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Purpose
This course offers the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of statistics in the research and development of projects.
Learning outcomes
By end of the course, students will be able to:
i understand the concept of a frequency distribution for sample data, and be able to summarise the distribution by diagrams and statistics,
ii Understand the principles of probability and the concept of probability distributions
iii Become familiar with binomial, Poisson, normal and log-normal probability distributions
iv Understand linear combinations of random variables and the Central Limit Theorem
v Understand the concepts of confidence intervals and hypothesis tests
vi Be able to make statistical comparisons of means (paired and unpaired samples), proportions and variances
vii Understand the concepts of ANOVA and be familiar with one-way, two-way, and two-way with interaction ANOVA,
viii Understand correlation and regression, and be able to make predictions and understand their limitations
ix Understand the concept of sample preparation error within a geostatistical sampling context
2
x Use a computer program (R – Programming) to analyse data
Content
Introduction to Statistics: Parametric Inference; Maximum Likelihood Estimation; Parametric Hypo-dissertation Testing; Testing Goodness of Fit; Regression & Correlation; Bayesian Statistics; Principal Component Analysis; Generalized Linear Models.
Practical use of statistical software’s (excel, SPSS, STATA, EVIEWS, R SOFTWARE, ATLAS, NVivo). Descriptive Statistics; Measures of central tendency, measures of dispersion; inferential statistics. Frequencies and descriptive; Correlation regression and cross tabulation (contingency tables); Compare means: t-tests, ANOVA (F-test); Chi-square; Factor and Factorial analyses; Multiple regression analysis; Time Series Analysis; Cross-sectional analysis; Panel data Analysis
Mode of Delivery
Lectures, student presentations, case studies presented in a seminar format, guest lectures, class discussions, and tutorials.
Instructional Materials
Textbooks, blackboard/whiteboard, handouts, overhead projector, laptop, LCD projector, and
DVDs. The course will primarily use R programming, a statistical computing environment and language
R Lab Sessions