MATLAB_GRAPHICS_2018_VT, information associated with a 2.5 hour presentation on MATLAB Graphics, given on 8 May 2018 to a group of students and staff working at the Virginia Tech Child Development Center.
The presentation involved a series of demonstrations of MATLAB graphics programs. As each program was discussed, some necessary information about MATLAB programming was thrown in.
CORVETTE_SCATTER considers the resale price for Corvettes by model year, displaying the results as a scatter plot.
GEYSER_SCATTER looks for relations between the duration in minutes of the eruption and following pause times for the Old Faithful geyser.
BULGARIA_PLOT takes data about the Bulgarian population over time and makes a plot of (time,population).
LYNX_PLOT makes a line plot of the yearly lynx harvest from 1821 to 1934. Data points are marked by circles.
NILE_PLOT makes a line plot of the yearly measurement of the height of the Nile at maximum flood.
DRUG_DOSAGE_PLOTS depicts measurements over 48 hours of the blood level concentration of a medicinal drug. The drug needs to reach a certain level to have an effect, but must not exceed the toxic level. A graphic is created which shows, on one plot, the concentration over time, the minimal effective level, and the maximum nonlethal leval, using a hold on/hold off combination to combine the plots.
ALBUM_SALES_BAR lists the year, and total number of music albums (LP's, cassettes, CD's and dowloads) sold each year from 2007 to 2017. This data is plotted as a bar graph.
GEYSER_BAR contains the waiting time in minutes between successive eruptions of the Old Faithful geyser. 299 values are recorded. The data ranges from 43 to 108. The program arranges the data into 35 bins of width 2 from 40 to 110, and then uses the MATLAB bar() program to display exactly these bins in a bar chart. The data comes from Martinez and Martinez.
PRESIDENT_HEIGHTS_BAR plots the heights of US presidents in inches, as a bar plot. The data file is in CSV format, so the first row and the first column need to be skipped.
NILE_HISTOGRAM makes a histogram of the yearly measurement of the height of the Nile at maximum flood. By lumping the data into bins, it is easier to see the range of flood heights, and the probability of various values in the range.
PRESIDENT_HEIGHTS_HISTOGRAM plots the heights of US presidents in inches, as a histogram. The data file is in CSV format, so the first row and the first column need to be skipped. By grouping the data by height, we lose the ability to identify specific cases, but we are better able to see the range of heights, and to judge the frequency of various heights.
SNOWFALL_HISTOGRAM makes a histogram of the yearly snowfalls totals at Michigan Tech from 1890 to 2017. Now we can tell what a mild or severe winter looks like, and what a typical amount of snow would be.
The class requested some information on displaying or identifying outliers in a data set.
NINETY considers 90 numeric values. Instead of using a bar graph, we want to create a histogram, to see how the data spreads out across its range. We spot outliers as histogram bins of low occupancy that are far from the rest of the data.
LOGISTIC plots (x,y) data that are sampled from a logistic curve, and then perturbed by a small random amount. However, a few data values have been more significantly perturbed. A simple call to isoutliers() won't detect these issues, because the outliers actually lie within the total range of the data, although they differ a lot from their local neighbors. We can detect them by using outliers() with a moving average test, which compares each Y value to the average of its nearest neighbors. data.
SINE_CURVE plots (x,y) data on a sine curve, except that two Y values have been noticeably (but not extremely) perturbed. A simple call to isoutliers() won't detect these issues. However, we can ask for a moving average test, which compares each Y value to the average of its nearest neighbors. This version of the test catches the bad data.
The class requested an example in which eye tracking data was read. Then, another example in which eye tracking data was read from a sequence of files, and averaged before displaying a bar plot.
TRACK_BAR considers an eye-tracking experiment in which the eye was focused on different regions for different durations. Each region has a text label. A bar graph is desired, in which the bar for each duration is given the appropriate label.
TRACKS_BAR considers an eye-tracking experiment in which the eye was focused on different regions for different durations. Each region has a text label. The data from several experiments is to be combined and averaged. A bar graph is desired, in which the bar for each duration is given the appropriate label.
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