Course Objectives:

The developed and illustrated statistical techniques and thought process are useful in designing a research study or experiment, and then analyze the data collected using an intuitive and proven approach. It is intended to prepare students to deal with solving problems encountered in research projects and decision making based on data.

Course Requirements:

It is intended to prepare students to deal with solving problems encountered in research projects and decision making based on data.

Course Contents:

Chapter I.1 Introductions

1. Strategy of experimentation

2. Typical applications of experimental design

3. Basic principles

4. Guidelines for designing experiments

Chapter II.2 Using surveys and scientific studies to gather data

1. Surveys

2. Scientific studies

3. Observational studies

4. Data management: preparing data for summary and analysis

Chapter II.3 Data description

1. Describing data on a single variable: graphical methods

2. Measures of central tendency

3. Measures of variability

4. The box-plot

5. Summarizing data from more than one variable

Chapter II.4 Probability and probability distributions

1. How probability can be used in making inferences

2. Finding the probability of an event

3. Basic event relations and probability laws

4. Conditional probability and independence

5. Variables: discrete and continuous

6. Probability distributions for continuous random variables

7. Normal distribution, random sampling

Chapter II.5 Inferences about population central values

1. Estimation of population mean

2. Statistical test for population mean

3. The level of significance of a statistical test

Chapter I.2 Simple comparative experiments

1. Basic statistical concepts

2. Sampling and sampling distributions

3. Inferences about the differences in means, randomized designs

4. Checking assumptions in t-test

5. Choice of sample size

6. Confidence intervals

7. The case where population variances are unknown

8. Comparing a single mean to a specified value

Chapter I.3 Experiments with a single factor: the analysis of variance

1. The analysis of variance

2. Analysis of the fixed effects model

3. Model adequacy checking

4. Practical interpretation of results

5. Sample computer output

6. Determining sample size

7. Discovering dispersion effects

8. The regression approach to the analysis of variance

9. Non-parametric methods in the analysis of variance

Computer labs review, exercise, homework and projects

Credits: 2