Statistics Syllabus and Study Guide: Key Concepts, Course Structure, and Academic Expectations

Course Overview

Introduction to Statistics

This course provides a comprehensive introduction to statistics, focusing on the organization and presentation of data, probability theory, and inferential statistics. Students will learn to analyze data, interpret results, and apply statistical reasoning to real-world problems.

• Course Format: Online asynchronous with on-campus testing.

• Instructor: Brian Shaw

• Prerequisites: Intermediate Algebra or equivalent.

• Textbook: Elementary Statistics, 14th edition, Triola.

Main Topics

1. Statistical Organization and Presentation of Data

Understanding how to collect, organize, and present data is foundational in statistics. This includes summarizing data using tables, charts, and descriptive measures.

• Data Types: Qualitative (categorical) and quantitative (numerical).

• Data Presentation: Frequency tables, histograms, bar charts, and pie charts.

• Descriptive Measures: Mean, median, mode, range, variance, and standard deviation.

• Example: Summarizing survey results using a frequency table and calculating the mean score.

2. Probability Theory

Probability theory forms the basis for inferential statistics, allowing us to quantify uncertainty and make predictions about random events.

• Probability: The likelihood of an event occurring, expressed as a number between 0 and 1.

• Key Formula: $P(A) = \frac{\text{Number of favorable outcomes}}{\text{Total number of outcomes}}$

• Applications: Calculating the probability of drawing a specific card from a deck.

3. Probability Distributions

Probability distributions describe how probabilities are distributed over the values of a random variable.

• Discrete Distributions: Binomial, Poisson, and geometric distributions.

• Continuous Distributions: Normal distribution, uniform distribution.

• Key Formula (Normal Distribution): $f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{-\frac{(x-\mu)^2}{2\sigma^2}}$

• Example: Modeling test scores using the normal distribution.

4. Inferential Statistics

Inferential statistics allow us to make conclusions about populations based on sample data. This includes hypothesis testing and confidence intervals.

• Hypothesis Testing: Procedure to test claims about population parameters.

• Confidence Intervals: Range of values within which a population parameter is likely to fall.

• Key Formula (Confidence Interval for Mean): $\bar{x} \pm z^* \frac{\sigma}{\sqrt{n}}$

• Example: Testing whether a new drug is effective based on sample results.

5. Critical Thinking and Quantitative Literacy

Students are expected to develop skills in critical analysis and quantitative reasoning, enabling them to interpret statistical results and make informed decisions.

• Evaluating Claims: Assessing the validity of statistical arguments.

• Application: Critically analyzing news reports that use statistical data.

Course Structure and Policies

Grading Scale

The course uses a standard grading scale based on total points earned. Students must average at least 66% on the midterm and final exams to pass, regardless of overall score.

Grade	Minimum %
A	92
A-	90
B+	88
B	82
B-	80
C+	78
C	70
D	60
F	Below 60

Assignments and Assessments

• Homework: Assigned weekly, with lowest score dropped.

• Quizzes: Timed online quizzes, limited attempts.

• Exams: On-campus midterm and final exams, covering major chapters.

Academic Integrity

• Cheating: Any form of academic dishonesty results in failing the course.

• Withdrawal: Students may withdraw by the posted deadline.

Technology and Support

• Calculator: TI-83 or TI-84 required for exams.

• Online Resources: Course website, video lectures, and practice problems.

• Instructor Support: Office hours and email communication.

Weekly Schedule Overview

Weeks 1-3: Foundational Concepts

• Chapters 1-4: Introduction, summarizing data, probability, and basic distributions.

• Assigned readings, video lectures, and book problems.

Weeks 4-7: Advanced Topics and Review

• Chapters 5-7: Probability distributions, inferential statistics, and hypothesis testing.

• Midterm exam in Week 6 covering Chapters 1-6.

• Continued practice and review using sample exams and textbook resources.

Key Terms and Definitions

• Population: The entire group being studied.

• Sample: A subset of the population used to make inferences.

• Parameter: A numerical summary of a population.

• Statistic: A numerical summary of a sample.

• Random Variable: A variable whose value is subject to chance.

• Hypothesis: A claim or assertion about a population parameter.

Additional info:

• Students are expected to spend 10-15 hours per week on coursework.

• FERPA and ADA policies are in effect to protect student privacy and provide accommodations.

• Cell phones are not allowed during exams.