Introduction to Biostatistics
Course Description and Objectives
The course is an introductory level to biostatistics, designed for biology professional’s students.
This course will cover the topics on data presentation techniques, describing data with numerical
summary measures, probability and probability distributions, sampling distributions, statistical
inferences from small and large samples, analysis of categorical data, analysis of variance,
correlation and simple linear regression analysis.
The primary objectives of this course are to
1. Teach data presentation techniques using graphs and summary statistics
2. Provide a basic foundation of probability and probability distributions
3. Teach how to formulate an appropriate hypothesis and make relevant inferences from
quantitative and qualitative data.
4. Educate how to apply analysis of variance technique to real life data and develop a model for
a response variable in order to identify the associated significant predictors.
Upon completion of the course, students will understand and be able to apply basic techniques in
descriptive and inferential statistics. These will include: graphical and numerical description of
data; elementary probability calculation and distributions; point and confidence interval
estimation, as well as hypothesis testing concerning population means and proportions; simple
contingency table analyses; analysis of variance; correlation and simple regression techniques.
Students will be introduced to basic operations and analytical procedures in SPSS on personal
computers. They will be able to understand and interpret the statistical analysis in research
articles published in biology journals.
Textbook
Principles of Biostatistics, Second Edition by Pagano and Gauvreau.
Prerequisites
One year undergraduate mathematics
Once a chapter is completed, you should expect a class quiz.
1
Week
Sections
1
introduction
2
3
Organization and
Description of Data
Descriptive study of
Bivariate Data
Probability
4-5
6-7
Probability Distributions
8-9
The Normal Distribution
Syllabus (Tentative)
Topics
What is statistics?
Statistics in our life
Statistics an aid of scientific inquiry
Two basic concepts- population and sample
The purpose of collection of data
Objectives of statistics
Introduction
Main types of Data
Describing Data by tables and graphs
Measures of central tendency
Measures of dispersion
Measures of Location and Measures of
Variation
Grouped data
Introduction
Summarization of Bivariate categorical
Data
Scatter Diagram of Bivariate measurement
Data
The correlation coefficient- A measure of
linear relation
Introduction
Probability of an event, Basic idea of
probability
Methods of assigning probability
Event relations and two laws of probability
Conditional probability and independence
Random sampling from a finite population
Random variable
Probability distributions of a Discrete
random variable
Expectation (Mean) and standard
Deviation of a probability distribution
Successes and failures – Bernoulli Trials
The Binomial Distribution
Probability Model for a continuous
Random variable
The Normal distribution- its general
features
The Standard Normal distribution
Other Continuous Distributions
2
Notes
(Exponential & Uniform) Applications
The normal approximation to the binomial
The sampling distribution of a statistics
Distribution of the sampling mean and the
Central limit theorem
Applications of the central limit theorem
10-11
12-13
Introduction
Point estimation of a population mean
Confidence interval for a population mean
Testing Hypothesis about a population
mean
Inferences about a population proportion
Drawing inferences
from large samples
Small –sample inferences
for normal populations
Introduction
Student’s t Distribution
Inferences about small sample size
Relationship between test and confidence
intervals
Inferences about the standard deviation σ
The Chi-Square distribution
Comprehensive Final Exam
Once a chapter is completed, you should expect a class quiz.
3