an estimate that was very close to the actual time of arrival. Considering these factors are essentielinformed estimates by the entrepreneur. Since the business he/she is venturing hasn't started vet oimportant that these factors affecting forecasting will be determined to better help him/her in making the bestdecisions for the business.B: Semantic WebDirections: Read the notes sent to your group chat entitled "Factors affecting forecasting revenues " thenexplain each factor using the semantic web below.o eongYAProcessing Question:1. Why is it important for an entrepreneur to determine the factors affecting forecasting revenues?

Forecasting Revenues: Forecasting revenues is the process of calculating the future revenue of the…

Answered: an estimate that was very close to the…

Understanding Business

12th Edition

ISBN:9781259929434

Author:William Nickels

Publisher:William Nickels

Chapter1: Taking Risks And Making Profits Within The Dynamic Business Environment

Section: Chapter Questions

Problem 1CE

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A pizza chain wants to forecast the demand rate for each store for each hour in the day.What type of forecasting method is it most likely to deploy?a. Automated forecastingb. Expert panel forecastingc. Weather forecastingd. Macroeconomic forecasting
Paraphrase this one. Analyze and elaborate in 200 words. Why organizations use time series data analysis? Time series analysis helps organizations understand the underlying causes of trends or systemic patterns over time. Using data visualizations, business users can see seasonal trends and dig deeper into why these trends occur. With modern analytics platforms, these visualizations can go far beyond line graphs. When organizations analyze data over consistent intervals, they can also use time series forecasting to predict the likelihood of future events. Time series forecasting is part of predictive analytics. It can show likely changes in the data, like seasonality or cyclic behavior, which provides a better understanding of data variables and helps forecast better.
You have a data set that includes time period and past sales data, and you want to use a time series forecasting technique. Which of the following forecasting methodologies is considered a time series forecasting technique? a.Group of answer choices b.Delphi method c.Simple movement smoothing d.Weighted moving average e.Simulation f.Exponential averaging
Assume that the data in columns A and B report the new constructions of residential homes, per month, in thousands, from 2015 to 2020 in the state of Oregon. a) Chart the data. Discuss any apparent trend and seasonal patterns. b) Use the "Tahoe Salt" method with linear trend to forecast new constructions for the 12 months of 2021. c) Add your model values and forecast to the data chart. d) Compute the MAD of your model. e) Compute and plot the tracking signal in a new chart. f) Discuss the quality of this model. What is the flagrant flaw? g) Suggest an approach that would yield better results in terms of forecast values, MAD, and tracking signal. An appropriate narrative argument is sufficient (i.e., one or two sentences). You do not need to do calculations for this part.
what are the differences between the following models? 1) Moving average models 2) Simple exponential smoothing 3) Holt's exponential smoothing 4) Winter's exponential smoothing 5) The Bass model In your answer, write down the equation for each model. What does each variable mean? Finally, why would I use one model as opposed to the others? For example, the Bass Model is used to forecast the adoption rate for new innovations, or sigmoid shaped growth.
Which of the following is important if forecasting is to have the desired positive effect on decisions? A. Communication between forecast staff and managment B. Ranges of forecast are reported to managment C. Forecast combination is examined D Forecast presentations should not be overly technical unless managment desires E All of the above Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.
Forecasting is said to be an "art" and a "science." Discuss how you would integrate both these very different techniques into a single forecast. What would be your goal in doing so?
Can you please solve all parts of the question manually. Thank you!
ontrast the reactive and proactive approaches to forecasting. Give several examples of types of organizations or situations in which each type is used.
If descriptive analytics focuses on summarizing historical data to gain insights into past events and trends and predictive analytics focused on forecasting future outcomes. What is the significance of the relationship between the past and the future?
Explain qualitative forecasting methods that managers can use to predict future behavior in various aspects of a company's operations. Use the following guide: Development Define and provide an example of each of the following qualitative forecasting techniques: Executive opinionSales force estimatesDelphi methodMarket research
There are two major ways of gaining data for the technology forecast. Which one you prefer using for technology forecasting?

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