The course includes: Introduction to Bio- Statistics ,Descriptive Statistics, Some Basic Probability Concepts , Some Important Sampling Distributions, Hypothesis Testing. (One Sample and Two Sample Testing), Analysis of Variance (ANOVA), Simple Linear Regression and Correlation, Multiple Regression and Correlation, Regression Analysis and Some Additional Techniques, The Chi-Square Distribution and the Analysis of Frequencies.
- At the end of this course, students should be able to
- 1. Describe the meaning of the terms “Statistics”and “Biostatistics”.
- 2. Construct and explain frequency distribution tables(eg. Frequency, relative frequency, cumulative frequency and relative cumulative frequency distribution), apply frequency diagrams (eg. Histrogram frequency, polygon, line graph, Ogive, bar graphs, and Pie chart).
- 3. Work with summation notation, define and compute measures of locations (eg. Mean, Median, Mode, Percentiles and Quartiles).
- 4. Explain the concept of Variation and compute measures of dispersion, (eg. Range, mean deviation, variance and standard deviation), define the concept of variability exists between two distributions, compute Z-Score or Standard Scores .
- 5. Decide whether two variables are related through scatter diagram, use coefficient correlation as a measure of strength of the relationship that exists between two variables, determine reliability of r , use least square method to construct estimated regression line, have the concept of multiple regression.
- 6. Analyze how sample data can be used to reject or accept the null hypothesis, discuss the type I and type II error, and distinguish between tests concerning means, difference between means and proportions.
- 7. Compute excepted frequencies, analyze contingency tables and discuss a Chi-Square test statistic to test goodness of fit.
- 8. Set up ANOVA charts, analyze how one-way or two- way ANOVA’s are used when comparing several sample means or when two factors may affect the sample mean.
- Set up confidence intervals for regression estimates the null hypothesis H_0 that β_1 is zero or not.
Books for this Course
- Fundamentals of Biostatistics, Bernard Rosner, 7th edition