# Course Details

Course Information Package

Course Unit TitleSTATISTICS I
Course Unit CodeAMAT112
Number of ECTS credits allocated5
Learning Outcomes of the course unitBy the end of the course, the students should be able to:
1. Recognise and identify the kinds of data (discrete and continuous, ordinal and nominal). Construct, present and interpret frequency tables, cumulative distributions and graphs (histograms, bar charts, pie charts). Understand and explain the shape of various distributions (skewed, and symmetric).
2. Summarizing, calculate and interpret the measures of location (mean, mode, and median) and measures of dispersion (variance, standard deviation, range). Identify extreme values and outliers and explain their significance in business applications.
3. Describe and explain the idea of probability, experiments, events, outcomes and sample space and construct the sample space given an experiment.
4. Calculate probabilities and basic relationships of probability (union of events, complement of event, intersection of events, conditional probability).
5. Distinct the difference between mutually exclusive, mutually exhaustive and independent events. Apply these in business problems.
6. Recognize and construct and explain probability distribution tables.
7. Recognize, use, apply and explain the theory and their applications in Business problems concerning the probability distributions (Binomial, Poisson, Normal distribution). Use the tables of the standard normal distribution for solving problems and interpret correctly the answers.
Mode of DeliveryFace-to-face
PrerequisitesNONECo-requisitesNONE
Recommended optional program componentsNONE
Course Contents

Tabular and graphical methods

Statistics in practice. Kinds of data (discrete and continuous, ordinal and nominal). Different kinds of variables. Frequency tables, cumulative distribution tables and graphs (histograms, bar charts, pie charts). Shape of various data distributions (skewed, and symmetric).

# Descriptive statistics: Numerical methods

Summarizing quantitative data. Measures of location (mean, mode, and median) and measures of dispersion (variance, standard deviation, range) for group data and raw data. Difference between measures of location and measures of dispersion and their significance. Extreme values, outliers and their importance.

Introduction to Probability

The idea of probability. Experiments, events, outcomes and sample space. Relative frequencies. Calculation of probabilities and basic relationships of probability (union of events, complement of event, intersection of events). Mutually excusive, mutually exhaustive and independent events. Conditional probability and multiplication law.

# Discrete Probability Distributions

Probability distribution tables. Theory and their applications in Business problems concerning the discrete probability distributions: Binomial, Poisson. Expected values and variance.

Continuous Probability Distributions

Theory and their applications in Business problems concerning the continuous probability distributions: Normal distribution. Standard normal distribution and table of the standard normal distribution. Applications in Business problems. Discrete versus Continuous distributions.