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Instructions:
elements of the time series regression
150 words reply agree or disagree to each question
Q1
The four major elements of the time series regression model consist of the trend component, seasonal component, cyclic component, and noise component. The trend component represents the pattern the observations take. Common trends are linear, exponential, and sshaped. The seasonal component represents a repeating pattern over time, and is not in every time series. The cyclic component is similar to the seasonal component, and uses historical data to predict future trends. However, compared to the seasonal component, the cyclic component is irregular and difficult to predict. The noise component is unpredictable and occurs randomly. The greater the volume of noise, the more difficult it is to determine patterns and trends of other components. For example, a farmer can use the seasonal component to predict ticket sales to their pumpkin patch. It is predictable that ticket sales will increase in the fall and begin to drop off after thanksgiving and into the winter, until the next fall year when they increase again.
Q2
Time series regression is a way to collect data of one single item over time (Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M., 2020). It is different from a simple regression because simple regression focuses on collecting data on an item and observing its performance for the current time rather than forecasting its performance over time. On the other hand, multiple regression requires two or more variables to collect data. With timeseries regression, one can forecast how a unit will perform a day, week, month, or years from now. The information gathered from a time series regression is pertinent when tracking the economy’s performance, inflation, weather, cigarette usage per capita, etc. (Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M., 2020). Time series regression requires using the dynamic causal effect, which is the data collected to estimate the impact of Y change to X; over time (Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M., 2020).
A realistic example that I will use to explain a time series regression is weather forecasting. Weather experts use time series regression to study the data from the past and today to foresee how the weather would be tomorrow (Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M., 2020). The model that is used for predicting weather is both a lag model and a vector autoregression model. While the lag model is used to analyze the “correlation between adjacent days and years”, the vector autoregression model is used to depict “historical data” (Liu, Y., Roberts, M.C. & Sioshansi, R., 2018).
Ebru
References
Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M. Introduction to Econometrics with R. September 15, 2020. University of DuisburgEssen
Essen, Germany. Retrieved from: Introduction to Econometrics with R (econometricswithr.org).
Liu, Y., Roberts, M.C. & Sioshansi, R. A vector autoregression weather model for electricity supply and demand modeling. J. Mod. Power Syst. Clean Energy 6,763776 (2018).
https://doi.org/10.1007/s4056501703651
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Excellent Quality 95100%

Introduction
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Literature Support 9184 points The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned. 
Methodology 5853 points Content is wellorganized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met. 

Average Score 5085% 
4038 points More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided. 
8376 points Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration. 
5249 points Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met. 

Poor Quality 045% 
371 points The background and/or significance are missing. No search history information is provided. 
751 points Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration. 
481 points There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met 

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Elements of The Time Series Regression