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Experience teaches us that the bulk of the technical material covered in this course will, unfortunately, be forgotten shortly thereafter. If you were to commit just three concepts you have learned in this course to your long-term memory, which concepts would you select and why?

Experience teaches us that the bulk of the technical material covered in this course will, unfortunately, be forgotten shortly thereafter. If you were to commit just three concepts you have learned in this course to your long-term memory, which concepts would you select and why?

respond in 150 words each.

  • Experience teaches us that the bulk of the technical material covered in this course will, unfortunately, be forgotten shortly thereafter. If you were to commit just three concepts you have learned in this course to your long-term memory, which concepts would you select and why?
  • Evaluate the applicability of each of the concepts that you’ve selected to a business environment in which you have worked in the past (or with which you might be familiar).
  • Describe how you would apply each of the concepts. Be specific.
  • Explain your goal in applying each concept to the environment that you have described.

One of the best tools available to business managers to help with the unknown is probability theory. Which offers a means of reducing uncertainty by estimating the chances of different outcomes occurring. For example, Flu season and the correct strand, how well does being vaccinated help with the flu. What happened in the past season to compare what the upcoming season will bring, (Anderson, 2018)

Data information and knowledge: Understanding the difference in all the pieces makes a great Impact. Data is pieces, information is structure, and knowledge is all the data information and put together and organized to complete collected information that will help make informed decisions and solve problems in everyday operations. For example, products that sell well and who purchases them will be your target customers to reach.

Population vs. sample: Understanding the relationship between them allows you to go beyond what is available. For the best analysis and for the best outcome you need to use a small sample of current or potential customers to get a solid answer. For example, using a discount card for services rendered in a company or organization which will allow you to group and analyze these groups. In the marketing world it is great to understand the difference of population vs sample and how to get the most out of the data available.

I have learned a lot in MBA5008 from added information to understanding in depth of old information. In this class my three favorite concepts were described above. Each concept holds a value in my everyday life and my business operation as a manager.

References

Anderson, D., Sweeney, D., Williams, T. (2018). (6th ed.) Modern Business Statistics with Microsoft Office Excel. Cengage.

ISBN: 9781337115186.

 

Time-series forecast is a technique used for predicting future aspects of data. It uses old data and incorporates the present data into estimates of future data. This forecast can be used in short-term and long-term decision-making and the outlook for the business. Time-series forecasting can be for daily, weekly, monthly, and yearly intervals. It is always best if the business can get good predictions for the future to know how to proceed with the business.

There are several circumstances that influence the reliability of the time-series forecasts. One is how the consumer feels about the product or business and how confident they are in the product or business. An example of this would be when Covid was so bad, and people were not going to stores as much, but when Covid was not as rampant and as bad, the consumers felt safer to go to the stores and spend money. Another influence is consumer disposable income. When a consumer does not have the money to spend, they will hold on to what little money they have left in case of an emergency.

An example of this is when businesses and companies were shutting down, and the consumers did not have jobs, so they had to hold on to all their extra money to buy what they needed. Several other circumstances influence the time series forecast, such as residential market, oil and gas prices, changes in the job market and wages, weather data, how much the currency is worth globally, raw material cost, and the production of key commodities. Many fields of study use time series forecasting in different applications. Some of the fields of study that uses time series forecasting are

  • Astronomy
  • Business planning
  • Control engineering
  • Earthquake prediction
  • Econometrics
  • Mathematical finance
  • Statistics
  • Weather forecasting

There are different types of models, and they are as follows: Decomposition model, smooth-based model, moving-average modeling, exponential smoothing model, Autoregressive models, and the TBATS models. The Stock Market uses time-series forecasting. The stock market is where investors buy and sell shares in public companies. The stock market is highly risky, and using a prediction to see the market trend helps people decide whether they want to invest in businesses. The stock market uses the ARIMA (autoregressive integrated moving average). The ARIMA model works well for the stock market because the stock market is never stationary and is constantly fluctuating. In the ARIMA model, it makes the non-stationary data to stationary data. This model predicts linear time series data. The ARIMA is usually mostly in the field of finance and economics.

The ARIMA model is the best model to use for the stock market. The ARIMA uses the dependent relationship between the current data and past values; it takes the data and makes it stationary by subtracting the observations from the previous values. It forecasts the outcome of the model depending linearly on the past values. In the ARIMA, forecasting the number of goods needed for the next period is based on the historical data, forecasting the seasonal changes in the sales and the impact of new products or stocks.

References

Bodily, Samuel. (2008). Time-Series Forecasting. Darden Case: Quantitative Analysis (Topic).

Elbasheer, Foriaa Ahmed, & Talab, Samani A. Intelligent Information Management, 2014, 6, 142-148

Published Online May 2014 in SciRes. http://www.scirp.org/journal/iim

http://dx.doi.org/10.4236/iim.2014.63015

Hayes, Adam. (2021, April 24) What is a time series? https://www.investopedia.com/terms/t/timeseries.asp

Pao, James J. & Sullivan, Danielle S. (2033, March 13) Time series sales forecasting.

https://www.cs229.stanford.edu/proj2017/final-reports/5244336.pdf

Time series forecasting. (2018, November 27). Learntek.  https://www.learntek.org/blog/time-series-forecasting/


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