Introduction to Statistics (English Edition)

Introduction to Statistics (English Edition), available at $22.99, has an average rating of 4.65, with 58 lectures, based on 19 reviews, and has 318 subscribers.

You will learn about Descriptive Statistics (Median/Mean/Variance/Standard Deviation/Standardization) Probability Distributions (Probability Models/Binomial Distribution/Normal Distribution) Point Estimation (Point Estimates of Population Mean and Population Variance) Interval Estimation I (Interval Estimation of the Population Mean) Interval Estimation II (T-Distribution/Central Limit Theorem) Interval Estimation III (Interval Estimation of the Population Proportion) Hypothesis Testing (Process of Hypothesis Testing/Testing of Population Mean) This course is ideal for individuals who are New to statistics or Tried to learn statistics but gave up or Wish to relearn statistics from the basics or Curious about what statistics is like or Frequently deal with data in business or Want to organize fragmented knowledge of statistics or Prefer to understand through diagrams and words rather than formulas and symbols or Want to learn statistics but don't have time to study textbooks It is particularly useful for New to statistics or Tried to learn statistics but gave up or Wish to relearn statistics from the basics or Curious about what statistics is like or Frequently deal with data in business or Want to organize fragmented knowledge of statistics or Prefer to understand through diagrams and words rather than formulas and symbols or Want to learn statistics but don't have time to study textbooks.

Enroll now: Introduction to Statistics (English Edition)

Summary

Title: Introduction to Statistics (English Edition)

Price: $22.99

Average Rating: 4.65

Number of Lectures: 58

Number of Published Lectures: 58

Number of Curriculum Items: 58

Number of Published Curriculum Objects: 58

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Descriptive Statistics (Median/Mean/Variance/Standard Deviation/Standardization)
Probability Distributions (Probability Models/Binomial Distribution/Normal Distribution)
Point Estimation (Point Estimates of Population Mean and Population Variance)
Interval Estimation I (Interval Estimation of the Population Mean)
Interval Estimation II (T-Distribution/Central Limit Theorem)
Interval Estimation III (Interval Estimation of the Population Proportion)
Hypothesis Testing (Process of Hypothesis Testing/Testing of Population Mean)

Who Should Attend

New to statistics
Tried to learn statistics but gave up
Wish to relearn statistics from the basics
Curious about what statistics is like
Frequently deal with data in business
Want to organize fragmented knowledge of statistics
Prefer to understand through diagrams and words rather than formulas and symbols
Want to learn statistics but don't have time to study textbooks

Target Audiences

New to statistics
Tried to learn statistics but gave up
Wish to relearn statistics from the basics
Curious about what statistics is like
Frequently deal with data in business
Want to organize fragmented knowledge of statistics
Prefer to understand through diagrams and words rather than formulas and symbols
Want to learn statistics but don't have time to study textbooks

This is a basic course designed for us to efficiently learn the fundamentals of statistics together!

(The English version* of the statistics course chosen by over 28,000 people in the Japanese market!”)

*Note: The script and slides are based on the original version translated into English, and the audio is generated by AI.

“Let’s make sure to standardize the data and check its characteristics.”
“Could we figure out the confidence interval for this data?”
“Let’s check if the results of this survey can be considered statistically significant.”

In the business world, there are many situations where statistical literacy becomes essential.

With the widespread adoption of AI/machine learning and a strong need for DX/digitalization, these situations are expected to increase.

This course is aimed at ensuring we’re well-equipped with statistical literacy and probabilistic thinking to navigate such scenarios.

We’ll carefully explore the basics of statistics, including “probability distributions, estimation, and hypothesis testing.”

By understanding “probability distributions,” we’ll develop a statistical perspective and probabilistic thinking.

Learning about “estimation” will enable us to discuss populations from data (samples), and grasping “testing” will help us develop statistical hypothesis thinking.

This course is tailored for beginners in statistics and will explain concepts using a wealth of diagrams and words, keeping mathematical formulas and symbols to the minimum necessary for understanding.

It’s structured to ensure that even beginners can learn confidently.

Let’s seize this opportunity to acquire lifelong knowledge of statistics together!

(Note: Please be aware that this course does not cover the use of tools or software like Excel, R, or Python.)

What we will learn together:

Basic statistical literacy Knowledge of “descriptive statistics” in statistics
Understanding of “probability” and “probability models” in statistics
Understanding of “point estimation” and “interval estimation” in statistics
Understanding of “statistical hypothesis testing” in statistics
Comprehension of statistics through abundant diagrams and explanations
Visual imagery related to statistics
Reinforcement of memory through downloadable slide materials

Course Curriculum

Chapter 1: Introduction/Descriptive statistics

Lecture 1: Introduction

Lecture 2: Lecture slides

Lecture 3: Population and Sample

Lecture 4: Variables

Lecture 5: Histogram (Frequency distribution)

Lecture 6: Scatter plot

Lecture 7: Descriptive statistics

Lecture 8: Representative value

Lecture 9: Median

Lecture 10: Mean

Lecture 11: Outlier

Lecture 12: Mean deviation

Lecture 13: Variance

Lecture 14: Standard deviation (SD)

Lecture 15: Standardization

Chapter 2: Point estimation

Lecture 1: Point estimation

Lecture 2: Unbiasedness

Lecture 3: Point estimation for the population mean

Lecture 4: Point estimation for the population variance

Lecture 5: Sample variance and Unbiased variance

Chapter 3: Probability distribution

Lecture 1: Why “probability” ?

Lecture 2: Random sampling

Lecture 3: Probability model

Lecture 4: Random variable

Lecture 5: Probability distribution

Lecture 6: Probability functions and parameters

Lecture 7: The notation of probability distributions

Lecture 8: Binomial distribution

Lecture 9: Normal distribution

Lecture 10: Standard normal distribution

Lecture 11: Population distribution and Sample distribution

Chapter 4: Interval estimation I

Lecture 1: Interval estimation

Lecture 2: Standard normal distribution (Review)

Lecture 3: Two-sided 5% points of the standard normal distribution

Lecture 4: Interval estimation for the population mean

Lecture 5: Confidence level and confidence interval

Lecture 6: Properties of the sample mean

Lecture 7: Interval estimation using the sample mean

Lecture 8: Variance and confidence intervals

Chapter 5: Interval estimation II

Lecture 1: t-distribution

Lecture 2: Interval estimation using the t-distribution

Lecture 3: Characteristics of estimation by t-distribution

Lecture 4: The population distribution unknown

Lecture 5: Central Limit Theorem

Lecture 6: Interval estimation using the Central Limit Theorem

Chapter 6: Interval estimation III

Lecture 1: Binary variable and proportion

Lecture 2: Bernoulli distribution

Lecture 3: Interval estimation for population proportion

Chapter 7: Hypothesis testing

Lecture 1: Hypothesis testing

Lecture 2: Null hypotheses and alternative hypotheses

Lecture 3: Significance level and rejection region

Lecture 4: Two-sided and one-sided tests

Lecture 5: Procedure in hypothesis testing

Lecture 6: Interpretation of hypothesis test results

Lecture 7: Type I and type II errors

Lecture 8: Hypothesis test of population mean

Lecture 9: Test for difference of population means (Welch's t-test)

Chapter 8: Conclusion

Lecture 1: Conclusion

Instructors

Miyamoto Shota
リサーチ全般 (Research) / 統計学 (Statistics) / R / Excel

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4 stars: 7 votes
5 stars: 12 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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