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Statistics in Radiological Sciences

From Radiological Sciences

Contents 1 Statistics in the Radiological Sciences 1.1 Course No.: 1.2 Instructor: 1.3 Text: 1.4 Credits: 1.5 Semester Offered: 1.6 Recommended References: 1.7 Evaluation Scheme: 1.8 Course Outline: 1.9 Topics by Lesson 1.10 Grading:

[edit] Statistics in the Radiological Sciences

[edit] Course No.:

RADI 5007

[edit] Instructor:

Geoffrey Clarke, Ph.D.

[edit] Text:

Reading Materials will come from publications in the Radiological and Statistical literature and postings and sites linked to the course web site.

The book, Introductory Statistics with R, is recommended (but not required) because it provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression.

[edit] Credits:

1

[edit] Semester Offered:

Summer

[edit] Recommended References:

Springer. ISBN 978-0-387-79053-4, 2008.
Paperback 229mm × 155mm, xvi+364 pages

Click here to see a listing of other books related to "R".

[edit] Evaluation Scheme:

Quizzes, Homework, Final Exam

[edit] Course Outline:

Students shall learn about the use of statistics in the radiological sciences following the theoretical developments and then applying the concepts to actual and simulated problems from various subfields of Radiological Science research.

Students will become familiar with the "R" programming language, a free, open-source analytical statistics environment that has been used for a myriad of applications worldwide. "R" is available for the Windows, Mac OS10 and Linux computing environments.

"R" can be downloaded and installed by going to the site: http://cran.r-project.org/

The ISwR library will be used for the examples in the book and in class and it can be downloaded from: http://cran.fyxm.net/web/packages/ISwR/index.html . (This entire folder should be put in the C:\Program Files\R\R-2.9.0\library directory after the archive has been unzipped.]

List of Topics by Lesson Number

[edit] Topics by Lesson

1. Measurements, Summarizing Data, Probabilities and Distributions
2. Experimental Design & the t-Test
3. Confidence Intervals & Normality
4. ANOVA, Standard Errors, Propagation of Errors, Error Reduction
5. QUIZ #1 + Statistical Significance & Statistical Power
6. Rates, Proportions & Goodness of Data
7. Linear Regression & Correlation
8. QUIZ #2 + Nonlinear Regression
9. General Linear Model: Analysis of Covariance (ANCOVA) & Multivariate Analysis
10. Nonparametric Statistics
11. QUIZ #3 + Measuring Statistical Agreement
12. Sensitivity, Specificity & Receiver Operating Curve Analysis
13. Bias in Experimental Design and Analysis
14. Analyzing Survival Data
15. QUIZ #4 + Introduction to Bayesian Analysis
16. Final Exam

[edit] Grading:

• Avg. of homework = 30%
• Quizzes = 40%
• Final Exam = 30% (comprehensive)

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