Introduction to Statistics (English Edition)

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Miyamoto Shota

4:01:33

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1. IntroductionDescriptive statistics

1. Introduction.mp4

03:51

2.1 lecture-slides ver.1.pdf

2. Lecture slides.html

3. Population and Sample.mp4

02:56

4. Variables.mp4

02:30

5. Histogram (Frequency distribution).mp4

02:54

6. Scatter plot.mp4

02:59

7. Descriptive statistics.mp4

01:58

8. Representative value.mp4

01:57

9. Median.mp4

02:57

10. Mean.mp4

02:52

11. Outlier.mp4

02:58

12. Mean deviation.mp4

02:21

13. Variance.mp4

03:19

14. Standard deviation (SD).mp4

02:40

15. Standardization.mp4

04:01

2. Point estimation

1. Point estimation.mp4

02:59

2. Unbiasedness.mp4

02:31

3. Point estimation for the population mean.mp4

02:25

4. Point estimation for the population variance.mp4

03:22

5. Sample variance and Unbiased variance.mp4

03:59

3. Probability distribution

1. Why probability .mp4

02:09

2. Random sampling.mp4

02:52

3. Probability model.mp4

02:02

4. Random variable.mp4

03:31

5. Probability distribution.mp4

03:18

6. Probability functions and parameters.mp4

04:48

7. The notation of probability distributions.mp4

04:46

8. Binomial distribution.mp4

07:46

9. Normal distribution.mp4

08:01

10. Standard normal distribution.mp4

02:50

11. Population distribution and Sample distribution.mp4

02:50

4. Interval estimation I

1. Interval estimation.mp4

01:58

2. Standard normal distribution (Review).mp4

03:11

3. Two-sided 5% points of the standard normal distribution.mp4

07:43

4. Interval estimation for the population mean.mp4

06:51

5. Confidence level and confidence interval.mp4

05:00

6. Properties of the sample mean.mp4

07:13

7. Interval estimation using the sample mean.mp4

05:47

8. Variance and confidence intervals.mp4

04:05

5. Interval estimation II

1. t-distribution.mp4

09:29

2. Interval estimation using the t-distribution.mp4

05:58

3. Characteristics of estimation by t-distribution.mp4

03:49

4. The population distribution unknown.mp4

02:49

5. Central Limit Theorem.mp4

03:49

6. Interval estimation using the Central Limit Theorem.mp4

05:55

6. Interval estimation III

1. Binary variable and proportion.mp4

03:03

2. Bernoulli distribution.mp4

05:06

3. Interval estimation for population proportion.mp4

07:28

7. Hypothesis testing

1. Hypothesis testing.mp4

02:24

2. Null hypotheses and alternative hypotheses.mp4

05:45

3. Significance level and rejection region.mp4

04:46

4. Two-sided and one-sided tests.mp4

03:59

5. Procedure in hypothesis testing.mp4

05:22

6. Interpretation of hypothesis test results.mp4

03:43

7. Type I and type II errors.mp4

04:34

8. Hypothesis test of population mean.mp4

08:36

9. Test for difference of population means (Welchs t-test).mp4

07:33

8. Conclusion

1. Conclusion.mp4

05:15

Description

This course is an introduction to statistics, covering probability distributions, estimation, and hypothesis testing.

What You'll 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 is this for?

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

What You Need to Know?

No specific requirements

More details

Description
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
Who this course is for:
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

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Statistics

Miyamoto Shota

Instructor's Courses

Miyamoto Shota: 講師 / リサーチャーDXの時代に不可欠となるデータ分析に関する学びを基礎からわかりやすく提供していきます。独学でデータ分析を学んだ後、シンクタンク在籍中に統計学や機械学習を基礎から丁寧に学び直しています。基礎的な内容への深い理解をベースとしながら、独学における苦労や難所に関する理解を踏まえ、初心者でもわかりやすく学べるようなコース設計を心がけています。この機会にぜひ一緒にデータ分析を学んで一生モノのスキルを身につけていきましょう！！《経歴》慶應義塾大学法学部卒業後、大手インフラ企業を経て国内シンクタンクにてデータ分析やリサーチ活動に従事。公的統計データやマーケティングデータの分析に加え、統計的手法や機械学習モデルを用いた需要予測、売れ行き要因分析等のリサーチ活動を行ってきました。その後、国内MBAを取得、現在は会社を設立しリサーチ活動や講師業を行っています。********************In the era of digital transformation, I am committed to providing a clear and foundational understanding of data analysis, an indispensable skill set. After self-learning data analysis, I revisited statistics and machine learning from the ground up while at a think tank.With a deep understanding of the basics, I design courses that are accessible to beginners, taking into account the struggles and challenges of self-learning. Let's learn data analysis together and acquire a skill set that will last a lifetime!Background:After graduating from the Faculty of Law at Keio University, I worked at a major infrastructure company, before engaging in data analysis and research activities at a domestic think tank. I have conducted research activities including analysis of public statistical data and marketing data, as well as demand forecasting and sales factor analysis using statistical methods and machine learning models. Following this, I obtained an MBA in Japan and currently run my own company, focusing on research activities and teaching.

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