| CODE | EMA1200 | ||||||
| TITLE | Business Statistics 2 | ||||||
| UM LEVEL | 01 - Year 1 in Modular Undergraduate Course | ||||||
| MQF LEVEL | 5 | ||||||
| ECTS CREDITS | 4 | ||||||
| DEPARTMENT | Faculty of Economics, Management and Accountancy | ||||||
| DESCRIPTION | This study-unit will cover the following content: Continuous Distributions: - Probability distribution for continuous variables; - Calculation of mean, variance and other estimates for continuous variables; - Normal distribution and the reading of standard normal tables; - Normal approximation to the Binomial and Poisson distribution; - Exponential distribution. Sampling theory and methodology: - The need for appropriate sample design; - Simple Random Sampling and Systemic Sampling; - Stratified Sampling with different conditions; - Cluster Sampling and Multi-Stage Sampling; - The Distribution of Sample Means; - Estimation of the sample size by using the standard error of the sample mean. Confidence intervals: - Confidence interval for the mean using the normal distribution (known variance); - Confidence interval for mean using Student t-distribution (unknown variance); - Confidence interval for the difference between two means; - Margin of error and sample size; - Confidence interval for proportion; - Confidence interval for the difference between proportions. Hypothesis testing involving one sample: - The meaning of the Null and Alternative Hypotheses; - The level of significance and application of one-tailed and two-tailed tests; - Types of Error and their interpretation; - Test of a sample mean using the normal distribution; - Test of a sample mean using the t-distribution; - Test of a sample proportion. Hypothesis testing involving two semples and chi-squared tables: - Test for the difference between two means; - Test for the difference between two means using a pooled variance; - Test for paired samples; - Test for the difference between proportions using a pooled proportion; - Chi-squared test applied to contingency tables; - Chi-squared goodness-of-fit test. Regression and correlation: - Coefficient of correlation and rank correlation between two variables; - Scatter diagrams and the normal equations for a simple linear regression; - The estimation of regression coefficients for a simple linear regression model; - Analysis of variance table for a simple linear regression model and its ; - Application in regression analysis; - Confidence intervals for the coefficients of a simple linear regression model; - Multiple Regression Analysis. Analysis of variance: - The importance of the method of analysis of variance; - One factor analysis of variance with equal samples; - One factor analysis of variance with unequal sample size; - Two factor analysis of variance without replicates. Study-unit Aims: - To enable students to properly identify the relevant statistical techniques appropriate to the empirical investigation under consideration, as well as to perform proper scientific analysis of the data collected for statistical purposes; - To equip the students with the necessary statistical and analytical skills to enable them to apply such skills to various business disciplines; - To underscore the importance of statistical methodology in business, economic and financial applications as found in real-life situations; - To provide an appropriate and adequate foundation for other study-units in the subsequent undergraduate years of the course (e.g., Operations Research, Introductory and Intermediate Econometrics and Macroeconomic modelling, Applied Forecasting Techniques,Managerial Decision Modelling, Research Methods, Derivative Markets, etc.). Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Master a wide range of statistical analysis techniques such as the valid interpretation and application of diverse discrete and continuous probability distributions; - Interpret sampling distributions of means as well as other population parameters; - Estimate of standard errors, sample size and margins of error as well as to to construct confidence intervals, perform hypothesis testing, and subsequently interpret the resulting parameter estimates and corresponding interval estimates; - Compute Type I and Type II errors, including the calculation of the power of a test together with the implication of these errors for the decision-making process; - Use one-tailed or two-tailed test according to the situation being analysed - the implications of different outcomes; - Use simple and multiple linear regression analysis to determine the nature of relationships within observed values of data, the computation of measures of association and correlation between sets of data, the interpretation of regression coefficients, and the performance of appropriate tests of significance on these parameter estimates; - Apply the knowledge they have acquired throughout this study-unit to other modules subsequently offered by the Faculty at a more advanced stage of their undergraduate studies; - Appreciate that statistical analysis provides the fundamental framework for the effective understanding of management, marketing, economics, insurance, finance, and other related disciplines; and that only through mastering the necessary data handling and statistical techniques will enable students to be more proficient in (a) the decision-making process, which is essential for strategic policy purposes, and (b) ability to communicate and implement effectively vital business concepts 2. Skills: By the end of the study-unit the student will be able to: - Perform effective technical analysis of numerical information, and the ability to generate meaningful and readily interpretable conclusions form available datasets; - Select the appropriate statistical technique from a wide range of both parametric and non-parametric statistical tools according to the particular sampling scenario and the nature of the data; - Identify the particular implicit data-generating process from the seemingly random behaviour of explicit real-world data patterns, and to correctly model the presumed specification of the underlying data-generating processes (DGP); - Correctly interpret the specific peculiarities of diverse probability density functions, and then applying the special properties of these modelled probability processes to different business and economic environments; - Formulate alternative analytical methodologies according to the different scenarios being faced; - Interpret statistical news releases issued by various official institutions (including governmental ones), as well as interpret figures published in reputable magazines, newspapers or academic journals; - Use various techniques to estimate the parameters of a statistical model, and subsequently apply appropriate tests of significance to determine the accuracy of the estimates obtained from such models; - Have mastered the elementary computer commands commonly featured in the more popular analytical software packages such as Microsoft Excel and SPSS, and be able to apply these commands and associated toolpacks/add-ins to productive use in day-to-day statistical analysis; - Differentiate between the alternative types of sampling techniques and methods of data collection, and their applications according to the scope of the empirical study being conducted as well as the nature, size and other characteristics of the population being investigated; - Give a meaningful interpretation of the empirical results and summary statistics arising from experimental outcomes. Main Text/s and any supplementary readings: Essential Text/s: Statistics for Business and Economics: [12th (International) edition] Authors: James T. McClave, P.George Benson, Terry Sincich Publisher: Pearson Education. Basic Business Statistics: Concepts and Applications [12th (Global) edition] Authors: Mark L. Berenson, David M.Levine, Timothy C. Krehbiel Publisher: Pearson Education. Supplementary readings: Schaums Outline of Statistics: Fourth Edition (Schaum's Outline Series) Authors: Murray R Spiegel, Larry J. Stephens Publisher: McGraw-Hill. A Concise Course in A-Level Statistics: with worked examples (4th revised edition) Authors: D J. Crawshaw, Joan Sybil Chambers Publisher: Nelson Thornes 2013. |
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| STUDY-UNIT TYPE | Lecture and Tutorial | ||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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