The credit is given on the completion of an individual project. The exam is written.

Obligatory:

• Rumsey, Deborah. Statistics for dummies. Hoboken: Wiley, 2003, xviii, 355 s. s. ISBN 0-7645-5423-9.
• Rumsey, Deborah. Intermediate statistics for dummies. Indianapolis: Wiley, 2007, xviii, 362 s. s. ISBN 978-0-470-04520-6.
• Motulsky, Harvey. Intuitive biostatistics, a nonmathematical guide to statistical thinking. New York: Oxford University Press, 2014, xxxv, 540 s. s. ISBN 978-0-19-994664-8.
• Venables, W. N., Smith, D. M.. An introduction to R. Bristol: Network Theory, 2001, 139 s. s. ISBN 0-9541617-4-2.

written

1. Descriptive statistics – graphical representation, measures of data centrality

2. Descriptive statistics – measures of variability in data

3. Normal distribution, Z distribution, lognormal distribution

4. Sampling distribution and the central limit theorem

5. Confidence interval

6. Hypothesis testing – critical region, one-tailed and two-tailed tests

7. Hypothesis testing – tests of the mean

8. Hypothesis testing – analysis of variance (ANOVA)

9. Covariance and correlation analysis

10. Linear regression – simple

11. Linear regression – multiple

12. Reserve

13. Summary

14. Early exam date

Online course materials at UCT e-learning.

Online textbooks:

OpenIntro Statistics

OnlineStatBook

Hyperstat

StatPrimer

Students will be able to:

• apply descriptive statistical methods, including graphical data representation and the calculation of measures of central tendency and variability,

• explain the properties of basic probability distributions and use the central limit theorem when working with sampling distributions,

• construct and interpret confidence intervals for selected statistical characteristics,

• perform and interpret statistical hypothesis testing, including tests of means and analysis of variance (ANOVA),

• analyze relationships between variables using covariance, correlation, and simple as well as multiple linear regression.

• use the R software (environment for statistical computing)

• do the exploratory analysis and hypotheses testing in R

• do the regression analysis in R

• analyze multidimensional data in R

• present results of statistics analysis using graphical capabilities of R

Mathematics