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Applied Statistics and Probability

3 Credits | 300 Level | 38 Contact hours

Newbold, P. (2013). Statistics for business and economics. Pearson
Navidi, W. C. (2006). Statistics for engineers and scientists (Vol. 2). New York: McGrawHill.

We will use the open-source software R to solve most problems in the latter half of the
semester.

The objective of the course is to provide students with a series of analytical procedures
and techniques used for Data Analysis and Decision Making. We put data first and use
computers, instead of math, as the engine for statistical inference. We use an informal
but rigorous style with minimal mathematical notation. We emphasize ideas rather than
mathematical calculations.

1. Sampling and Descriptive Statistics
2. Probability
3. Propagation of error
4. Commonly used distributions
5. Hypothesis testing
6. Confidence intervals
7. Correlation, simple regression models
8. Factorial experiments

Upon successful completion of this course, students will be able to:
• Use statistical reasoning in science areas that involve decision-making.
• Perform data analysis to answer relevant questions, producing professional visuals and
thoughtful discussion.
• Raise awareness of basic statistical issues such as randomization, confounding, and the role of experiments.
• Carry out statistical analyses in their professional career and report their findings
professionally.
• Describe the goals of statistical methodologies conceptually, and use the appropriate statistical tools to analyze a particular problem.
• Develop a healthy scepticism toward statistical studies and their results based on a sensible consideration of the data and techniques employed.
• Know the importance of literate programming. Write code that expresses what your analysis
is doing and how you did it so that your research is reproducible.

Class Participation: 10%
Quizzes: 25%
Reports of lab sessions: 30%
Midterm exams: 35%