Descriptive Statistics:
Statistical data. Types, sources and methods of collection. Presentation of data. Tables chart and graph. Errors and approximations. Frequency and cumulative distributions. Measures of location, partition, dispersion, skewness and Kurtosis. Rates, ratios and index numbers. New Computing
Learning outcomes
At the end of the course, students should be able to: 1. explain the basic concepts of descriptive statistics. 2. present data in graphs and charts. 3. differentiate between measures of location, dispersion and partition. 4. describe the basic concepts of Skewness and Kurtosis as well as their utility function in a given data set. 5. differentiate rates from ratio and how they are use. 6. compute the different types of index number from a given data set and interpret the output.
Course contents
Statistical data. Types, sources and methods of collection. Presentation of data. Tables chart and graph. Errors and approximations. Frequency and cumulative distributions. Measures of location, partition, dispersion, skewness and Kurtosis. Rates, ratios and index numbers. New Computing