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LinkedIn Learning

Statistics Foundations: 2

via LinkedIn Learning

Overview

Get practical, example-based learning of the intermediate skills associated with statistics: samples and sampling, confidence intervals, and hypothesis testing.

Statistics are a core skill for many careers. Basic stats are critical for making decisions, new discoveries, investments, and even predictions. But sometimes you need to move beyond the basics. Statistics Fundamentals – Part 2 takes business users and data science mavens into practical, example-based learning of the intermediate skills associated with statistics: samples and sampling, confidence intervals, and hypothesis testing.

Eddie Davila first provides a bridge from Part 1, reviewing introductory concepts such as data and probability, and then moves into the topics of sampling, random samples, sample sizes, sampling error and trustworthiness, the central unit theorem, t-distribution, confidence intervals (including explaining unexpected outcomes), and hypothesis testing. This course is a must for those working in data science, business, and business analytics—or anyone else who wants to go beyond means and medians and gain a deeper understanding of how statistics work in the real world.

Syllabus

Introduction
  • Welcome
  • What you should know
1. Beyond Data and Probability
  • Understanding data and distributions
  • Probability and random variables
  • What's next in stats 2?
2. Sampling
  • Sample considerations
  • Random samples
  • Alternative to random samples
3. Sample Size
  • The importance of sample size
  • The central limit theorem
  • Standard error (for proportions)
  • Sampling distribution of the mean
  • Standard error (for means)
4. Confidence Intervals
  • One sample is all you need
  • What exactly is a confidence interval?
  • 95% confidence intervals for population proportions
  • Do you want to be more than 95% confident?
  • Explaining unexpected outcomes
  • 95% confidence intervals for population means
5. Hypothesis Testing
  • Is this result even possible?
  • How to test a hypothesis in four steps
  • One-tailed vs. two-tailed tests
  • Significance test for proportions
  • Significance test for means (acceptance sampling)
  • Type I and type II errors
Conclusion
  • Next steps

Taught by

Eddie Davila

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