Unit 2 Module Summative Assignment Final · Unit 2 Module Assignment o Your module assignment: This...

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Unit 2 Module Assignment o Your module assignment: This assignment will be graded, using the rubric as provided. Assignment Overview: Each student will collect tide gauge data for two different sites located on different continents (or at least from different oceans) using the Permanent Service for Mean Sea-Level website (http://www.psmsl.org/data/obtaining/map.html). Students will output their chosen datasets to a spreadsheet program (Microsoft Excel - provided) where the raw data will be plotted and used to calculate sea level change rates for the duration of their specific data records. Students will use these data to formulate forecasts for sea-level rise in the future by projecting recent trends into the future. Students will need to submit a report showing a map of their data locations, data tables used to produce their sea-level curves, and graphs showing their data and their forecast for future sea-levels. Instructions: Download these detailed instructions so you have them at your side. We have also provided a video showing you many of these steps should you need help with these elements. Find two sites from different areas of the globe, preferably in different oceans/countries. You may not choose more than one of the sites used for examples here, we have already provided you one. Select sites that have data records that extend back at least 30 years (preferably 50 or more years of data). It is best if you start with the longest and oldest data set first! Step 1: Use the PSMSL visual data explorer tool to locate your sites. An example of the interface is shown in Figure 1. Figure 1: Screenshot of the PSMSL database user interface. Tide records from Balboa, Panama are shown. This record shows that there are data from 1908 to 2012, with 99.4% complete. Screen clipping taken: 1/15/2014 8:45 PM

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Unit 2 Module Assignment

o Your module assignment: This assignment will be graded, using the rubric as provided.

Assignment Overview: Each student will collect tide gauge data for two different sites located on different continents (or at least from different oceans) using the Permanent Service for Mean Sea-Level website (http://www.psmsl.org/data/obtaining/map.html). Students will output their chosen datasets to a spreadsheet program (Microsoft Excel - provided) where the raw data will be plotted and used to calculate sea level change rates for the duration of their specific data records. Students will use these data to formulate forecasts for sea-level rise in the future by projecting recent trends into the future. Students will need to submit a report showing a map of their data locations, data tables used to produce their sea-level curves, and graphs showing their data and their forecast for future sea-levels.

Instructions: Download these detailed instructions so you have them at your side. We have also provided a video showing you many of these steps should you need help with these

elements. Find two sites from different areas of the globe, preferably in different oceans/countries. You may

not choose more than one of the sites used for examples here, we have already provided you one. Select sites that have data records that extend back at least 30 years (preferably 50 or more years of

data). It is best if you start with the longest and oldest data set first! Step 1: Use the PSMSL visual data explorer tool to locate your sites. An example of the interface is

shown in Figure 1.

Figure 1:  Screenshot of the PSMSL database user interface.  Tide records from Balboa, Panama are shown.  This recordshows that there are data from 1908 to 2012, with 99.4% complete. Screen clipping taken: 1/15/2014 8:45 PM

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1. Here we have selected Balboa a site near the Panama Canal. This site has data from 1908 to2012 and is nearly 99.4% complete. This is an excellent dataset and would be a great candidatefor selection due to this completeness.

2. To quickly see the data visually, click on the “Plots” tab next to Metadata. This will bring uptwo plots one for monthly and one for annual means. In this example, only one year 1987 ismissing. Why? What happened in Panama in 1987 that may have interrupted datacollection?

3. Once you are convinced that your dataset is useful, you need to download it. There are severalways to do this, but the easiest is to click on the "Other Information" tab and then click on"Documentation" under Station Information. A new page should open that would looksomething like the image in Figure 2. You will recognize the Tide Gauge Data plots below theStation Information section.

4. At this point, it is recommended that you click on the tide gauge plots and download the actualplots in .png image format so that you have these as models to use for plotting up the raw datain Excel. Make sure you name the plots when you save them so you know what plot belongs towhat site. Remember you will have six of these (3 for each location) so keep organized fromthe start.

Step 2: Now to download the raw data. To do this, click on the "Download monthly mean sea leveldata" link. You will repeat this for the annual mean data as well. Once you click the link, the datawill show up as columns of data separated by semicolons.

1. Information about how these data are arranged is included here:http://www.psmsl.org/data/obtaining/notes.php

2. So what do the columns mean? http://www.psmsl.org/data/obtaining/psmsl.hel for moreinformation you should read the help document provided. It will help you if you get stuck, butin general the columns look something like the image the follows (Figure 3).a. The first column is the date, month of the observation according to a Fortran code. We

don't need to worry too much about the details, but the first data point shows data collectionbegan in 1908. If you look there are 12 rows of 1908 data, one for each month of 1908.The same is true for 1909.

b. The second column, starting with 6836 is the actual average water level reading in mmrelative to the selected revised local reference (RLR) datum for that time increment (in thiscase the month - January). RLR for corrected datasets is set to be 7000 mm below meansea level by convention of the PSMSL. So here 6836 is actually a reading of 6836 mmabove the RLR, which itself is 7000 mm below mean sea level. So this indicates that if weconverted the 6836 reading to mean sea level position, it would be -164 mm (or 164 mmbelow mean sea level). PSMSL chose to use RLR in order to minimize the use of negativenumbers in their work.

c. The third and fourth columns, here identified by 0; and 000, provide information aboutthe integrity of the sea level measurements used to calculate the time increment's average.A value of 0 in both indicates that these data are complete and no adjustments/concernsarose in calculating those averages. As much as possible, try to avoid datasets that have toomany rows of non 0 values.

d. To actually download the data you will need to select all the data. You can do this quicklyby hitting control A, or use your mouse to highlight all of the data in your web browser.Then you will need to select copy, by hitting control C, or by right clicking and hittingcopy.

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Figure 2: Station information screen for the Balboa site in Panama.  Tide Gauge Data for this site and other data sources are available by clicking the links provided.  Screen clipping taken: 1/15/2014 8:58 PM

Step 3: Now to download open the data in Excel.1. Next open the Excel spreadsheet and in the first row and column paste the data you copied in Step

2. This will paste all the data as a single column.2. You will now need to separate the data into four columns. You will do this using the "Data" tab

and the "Text to Columns" tool. When you open the tool, your screen should look like Figure 4.Make sure you have all of your data selected, or the tool won't open.

a. In the wizard step 1, select the "Delimited" option, then hit next to move to step 2.b. In step 2, select the "semicolon" option, at which point the one column of data should

appear in four separate columns separated by a vertical line.c. At this point you can hit "Finish" and your Excel sheet should have 4 columns (A, B, C,

and D) of data.

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Figure 3: Screen shot of the data output from the Balboa Site. Screen clipping taken: 1/15/2014 9:27 PM

Step 3: Now, let's prep the dataset for analysis. 1. First let’s label the dataset so you know what each column contains for data. Do this before you

add data records from your other sites so you keep it organized and less complicated.2. We recommend that you insert a new row at the top of your data and provide labels similar to

those shown in Figure 5.3. While you are at it, go ahead and make a new column and type in an equation in cell F2 to

calculate the elevation relative to Mean Sea Level (MSL). This is easy. All you have to do is typein the equation “= B2 – 7000”, where B2 is the reading for sea-level you just downloaded. Copythis calculation for the rest of the values all the way down the page, as shown in Figure 5.

4. Next add a column for Date (Month, Year). In the screenshot (Figure 6) you will see that weadded this to column E.

a. In order to do this, you will have to manually type in the month, date, and year for thefirst 12 months so Excel understands the pattern we want repeated the rest of the waythrough the data.

b. You will also need to tell Excel using the format cells tool that this column will be a dateformatted column (select the entire column by clicking on column E). You can then

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Figure 4: Screenshot for Step 3.  This is the first step in converting your pasted data from a single column of data into separate columns for each variable as described above. 

Figure 5: Screenshot for Step 3.  This is the first step in converting your pasted data from a single column of data into separate columns for each variable as described above. 

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Figure 6: Screenshot for Step 3.  This is the first step in converting your pasted data from a single column of data into separate columns for each variable as described above. Screen clipping taken: 1/16/2014 8:49 AM

c. bring this tool up by right clicking and hitting format cells, or you can use the menu barat the top of the screen.

d. In the number menu, select "Date" as your "Category" and scroll down through the date"Type" until you find M-01. This simply tells Excel that your data is in month yearformat. Now go ahead and type in the Month/Date/Year in E2.

e. In figure 6 above we typed: 1/15/1908 - since the 15th of January is the middle of themonth, and because the sea-level value represents the month's average. The reasoning forthis is explained in the PSMSL help guide on the website. Do this same thing for the first12 months of your dataset. i.e. 2/15/1908, 3/15/1908, etc.

f. Next, copy and paste the pattern the rest of the way through the dataset. Make sure youselect the 12 months, and then drag downward. In our example row 14 E shows "J-09",and row 15 E shows "F-09" so we know that we have done it right.

5. At this point, it is important to "clean-up" the data by handling any intervals that have incompleteor discrepant data. As you scroll down through your dataset, you may find some data that has a "-99999" value in column B. It is critical that you don't just delete the entire row (date of reading),but we need to ensure that any data that has lost its integrity for that month/year for whateverreason not be used to influence the rest of the data in an inaccurate or artificial way.Geoscientists have to make decisions about how to handle these data points. Sometimes, theywill omit the data by treating the month as a blank, or sometimes they will average the values ofthe immediately preceding and following data points and use that value to fill the blank. This

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way the data point still holds its place, but its signal doesn't interfere with the rest of the data points in the dataset. This ensures continuity in data analysis methods (i.e. calculating running averages for instance). Ultimately you should make a decision on how to handle these.

a. In our dataset we have decided to blank the data for now, but this will have repercussionsfor later on, so you may want to do something about filling the gap at this point. I.eaveraging the two points…For the example dataset (Figure 7), this could be done in acouple of ways:

by entering a formula in B 880 to add the values from B 879 and B 881 and thendivide them by 2, or

by using the Average function within the formula menu. Either in our example you would see "7020" as a result in box B 880.

b. Whatever decision you make for handling discrepant data, make sure you are consistentin your methodology and apply that methodology to all instances in all of your datasets.

Figure 7: Screenshot for Step 3.  Here you will notice ‐99999 in column B (in cell B880), and as a result you see a ‐106999 in the elevation relative to MSL.  This is clearly a bad data point that needs to be culled or replaced by a reasonable value that won’t influence the rest of the dataset in an artificial way.  This data point needs to be fixed before any quantitative analysis is performed. Screen clipping taken: 1/16/2014 9:14 AM

In figure 8 below -we have simply removed the -99999 reading from B 880

Figure 8: culling the “incomplete data” from the dataset.

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6. Once initial data cleaning has been completed, repeat the process for your other two datasets. Werecommend you place all data into a single spreadsheet, you can use different sheets (see bottomleft of the screen shot in part l, Figure 5 above).

a. You can use Sheet1 for one site (named Panama_Data in the example), and then useSheet2 for your second, and Sheet3 for the other.

b. Alternatively, you could put all the data in the same sheet, which has its advantages lateron, but make sure you keep it organized.

Step 4: Plotting your Data 1. At this point, you should have 3 datasets downloaded into your one Excel Spreadsheet, and they

should all be labeled and prepared for plotting. Remember that you were asked to download youroldest and longest record first, so we will start plotting that one first.

2. Select the data you want to plot, in this example we will plot the mean sea level data (column F)on the y or vertical axis, and the time (column E) on the x or horizontal axis (See Figure 9). Sowe will plot up the first three years of the data record for demonstration. So we will select 1908,1909, and 1910 and click "Insert" a "Scatter" plot using the "Scatter with smooth lines andmarkers" option.

3. The graphed result should look something like the plot shown in Figure 9. A scatterplot isgenerated and is inserted into the spreadsheet.

4. The plot is pretty ugly at this point, in part because of the negative numbers on the y-axis andissues with scaling of the x axis. Now you know why the PSMSL folks like to use the RLRconvention when plotting their sea-level data. But let's roll, because we can easily fix theseproblems.

5. So to fix the "y axis" issue, click on the y axis to select the axis itself. A box should appeararound the y axis in this case from -300 to 150. Once highlighted, right click and select "FormatAxis" as shown in Figure 10.

a. At this point, change the position where the X axis crosses the y axis. In the "FormatAxis" window, under "Axis Options" near the bottom of the box is a section that says"Horizontal axis crosses:"

b. Here you should select the option box for "Axis value:" and enter a negative value (themost negative of all of your data points). We will use “-300” since that is the mostnegative in our example data.

c. When you hit close, you should see the X axis labels drop to the bottom of the graph asshown in Figure 10.

6. Alright so now the X-Axis… Notice that the first data point January, 1908 is at the middle of theplot. So we don't need all the empty space to the left of it. Excel arbitrarily set J-1900 as theorigin for this axis, so we need to tell Excel where we want to start plotting data. RememberExcel was told the X axis values are date values, so it has plotted the data in days from 1900 tothe end of the dataset (arbitrarily ending the plot at September 1913).

a. So to fix this, click on the x-axis and make sure a box surrounds the labels from J-00 toS-13. Then, right click and select "Format Axis”. The format axis box should come up.

b. In Axis Options, you will see that the Minimum: was Auto selected as “0”.c. Change this to a "Fixed" number. In our case, we want our first data point to be January

1908, so to figure out what number to use we multiply 365 x 8 years.i. 365 because Excel is plotting days each year and 8 because we are 8 years into

the 1900's. So we would enter 2920 in the Minimum "Fixed" box.ii. Now, since we are plotting 3 years of data, we would multiple 365 x 10 and type

4015 in the Maximum "Fixed" box. Once you do this, Excel may automaticallyadd one so you might see "4016.0" show up. That is ok. You should seesomething that looks like Figure 11.

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Figure 9: Plotting data from the Panama example. Time (Column E) will be plotted against MSL (Column F) to generate a “sea‐level curve through time” plot as discussed in Step 4.  Screen clipping taken: 1/16/2014 9:37 AM

d. One more thing to fix… The labeling interval on the x-axis needs to be changed. If youwant one label per year, you can change the "Major unit: Fixed" value to "365." Goahead and set "Minor unit Fixed" value to "0" as shown below. The graph should beupdated as you make changes to each axis option field. Now you should have a decentlooking graph, with nice, well-labeled axes.

e. So now that the plot looks better let's make it a sheet in the spreadsheet, rather than anobject in another. Select the chart, right click on it, and click "Move Chart." A windowwill open and select "New sheet" and enter a name changing the name Chart1 tosomething like Panama Sea Level. When this happens, the font might appear reallysmall. You can easily change that. Make sure the chart is selected and you can changethe font size to something like 24 or larger - whatever suits you. See Figure 12.

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Figure 10: Screenshot for step 4 part 5.  Here we are modifying the axis labels and ranges to improve the look of the plot.

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Figure 11: Screen shot showing how to improve the axes and labels of your sea‐level plot.

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Figure 12: Screenshot showing the result of Step 4 part e.

f. Before you go any further, let's fix the legend on the right side of the chart (Figure 12above). You will notice that the chart title is the same as the legend, so let's change thelegend so it shows that this is Panama. This is important because you will soon be addingdata from additional sites.

i. So first, click on the line of data points, and then right click to bring up the"Select Data" then "Select Data Source" window. Your screen should looksomething like what is shown in Figure 13.

ii. Now, click on "Edit" in the Legend Entries (Series) box. Figure 14 will appear.iii. In the "Series name:" box - type Panama Tides or whatever title describes the

location of your dataset. Hit "OK", and "OK" again until you are back at the chartscreen. The legend on the right should say exactly what you typed in the "Seriesname:" box.

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Figure 13: Screenshot for Step 4 part f i.

Figure 14: Screenshot for Step 4 part f ii.

Step 5: Interpreting the Data & Quantitative Analysis 1. At this point, it is a good idea to look at the data and see what is going on with tides. You will begin

to see patterns or trends in the data. In fact you may see trends and patterns that we discussedthroughout the module.

a. In the dataset example used here, remember, we are looking at monthly averages, so the datawe downloaded is the 30-day average for each month therefore all tides in a given month areaveraged to a single value.

b. Looking at our dataset (Figure 12 above), it looks like there are annual highs and lows wheretides are high for about 8 months (May-December) every year, and are relatively low for 4months (January-April). This suggests Panama experiences a persistent seasonal sea-levelchange. This is pretty cool!

c. You could even calculate the average seasonal range of sea-level from your dataset becauseyou now have the data. In this case, the seasonal fluctuation is about 30 cm (300 mm).

d. Can you think of any reasons why this location in Panama would experience this pattern?What currents/climate factors might change like this to influence water levels annually? Ifyou looked at a longer time increment, would there be any decadal patterns?

e. Obviously we only plotted 3 years of data, but you should have plotted at least 30 years ormore from your datasets so you could look at this issue in your data.

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2. Alright, so now we are going to add a trend line to see if there isn't a longer-term signalembedded in the monthly data. So it is pretty easy. Simply click on the data series line inyour graph, and each data point should then be highlighted. Right click and select "AddTrendline…"

Figure 15: Screenshot showing the “Format Trendline” tool window as discussed in Step 5 part 2.

. This will open the "Format Trendline" tool window (Figure 15 above). Excel will ask you what statistical method you want to employ to calculate a trend. You have several options to choose from.

i. We recommend you start with the "Linear" option, but you could also repeat theprocess and use the "Moving Average" option later.

ii. In this case, while the tool window is open, change the averaging period. Itautomatically starts with "2", which means that it will average 2 month increments toestablish a data point, but you can increase the increment. For instance, if you wantto do a yearly average, you would select "12" as your period.

iii. Before you exit the window, ensure that you: label the "Trendline Name". Excel will do an “Auto” name, but if you want

to change it, now is the chance. display the equation for the trend line. This option is at the bottom of the

window. It will show up in your graph if you select "Display Equation onchart".

display the "R-squared value on chart". You may recall from a high-schoolstatistics/algebra class that an R2 value is actually the "goodness-of-fit" foryour data. When your data are tightly distributed around the trend, your R2value will be close to 1.0. When the data points have a greater spread, the R2value will be closer to 0. In this case, if the R2 value is 0, the regressionequation will not be able to accurately determine the Y value.

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Finally take a look at the Forecast option. You can use this function toextend any trend into the future ("Forward" = forecasting) or into the past("Backward" - which is typically called hindcasting). You won't do this rightnow, but you will eventually so make note of it.

When you do use this function, remember the periods will be 365 days forone year. So if you want to forecast one year into the future, you would putan increment of 365 in the "Forward: periods" box.

When you do this, you will need to change the scale on the x-axisaccordingly so the forecast trendline will show up on your graph.

Figure 16: Screenshot showing the trendline (linear regression) for the data in the 3‐year record from Panama (1907 to 1910).  The equation for the line is given as is the R2 value.  Here the volatility in seasonal water level produces a very low R2 value that suggests the line can’t be used to predict the accurate water elevation at any given time period, but over the long‐term the behavior of the data shows an upward increase in water level for the duration.

iv. Your screen might look something like what is shown in Figure 16 above. Here youcan see the monthly data and a calculated average trend (linear regression) of thedata. The equation for the line is shown in the format y=mx+b .

You will remember from high school math, that you can solve for any pointon that line when you know one or the other of the variable.

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For instance, you can solve for "y" mean sea-level height in mm if you knowthe x value. This is the equation that you could use to calculate a possiblefuture sea-level position.

Remember also that your equation was based on the premise that the x-axisorigin was January 1, 1900, so future dates would have to be multiplied by365. So if you wanted to use this equation to predict the elevation of meansea-level in January 1913, your x value would have to be 365 x 13.

v. Now using the trendline projected into the future, you could also visuallyapproximate the mean sea-level in 1913 as well. That is the difference betweenquantitative reasoning (true calculation) and qualitative reasoning (approximating thevalue).

vi. In terms of the R2 value, in this example our value is 0.1113. This value is prettylow, because of the seasonal spread of monthly tide values.

Clearly sea-level change at this location in Panama for these three years wasdominated by the seasonal pattern.

However, there is a longer temporal trend that explains additional sea-levelchange not explained by the seasonal process.

As a scientist, it is your job to think about the "background" signal that mightbe causing the trend in increasing sea-level here. Is it a local process? i.e.subsidence? Is it part of a decadal rhythm? i.e. El Niño? Is it a globalprocess? i.e. eustatic sea-level rise?

This is what we are trying to figure out. You need to think about thegeographic/geologic setting and perhaps patterns that exist at each of yourthree sites. More on this later.

Step 6: Integrating data from other sites. 1. So now, what about the other sites? You will follow the same steps that you have above and produce

scatter plots for each dataset. You should have 3 scatterplots, each with "linear" regression and"moving average" averaged over 12 periods.a. Note the moving average will not produce an equation or an R2 value, so this cannot be used for

forecasting.b. Anyway think about each dataset individually in the same fashion that we just did.c. What do the data show? Why do they show that? Use inference/deduction skills to draw logical

and thoughtful conclusions supported by the data. Identify any questions or concerns you mighthave? What additional ideas/hypotheses do you have?

d. Write down all observations and interpretations in your course notebook, and export each of yourgraphs to a PowerPoint for later use. Save your Excel data chart often so it isn’t lost!!!

e. Make sure you answer the questions above, at the minimum. If you get stuck in your graphing,remember, that you have already downloaded example plots from the PSMSL website. Youshould be able to verify if you are on the right track. If your graph looks absolutely nothing likethe PSMSL plot, you should go back through and see if you made any errors. It is best to gothrough this step-by-step the first time.

2. Now to make and draw comparisons between your sites. In order to begin to look at differences andsimilarities, you need to produce a composite graph. This means you need to put all 3 datasets on thesame graph. If you have been methodical and careful in your labeling this step should be relativelyeasy as well. So what do you do now?

a. If you have each data on a different sheet in your Excel. Go ahead and copy each data set to afourth sheet. Label that sheet "composite data".

b. Next make sure your data are aligned by date and that each dataset has the same date format. Inthe example that follows (Figure 17), I have 5 yearly datasets (not monthly as in the previous

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example) that I have aligned accordingly. Note in the example below, I have plotted data since 1937 for Cuba, New Zealand, Virginia, Canada and Uruguay. Note that Cuba is the oldest record and should be plotted first (column B). The data record for New Zealand doesn't start until 1944, so it is aligned with the Cuba dataset. The same goes for the other sites.

c. Note here that I am using the RLR datum data, rather than the corrected to mean sea level (MSL)data. It is easier to demonstrate the graphing process here. You would want to use the MSL data,which would require that you align the axes again as you did previously.

Figure 17: Plotting data from all of your sites on one composite graph.  Here sea‐level elevations from several sites are shown plotted by date (year).

3. Once the data are aligned, you can easily create the combined scatter plot. In the end you will likelyproduce a diagram that looks like the one below (i.e. Figure 18). Your graph will have MSL as the y-axis rather than sea-level relative to RLR.

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Figure 18: An example of a combined sea‐level plot for 5 different localities.  Although each site shows annual variability, the long‐term trend is clearly toward higher sea‐levels.  

4. You will note the plot above doesn't include regression lines for the datasets, which yours will includeso you can compare long-term trends between each of the three sites you evaluated. We have alreadydiscussed how to include a regression line so make sure you do that.

5. But wait? Once you have a single data set plotted, how do you add the other datasets to make acombined plot?

a) It’s actually pretty simple… To help demonstrate the process, we have plotted data for Cuba inFigure 19. Note the omitted data in the 1940's. Wonder why data is missing? So as you didpreviously when you changed the title of the legend, you will use the "Select Data Source"function as shown below.

b) You can then click "Add" in the Legend Entries (Series) box - shown below. An "Edit Series"window will open (Figure 20).

c) In the box - type the name of the data series in the "Series name:" box. In this case, it will be NewZealand.

d) Then click in the "Series values:" box, click on the spreadsheet icon to the right of the blank box.The window will minimize and you can navigate to the y value data in your spreadsheet and click

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Figure 19: In order to insert additional datasets into an existing scatter plot you will use the “Add” data button in the “Select Data Source” window as shown above.

Figure 20: The “Edit Series” dialogue box will appear after clicking “Add” in Figure 19.  You will name the series, and include the values that are to be plotted along with the previous dataset. 

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and drag until all data points are included. In this case the white Series values: box will be populated by the appropriate variables you clicked on. Here we selected column C beginning with row C9 through C76. Once, you have done this for your second data set, hit OK.  

e) You can go back to your "Select Data Source" window and hit "Add" again to add your third dataset. Here "Virginia, USA" and variable range from D16 to D76 will be added (Figure 21).

Figure 21: Edit Series Box for the third and final dataset to be added to the composite scatter plot.

f. Once you have added all three, you can click "OK" all the way out of the tool.g. Your chart might look something like what is shown below in Figure 22 below.

Figure 22: Combined scatterplot showing all three datasets and their linear regression lines.  Each linear regression line has been color coded to the appropriate dataset.  All three datasets show long‐term sea‐level rise trends, however each has a slightly different rate of rise.  The slowest rate of rise was located in CUBA, whereas the highest rate of rise was in Virginia.

h. Once you have added all of the datasets to the scatterplot, you will want to clean up the axes, andchange any chart labels to make the chart look professional again. Notice in Figure 22, becausewe used Cuba's dataset first (which ended in 1971), we had to fix the x-axis to show data through2004.

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i. Also, notice the legend was placed inside the graph. Placement is up to you, but locate the legendso you can maximize visualization of your composite dataset.

j. To aid visualization, we have also color-coded the regression lines, the equations and R2 values,to the original color of the data line. Thus the blue regression line corresponds with the blue datapoints for New Zealand. This practice helps with visual acuity and improves the ability ofreaders to interpret the data. We also took time to change the orientation of the labels on the Xaxis so they are rotated. You can do this in the Format Axis, Alignment tool box.

Step 7: Data Interpretation 1. Now, from the data shown, we are able to begin to compare the data sets. We can begin to look

for similarities and differences in the datasets and draw inferences and conclusions accordingly.a. Remember, what is shown in Figure 22 are the yearly data points (not monthly), so we can’y

think about seasonal-scale trends using this dataset, but we could look for decadal-scale andlonger-term trends. Thus, you might want to use the "moving average" technique as well.

b. When you do this, and increase the period averaged, you will see that the amplitude of theannual data becomes subdued (less volatile), and as you approach 7 or 8 periods, it is clearthat there is likely a decadal-scale pattern of sea-level rise/fall superimposed on the longer-term sea-level rise pattern.

c. There is the obvious longer-term trend in the data shown by the linear regression equationsand R2 value. In this case the R2 value for the Virginia dataset is 0.7865. This is pretty closeto 1.0 which means the data are pretty well constrained. This gives us the ability, when weforecast sea-level positions in the future, to be in the ballpark, as long as the existing controlson sea-level remain in place (i.e. no additional changes in sea-level forcing occurs).

d. As we all have learned, scientists are debating changing baselines and changes in forcingmechanisms (i.e. anthropogenic vs natural effects), but again that isn't the purpose of thisassignment.

2. As you analyze and interpret your data. Think about quantitative and qualitative measurements.What can you definitively say the data is showing, unequivocally?a. What have the actual sea-level changes been? Give some numbers from your data plots.b. What inferences can you make and deductive conclusions might you draw from this?c. Does your data show, seasonal, decadal, or longer-term trends? Do all sites agree? Why or

Why not?d. In this case, Virginia shows the greatest slope of the regression line (m in the equation

y = mx + b) is 3.4761. Cuba has the lowest slope (1.4185) in the long-term trend, perhaps ifdata were collected beyond 1971 maybe this slope would be different.

e. What do these factual observations mean?f. What is different about the geography/geology of Virginia, USA relative to Cuba? What are

the similarities between Virginia and Cuba? Based on unit 1 discussions you might havesome insights to bring to the discussion, but certainly a look at a map might help you infersome relationships.

g. As you work, you might want to return to the PSMSL website and look at global trends oranomaly's. PSMSL offers a great set of visual tools that might be of help to you as you work.http://www.psmsl.org/products/anomalies/ There is a slider scale at the bottom and a fewother buttons that you can toggle to look at the data in different ways using the 1960-1990 (30year) interval average.

h. You may also want to use Google Earth, or any of the other viewer tools used previously inthe course, to help derive ideas and infer facts about geography, climate setting, recentgeologic history, rates and proximity of tectonic activity, rates of sedimentation, etc.

i. You should answer these types of questions at the minimum in your own data sets.

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j. As every student will likely use a different set of 3 sites, you may find different direct and indirect correlations. In the context of the entire module, you should have some practical, logical, well-thought ideas to help support your deductive reasoning.

Step 8: Completing the assignment 1. In order to complete the assignment, you will need to export all of your graphics from Excel and paste

them into Microsoft Power Point. 2. It is best if you paste all figures and data as images, not as object. 3. The PowerPoint should include observations and inferences about each individual dataset, and the

combined dataset. 4. In the PowerPoint, you will want to summarize key observations and "Facts," "Inferences," and

"Conclusions". These should be detailed, but concise by use of bullets. You should have some quantitative and qualitative statements for each of the three data sets.

5. You might want to use tools in PowerPoint to label or highlight features of your graphics (i.e. arrows, circles, etc.)

6. All slides should proceed logically from one to the next, and your presentation should transition into the comparative components where you discuss shared similarities and dissimilarities between the data sets and finally end with final conclusions and references.

7. Be sure to proofread, be consistent in grammar and spelling, and make your document a cohesive, professional product. Use page numbers on every page.

8. Provide a cover/title slide, a purpose/introductory statement that explains what the report is about, and a slide that shows the geographic location where each dataset was derived.

9. Use an easy-to-read, attractive text and a color scheme that isn't distracting. 10. Include your name as a copyright on every graphic and slide you produce. You might want to create

a unique logo, not required, but would give you ownership of your ideas and a cool professional look. Make sure it isn't too busy that it distracts from your content.

11. As you use references and data (and it is required), or images that are not your own… Make sure you provide a citation to the resource. This means you MUST provide reference to the data you used (i.e. to PSMSL, NOAA, NASA or other reference). The PSMSL website provides an example on the website for you to follow when citing their work. Citation format is up to you, as long as it is consistent and clear so anyone can retrieve the data/resources you used if necessary.

12. As you assemble this, assume that you will be giving this presentation to an educated community group, who is interested in sea-level, but who might not be science-oriented.

13. You should produce a presentation that might be 8-10 slides, and could be given in~10 minutes, if you were to give this orally.

14. Remember, ALL conclusions drawn from your analysis should be supported by fact, and clear. 15. If you are short of the 8-10 slides, you might want to include some "outstanding questions" you have

or "ideas for future research" as part of the Power Point? Step 9: Turning in the assignment 1. You will want to upload your Excel document and your finished Power Point to the course dropbox. 2. Save each file titled as follows:

a. LastName_FirstName_Module4_TideRecordAssignment_PPT.pptx OR b. LastName_FirstName_Module4_TideRecordAssignment_XL.xlx

3. If your PowerPoint becomes too large to upload to the course management system, you should save it as a pdf document.

4. Make sure you open the assignment once it is closed/saved to ensure it is of the quality you want it to be. Your assignment will be graded "as is" and re-submissions will not be possible.