Introduction to Statistics Course Syllabus

Course Description

This course provides an introduction to the fundamental concepts and techniques of statistics. Students will learn to collect, analyze, interpret, and present data. The course emphasizes statistical reasoning, understanding of statistical methods, and practical application through hands-on projects and use of statistical software.

Course Objectives

• Develop an understanding of statistical reasoning and concepts.

• Learn to perform and interpret basic statistical methods.

• Gain experience with real or realistic data sets.

• Enhance skills in communicating statistical findings.

• Prepare for further study or application of statistics in various fields.

Week-by-Week Breakdown

Week 1: Introduction to Statistics

• Overview of the course and expectations.

• Introduction to statistical reasoning and the importance of statistics in everyday life.

Week 2: Descriptive Statistics I

• Types of data and measurement scales.

• Measures of central tendency: mean, median, mode.

Week 3: Descriptive Statistics II

• Measures of variability: range, variance, standard deviation.

• Introduction to graphical representations: histograms, box plots.

Week 4: Probability Concepts

• Basic probability rules and concepts.

• Discrete and continuous probability distributions.

Week 5: Discrete Distributions

• Binomial and Poisson distributions.

• Applications and assumptions.

Week 6: Continuous Distributions

• Normal distribution and properties.

• Standardization and the use of z-scores.

Week 7: Sampling Distributions

• Concept of a sampling distribution.

• Central Limit Theorem and its implications.

Week 8: Confidence Intervals

• Constructing and interpreting confidence intervals.

• Margin of error and sample size considerations.

Week 9: Hypothesis Testing I

• Null and alternative hypotheses.

• Type I and Type II errors.

Week 10: Hypothesis Testing II

• t-tests for means (one-sample, independent samples, paired samples).

• Assumptions and interpretations.

Week 11: Chi-Square and ANOVA

• Chi-square tests for independence and goodness-of-fit.

• One-way ANOVA and assumptions.

Week 12: Correlation and Regression

• Scatterplots and correlation coefficients.

• Simple linear regression and interpretation of results.

Week 13: Project Work

• Students work on their projects, collect data, and perform preliminary analyses.

• Group discussions and feedback sessions.

Week 14: Project Presentations

• Students present their projects and findings.

• Peer review and discussion.

Week 15: Review and Final Exam Preparation

• Review of key concepts and methods.

• Preparation for the final exam.

Assessment

• Weekly assignments (30%)

• Midterm exam (20%)

• Final project (20%)

• Final exam (30%)

Textbook and Materials

• Required textbook: [To be determined by the instructor]

• Access to statistical software (e.g., SPSS, R, or similar).

Policies

• Attendance and participation are expected in all classes.

• Late assignments will be penalized unless prior arrangements are made.

• Academic integrity is strictly enforced according to university guidelines.

Office Hours

• The instructor will be available for additional help during scheduled office hours or by appointment.

Note: This syllabus is subject to change to better meet the needs of the class. Any changes will be communicated to students in a timely manner.