Last updated 3/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 413.18 MB | Duration: 2h 39m

Introductory Statistics as Covered in the Social, Behavioral, and Natural Sciences

What you'll learn
Understand and learn how to calculate a number of different descriptive statistics
Increase your quantitative and numerical reasoning skills!

Increase marketable job skills in data analytics
Requirements
No special software or other materials are required.
Description
November, 2019In the course, you will learn how to easily and effectively analyze and interpret data involving introductory statistics. The following topics are covered in this course:Scales of measurement - nominal, ordinal, interval, ratio. Goal/Learning Objective: Easily understand the often-confused scales of measurement covered in most statistics texts.Central Tendency - mean, median, and mode are illustrated along with practice problems; measures of central tendency and skewed distributions are explained, as well as how to calculate the weighted mean. Goals/Learning Objectives: Summarize a set of data, find the center location in a distribution of scores, understand and identify the location of measures of central tendency in skewed distributions, understand and interpret how to find the overall or combined mean for two different sets of data.Variability - How to calculate the standard deviation and variance as well as how to interpret percentiles are provided in simple and clear language. Goals/Learning Objectives: Understand and explain variability (spread) in a set of numbers, including how to rank data and interpret data such as standardized test scores (for example, the 95th percentile).Charts and Graphs - How to calculate a cumulative frequency distribution table as well as how to calculate a stem and leaf plot is illustrated. Goals/Learning Objectives: Learn how to easily organize, summarize, understand, and explain a set of numbers.Probability, the Normal Curve and z-Scores - An introduction to probability is provided, along with properties of the normal distribution and how to calculate and interpret z-scoresGoals/Learning Objectives: Understand bning probability including important characteristics of the normal (Gaussian) distribution, as well as how to calculate and interpret z-scores.Bonus Features: Cement understanding with practice opportunities including several quizzes with complete video coverage of the solutions. Update: New Videos Added on Hypothesis Testing and on Correlation! (See Sections 6 and 7 of the Course.)

Overview

Section 1: Course Introduction and Introduction to Statistics

Lecture 1 Course Introduction

Lecture 2 Scales of Measurement

Section 2: Central Tendency

Lecture 3 Mean, Median, and Mode (Measures of Central Tendency)

Lecture 4 Video Review of Quiz - Mean, Median, and Mode

Lecture 5 Central Tendency and Skewed Distributions

Lecture 6 Video Review of Quiz - Central Tendency and Skewed Distributions

Lecture 7 The Weighted Mean

Lecture 8 Video Review of Quiz - The Weighted Mean

Section 3: Variability

Lecture 9 Percentiles

Lecture 10 Calculating the Standard Deviation and Variance – Step by Step

Lecture 11 Video Review of Quiz - Standard Deviation and Variance

Section 4: Charts, Tables, and Graphs

Lecture 12 How to Create a Frequency Distribution Table

Lecture 13 How to Create a Cumulative Frequency Distribution Table

Lecture 14 How to Create Stem and Leaf Plot

Section 5: Probability, the Normal Curve and z-Scores

Lecture 15 Probability

Lecture 16 Normal Curve and z-Scores (68, 95, 99.7 Rule)

Lecture 17 Properties of the z Score Normal Distribution

Lecture 18 Video Review of Quiz - Properties of the z-Score Normal Distribution

Lecture 19 Solving for z-Scores

Lecture 20 Video Review of Quiz - Solving for z-Scores

Lecture 21 Solving for X Given a z-Score

Lecture 22 Video Review of Quiz - Solving for X Scores Given a z-Score

Section 6: Hypothesis Testing

Lecture 23 Hypothesis Testing - Two Tailed Tests

Lecture 24 Two Tailed Hypothesis Tests - Examples

Lecture 25 Hypothesis Testing - One Tailed Tests

Lecture 26 One Tailed Hypothesis Tests - Examples

Lecture 27 Type I and Type II Errors Explained

Lecture 28 What is a P-Value

Lecture 29 P Value = .000

Lecture 30 Power

Section 7: Correlation

Lecture 31 How To Calculate a Correlation

Lecture 32 How To Test a Correlation for Significance

Section 8: Conclusion

Lecture 33 Conclusion

Those interested in learning more about descriptive statistics should take this course (those interested only in inferential statistics should not take the course)

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