Learning objectives
The spread of computers and information technology has introduced and approached everyone, not only researchers, to statistical tests. However, the comprehension of the logic and concepts of statistics is still a problem: how to plan data collection and samples, which tests use, what are the validity assumptions in order to run valid and reliable tests. From thesis work to international reports, from surveys to opinion polls, each publication based on interpretation of data should be grounded on a correct statistical analysis to be regarded as scientifically correct and to allow for comparison with results from other studies. <br />
The course will cover issues of descriptive statistics and will also focus on arguments of inferential statistics.
Prerequisites
Basic notions of mathematics and combinatory calculus
Course unit content
1. Introduction to statistics. Measurement Scales, Types of Variables. Descriptive statistics for univariate distribution. Tables and graphic representations. Central tendency and dispersion, symmetry and kurtosis measures. <br />
2. Introduction to probability. Some theoretical distributions: binomial, Poisson and normal.<br />
3. The chi-square distribution and the comparison between observed and theoretical distributions. Goodness-of-fit test. Contingency tables (2x2 and MxN) and the test of independence.<br />
4. Inferential procedure and the logic of statistical tests. I and II type errors. <br />
5. t test. t test for one sample and for two samples (paired and unpaired samples). Confidence interval for the mean. <br />
6. Analysis of variance for the comparison of means. Validity assumptions: test for the omoschedasticity and data transformations. Post-hoc tests: Bonferroni. Interaction effects between factors.<br />
7: Descriptive statistics for bivariate distributions. Simple linear regression: parameter estimates, their statistical significance, and confidence interval. Correlation. R-square and r estimation.
Full programme
- - -
Bibliography
L. Soliani, M. Manfredini, Statistica applicata, Uninova, Parma.<br />
<br />
M.R. Spiegel, Statistica, Collana Schaum, Mc Graw-Hill.
Teaching methods
The course will consist of of lectures of about two hours each.<br />
<br />
The conclusive assessment will be either in the form of an oral exam at the end of the course or two intermediate written exams during the course.
Assessment methods and criteria
- - -
Other information
- - -
2030 agenda goals for sustainable development
- - -
