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.
Video List
Introduction1. Welcome
2. What you should know before watching this course
3. Using the exercise files
1. The World of Statistics
4. Why statistics matter in your life
5. Is my data set good?
6. Understanding statistics with the use of charts
2. The Center of the Data
7. The middle of the data: Means and medians
8. Medians for data sets with even numbers of data points
9. Weighted mean
10. The mode: Find it and understand it
3. Data Variability
11. The range
12. Standard deviation: Calculate it and understand it
13. How many standard deviations?
14. Outliers
4. Distribution and Relative Position
15. Z-score: Measuring by using standard deviations
16. Empirical rule: What symmetry tells us
17. Calculating percentiles: Where do you stand?
5. Probability Explained
18. Defining probabilty
19. Examples of probability
20. Types of probability
6. Multiple Event Probability
21. Probability of two events: Either event? Both events?
22. Explanation of conditional probability: If X happens, then...
23. Relationship between two events: Independence vs. dependence
24. Bayes theorem and false positives
25. Even more of Bayes theorem
7. How Objects Are Arranged
26. Permutations: The order of things
27. Combinations: Permutations without regard for order
8. Discrete vs. Continuous Probability Distributions
28. Discrete vs. continuous: What's the difference?
9. Discrete Probability Distributions
29. Mean and standard deviation of discrete probability distributions
30. Expected monetary value
31. Binomial experiments: When there are only two possible outcomes
10. Continuous Probability Distributions
32. Probability densities: Curves and continuous random variables
33. The famous bell-shaped curve: Introduction
34. Fuzzy central limit theorem
35. Using the Z transformation to find probabilities
Conclusion
36. What's next and what's ahead in stats 2 and 3 courses?
