Predictive analytics is the process of using data to forecast future outcomes. The process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behavior. Organizations can use historic and current data to forecast trends and behaviors seconds, days, or years into the future with a great deal of precision.
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Predictive analytics is the process of using data to forecast future outcomes. The process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behavior. Organizations can use historic and current data to forecast trends and behaviors seconds, days, or years into the future with a great deal of precision.
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- The file P13_27.xlsx contains yearly data on the proportion of Americans under the age of 18 living below the poverty level. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Create a chart of the series with the forecasts superimposed from this optimal smoothing constant. Does it make much of an improvement over the model in part b? d. Write a short report to summarize your results. Considering the chart in part c, would you say the forecasts are good?Stock market analysts are continually looking for reliable predictors of stock prices. Consider the problem of modeling the price per share of electric utility stocks (Y). Two variables thought to influence this stock price are return on average equity (X1) and annual dividend rate (X2). The stock price, returns on equity, and dividend rates on a randomly selected day for 16 electric utility stocks are provided in the file P13_15.xlsx. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Also, interpret the standard error of estimate and the R-square value for these data.The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
- 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.Paraphrase this one. Analyze and elaborate in 85 words. The accuracy of predictive analysis is intricately linked to the currency of its data. Temporal factors play a pivotal role in the precision of predictions. Data from a year past might prove too antiquated to effectively forecast trends and patterns in today's dynamic global market. Neglecting this temporal dimension could potentially expose organizations to substantial financial risks.If Mr. Ishaq is the Director of Lexus Company in Nizwa, what does he need to do to predict customer sales or product demand? a. Forecasting b. Marketing c. Production d. Coordination
- 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?Business analytics can be of three types: descriptive, predictive, and prescriptive. Describe each of those. Imagine you are a data analyst in FedRetail, an online retail shop. Your manager wants a forecast of the sell in the upcoming Christmas season. What kind of analysis you should do to find out your manager's query? Explain.For this final assignment I’d like you to call upon your knowledge of forecasting to help project your firm’s revenue stream for the upcoming year. In the Microsoft Excel that is attached to this assignment, you will find the past four years of quarterly revenue data for your nonprofit. Your revenue comes from two major sources: individual donations and donations from corporations. I’d like you to use the past four years’ worth of data to forecast the individual donations and donations for corporations for the next fiscal year (year 5 quarters 1-4). Once you have forecasted both individual revenue streams you can simply add them together to get your overall revenue forecast for the next fiscal year. Please discuss what forecasting method you chose and how you made your decision. You can do this in your excel document or write up a separate paragraph or two in a Microsoft Word document.
- Explain the term Forcasting. Why forcasting are Forecasting is one of the important functions of management. It is a part and parcel of planning function. Forecasting means prediction about future. Forecasting means analysis of future about the operations of an enterprise. It involves looking ahead for future event. Forecasting means a process of providing the details supported by budget. Forecasting means drawing a conclusion about production, sales, profit on the basis of research, study & survey.Distinguish between Historical Analogy and the Delphi Method forecasting and discuss a business application suitable for each methodology. In each example, discuss why the chosen method (Historical Analogy or Delphi) is more appropriate than the other. Use -- Evans, J. (2020). Business analytics (3rd ed.). Pearson.Although the unpredicted, yet disruptive, impact of the CoViD-19 pandemic is global and has negatively affected all organizations, identify a business organization that you think has been affected the most. In the new normal, which forecasting time horizon (short-range, medium-range, or long-range) would you suggest should be adopted by this organization in forecasting the demand for its main product? Justify your answer. Cite your assumptions. Cite 2 to 3 factors that this business organization should use as a basis for forecasting the demand for its main product. Justify your answer.