worksheet
Research Paper: Simple Time Series Analysis Assignment Guide
ASSIGNMENT for Week 8:
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PLEASE NOTE THE CHANGE IN THE PATTERN FOR THE SUBMISSION TIMES
The assignment is due by 11:59 p.m. (ET) on Friday, July 9 - Classes End.
Failure to submit your worksheet with your assignment for Week 8, Research Paper Simple Time Series Analysis will result in reduction of points.
If I do not receive your assignment for Week 8, Research Paper Simple Time Series Analysis by that date and time, you will receive a zero for the assignment because I will be grading them on Saturday morning and submitting grade shortly thereafter. Extensions are not possible after the due date.
Also, any other projects that have not been submitted by Wednesday, May 12 will receive a zero for the assignment.
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Research Paper: Simple Time Series Analysis Assignment
Another way to analyze fiscal data over time is via Time Series Analysis.
Time series analysis enables public administrators and policy analysts to examine values of a variable over equally spaced intervals of time (e.g., income figures monthly or yearly). Using times series analyses, administrators and analysts can discern patterns in the revenues and expenditures that then enable them to forecast revenues and expenditures based on historical and existing patterns.
A time series plot allows an analyst to look for (1) outliers and sudden shifts in data patterns, (2) unusual observations or shifts, and (3) long-term increase or decrease in the data values. A trend plot also will show whether the data pattern is linear, or nonlinear, as the time series plot. Then, the question to be asked is, why?
For this Module Assignment, students will compute and interpret a Simple Time Series Analysis called Exponential Smoothing and write a four-page report stating their findings, including chart(s). (The paper format should consist of (1) a couple of pages describing time series analysis and how it can be used to analyze budgets, (2) present the charts, and (3) analyze the charts based on the information provided below. See the end of this assignment, How to Interpret the key results for Time Series Plot.)
I. Getting Started:
· To use the Exponential Smoothing feature in Excel, the Data Analysis ToolPak must be activated.
· To see if Data Analysis ToolPak has been activated:
1) Look at the top middle of the excel screen and select Data.
2) Look at the top right of the Excel screen and find Data Analysis.
If you see this, you do have the Data Analysis ToolPak activated. Please proceed to the next section: II. Calculating a Simple Time Series Analysis (Exponential Smoothing)
If you do not see this: Install the Data Analysis ToolPak by following the instructions outlined below.
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Step 1: Click on File and select Options |
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Step 2: Select Add-ins from the left sidebar.
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Step 3: Under Add-Ins select Excel Add-Ins under the Manage options and click on Go…
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Step 4: Now from the Add-ins window select Analysis ToolPak and click on OK to enable “Data Analysis.”
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Now you should see “Data Analysis” under the “DATA” tab in Excel. |
If you have any difficulties, contact me immediately by email or phone. I will walk you through the process.
II. Calculating a Simple Time Series Analysis (Exponential Smoothing):
Step 1:
Open and save the Income Data located in the Assignment Resource Income Data Worksheet. This can be found in the Assignment Instructions folder.
Select Data column (e.g., Median Income)
Follow the instructions below using the Median Income data
Step 2: Click on the Data tab and Data Analysis.
Step 3: Select the Exponential Smoothing option.
Step 4: For the Input Range, specify the available data points. The data range in this example is B1:B11.
Our data range is B2:B45.
Step 5: Set the Damping Factor to 0.3. This shows for recent values (recent years revenue values) has given a weight of 70% and for relatively old values has a weight of 30%. (The more recent the data, the more important it is to the trend.)
Step 6: Since the column heading was selected in the Input Range, select the checkbox Labels.
Step 7: Now select the range where to display the output range. Select the next empty column, i.e., C2.
Step 8: Now, select Chart Output.
Step 9: Click on Ok to get the results. The chart will look like this:
Do not panic!
Place cursor on the chart and click You will see a round white dot, just below the term Data Point and drag down until the chart look like this:
Wala!
Now, select Design at the top middle of the excel screen, then
select Add Chart Element
Select Chart Title >Above Chart> type in Median Household Income
Then the chart looks like this:
To Change Vertical Axis Values -- Change Value to Median Household Income
To Change Horizontal Axis Values -- Change Data Point to Years
To change Data Point to Years:
Select the Chart that you have created and navigate to the Axis you want to change.
Right-click the axis you want to change (Horizontal Axis) and navigate to Select Data and the Select Data Source window will pop up,
In the Horizontal (Category) Axis Label box, select Edit and place cursor in Axis label range, navigate to data sheet and click and drag A3 to A45
click OK
Then the chart looks like this:
Chart 1
How to Interpret the key results for Time Series Plot
II. Interpreting the Key Results for Time Series Plot
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Step 1: Look for Outliers and Sudden Shifts: Use knowledge to determine whether unusual observations or shifts indicate errors or a real change in the process. · · Look for unusual observations, also called outliers. Outliers can have a disproportionate effect on time series models and produce misleading results. Try to identify the cause of any outliers and correct any data-entry errors or measurement errors. Consider removing data values that are associated with abnormal, one-time events, which are also called special causes. · Figure 4
· Sudden Shifts · Look for sudden shifts in the series or sudden changes to trends. Try to identify the cause of such changes. · For example, the following time series plot (Figure 5) shows a drastic shift in the cost of a process after 15 months. You should investigate the reason for the shift. Figure 5 |
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Step 3: Look for Trends. ·
· The following time series plot shows a clear upward trend. There may also be a slight curve in the data because the increase in the data values seems to accelerate over time.
Source: 1 Source: https://support.minitab.com 1
Figure 6
Once the plot is completed, write a four-page report, including chart(s), interpreting the findings. The format should consist of (1) a couple of pages describing time series analysis and how it can be used to analyze budgets, (2) present the table, and (3) analyze the table based on the information provided in section How to Interpret the key results for Time Series Plot.
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How to analyze TSA chart
Assigned readings are not going to help you analyze primary data. You must learn to do that yourself by researching how to analyze data. The author(s) have not seen your data, so they cannot help with the interpretation.
Your analysis should address the following measures:
· Describe the trend that exist in your data.
What does it tell you about median income growth over the years?
The chart shows that there is a leveling of median household income beginning at point 35. (Normally, a chart will have data point, not years.) Look at the lower axis on the table below. Now, look at your data as see that the 35th point was 2011 and the 43rd point was 2019. What plausibly could have happened between 2011 and 2019 that would have dampened growth in median household income?
· Are there any outliers and/or sudden shifts? (hint, none exists)
· Are there any seasonal patterns or cyclic movements (hint, none exists)?
· Make sure that you support research with scholarly and/or biblical references in APA format.
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To create a preliminary visual forecast
1. Select the data that contains timeline series and values.
2. Go to Data > Forecast Sheet
3. Click the Create
Your actual data will be moved into a new sheet with the addition of a few columns, and the chart of your selection that matches what you have seen in the preview will be placed on this page.
You will need to expand your research beyond text authors’ comments, which normally are not specific enough to provide sufficient information to help you determine the significance and implications of the conclusions.
Make sure that you support research with scholarly and/or biblical references in APA format.
The assignment is due by 11:59 p.m. (ET) on Friday, July 9 - Classes End
Exponential Smoothing
Actual 40107.754010695186 41836.106346483713 43391.379310344826 43054.929577464791 42635.982008995503 44462.227074235809 45360 43923.474470734749 42773.302646720367 43483.025027203483 43758.239508700099 45451.724137931036 45325.305944055945 43891.817466561763 43163.146551724145 43047.865853658543 42747.076023391812 44074.438202247191 44898.430899215455 46488.174273858924 47071.1869266055 47432.93694690265 48278.844114528103 47636.590909090904 46268.600682593853 45888.445078459343 45665.498839907188 46175.193094048162 47621.739130434777 48314.520311149528 49308.692893401014 51100.438047559459 52387.576624438087 52300.711743772241 51160.846153846149 50563.342165026494 50518.955942243607 50343.222782984856 50898.814091384724 51277.659574468082 51965.172413793109 50112.096774193546 49777Data Point
Value
Exponential Smoothing
Actual 40107.754010695186 41836.106346483713 43391.379310344826 43054.929577464791 42635.982008995503 44462.227074235809 45360 43923.474470734749 42773.302646720367 43483.025027203483 43758.239508700099 45451.724137931036 45325.305944055945 43891.817466561763 43163.146551724145 43047.865853658543 42747.076023391812 44074.438202247191 44898.430899215455 46488.174273858924 47071.1869266055 47432.93694690265 48278.844114528103 47636.590909090904 46268.600682593853 45888.445078459343 45665.498839907188 46175.193094048162 47621.739130434777 48314.520311149528 49308.692893401014 51100.438047559459 52387.576624438087 52300.711743772241 51160.846153846149 50563.342165026494 50518.955942243607 50343.222782984856 50898.814091384724 51277.659574468082 51965.172413793109 50112.096774193546 49777 Forecast #N/A 40107.754010695186 41317.600645747152 42769.245710965522 42969.224417515004 42735.954731551348 43944.345371430471 44935.303611429139 44227.023212943066 43209.418816587175 43400.943164018587 43651.050605295641 44911.52207814042 45201.170784281283 44284.623461877614 43499.589624770182 43183.382984992029 42877.968111871873 43715.497175134595 44543.550781991195 45904.787226298606 46721.267016513426 47219.435967785881 47961.021670505434 47733.920137515262 46708.196519070276 46134.370510642621 45806.160341127819 46064.483268172058 47154.562371755965 47966.532929331457 48906.044904180148 50442.120104545662 518 03.939668470353 52151.680121181678 51458.096344046804 50831.768418732579 50612.799685190301 50424.09585364649 50756.39862006325 51121.281288146623 51712.005076099158 50592.069264765225Data Point
Value
Median Household Income
Actual 40107.754010695186 41836.106346483713 43391.379310344826 43054.929577464791 42635.982008995503 44462.227074235809 45360 43923.474470734749 42773.302646720367 43483.025027203483 43758.239508700099 45451.724137931036 45325.305944055945 43891.817466561763 43163.146551724145 43047.865853658543 42747.076023391812 44074.438202247191 44898.430899215455 46488.174273858924 47071.1869266055 47432.93694690265 48278.844114528103 47636.590909090904 46268.600682593853 45888.445078459343 45665.498839907188 46175.193094048162 47621.739130434777 48314.520311149528 49308.692893401014 51100.438047559459 52387.576624438087 52300.711743772241 51160.846153846149 50563.342165026494 50518.955942243607 50343.222782984856 50898.814091384724 51277.659574468082 51965.172413793109 50112.096774193546 49777 Forecast #N/A 40107.754010695186 41317.600645747152 42769.245710965522 42969.224417515004 42735.954731551348 43944.345371430471 44935.303611429139 44227.023212943066 43209.418816587175 43400.943164018587 43651.050605295641 44911.52207814042 45201.170784281283 44284.623461877614 43499.589624770182 43183.382984992029 42877.968111871873 43715.497175134595 44543.550781991195 45904.787226298606 46721.267016513426 47219.435967785881 47961.021670505434 47733.920137515262 46708.196519070276 46134.370510642621 45806.160341127819 46064.483268172058 47154.562371755965 47966.532929331457 48906.044904180148 50442.120104545662 51803.939668470353 52151.680121181678 51458.096344046804 50831.768418732579 50612.799685190301 50424.09585364649 50756.39862006325 51121.281288146623 51712.005076099158 50592.069264765225Data Point
Value
Median income 40107.754010695186 41836.106346483713 43391.379310344826 43054.929577464791 42635.982008995503 44462.227074235809 45360 43923.474470734749 42773.302646720367 43483.025027203483 43758.239508700099 45451.724137931036 45325.305944055945 43891.817466561763 43163.146551724145 43047.865853658543 42747.076023391812 44074.438202247191 44898.430899215455 46488.174273858924 47071.1869266055 47432.93694690265 48278.844114528103 47636.590909090904 46268.600682593853 45888.445078459343 45665.498839907188 46175.193094048162 47621.739130434777 48314.520311149528 49308.692893401014 51100.438047559459 52387.576624438087 52300.711743772241 51160.846153846149 50563.342165026494 50518.955942243607 50343.222782984856 50898.814091384724 51277.659574468082 51965.172413793109 50112.096774193546 49777 Forecast(Median income ) 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 49777 49720.639701859611 49507.686634679601 50483.403070335808 51284.366658803126 52796.510361285938 54083.681180313855 53905.402268104677 53797.007433974111 53748.78471955358 53284.419691113464 53756.266034030348 53649.353033632957 53549.131185113365 53336.509789510877 53280.011244515539 53067.05817733553 54042.774612991736 54843.738201459048 56355.881903941867 57643.052722969784 57464.773810760598 57356.37897663004 57308.156262209508 56843.791233769392 57315.637576686277 57208.724576288885 57108.502727769293 56895.881332166806 Lower Confidence Bound(Median income ) 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 49777 47609.007804680667 46665.363427003176 47061.904462669743 47367.409586884096 48439.159092698384 49325.684357694634 48777.183017739677 48322.811991434748 47948.518184992703 47174.798591218881 47351.583450319587 46962.009178905726 46590.014473772346 46115.278549051262 45748.619793557838 45291.774614760405 46030.51050234 8916 46600.80843066339 47888.084759515652 48955.735708187618 48562.88831201593 48244.52609428815 47990.625560175475 47324.593754335889 47598.534166989564 47297.250348496906 47005.988305568892 46605.471406392942 Upper Confidence Bound(Median income ) 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 49777 51832.271599038555 52350.009842356027 53904.901678001872 55201.323730722157 57153.861629873492 58841.678002933077 59033.621518469678 59271.202876513475 59549.051254114456 59394.040791008047 60160.948617741109 60336.696888360188 60508.247896454384 60557.741029970493 60811.40269547324 60842.341739910655 62055.038723634556 63086.667972254705 64823.679048368082 66330.369737751957 66366.659309505267 66468.231858971936 66625.686964243534 66362.988713202896 67032.740986382996 67120.198804080865 67211.017149969688 67186.291257940669
Microsoft Excel
Worksheet
Sheet1
| Household Income for Kentucky | |
| Year | Median income |
| 1977 | $40,108 |
| 1978 | $41,836 |
| 1979 | $43,391 |
| 1980 | $43,055 |
| 1981 | $42,636 |
| 1982 | $44,462 |
| 1983 | $45,360 |
| 1984 | $43,923 |
| 1985 | $42,773 |
| 1986 | $43,483 |
| 1987 | $43,758 |
| 1988 | $45,452 |
| 1989 | $45,325 |
| 1990 | $43,892 |
| 1991 | $43,163 |
| 1992 | $43,048 |
| 1993 | $42,747 |
| 1994 | $44,074 |
| 1995 | $44,898 |
| 1996 | $46,488 |
| 1997 | $47,071 |
| 1998 | $47,433 |
| 1999 | $48,279 |
| 2000 | $47,637 |
| 2001 | $46,269 |
| 2002 | $45,888 |
| 2003 | $45,665 |
| 2004 | $46,175 |
| 2005 | $47,622 |
| 2006 | $48,315 |
| 2007 | $49,309 |
| 2008 | $51,100 |
| 2009 | $52,388 |
| 2010 | $52,301 |
| 2011 | $51,161 |
| 2012 | $50,563 |
| 2013 | $50,519 |
| 2014 | $50,343 |
| 2015 | $50,899 |
| 2016 | $51,278 |
| 2017 | $51,965 |
| 2018 | $50,112 |
| 2019 | $49,777 |
$0$10,000$20,000$30,000$40,000$50,000$60,0001977197819791980198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120122013201420152016201720182019Meddan Household IncomeYearsMedian Household IncomeActualForecast