TECH-4025 (Applied Statistics) – Course Timeline and Chapter Overview

Course Overview: TECH-4025 (Applied Statistics)

Introduction

This course timeline outlines the structure and content coverage for a college-level Applied Statistics course. The schedule is organized by week, with each week focusing on a specific chapter or set of chapters from a statistics textbook. The chapters align closely with foundational and advanced topics in statistics, providing a comprehensive roadmap for students to follow throughout the term.

Course Timeline and Chapter Topics

Week	Chapter(s)	Main Topic
1	Chapter 1	Stats Starts Here
2	Chapter 2	Displaying and Describing Categorical Data
3	Chapter 3	Displaying and Summarizing Quantitative Data
4	Chapter 4	Understanding and Comparing Distributions
5	Chapter 5	The Standard Deviation as a Ruler and the Normal Model
6	Chapters 6, 7, 8	Scatterplots, Association, Correlation, and Linear Regression
7	Chapter 9	Sample Surveys
8	Chapter 10	Experiments and Observational Studies
9	Chapters 11, 12	From Randomness to Probability and Probability Rules
10	Chapter 13	Random Variables
11	Chapter 14	Sampling Distribution Models
12	Chapter 15	Confidence Intervals for Proportion
13	Chapters 16, 17	Testing Hypotheses About Proportions and More About Tests
14	Chapter 18	Inferences about Means
15	Chapters 19, 20	Comparing Means & Paired Samples and Blocks
16	Chapter 21	Comparing Two Proportions
17	Chapters 23, 24	Inferences for Regression and Analysis of Variance
18	Chapter 25	Multifactor Analysis of Variance
19	Chapter 26	Multiple Regression
20	Chapter 27	Multiple Regression Wisdom
21	Chapter 28	Nonparametric Tests
22	Chapter 29	The Bootstrap
23	Chapter 30	Introduction to Statistical Learning and Data Science

Key Features of the Course Timeline

• Sequential Learning: The course is structured to build foundational knowledge before advancing to more complex statistical methods.

• Assessment Integration: Self-tests, assignments, and quizzes are interspersed to reinforce learning and assess understanding.

• Comprehensive Coverage: Topics range from basic descriptive statistics to advanced inferential techniques, regression, ANOVA, and modern data science concepts.

Example: Chapter Progression and Academic Context

• Chapter 1 – Stats Starts Here: Introduction to statistics, types of data, and the role of statistics in research and decision-making.

• Chapter 2 – Displaying and Describing Categorical Data: Methods for summarizing categorical variables, including frequency tables and bar charts.

• Chapter 5 – The Standard Deviation as a Ruler and the Normal Model: Understanding variability, standard deviation, and the properties of the normal distribution. Formula: $\sigma = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (x_i - \mu)^2}$

• Chapters 6, 7, 8 – Scatterplots, Association, Correlation, and Linear Regression: Visualizing relationships between variables, measuring correlation, and fitting linear models. Formula (Correlation Coefficient): $r = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}}$

• Chapters 11, 12 – Probability: Introduction to probability concepts, rules, and their application in statistical inference.

• Chapters 15–17 – Inference for Proportions: Constructing confidence intervals and hypothesis tests for population proportions. Formula (Confidence Interval for Proportion): $\hat{p} \pm z^* \sqrt{\frac{\hat{p}(1-\hat{p})}{n}}$

• Chapters 18–20 – Inference for Means and Comparing Groups: Techniques for comparing means, including paired and independent samples.

• Chapters 23–27 – Regression and ANOVA: Advanced modeling techniques for analyzing relationships among variables and comparing group means.

• Chapters 28–30 – Nonparametric Tests, Bootstrap, and Data Science: Modern statistical methods for situations where traditional assumptions do not hold, and an introduction to statistical learning.

Assessment and Review

• Regular self-tests and assignments are scheduled to reinforce learning.

• Midterm and final exams assess cumulative understanding of the material.

Additional info: This timeline provides a week-by-week guide for students, ensuring systematic coverage of all major topics in a typical college statistics course. The structure supports both foundational learning and advanced application, preparing students for further study or professional application of statistical methods.