Appendix 1.A on pages 32-39.) You may want to make a copy of the strategic audit worksheet (Figure 12–1) to use to keep track of your comments as you read the case.
■The questions in the strategic audit parallel the strategic decision-making process shown in Figure 1–5 (pages 26-27).
■The audit provides you with a conceptual framework to examine the company’s mission, objectives, strategies, and policies, as well as problems, symptoms, facts, opinions, and issues.
■Perform a financial analysis of the company, using ratio analysis (see Table 12–1), and do t.
8 9 forecasting of financial statementsJohn McSherry
1) Lectures 8 and 9 cover forecasting techniques and credit risk analysis. Readings are provided on analyst forecasts and credit risk assessment models.
2) There are two general approaches to forecasting - non-econometric qualitative methods typically used by analysts, and econometric quantitative methods. Top-down and bottom-up are common non-econometric techniques.
3) Financial ratios tend to revert to historical norms over time. An analysis of a company's ratios should consider the typical behavior of those ratios and anchor forecasts accordingly.
This document provides an overview of decision making techniques covered in Chapter 17. It begins by listing the learning objectives, which are to use payoff tables, decision trees, and criteria to evaluate alternative courses of action. It then outlines the steps in decision making, which include listing alternatives and uncertain events, determining payoffs, and adopting evaluation criteria. Several decision making criteria are introduced, including maximax, maximin, expected monetary value, expected opportunity loss, value of perfect information, and return-to-risk ratio. Payoff tables and decision trees are presented as methods for displaying decision problems. The chapter concludes by discussing how sample information can be used to revise old probabilities when making decisions.
This document provides an overview of multiple regression analysis. It introduces the concept of using multiple independent variables (X1, X2, etc.) to predict a dependent variable (Y) through a regression equation. It presents examples using Excel and Minitab to estimate the regression coefficients and other measures from sample data. Key outputs include the regression equation, R-squared (proportion of variation in Y explained by the X's), adjusted R-squared (penalized for additional variables), and an F-test to determine if the overall regression model is statistically significant.
This document provides an overview of simple linear regression analysis. It defines key concepts such as the regression line, slope, intercept, and correlation coefficient. It also explains how to evaluate the fit of a regression model using the coefficient of determination (R2), which measures the proportion of variance in the dependent variable that is explained by the independent variable. The document includes an example using house price and square footage data to demonstrate how to apply simple linear regression and interpret the results.
This chapter discusses chi-square tests and nonparametric tests. It covers chi-square tests for contingency tables to test differences between two or more proportions, including computing expected frequencies. The Marascuilo procedure is introduced for determining pairwise differences when proportions are found to be unequal. Chi-square tests of independence are discussed for contingency tables with more than two variables to test if the variables are independent. Nonparametric tests are also introduced. Examples are provided to demonstrate chi-square goodness of fit tests and tests of independence.
This chapter discusses analysis of variance (ANOVA) techniques. It covers one-way and two-way ANOVA for comparing the means of three or more groups or populations. The chapter explains how to partition total variation into between-group and within-group components using sum of squares calculations. It also describes how to conduct the F-test and make inferences about differences in population means using ANOVA tables and significance tests. Multiple comparison procedures for identifying specific mean differences are also introduced.
This chapter discusses two-sample hypothesis tests for comparing population means and proportions between two independent samples, and between two related samples. It introduces tests for comparing the means of two independent populations, two related populations, and the proportions of two independent populations. The key tests covered are the pooled variance t-test for independent samples with equal variances, separate variance t-test for independent samples with unequal variances, and the paired t-test for related samples. Examples are provided to demonstrate how to calculate the test statistic and conduct hypothesis tests to compare sample means and determine if they are statistically different. Confidence intervals for the difference between two means are also discussed.
This chapter discusses confidence interval estimation for means and proportions. It introduces key concepts such as point estimates, confidence intervals, and confidence levels. For a mean where the population standard deviation is known, the confidence interval formula uses the normal distribution. When the standard deviation is unknown, the t-distribution is used instead. For a proportion, the confidence interval adds an allowance for uncertainty to the sample proportion. The chapter also covers determining sample sizes and interpreting confidence intervals.
This chapter discusses sampling and sampling distributions. It defines key sampling concepts like the sampling frame, population, and different sampling methods including probability and non-probability samples. Probability sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. The chapter also covers sampling distributions and how the distribution of sample means approaches a normal distribution as the sample size increases due to the Central Limit Theorem, even if the population is not normally distributed. This allows inferring properties of the population from a sample.
This document discusses the normal distribution and other continuous probability distributions. It begins by listing the learning objectives, which are to compute probabilities from the normal, uniform, exponential, and binomial distributions. It then defines continuous random variables and describes key properties of the normal distribution, including its bell shape, equal mean, median and mode, and symmetry. Several examples are provided to illustrate how to compute probabilities using the normal distribution and standardized normal table. The empirical rules for the normal distribution are also discussed.
This chapter discusses important discrete probability distributions used in business statistics. It introduces discrete random variables and their probability distributions. It defines the binomial distribution and explains how to calculate probabilities using the binomial formula. Examples are provided to demonstrate calculating the mean, variance, and covariance of discrete random variables, as well as the expected value and risk of investment portfolios. Counting techniques like combinations are also discussed for calculating binomial probabilities.
This document provides an overview of basic probability concepts covered in Chapter 4 of Basic Business Statistics, 11th Edition. It introduces key probability terms like simple events, joint events, sample space, and contingency tables for visualizing events. It covers how to calculate probabilities of events both with and without conditional dependencies. Formulas are provided for computing joint, marginal, and conditional probabilities using contingency tables. The chapter also explains Bayes' Theorem for revising probabilities based on new information. An example demonstrates how to apply Bayes' Theorem to calculate the probability of a successful oil well given a positive test result.
This document discusses various methods for organizing and presenting categorical and numerical data using tables, charts, and graphs. It covers summarizing categorical data using summary tables, bar charts, pie charts, and Pareto diagrams. For numerical data, it discusses organizing data using ordered arrays, stem-and-leaf displays, frequency distributions, histograms, frequency polygons, ogives, contingency tables, side-by-side bar charts, and scatter plots. The goal is to effectively communicate patterns and relationships in the data.
The document discusses the economic theory of consumer choice. It addresses how consumers make decisions based on their preferences between goods, income constraints, and prices. The key points covered are:
1) Consumer preferences are represented by indifference curves, which show combinations of goods that make the consumer equally satisfied.
2) The budget constraint depicts the combinations of goods a consumer can afford based on income and prices.
3) Consumers seek to maximize satisfaction by choosing the highest indifference curve possible, given their budget constraint. The optimal choice occurs where the indifference curve is tangent to the budget constraint.
This document discusses income inequality and poverty. It provides data on the distribution of income in the United States from 1998 to 1935, showing that income inequality has increased in recent decades. Factors that have contributed to rising inequality include increases in international trade, changes in technology, and the falling wages of unskilled workers relative to skilled workers. The document also examines poverty rates in the US and issues with measuring inequality, such as accounting for in-kind transfers, economic life cycles, and transitory versus permanent income. It concludes by discussing different political philosophies around redistributing income.
1) Workers earn different wages due to factors like human capital, job attributes, ability, and discrimination. More education leads to higher wages.
2) While competitive markets reduce discrimination, it can persist due to customer preferences or government policies that support discriminatory practices.
3) There is debate around the doctrine of "comparable worth" and whether jobs of equal value or importance should receive equal pay.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
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