Bivariate+Data+-+II

Bivariate+Data+-+II - Bivariate Data - II Quantitative...

Info iconThis preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Bivariate Data - II Quantitative & Qualitative Example 2: Cancer Survival Data Patients with advanced cancers of the stomach, bronchus, colon, ovary or breast were treated with ascorbate. The purpose of the study was to determine if patient survival differed with respect to the organ affected by the cancer. The cancer type and the survival (in days) of each patient was recorded. http://lib.stat.cmu.edu/DASL/Datafiles/Can cerSurvival.html Example 2: Cancer Survival Data Clearly cancer type is a nominal variable and survival time is quantitative variable. One possible way to visualize the data is to treat the data set as several different datasets of a single quantitative variable. All the observations that have the same category for the qualitative variable are grouped into the same dataset. Colon Bronchus Stomach Breast Ovary 248 81 124 1235 1234 377 461 42 24 89 189 20 25 1581 201 1843 450 45 1166 356 180 246 412 40 2970 537 166 51 727 456 519 63 1112 3808 455 64 46 791 406 155 103 1804 365 859 876 3460 942 151 146 719 776 166 340 372 37 396 163 223 101 138 20 72 Example 2: Cancer Survival Data One can then resort to the usual displays and descriptive statistics to visualize the data. i.e. histograms, dotplots, stem-and-leaf plots. It is important in this paradigm to make sure that the scales are the same for each graph in order to make them comparable For the cancer survival dataset, one could 1000 2000 3000 4000 Colon Cancer Survival Time in Days Five-Number Summary For a quantitative dataset, the five-number provides a quick summary of how it is spread out. The five number summary reports the min, Q1, Q2, Q3 and the max. Five-Number Summaries for Each Cancer Type Colon Bronchu s Stomac h Breast Ovary min 20 20 25 24 89 Q 1 189 72 46 723 201 Q 2 372 155 124 1166 406 Q 3 519 245 396 1396 1234 max 1843 859 1112 3808 2970 Box-and-Whiskers Plots (Boxplots) The five-number summary for each group can be summarized in a boxplot plotted on a vertical or horizontal axis. a vertical or horizontal axis....
View Full Document

This note was uploaded on 02/01/2012 for the course STATS 285 taught by Professor Popel during the Spring '09 term at Rutgers.

Page1 / 38

Bivariate+Data+-+II - Bivariate Data - II Quantitative...

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online