Find some data set where conducting a time series analysis would be appropriate. Preferably the data set should be from your place of employment, but if this is not possible, then you may find any...

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Find some data set where conducting a time series analysis would be appropriate. Preferably the data set should be from your place of employment, but if this is not possible, then you may find any applicable data set on the internet. There is no minimum or maximum number of entries or variables that should be in the data, but the data should be complex enough that meaningful analysis can be conducted. For example, if the data is quarterly, having 16 quarters of data (4 years) should be sufficient for this exercise.



Use the tool of your choice to conduct your analysis, but Microsoft Excel is more than capable of doing this.




In a Word document, complete the following:




    • Describe the data set (where you found it, what it means, etc.)

    • Conduct a time series analysis

    • You must use one of the following: moving average, exponential smoothing, double exponential smoothing, or Holt-Winters method

    • Note: Moving average and exponential smoothing is the easiest (and sufficient for this exercise)

    • Interpret the output

    • Insert images of output into Word document and explain what it means both statistically and in layman’s terms.




3/4 pages double-spaced is sufficient. Include both your analysis and the data set in your submission.




Time Series Analysis (Assignment) Top of Form Find some data set where conducting a time series analysis would be appropriate. Preferably the data set should be from your place of employment, but if this is not possible, then you may find any applicable data set on the internet. There is no minimum or maximum number of entries or variables that should be in the data, but the data should be complex enough that meaningful analysis can be conducted. For example, if the data is quarterly, having 16 quarters of data (4 years) should be sufficient for this exercise. Use the tool of your choice to conduct your analysis, but Microsoft Excel is more than capable of doing this. In a Word document, complete the following: · Describe the data set (where you found it, what it means, etc.) · Conduct a time series analysis · You must use one of the following: moving average, exponential smoothing, double exponential smoothing, or Holt-Winters method · Note: Moving average and exponential smoothing is the easiest (and sufficient for this exercise) · Interpret the output · Insert images of output into Word document and explain what it means both statistically and in layman’s terms. 3/4 pages double-spaced is sufficient. Include both your analysis and the data set in your submission. Bottom of Form
Answered Same DaySep 15, 2021

Answer To: Find some data set where conducting a time series analysis would be appropriate. Preferably the data...

Pritam Kumar answered on Sep 16 2021
155 Votes
About the dataset
Reymons Electricals is an electrical home appliances manufacturing company in India. The company has been traditionally catering to the developed economies since it started its operations (predominantly US & Canada). Reymons works on bulk orders from third-party organizations for home appliances such as lighting solutions for homes.
We
have a dataset that is collected from Reymons which provides information on yearly cost and yearly sales amounts from the year 1986 till 2009 (24 years). The original dataset that is collected from Reymons looks like this:
Table 1: Yearly cost & sales performance for Reymons Electricals
    sl.no.
    year
    total cost
    total sales
    1
    1986
    944.75
    1011.46
    2
    1987
    1344.84
    1453.65
    3
    1988
    1858.37
    1999.54
    4
    1989
    2553.54
    2758.53
    5
    1990
    3516.72
    3815.35
    6
    1991
    4705.32
    5136.62
    7
    1992
    6533.46
    7148.43
    8
    1993
    8446.79
    9238.76
    9
    1994
    11359.89
    12476.69
    10
    1995
    14109.29
    15470.35
    11
    1996
    17769.51
    19535.53
    12
    1997
    21857.68
    24156.37
    13
    1998
    27185.16
    30219.58
    14
    1999
    34176.56
    38434.34
    15
    2000
    40946.23
    45738.78
    16
    2001
    47812.39
    53553.25
    17
    2002
    51514.36
    58247.68
    18
    2003
    56894.27
    64816.16
    19
    2004
    63849.21
    73094.23
    20
    2005
    70585.76
    81511.45
    21
    2006
    79402.34
    90837.28
    22
    2007
    68317.89
    77347.43
    23
    2008
    64195.33
    71288.26
    24
    2009
    59303.82
    65955.96
The dataset contains three variables (columns), year as the time index, total cost amount in INR[footnoteRef:1] million and total sales amount in INR million. For our analysis, we will consider two of these three variables, year and total sales as independent and dependent variables. As “year” is a time variable, “total sales” is a continuous variable. We calculate the autocorrelation for total sales at lag 2 and lag 3. These values come out to be 0.8338 and 0.7106 respectively. As it is in most of the cases, autocorrelation is very common among time series data points. Here we see that there is a strong autocorrelation, around 0.8 between two data points that are two time-indices apart, for example autocorrelation between 1986 and 1988, 1987 and 1989, and so on. Similarly, there is a strong correlation around 0.7 between two data points that are three time-indices apart, for example between 1986 and 1989, 1987 and 1990, and so on. [1: INR: India Rupee]
Now that we have taken a close look at our independent and dependent variables, in the next step, we will do some descriptive statistical analysis with our data.
Descriptive analysis of the time series data
Descriptive Analysis (Peter Brockwell, 2016) is the type of analysis that describes or summarizes the data points. This analysis helps in getting information such as central tendency (Chatfield, 2000) and the degree of scatter (Chatfield, 2000) for the data points. It is a very important step for conducting statistical data analysis. The descriptive analysis for our data looks like this:
Table 2: Descriptive analysis for total sales
    Mean
    35635.23667
    Standard Error
    6265.977207
    Median
    27187.975
    Mode
    #N/A
    Standard Deviation
    30696.89379
    Sample Variance
    942299288.6
    Kurtosis
    -1.457611134
    Skewness
    0.365208162
    Range
    89825.82
    Minimum
    1011.46
    Maximum
    90837.28
    Sum
    855245.68
    Count
    24
    Confidence Level (95.0%)
    12962.16144
In the summary statistics, we find the mean for total sales is INR 35,635.23 million. The median value is INR 27,187.97 million. Standard...
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