Statistics

Course Description

COURSE CONTENT

Definition of statistics. Statistics in scientific research. Graphic view of data. Average sample values. Measures of dispersion or variability. Measures of location. Measures of shape. Linear regression. (Pearson) coefficient of correlation. Computer implementation with the help of the MS Excel program package.

A random trial. Probability. Setting probability. Laplace's probability model. Combinatorics. Combinatorial application to solve elementary probability problems. Conditional probability. Independent events. Bayes’ formula. Discrete random variables (Binomial and Bernoulli random variables, Poisson random variables, Hypergeometric random variables). Continuous random variables (Normal random variables).

A random sample. Point estimation of population mean and variance. Confidence intervals for means of a normal population. Confidence intervals for means based on large samples. Testing hypothesis about population mean of a normally distributed population. Testing hypothesis about population mean based on large samples. Comparing two means of two normally distributed populations (t-test). Comparison of proportions. Comparing two population variances of normally distributed populations (F-test). Χ²-test for goodness of fit. Χ²-homogeneity test population. One-way ANOVA test. The correlation test of two variables. Linear regression model. Testing of statistical hypotheses by using the MS Excel program package.

LEARNING OUTCOMES

• explain the statistical procedure
• define and describe the methods of solving combinatorial problem
• link different concepts and results and apply them in terms of probability
• perform basic statistical analysis for the given data
• select and apply the appropriate data analysis test
• formulate and test the appropriate hypothesis
• use the MS Excel program for statistical data processing