| CODE | MGT1924 | ||||||
| TITLE | Quantitative Techniques | ||||||
| UM LEVEL | 01 - Year 1 in Modular Undergraduate Course | ||||||
| MQF LEVEL | 5 | ||||||
| ECTS CREDITS | 4 | ||||||
| DEPARTMENT | Business and Enterprise Management | ||||||
| DESCRIPTION | Objectives:Content: Statistics and the Research Process - The use of descriptive and inferential procedures; - The meaning of the terms statistic and parameter; - The difference between and experiment and a correlational study; - Types of measurement scales. Frequency Distributions and Percentiles - How simple frequency, relative frequency, cumulative frequency and percentiles are computed and interpreted; - How frequency tables, pie charts, bar graphs, histograms and box plots are created; - What normal, skewed, bimodal and rectangular distributions are and how to interpret them. Measures of Location and Dispersion - How measures of central Tendency (mean, mode, and median) describe data and when each is appropriate; - What is meant by ‘deviations around the mean’ and what they convey about each score’s location and frequency in a normal distribution; - What is meant by dispersion; - How measures of dispersion (range and interquartile range) are computed and how to interpret them; - How to compute th sample variance and standard deviation, the estimated population variance and standard deviation and the true population variance and standard deviation. Z-scores and the Normal Curve Model - How to compute and interpret z-scores; - Applying the standard normal curve model; - Using z-tables to obtain probability; - How to compute and interpret the standard error of the mean. Describing Relationships - The logic of correlational research and how it is interpreted; - How to identify the type and strength of a relationship; - How to interpret the Pearson r correlation coefficient; - The logic of inferring a population based on sample correlation. Introduction to Regression Analysis - How a regression lines summarises a scatterplot - How the regression equation is used to predict Y scores at a given X; - Measuring errors in prediction when using regression; - Computing and interpreting the proportion of variance accounted for. Overview of statistical Hypothesis Testing - What Type 1, Type II and statistical power are; - When experimental hypothesis leads to a one-tailed or two-tailed statistical test; - How to set up a sampling distribution for one- and two-tailed tests; - How to interpret significant and non-significant results; - Hypothesis testing for a single mean and two-sample means, and the test of independence (i.e these include the z-test, the single/paired/independent t-tests, ANOVA and the Chi-square test). Main Text/s and any supplementary readings: Reading List: - Basic Statistics for the Behavioural Sciences, by Gary W. Heiman (Cengage) - Basic Statistics for Business and Economics, by Douglas Lind, William Marchal and Samuel Wathen (McGraw Hill). |
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| ADDITIONAL NOTES | This study-unit is only offered at the Gozo centre. | ||||||
| STUDY-UNIT TYPE | Lecture | ||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Frank H. Bezzina |
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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 2024/5. It may be subject to change in subsequent years. |
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