Statistics Foundations 3: Using Data Sets
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 40m | 605 MB
Instructor: Eddie Davila
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 40m | 605 MB
Instructor: Eddie Davila
Statistics are a core skill for many careers. Basic stats are critical for making decisions, discoveries, investments, and even predictions. But sometimes you need to move beyond the basics. This third course in the Statistics Foundations series gives you practical, example-based lessons on the intermediate skills associated with statistics: Samples and sampling, standard errors, confidence intervals, and hypothesis testing.
Eddie Davila takes a look at topics like sampling, random samples, sample sizes, sampling error, trustworthiness, the central unit theorem, confidence intervals, and hypothesis testing. This course is a must for those working in data science, business, and business analytics—or anyone who wants to go beyond means and medians and gain a deeper understanding of how statistics work in the real world.
Learning objectives
- Describe how non-representative samples can lead to biased conclusions in the context of polling.
- Recognize the characteristics and process of obtaining a convenience sample.
- Explain how concepts such as the law of large numbers and the central limit theorem are relevant to understanding the impact of sample size.
- Assess how confidence intervals contribute to the overall reliability and validity of election poll results.
- Discuss the role of test statistics in hypothesis testing and how they are used to find the p-value.