Short Course

Statistics Fundamentals: Bundled

Theory and Python

9 Hours of video content

Beginner

Certificate of Completion Included

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Overview What You Will Learn Curriculum Instructor

Course Overview

As a science field, statistics is a discipline that concerns collecting data, and mathematical analysis of the collected data, describing data and making inference from the data. Statistical Analysis is now applied in various scientific and practical fields. It is essential in both natural science and social science. In business practice, statistical analysis is applied as business analytics such as human resource analytics and marketing analytics. And now, it is an essential tool in medical practice and government policymaking. Besides, baseball teams utilize it for strategy formation.

This course is a comprehensive program for learning the basics of statistics and it covers theory and basic Python coding. Using statistical methods, we can obtain insights from data, and use the insights for answering various questions and decision making. To obtain meaningful insights from data, we need to learn statistics both in practical and theoretical viewpoints. This intends to provide you with theoretical knowledge as well as Python coding. Theoretical knowledge enables us to implement appropriate analysis in various situations. And it can be a useful foundation for more advanced learning.

By the end of this course, you will reach an intermediate level of expertise in statistics and will be familiarized with basic theories in statistics and Python coding for statistical analysis.

What You Will Learn

Familiarize with Basic theories in Statistics.
Learn Python coding for statistical analysis.
Learn about descriptive statistics.
Familiarize with Sampling.
Introduction to ANOVA.
Understand the concepts of Correlation & Regression.

Program Curriculum

What are Statistics?
Types of Statistics
What is Data?
Stevens’ Typology
How to Distinguish?
Independent and Dependent Variables
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Chapter 1 Quiz

Introduction
Display Data 1: Frequency Table
Display Data 2: Create Frequency Table with Python
Display Data 3: Stem and Leaf Diagram
Display Data 4: Stem and Leaf Diagram with Python
Display Data 5: Histogram
Display Data 6: Create Histograms with Python
Display Data 7: Dot Plot
Central Tendency 1: Mean
Central Tendency 2: Median
Central Tendency 3: Mode
Central Tendency 4: Mean Median and Mode with Python
Central Tendency 5: Geometric Mean
Central Tendency 6: Harmonic Mean
Central Tendency 7: Trimmed Mean
Central Tendency 8: Moving Average
Central Tendency 9: Expected Value
Central Tendency 10: Proportions for Binary Data
Central Tendency 11: Various Means with Python
Variability 1: What is Variability?
Variability 2: Range and Residual
Variability 3: Mean Absolute Deviation
Variability 4: Variance
Variability 5: Standard Deviation
Variability 6: Coefficient of Variation
Variability 7: Variability with Python
Relative Position 1: Percentile
Relative Position 2: Interquartile Range
Relative Position 3: The Empirical Rule
Relative Position 4: Chebyshev's Theorem
Relative Position 5: Relative Position with Python
Data Visualization 1: Why Visualization?
Data Visualization 2: Box Plot
Data Visualization 3: Box Plot with Python
Data Visualization 4: Bar Chart
Data Visualization 5: Bar Plot with Python
Data Visualization 6: Pie Chart
Data Visualization 7: Pie Chart with Python
Data Visualization 8: Line Plot
Data Visualization 9: Line Plot with Python
Data Visualization 10: Cross Tabulation Table
Data Visualization 11: Stacked Bar Chart
Data Visualization 12: Crosstab and Stacked Bar Chart with Python
Data Visualization 13: Mosaic Plot with Python
Data Visualization 14: Ternary Plot
Data Visualization 15 Ternary Plot with Python
Chapter 2 Quiz

Introduction
Permutation and Combination 1: Factorial
Permutation and Combination 2: Permutation
Permutation and Combination 3: Combination
Permutation and Combination 4: Permutation and Combination with Python
Set Theory 1: Experiment and Event
Set Theory 2: Set
Set Theory 3: Event and Element
Set Theory 4: Venn Diagram
Set Theory 5: Complementary Event
Set Theory 6: Intersection
Set Theory 7: Union
Set Theory 8: Set Difference
Set Theory 9: Set in Python
Probability Theory 1: What is Probability?
Probability Theory 2: Calculate Probability
Probability Theory 3: Combination and Probability
Probability Theory 4: Statistical Independence
Probability Theory 5: Expected Value
Conditional Probability 1: What is Conditional Probability?
Conditional Probability 2: Statistical Independence
Conditional Probability 3: Multiplication Theorem
Conditional Probability 4: Simpson's Paradox
Conditional Probability 5: Conditional Probability with Python
Conditional Probability 6: Bayes' Theorem
Conditional Probability 7: Bayes' Theorem with Python
Chapter 3 Quiz

Introduction
Random Variable
Discrete Probability Distribution
Continuous Probability Distribution
Probability Density Function
Cumulative Distribution Function
Expected Value of Random Variables
Variance of Random Variables
Find Variance from Expected Value
Additivity of Variance
Normal Distribution
Standard Normal Distribution
Standard Normal Distribution Table
Skewness and Kurtosis
Normal Distribution with Python
Binomial Distribution
Expected Value of Binomial Distribution
Variance of Binomial Distribution
Binomial Distribution with Python
Poisson Distribution
Expected Value of Poisson Distribution
Variance of Poisson Distribution
Examples of Poisson Distribution
Poisson Distribution with Python
Geometric Distribution
Expected Value of Geometric Distribution
Variance of Geometric Distribution
Geometric Distribution with Python
Exponential Distribution
Expected Value of Exponential Distribution
Variance of Exponential Distribution
Memorylessness
Exponential Distribution with Python
Discrete Uniform Distribution
Continuous Uniform Distribution
Uniform Distribution with Python
Joint Probability Distribution
Chapter 4 Quiz

Introduction
Population and Sample
Complete Survey and Sampling Survey
Probability Sampling and Non-probability Sampling
Probability Sampling Methods
Random Sampling with Python
Law of Large Numbers
Law of Large Numbers with Python
Central Limit Theorem
Central Limit Theorem with Python
Experimental and Observational Studies
Fisher’s Principle
Chapter 5 Quiz

Introduction
What is Point Estimation?
Point Estimation of Population Mean
Unbiased Variance
Standard Error
Point Estimation by Python
What is Interval Estimation?
Interval Estimation of Population Mean (Population Variance Known)
What is 95% Confidence Interval?
Sample Size and Confidence Interval
When Population Variance is Unknown . . . (t-distribution)
Interval Estimation of Population Mean (Population Variance Unknown)
Interval Estimation of Population Mean Difference
Interval Estimation of Population Proportion
Interval Estimation and Minimum Sample Size
Chi-Square Distribution
Properties of Chi-Square Distribution
Interval Estimation of Population Variance
Interval Estimation by Python
Chapter 6 Quiz

Introduction
What is Hypothesis Testing?
Process of Hypothesis Testing
Significance Level
Test Statistic
One- and Two-Tailed Test
Hypothesis Testing for Population Mean
Hypothesis Testing for Population Mean with Python
Exercise Hypothesis Testing for Population Mean
Two-Sample t-Test
Two-Sample t-Test Dependent Sample with Python
Exercise Two-Sample t-Test Dependent Sample
Two-Sample t-Test Independent Sample
Two-Sample t-Test Independent Sample with Python
Exercise Two-Sample t-Test Independent Sample
Hypothesis Testing for Population Proportion
Hypothesis Testing for Population Proportion with Python
Exercise Hypothesis Testing for Population Proportion
Goodness of Fit Test
Goodness of Fit Test with Python
Exercise Goodness of Fit Test
Test of Independence
Test of Independence with Python
Exercise Test of Independence
Test of Population Proportion Difference
Test of Population Proportion Difference with Python
Exercise Test of Population Proportion Difference
Chapter 7 Quiz

Introduction
Scatter Plot
Correlation
Correlation Coefficient
Covariance
Correlation Coefficient Revisited
Exercise Correlation Coefficient
Test of Non-Correlation
Spurious Correlation
Regression Analysis
Ordinary Least Squares
Ordinary Least Squares Math
The Difference between Correlation and Regression
Multiple Regression Analysis
Multiple Regression Analysis Math
Assumptions of Linear Regression
Hypothesis Testing in Multiple Regression Analysis
Coefficient of Determination
Residual Analysis
Multicollinearity
Variance Inflation Factor
F-test
Dummy Variable
Effect Size
Statistical Power
Correlation Analysis with Python
Regression Analysis with Python
Get Dummy Variables with Python
Chapter 8 Quiz

Introduction
What is ANOVA?
F-Test
Example F-Test
One-Way ANOVA
Tukey’s HSD Test
Assumptions in ANOVA
One-Way ANOVA with Python
Two-Way ANOVA
Two-Way ANOVA with Python
Chapter 9 Quiz

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Instructor

Takuma Kimura

Dr. Takuma Kimura is an internationally recognized scholar in business and management fields. His expertise includes research in organizational behavior, and practical business analytics in human resource management and marketing. He teaches these subjects in universities and industrial companies. He published more than 10 academic papers in internationally prominent journals such as Journal of Business Ethics, International Journal of Management Reviews, Industrial Marketing Management. He is awarded as one of the World Top Reviewers from Publons, and as a Recognized Reviewer from European Management Journal.

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