Course Content
09 – Practical example inferential statistics
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001 Practical example inferential statistics.mp4
10:05
17 – Practical example regression analysis
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001 Practical example regression analysis.mp4
14:09
16 – Dealing with categorical data
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001 Dummy variables.mp4
05:03
15 – Assumptions for linear regression analysis
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001 OLS assumptions.mp4
02:21
003 A1. Linearity.mp4
01:50
005 A2. No endogeneity.mp4
04:09
007 A3. Normality and homoscedasticity.mp4
05:47
009 A4. No autocorrelation.mp4
03:14
011 A5. No multicollinearity.mp4
03:26
14 – Subtleties of regression analysis
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001 Decomposing the linear regression model – understanding its nuts and bolts.mp4
03:37
003 What is R-squared and how does it help us.mp4
05:23
005 The ordinary least squares setting and its practical applications.mp4
02:23
007 Studying regression tables.mp4
04:54
010 The multiple linear regression model.mp4
02:55
012 The adjusted R-squared.mp4
05:24
014 What does the F-statistic show us and why do we need to understand it.mp4
02:01
13 – The fundamentals of regression analysis
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001 Introduction to regression analysis.mp4
01:02
003 Correlation and causation.mp4
04:12
005 The linear regression model made easy.mp4
05:50
007 What is the difference between correlation and regression.mp4
01:43
009 A geometrical representation of the linear regression model.mp4
01:25
011 A practical example – Reinforced learning.mp4
05:45
12 – Practical example hypothesis testing
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001 Practical example hypothesis testing.mp4
07:16
11 – Hypothesis testing Let’s start testing!
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001 Test for the mean. Population variance known.mp4
06:34
003 What is the p-value and why is it one of the most useful tools for statisticians.mp4
04:13
005 Test for the mean. Population variance unknown.mp4
04:48
007 Test for the mean. Dependent samples.mp4
05:17
009 Test for the mean. Independent samples (Part 1).mp4
04:21
011 Test for the mean. Independent samples (Part 2).mp4
04:26
10 – Hypothesis testing Introduction
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001 The null and the alternative hypothesis.mp4
05:51
004 Establishing a rejection region and a significance level.mp4
07:05
006 Type I error vs Type II error.mp4
04:14
01 – Introduction
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001 What does the course cover.mp4
03:54
08 – Confidence intervals advanced topics
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001 Calculating confidence intervals for two means with dependent samples.mp4
06:04
003 Calculating confidence intervals for two means with independent samples (part 1).mp4
04:31
005 Calculating confidence intervals for two means with independent samples (part 2).mp4
03:57
007 Calculating confidence intervals for two means with independent samples (part 3).mp4
01:27
07 – Estimators and estimates
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001 Working with estimators and estimates.mp4
03:07
003 Confidence intervals – an invaluable tool for decision making.mp4
02:41
005 Calculating confidence intervals within a population with a known variance.mp4
08:01
007 Confidence interval clarifications.mp4
04:38
008 Student’s T distribution.mp4
03:22
010 Calculating confidence intervals within a population with an unknown variance.mp4
04:36
012 What is a margin of error and why is it important in Statistics.mp4
04:52
06 – Distributions
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001 Introduction to inferential statistics.mp4
01:00
002 What is a distribution.mp4
04:33
004 The Normal distribution.mp4
03:54
006 The standard normal distribution.mp4
03:30
009 Understanding the central limit theorem.mp4
04:19
011 Standard error.mp4
01:26
05 – Practical example descriptive statistics
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001 Practical example.mp4
16:15
04 – Measures of central tendency, asymmetry, and variability
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001 The main measures of central tendency mean, median and mode.mp4
04:20
003 Measuring skewness.mp4
02:37
006 Measuring how data is spread out calculating variance.mp4
05:55
008 Standard deviation and coefficient of variation.mp4
04:40
011 Calculating and understanding covariance.mp4
03:23
013 The correlation coefficient.mp4
03:17
03 – The fundamentals of descriptive statistics
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001 The various types of data we can work with.mp4
04:33
003 Levels of measurement.mp4
03:43
005 Categorical variables. Visualization techniques for categorical variables.mp4
04:52
008 Numerical variables. Using a frequency distribution table.mp4
03:09
011 Histogram charts.mp4
02:14
014 Cross tables and scatter plots.mp4
04:44
02 – Sample or population data
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001 Understanding the difference between a population and a sample.mp4
04:02
Statistics for Data Science and Business Analysis (Udemy – Vietsub and Engsub)
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