S1 Course Content
MATHEMATICAL MODELS: Basic ideas of modelling in probability and statistics.
REPRESENTATION OF DATA: Histograms, stem and leaf, box plots, mean, mode, median, variance, standard deviation, coding, range, interpercentile ranges, outliers, skewness.
PROBABILITY: Sample space, exclusive and complementary events, independence, sum and product laws, conditional probability.
CORRELATION AND REGRESSION: Scatter diagrams, linear regression, independent/dependent variables, applications and interpretations, product moment correlation coefficient.
DISCRETE RANDOM VARIABLES: Concepts, probability density function, cumulative distribution function, expectation, variance.
DISCRETE DISTRIBUTIONS: discrete uniform distribution.
NORMAL DISTRIBUTION: Mean, variance, use of tables.
Course Programme
0915 Coffee
0930 INTRODUCTION AND SESSION I: Mathematical models, representation of data
1115 Coffee
1115 SESSION II: Probability, Correlation and regresssion
1300 Lunch
1345 SESSION III: Discrete random variables, Discrete distributions, Normal distribution
1515 Tea and depart
Contact details: