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Post a total of 3 substantive responses over 2 separate days for full participat

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Post a total of 3 substantive responses over 2 separate days for full participation. This includes your initial post and 2 replies to other students or your faculty member.
Due Day 3
Time series are particularly useful to track variables such as revenues, costs, and profits over time. Time series models help evaluate performance and make predictions. Consider the following and respond in a minimum of 175 words:
Time series decomposition seeks to separate the time series (Y) into 4 components: trend (T), cycle (C), seasonal (S), and irregular (I). What is the difference between these components?
The model can be additive or multiplicative.When we do use an additive model? When do we use a multiplicative model?
The following list gives the gross federal debt(in millions of dollars) for the U.S. every 5 years from 1945 to 2000:
Year Gross Federal Debt ($millions)
1945 260,123
1950 256,853
1955 274,366
1960 290,525
1965 322,318
1970 380,921
1975 541,925
1980 909,050
1985 1,817,521
1990 3,206,564
1995 4,921,005
2000 5,686,338
Construct a scatter plot with this data. Do you observe a trend? If so, what type of trend do you observe?
Use Excel to fit a linear trend and an exponential trend to the data. Display the models and their respective r^2.
Interpret both models. Which model seems to be more appropriate? Why?
Due Day 7
Reply to at least 2 of your classmates or your faculty member. Be constructive and professional.
RESPONSE 1: ANITRA
Time series decomposition seeks to separate the time series (Y) into 4 components: trend (T), cycle (C), seasonal (S), and irregular (I). What is the difference between these components?
Trend is general movement over all years. A Trend can be steadily growing, declining, or not moving at all. For example, a trend can measure whether unemployment rates have grown, declined, or stayed the same over a time period.
A cycle is a repetitive up and down movement around the trend that covers several years. For example, analysts can study cycles for sales of inventory.
Seasonal is a repetitive cyclical pattern within a year. For example, people tend to spend more money at restaurants and bars during the Summer months in Ohio.
Irregular is a random disturbance that follows no apparent pattern. When faced with irregular data, some analysts must make their own judgement forecasts.
The model can be additive or multiplicative. When we do use an additive model? When do we use a multiplicative model?
It is best to use additive model when data is similar in magnitude, short run, or trend free, with constant absolute growth or decline.
The multiplicative model is more useful when data vary over a range of magnitudes.
Construct a scatter plot with this data. Do you observe a trend? If so, what type of trend do you observe?
I do observe a trend. The scatter plot shows that there is a positive relationship between the gross federal debt and the growing economy.
Interpret both models. Which model seems to be more appropriate? Why?
The exponential model seems to be more appropriate, as the r^2 is higher than the r^2 on the linear model
RESPONSE 2:
S01 Time series (Y) consist of four components (1) Seasonal (S) variation that repeat over a specific period such as a day, week, month, season ect. (2) Trend (T) variations that move up and down in a seasonally predictable pattern. (3) Cyclical © variations that correspond with business and economic ‘boom bust’ cycles and follow their own peculiar cycles. (4) Irregular (I) random variations that do not fall under any of the above those classifications. Additive and multiplicative time series models are different in how the components of seasonality trends and errors are different in addictive models, the seasonality trend and error components are added. In multiplicative models, these components are multiplied.
Additive model
Data is represented in terms of addition of seasonality, trend cyclical and residual components.
Used where change is reassured in absolute quantity.
Data is modeled as – is In an additive model the time series is expressed as: Y = T+S+C+T
Y = time series data
S = seasonal
T = trend
C = cyclical
I = irregular
Multiplicative Model
Data is represented in terms of multiplication of seasonality, trend, cyclical and residual components.
Used where change is measured in percent (%) change.
Data is modeled just as additive but after taking logarithmic.
scatter plot of data is,

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