This course provides an introduction to data analysis and statistical inference illustrated with biological applications. Topics include graphical display, populations and sampling, probability distributions, expectation and variance, estimation, testing, correlation, regression, and the design of experiments. Emphasis is on concepts and the careful modeling of biological data, so that statistical methods are applied properly.


• What is Statistics? Descriptive Statistics and Inferential Statistics, Characteristics of Statistics, Introduction to Some Basic Terms,Types of Classification, Bases of Classification and Application of Statistics.
• Frequency, Frequency Distribution table, Graphical Representation of Data, Types of Graphs and Charts, Histogram, Cumulative Frequency Curve or Ogive, Types of Ogive and Outlier.
• Measures of Central Tendency or Averages: The Arithmetic mean The Harmonic mean, The Geometric mean, Median, Mode for group and ungroup data se.
• Quartiles, The Five Number Summary, Deciles and Percentiles, Box Plot
• Measure of Dispersion: Alegebric and Graphical measures, Range, Quartile Deviation, The Mean Deviation, Variance, Standard Deviation
• Shape of Data: Skewness, Quartile Coefficient of Skewness and Kurtosis
• Probability Rules: Addition Rule, Mutually Exclusive, Not Mutually Exclusive, Collectively Exhaustive Events
• Bayes’ Rule, Multiplication Rule, Independent events, dependent events and Conditional Probability
• Random variables.
• Discrete and continuous Random variables
• Random variables.
• Discrete and continuous Random variables
• The Binomial Probability Distribution
• The Poisson Probability Distribution
• Continuous Probability Distributions
• Properties of Continuous Probability Distributions.
• The Uniform Probability Distribution