4 Tutorial 1 - Linear_graphs (Continious Data)

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    Dr Mark J.P. Kerrigan - 2009

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    Microsoft Excel is one of the most powerful computer programs you will

    encounter during your time at the University of Westminster. It is a

    spreadsheet program design to manage complex data sets with ease of use

    and functionality. Furthermore, it has graphical and analytical tools for data

    analysis and presentation.

    By the end of this tutorial you should be able to draw a line graphs, complete

    a regression analysis and also understand the ethics of representing data.

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    One of the most common types of graph that you will need to be able to draw

    is a line graph. This is a representation of continuous data for example

    something plotted against time. There is an important note here and it isthat in order to plot a line graph in Excel you must actual use the graph type

    called Scatter Plot. There is a Line Graph option in Excel but please do not

    use it as it always produces an incorrect graph. When reading this, I

    appreciate that it sounds rather confusing so I thought I would have you a

    short video and show you. Before continuing, please look at the video called

    when is a line graph a scatter plot.

    Now that we all know what a line graph is, now let us compile one.

    1. Log onto blackboard and save the file titled Excel tutorial 1 to your

    computer hard drive or USB stick depending upon how you like to

    work.

    2. Open the file.

    3. There is a table of data that was acquired during an experiment I

    performed a few years ago. If you are interested in what this data is

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    and why the experiments were performed please look at the video

    called calcium and chondrocytesbefore continuing.

    4. Select the data and plot a scatter graph of this data with time on the x-

    axis and absorbance on the y-axis. Just as a reminder, to draw a graph

    click on the chart wizard button at the top of the Excel window.

    5.

    6. When drawing the graph select option that does not join up the points

    as we are going to add a line of best fit later on.

    Okay, we now have a basic line graph drawn in Excel but it is not ready;

    i.e. to a standard acceptable to a scietist. It requires formatting to make

    it look good and easy to read. Please look at the video called Scatter

    plots & tidyingbefore continuing.

    1. Having got the basics of the graph drawn it is necessary to tidy it up.

    Right click on the grey area of the chart area and this will open a menu.

    Select Chart options. From here, you can label the axis, remove

    gridlines and alter the legend. Label the graph with the title Change in

    Fura-2 fluorescence, measured in bovine articular chondrocytes in

    response to a change in osmolality.

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    2. Having labelled the graph, double click (left) on the chart area and

    change it to white. This is convention.

    3. The graph is now presented in a format that is acceptable in science.

    Right, now that the graph is draw and is acceptable to the scientific

    community let us begin some analysis. We are going to do something

    called Regression Analysis or sometimes know as adding a trend line.

    1. If you look at the data, it would appear to fit a trend and this would

    seem to follow a straight line. If you remember the equation for a

    straight line is y=mx+c and Excel is able to apply this to a data set as aline of best fit. The technical term is a trend line. What does this

    equation mean? What is m? What is C?

    2. Left click on a data point, and then right click on the same data point. A

    menu will appear. Select add trend line.

    3. The trend line you want to add is linear. Having selected this, click on

    the options tab.

    4. Select the show equation and R-squared value. Having clicked OK,

    you will now have a line on your graph and an equation. The R-

    squared value shows you how well the data fits the straight line.

    Anything above 0.9 is deemed good. Above 0.95 is excellent.

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    Now we have the graph and the trend line we can see if there is anything

    we can to do improve the data. How good is the data set? Can the trend

    line be improved? Is there a data point that should be removed as it is

    an outlier? What is the new R-squared value? Think about these

    questions and then let us all have a discussion on the discussion board.

    As well as talking about regression analysis, these comments also

    relate to the ethics of data; in other words when is it deemed acceptable

    to change data and why. How do we communicate this? As a scientist

    this is a key skill as we often have to communicate to the public and so

    how do we ensure that they trust us?

    The final use of a regression is being able to predict values. Or example,

    assuming our data remains linear, what would the value be at 500 seconds?

    This is where a little bit of maths is need as well as the regression information.

    The line has the equation format of y = mx+c, whereby y is fluorescence, x

    is time, m is the gradient and c is where the graph crosses the y axis. In

    order to find out the value at 500 seconds we need to insert these values.

    1. Calculate the fluorescence at 500 seconds and then paste your answer

    on the discussion board.

    Another use is to be able to predict a time based on an fluorescence.

    For example, if the fluorescence is 0.65 AUs, when time did this occur?

    For this you need to re-arrange the equation and solve for x.

    1. Re-arrange the equation and calculate the time (i.e. x) when y = 0.65.

    Paste your answer on the discussion board. This bit can be tricky, so if

    not sure post a comment on the discussion board and we can talk

    about this.

    This is the end of the tutorial on linear graphs. Well done for your

    hard work.