1 / 18

Plotting of the data Dot diagram

Plotting of the data Dot diagram. When Analyzing data, always plot the data! A dot diagram: XL XT Stren 11.8 * * 11.7 * * * * * 11.6 * * * * * 11.5 * * * * 11.4 * * * 11.3 * * 11.2 * * 11.1 * * * * 11.0 * * 10.9 *. Plotting Original Data.

marin
Download Presentation

Plotting of the data Dot diagram

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Plotting of the dataDot diagram When Analyzing data, always plot the data! A dot diagram: XLXTStren 11.8 * * 11.7 * * * * * 11.6 * * * * * 11.5 * * * * 11.4 * * * 11.3 * * 11.2 * * 11.1 * * * * 11.0 * * 10.9 *

  2. Plotting Original Data • Always plot original data points. • This is the first thing to do when analyzing data • This is very important!

  3. Plotting Cancer Study Results • The following plots are from a study by Dr. Terry Rose-Hellekant in the Medical School Duluth • Treatments • Tamoxifen • Placebo • Some mice develop breast cancer

  4. The data are RT-PCR expressions corresponding to particular genes • In RT-PCR the values are roughly a log base 2 scale of the RNA content. • PUM1 Is a “housekeeping” gene • Account for RNA quality in the sample • For example time since death for a study of schizophrenia on deceased patients’ brains

  5. Two groups can be compared with back to back stem and leaf diagrams E.g. Stopping distances of bikes Treaded tire Smooth tire 34 1 8 9 35 5 5 36 6 4 37 5 38 1 39 1 2 0 40 Or dot diagrams | | | * | ** | | * |** Treaded 340 350 360 370 380 390 400 |*** | * | | * | | * | Smooth

  6. When there are associations between sets of data values, plot the data accordingly. E.g., Snowfall for duluth and White Bear Lake 1972-2000 A not very good way to plot the data WB Lake Duluth 130 * 120 * 110 ** ** 100 *** * 90 ***** 80 ****** ****** 70 ** *** 60 ** ********** 50 **** *** 40 *** *** 30 * *** 20

  7. Duluth White Bear

  8. A study of trace metals in South Indian River 5 3 1 6 2 4 T=top water zinc concentration (mg/L) B=bottom water zinc (mg/L) 1 2 3 4 5 6 Top 0.415 0.238 0.390 0.410 0.605 0.609 Bottom 0.430 0.266 0.567 0.531 0.707 0.716

  9. One of the first things to do when analyzing data is to PLOT the data • This is not a useful way to plot the data. There is not a clear distinction between bottom water and top water zinc • even though Bottom>Top at all 6 locations.

  10. A better way Top Bottom Connect points in the same pair.

  11. A better way Bottom=Top

  12. This following plot would imply a natural ordering of sites from 1 to 6. This would not be the best way to plot the data unless the sites 1-6 correspond to a natural ordering such as distance downstream of a factory.

More Related