# Statistics Foundations: Using Data Sets

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 41m | 645 MB

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.

Introduction
1 Discover samples, confidence intervals, and hypothesis testing

Sampling
2 Sample considerations
3 Random samples
4 Alternative to random samples

Sample Size
5 The importance of sample size
6 The central limit theorem

Standard Error
7 Standard error for proportions
8 Sampling distribution of the mean
9 Standard error for means

Confidence Intervals
10 Introduction to confidence intervals
11 Components of a confidence interval
12 Creating a 95% confidence interval for a population
13 Alternative confidence intervals
14 Confidence intervals with unexpected outcomes

Hypothesis Tests
15 Hypothesis test introduction
16 Hypothesis test Step-by-step
17 One-tailed vs. two-tail tests
18 Significance test for proportions
19 Significance test for means
20 Type one and type two errors