- Course type: Self-paced
- Available Lessons: 104
- Average Lesson Length: 8 min
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Watch a preview:chapter 1 / lesson 1Descriptive & Inferential Statistics: Definition, Differences & Examples
Course SummaryThis Statistics 101 Syllabus Resource & Lesson Plans course is a fully developed resource to help you teach introductory statistics. You can easily adapt the video lessons, transcripts, and quizzes to take full advantage of the comprehensive and engaging material we offer. Make planning your course easier by using our syllabus as a guide.
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Course Practice TestCheck your knowledge of this course with a 50-question practice test.
- Comprehensive test covering all topics
- Detailed video explanations for wrong answers
How It Works
You can use this statistics course as a template for designing and implementing your course. Here are the key components of the course and how you can use them:
- Chapters - Each chapter covers a unit of introductory statistics, from tables and plots to statistical estimation and hypothesis testing. Use these chapters as mile markers as you map out your course. We recommend planning to spend a week on each chapter, but you can always allocate the chapters according to the length of your specific statistics course.
- Lessons - Within each chapter are video lessons that further break down topics into bite-sized chunks. These lessons cover single topics like standard deviation or mathematical sets. Each one is often appropriate for a single class.
- Key Terms - Within each lesson are key terms. These are emphasized on screen and in the transcript. As you develop your syllabus, these key terms help you focus on the most important learning objectives. For example, the lesson on quantitative data includes key terms like discrete data and continuous data.
As you work on your statistics lesson plans, save time by incorporating video lessons from this resource. Here's how:
- Introduce Topics - Your students will be in the right mindset for understanding topics like probability if you begin class with a short video. It can be a jumping-off point for a lecture, group activity, or class discussion.
- Break Up Lectures - The video format, which often includes animation, helps students visualize topics like z-scores and t-distributions.
- Assign For Homework - Each lesson in the course, from descriptive and inferential statistics to the coefficient of determination, can be assigned to your students as homework.
Each video lesson includes a complete transcript. You can utilize these transcripts in several ways:
- Lecture Notes - Do you need a guide as you plan a lecture, such as one on sampling or statistical estimation? The transcripts cover each topic in depth, with key terms highlighted for quick reference.
- Student Reading - Perhaps you'd like your students to learn about stem and leaf displays, but you don't have class time available. Assign the transcript as extra reading.
- Study Tools - When it's time for a unit exam on hypothesis testing, you can point your students to the transcripts on effect size, type I and type II errors, and related topics to help them study.
Each video lesson has a corresponding quiz. Here's how to use the quizzes:
- Homework - Assign a quiz to your students as homework. You'll receive an email with the results, which enables you to verify they've completed the assignment and that they've understood the material. Questions cover everything from methods for finding expected values in games of chance to techniques for interpreting graphical representations of data, such as histograms and frequency polygons.
- Tests - You can meld the material in the quizzes into your own student assessments, saving you valuable time. Need a few questions on regression and correlation? There are plenty!
- Discussions - Jump-start a discussion with questions like: What differentiates stratified random samples from other sampling techniques?
Below is a sketch of the statistics syllabus modeled on a 10-week course. This sample can be adapted based on your course schedule. Navigate the chapters and lessons for more detail.
|Week||Unit||Sample of Topics Covered|
|Week 1||Overview of Statistics||Descriptive and inferential statistics, quantitative and categorical data, discrete and continuous data, convenience sampling, random selection and allocation, confounding and bias|
|Week 2||Summarizing Data||Data sets, measures of central tendency, unimodal and bimodal distribution, spread, the interquartile range, standard deviation, population and sample variance|
|Week 3||Tables and Plots||Relative and cumulative frequency tables, stem and leaf displays, histograms, dot and box plots, bar graphs, pie charts, univariate and bivariate data|
|Week 4||Probability||Probabilities of simple, compound, and complimentary events; probabilities of independent and dependent events; simple conditional probabilities; the addition and multiplication rules of probability; permutations|
|Week 5||Discrete Probability Distributions||Discrete random variables, expected values in games of chance, game strategies, binomial probabilities, standard deviation of a binomial random variable|
|Week 6||Continuous Probability Distributions||Expected values of continuous random variables, the normal distribution, z-scores|
|Week 7||Sampling||Simple and stratified random samples, cluster random samples, systematic random samples, sampling distributions, the law of large numbers, the central limit theorem|
|Week 8||Statistical Estimation||Point and interval estimations, confidence intervals for sample means and proportions, t-distributions, biased and unbiased estimators|
|Week 9||Hypothesis Testing||Effect size, type I and type II errors, hypothesis tests for matched pairs and proportions|
|Week 10||Regression & Correlation||Scatterplots, simple linear regression, the correlation coefficient, causation, the coefficient of determination|
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