Mô tả
Welcome to Statistics Fundamentals! This course is for beginners who are interested in statistical analysis. And anyone who is not a beginner but wants to go over from the basics is also welcome!
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. Using statistical methods, we can obtain insights from data, and use the insights for answering various questions and decision making.
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. It is well known a SABRmetrics.
However, if we do not use appropriate methods, statistical analysis will result in meaningless or misleading findings. To obtain meaningful insights from data, we need to learn statistics both in practical and theoretical viewpoints. This course 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.
This course is a comprehensive program for learning the basics of statistics. It consists of the 9 sections. They cover theory and basic Python coding. Even if you do not have Python coding experience, I believe they are easy to follow for you. But this program is not a Python course, so how to install Python and construct environment is not covered in this course.
This course is designed for beginners, but by learning with this course, you will reach an intermediate level of expertise in statistics. Specifically, this course covers undergraduate level statistics. After enrollment, you can download the lecture presentations, Python code files, and toy datasets in the first lecture page.
I’m looking forward to seeing you in this course!
*In some videos, the lecturer says "... will be covered in later courses", but it should be "later sections."
Table of Contents
1. Introduction
2. Descriptive Statistics:
3. Probability
4. Probability Distribution
5. Sampling
6. Estimation
7. Hypothesis Testing
8. Correlation & Regression
9. ANOVA
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10 sections
Let's Get Started with Python!
07:44
1-1 What is Statistics?
08:19
1-2 Types of Statistics
05:49
1-3 What is Data?
06:39
1-4 Stevens’ Typology
06:01
1-5 How to Distinguish?
04:52
1-6 Independent & Dependent Variables
01:53
Data
16 questions
2-0 Introduction
03:01
2-1 Display Data 1: Frequency Table
03:57
2-2 Display Data 2: Create Frequency Table with Python
06:23
2-3 Display Data 3: Stem and Leaf Diagram
02:32
2-4 Display Data 4: Stem and Leaf Diagram with Python
01:38
2-5 Display Data 5: Histogram
09:18
2-6 Display Data 6: Create Histograms with Python
05:27
2-7 Display Data 7: Dot Plot
01:28
2-8 Central Tendency 1: Mean
03:14
2-9 Central Tendency 2: Median
03:45
2-10 Central Tendency 3: Mode
01:56
2-11 Central Tendency 4: Mean Median & Mode with Python
08:54
2-12 Central Tendency 5: Geometric Mean
03:27
2-13 Central Tendency 6: Harmonic Mean
03:58
2-14 Central Tendency 7: Trimmed Mean
01:18
2-15 Central Tendency 8: Moving Average
01:58
2-16 Central Tendency 9: Expected Value
02:41
2-17 Central Tendency 10: Proportions for Binary Data
02:02
2-18 Central Tendency 11: Various Means with Python
06:54
2-19 Variability 1: What is Variability?
03:05
2-20 Variability 2: Range and Residual
02:12
2-21 Variability 3: Mean Absolute Deviation
03:15
2-22 Variability 4: Variance
01:57
2-23 Variability 5: Standard Deviation
03:13
2-24 Variability 6: Coefficient of Variation
02:52
2-25 Variability 7: Variability with Python
05:35
2-26 Relative Position 1: Percentile
02:20
2-27 Relative Position 2: Interquartile Range
02:20
2-28 Relative Position 3: The Empirical Rule
02:10
2-29 Relative Position 4: Chebyshev's Theorem
01:32
2-30 Relative Position 5: Relative Position with Python
03:54
2-31 Data Visualization 1: Why Visualization?
01:58
2-32 Data Visualization 2: Box Plot
06:10
2-33 Data Visualization 3: Box Plot with Python
04:49
2-34 Data Visualization 4: Bar Chart
01:55
2-35 Data Visualization 5: Bar Plot with Python
04:55
2-36 Data Visualization 6: Pie Chart
02:39
2-37 Data Visualization 7: Pie Chart with Python
05:50
2-38 Data Visualization 8: Line Plot
01:36
2-39 Data Visualization 9: Line Plot with Python
05:11
2-40 Data Visualization 10: Cross Tabulation Table
02:51
2-41 Data Visualization 11: Stacked Bar Chart
03:02
2-42 Data Visualization 12: Crosstab and Stacked Bar Chart with Python
06:19
2-43 Data Visualization 13: Mosaic Plot with Python
01:34
2-44 Data Visualization 14: Ternary Plot
02:49
2-45 Data Visualization 15 Ternary Plot with Python
04:25
Descriptive Statistics
26 questions
3-0 Introduction
02:23
3-1 Permutation & Combination 1: Factorial
08:35
3-2 Permutation & Combination 2: Permutation
03:02
3-3 Permutation & Combination 3: Combination
05:56
Permutation & Combination
8 questions
3-4 Permutation & Combination 4: Permutation and Combination with Python
06:13
3-5 Set Theory 1: Experiment & Event
02:39
3-6 Set Theory 2: Set
02:27
3-7 Set Theory 3: Event & Element
02:21
3-8 Set Theory 4: Venn Diagram
02:36
3-9 Set Theory 5: Complementary Event
02:42
3-10 Set Theory 6: Intersection
03:16
3-11 Set Theory 7: Union
02:02
3-12 Set Theory 8: Set Difference
02:37
Set Theory
9 questions
3-13 Set Theory 9: Set in Python
09:16
3-14 Probability Theory 1: What is Probability?
03:41
3-15 Probability Theory 2: Calculate Probability
04:12
3-16 Probability Theory 3: Combination & Probability
05:07
3-17 Probability Theory 4: Statistical Independence
06:16
3-18 Probability Theory 5: Expected Value
02:03
Probability Theory
7 questions
3-19 Conditional Probability 1: What is Conditional Probability?
05:50
3-20 Conditional Probability 2: Statistical Independence
03:04
3-21 Conditional Probability 3: Multiplication Theorem
02:39
3-22 Conditional Probability 4: Simpson's Paradox
04:27
3-23 Conditional Probability 5: Conditional Probability with Python
06:32
3-24 Conditional Probability 6: Bayes' Theorem
11:38
3-25 Conditional Probability 7: Bayes' Theorem with Python
04:13
Conditional Probability
4 questions
4-0 Introduction
01:55
4-1 Random Variable
02:44
4-2 Discrete Probability Distribution
02:40
4-3 Continuous Probability Distribution
02:16
4-4 Probability Density Function
04:35
Probability Density Function
4 questions
4-5 Cumulative Distribution Function
04:16
4-6 Expected Value of Random Variables
12:39
Expected Values of Random Variables
3 questions
4-7 Variance of Random Variables
07:17
4-8 Find Variance from Expected Value
02:03
4-9 Additivity of Variance
05:38
Variance of Random Variables
4 questions
4-10 Normal Distribution
05:21
4-11 Standard Normal Distribution
02:06
4-12 Standard Normal Distribution Table
07:39
4-13 Skewness & Kurtosis
03:43
Normal Distribution
4 questions
4-14 Normal Distribution with Python
01:45
4-15 Binomial Distribution
07:55
4-16 Expected Value of Binomial Distribution
07:07
4-17 Variance of Binomial Distribution
05:30
Binomial Distribution
3 questions
4-18 Binomial Distribution with Python
04:50
4-19 Poisson Distribution
08:53
4-20 Expected Value of Poisson Distribution
02:49
4-21 Variance of Poisson Distribution
04:13
4-22 Examples of Poisson Distribution
03:20
Poisson Distribution
3 questions
4-23 Poisson Distribution with Python
03:55
4-24 Geometric Distribution
03:59
4-25 Expected Value of Geometric Distribution
04:08
4-26 Variance of Geometric Distribution
05:18
Geometric Distribution
3 questions
4-27 Geometric Distribution with Python
04:01
4-28 Exponential Distribution
04:41
4-29 Expected Value of Exponential Distribution
02:35
4-30 Variance of Exponential Distribution
02:20
4-31 Memorylessness
05:54
Exponential Distribution
2 questions
4-32 Exponential Distribution with Python
03:25
4-33 Discrete Uniform Distribution
05:07
4-34 Continuous Uniform Distribution
05:54
Uniform Distribution
3 questions
4-35 Uniform Distribution with Python
01:19
4-36 Joint Probability Distribution
04:26
5-0 Introduction
01:44
5-1 Population and Sample
06:07
5-2 Complete Survey and Sampling Survey
04:00
Sampling Survey
3 questions
5-3 Probability Sampling and Non-probability Sampling
05:09
5-4 Probability Sampling Methods
06:34
Sampling
4 questions
5-5 Random Sampling with Python
03:08
5-6 Law of Large Numbers
03:06
5-7 Law of Large Numbers with Python
03:13
5-8 Central Limit Theorem
05:44
Law of Large Numbers & Central Limit Theorem
2 questions
5-9 Central Limit Theorem with Python
01:30
5-10 Experimental and Observational Studies
08:52
5-11 Fisher’s Principle
03:40
Experiment Design
5 questions
6-0 Introduction
01:26
6-1 What is Point Estimation?
02:22
6-2 Point Estimation of Population Mean
03:59
6-3 Unbiased Variance
05:21
6-4 Standard Error
02:17
Point Estimation
4 questions
6-5 Point Estimation by Python
02:33
6-6 What is Interval Estimation?
04:48
6-7 Interval Estimation of Population Mean (Population Variance Known)
03:42
6-8 What is 95% Confidence Interval?
04:02
6-9 Sample Size and Confidence Interval
01:53
6-10 When Population Variance is Unknown . . . (t-distribution)
05:19
6-11 Interval Estimation of Population Mean (Population Variance Unknown)
04:30
Interval Estimation
2 questions
6-12 Interval Estimation of Population Mean Difference
04:43
6-13 Interval Estimation of Population Proportion
07:27
6-14 Interval Estimation and Minimum Sample Size
02:02
6-15 Chi-Square Distribution
01:45
6-16 Properties of Chi-Square Distribution
01:41
6-17 Interval Estimation of Population Variance
03:40
Interval Estimation Part 2
2 questions
6-18 Interval Estimation by Python
03:20
7-0 Introduction
01:43
7-1 What is Hypothesis Testing?
03:16
7-2 Process of Hypothesis Testing
05:05
Null and Alternative Hypotheses
4 questions
7-3 Significance Level
05:46
Significance Level
4 questions
7-4 Test Statistic
03:01
7-5 One- and Two-Tailed Test
04:36
7-6 Hypothesis Testing for Population Mean
05:03
7-7 Hypothesis Testing for Population Mean with Python
03:05
7-8 Exercise Hypothesis Testing for Population Mean
02:14
7-9 Two-Sample t-Test
05:45
7-10 Two-Sample t-Test Dependent Sample with Python
02:23
7-11 Exercise Two-Sample t-Test Dependent Sample
02:18
7-12 Two-Sample t-Test Independent Sample
05:52
7-13 Two-Sample t-Test Independent Sample with Python
03:03
7-14 Exercise Two-Sample t-Test Independent Sample
02:10
7-15 Hypothesis Testing for Population Proportion
03:57
7-16 Hypothesis Testing for Population Proportion with Python
01:56
7-17 Exercise Hypothesis Testing for Population Proportion
01:43
7-18 Goodness of Fit Test
03:01
7-19 Goodness of Fit Test with Python
01:30
7-20 Exercise Goodness of Fit Test
01:48
7-21 Test of Independence
06:53
7-22 Test of Independence with Python
01:35
7-23 Exercise Test of Independence
01:50
7-24 Test of Population Proportion Difference
03:37
7-25 Test of Population Proportion Difference with Python
02:26
7-26 Exercise Test of Population Proportion Difference
02:05
8-0 Introduction
01:55
8-1 Scatter Plot
03:02
8-2 Correlation
01:59
8-3 Correlation Coefficient
05:02
8-4 Covariance
04:09
8-5 Correlation Coefficient Revisited
06:50
8-6 Exercise Correlation Coefficient
03:36
8-7 Test of Non-Correlation
04:32
8-8 Spurious Correlation
05:12
Correlation
8 questions
8-9 Regression Analysis
03:05
8-10 Ordinary Least Squares
04:48
8-11 Ordinary Least Squares Math
03:01
8-12 The Difference between Correlation and Regression
03:21
Regression Analysis
5 questions
8-13 Multiple Regression Analysis
06:25
8-14 Multiple Regression Analysis Math
05:08
8-15 Assumptions of Linear Regression
05:09
8-16 Hypothesis Testing in Multiple Regression Analysis
04:55
8-17 Coefficient of Determination
12:29
8-18 Residual Analysis
04:24
8-19 Multicollinearity
08:39
8-20 Variance Inflation Factor
02:04
8-21 F-test
07:16
8-22 Dummy Variable
05:11
Multiple Regression Analysis
8 questions
8-23 Effect Size
04:21
8-24 Statistical Power
04:50
8-25 Correlation Analysis with Python
06:39
8-26 Regression Analysis with Python
13:31
8-27 Get Dummy Variables with Python
01:53
9-0 Introduction
00:53
9-1 What is ANOVA?
03:05
9-2 F-Test
04:20
9-3 Example F-Test
02:12
9-4 One-Way ANOVA
06:37
One-Way ANOVA
8 questions
9-5 Tukey’s HSD test
03:26
9-6 Assumptions in ANOVA
01:46
Tukey’s HSD and Assumptions in ANOVA
3 questions
9-7 One-Way ANOVA with Python
05:17
9-8 Two-Way ANOVA
05:19
9-9 Two-Way ANOVA with Python
04:49
Congratulations!
00:16
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