This assignment provides practice in decomposing the seasonal time series data and then subtract that effect from the data and then to show how to address issues of correlations between successive values of the time series. Before beginning this assignment, review the learning resources for this module, especially focus on Decomposing Time Series, Forecasting using Exponential Smoothing and ARIMA Models sections of A Little of R for Time Series by Avril Coghlan. Complete the following steps and write a report to record your work, results and analysis. You may use the attached data sets (average price data and NY benchmarked employment data) or one of time series data from data( ). Please note that some time series are more amenable to time series analysis than others.It is important that you understand data structure and be able to appropriately transform/clean raw data prior to analysis. "Average price data.xlsx" is in the long format. The data series are stacked on top of each other. That is how the data is provided the Bureau of Labor Statistics. The names of variables are provided in the "series" worksheet. You need to pick one series name and use it to filter or subset the data prior to analysis. Some series are better than others. You may want to use only the portions of the series after 2009, since the Great Recession was an one-time event that cannot be modeled.A. Decompose seasonal time series data and subtract that effect from the data: Identify an appropriate time series data set, this can be a data set in R or a data set you find. Then, use R to display decompose the seasonal time series and seasonally adjust to subtract the seasonal components from the time series. In your report, provide insights to the results. B. Address issues of correlations between successive values of the time series: Identify an appropriate data set, this can be this can be a data set in R or a data set you find. Then use R time series functions to address autocorrelation issues in data sets. In many cases you can make a better predictive model by taking correlations in the data into account. Address autocorrelation issues (irregular components of the time series) using a technique called ARIMA (Autoregressive Integrated Moving Average) models for irregular components of time series. In your report, provide insights to the results. Report Your assignment/project should have a good cover/title page, introduction of what the goals of the project and the methods you use. It also should follow APA format with at least 1000 words (excluding title page and references page) and references page. In the body of your project you should incorporate the R codes and R outputs with interpretation of your results. Finally, you need to make sense of your results to make good points with proper conclusions, to show your understanding of the course material and its application to the dataset. Graphs, figures, charts, tables are very useful to increase visual effects to impress your readers. You also should do your best to give insight and understanding to the project with a good conclusion. Please use subtitles to make your assignment more reader friendly as well.

Covenant Hospital operates a main hospital and three outlying clinics in a medium-size city. All four locations perform similar outpatient services, and patients typically visit the location nearest them. The hospital has recently had more congestion—longer waiting lines—than it (or its patients) would like. As part of a study to learn the causes of these long lines and to suggest possible solutions, all locations have kept track of customer arrivals during 1-hour intervals for the past 10 weeks. All locations are open Monday through Friday from 9 a.m. until 5 p.m. and on Saturday from 9 a.m. until noon. For each location, the hospital and clinics collected data that contains the number of customer arrivals during each hour of a 10-week period. The manager of Covenant has hired you to make some sense of these data. Specifically, your task is to present charts and/or tables that indicate how customer traffic into the hospital locations varies by day of week and hour of day. There is also interest in whether any daily or hourly patterns you observe are stable across weeks. Although you don't have full information about the way the hospital currently runs its operations—you know only its customer arrival pattern and the fact that it is currently experiencing long lines—you are encouraged to append any suggestions for improving operations based on your analysis of the data.Review the Covenant Hospital data files. Reflect on what analyses might be required to address the manager's request.The Assignment: (3–5 pages)Submit your answers and embedded Excel analysis as a Microsoft Word management report.Be sure to adhere to APA format when generating your management report.Albright, S. C., & Winston, W. L. (2015). Business analytics: Data analysis and decision making (5th ed.). Stamford, CT: Cengage Learning. Chapter 1, "Introduction to Data Analysis and Decision Making" (pp. 1–16)Chapter 2, "Describing the Distribution of a Single Variable" (pp. 19–78)Chapter 3, "Finding Relationships Among Variables" (pp. 79–136)Kohane, I. S. (2015). Health care policy: Ten things we have to do to achieve precision medicine. Science, 349(6243), 37–38.Note: Retrieved from the Walden Library databases.Zwitter, A. (2014). Commentary: Big data ethics. Big Data & Society, 1–6.Note: Retrieved from the Walden Library databases.Document: Covenant Data File [Excel spreadsheet]

1.  In your own words, what is meant by the statement that correlation does not imply causality (Section 10-2)? 2.  In your own words, please describe the difference between the regression equation    and the regression equation   (Section 10-3). 3.  A geneticist wants to develop a method for predicting the eye color of a baby, given the eye color of each parent.  In your own words, can the methods of Section 10-5 be used?  Why or why not?