This course is designed to give students the statistical background required to apply statistical techniques to data both of general interest and of interest specific to business activity. This involves 1) presenting data using descriptive measures and graphical means, 2) presenting hypotheses that can be tested statistically, together with an appropriate interpretation of the test results and 3) analysing time series data and prediction. In order to deal with large data sets, the lectures are accompanied by computer laboratories using a statistical computer package (SPSS).

1. Sampling methods and descriptive statistics - collection and tabulation of data. Descriptive measures and graphical presentation of data. 2. Basic concepts of probability - probabilities of the union and intersection of events, conditional probability, contingency tables. 3. Discrete probability distributions - the binomial distribution. Expected values. 4. Continuous probability distributions - the normal and Pareto distributions - relevance to natural and economic phenomena. 5. Applications of the central limit theorem - interval estimation. 6. Hypothesis testing - one and two sample tests for population proportions and means. Tests of association. 7. The Pearson and Spearman correlation coefficient and simple linear regression. 8. Time Series Analysis. Trends and Seasonal Variation. Use of moving averages. Prediction. 9. Use of a statistical package (SPSS) for data input and transformation, as well as carrying out the statistical methods described above.

1. Differentiate between sampling and non-sampling errors, bias and precision and discuss their relation to the process of data collection - written examination. 2. Present data using descriptive measures and graphical means (e.g. histograms, bar charts) - written examination. 3. Use the basic concepts of probability to calculate the probabilities of events, calculate the expected value of a discrete random variable and analyse contingency tables - written examination. 4. Apply the concepts of the central limit theorem - written examination. 5. Calculate confidence intervals for the parameters of a distribution (the population mean and proportion) - written examination. 6. Carry out tests regarding the parameters of distributions and association of categorical variables - written examination. 7. Interpret correlation coefficients and the results obtained from linear regression models, as well as understanding the limitations of these methods - written examination. 8. Analyse trends and seasonal variation in time series data. Use moving averages to smooth time series. - written examination. 9. Use a statistical computer package (SPSS) to carry out the methods of data analysis contained in this course - project.

1. Display the ability to discuss the merits of different methods of data collection and appraise the manner in which data is presented - Written examination

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