Introduction To Statistics (English Edition)

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)

Requirements

No specific requirements

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!")"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 statisticsUnderstanding of "probability" and "probability models" in statisticsUnderstanding of "point estimation" and "interval estimation" in statisticsUnderstanding of "statistical hypothesis testing" in statisticsComprehension of statistics through abundant diagrams and explanationsVisual imagery related to statisticsReinforcement of memory through downloadable slide materials

Overview

Section 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

Section 2: Point estimation

Lecture 16 Point estimation

Lecture 17 Unbiasedness

Lecture 18 Point estimation for the population mean

Lecture 19 Point estimation for the population variance

Lecture 20 Sample variance and Unbiased variance

Section 3: Probability distribution

Lecture 21 Why “probability” ?

Lecture 22 Random sampling

Lecture 23 Probability model

Lecture 24 Random variable

Lecture 25 Probability distribution

Lecture 26 Probability functions and parameters

Lecture 27 The notation of probability distributions

Lecture 28 Binomial distribution

Lecture 29 Normal distribution

Lecture 30 Standard normal distribution

Lecture 31 Population distribution and Sample distribution

Section 4: Interval estimation I

Lecture 32 Interval estimation

Lecture 33 Standard normal distribution (Review)

Lecture 34 Two-sided 5% points of the standard normal distribution

Lecture 35 Interval estimation for the population mean

Lecture 36 Confidence level and confidence interval

Lecture 37 Properties of the sample mean

Lecture 38 Interval estimation using the sample mean

Lecture 39 Variance and confidence intervals

Section 5: Interval estimation II

Lecture 40 t-distribution

Lecture 41 Interval estimation using the t-distribution

Lecture 42 Characteristics of estimation by t-distribution

Lecture 43 The population distribution unknown

Lecture 44 Central Limit Theorem

Lecture 45 Interval estimation using the Central Limit Theorem

Section 6: Interval estimation III

Lecture 46 Binary variable and proportion

Lecture 47 Bernoulli distribution

Lecture 48 Interval estimation for population proportion

Section 7: Hypothesis testing

Lecture 49 Hypothesis testing

Lecture 50 Null hypotheses and alternative hypotheses

Lecture 51 Significance level and rejection region

Lecture 52 Two-sided and one-sided tests

Lecture 53 Procedure in hypothesis testing

Lecture 54 Interpretation of hypothesis test results

Lecture 55 Type I and type II errors

Lecture 56 Hypothesis test of population mean

Lecture 57 Test for difference of population means (Welch's t-test)

Section 8: Conclusion

Lecture 58 Conclusion

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