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Statistics for Data Science and Business Analysis


Introduction :
What does the course cover?

Sample or population data? :
Understanding the difference between a population and a sample
Population vs sample
2 questions

The fundamentals of descriptive statistics :
The various types of data we can work with
Types of data
2 questions
Levels of measurement
Levels of measurement
2 questions
Categorical variables. Visualization techniques for categorical variables
Categorical variables. Visualization techniques. Exercise
Numerical variables. Using a frequency distribution table
Numerical variables. Using a frequency distribution table. Exercise
Histogram charts
Histogram charts. Exercise
Cross tables and scatter plots
Cross tables and scatter plots. Exercise

Measures of central tendency, asymmetry, and variability :
The main measures of central tendency: mean, median, mode
Mean, median, mode. Exercise
Measuring skewness
Skewness. Exercise
Measuring how data is spread out: calculating variance
Variance. Exercise
Standard deviation and coefficient of variation
Standard deviation and coefficient of variation. Exercise
Calculating and understanding covariance
Covariance. Exercise
The correlation coefficient
Correlation coefficient

Practical example: descriptive statistics :
Practical example
Practical example: descriptive statistics

Distributions :
Introduction to inferential statistics
What is a distribution?
What is a distribution
1 question
The Normal distribution
The Normal distribution
1 question
The standard normal distribution
Standard Normal Distribution. Exercise
Understanding the central limit theorem
The central limit theorem
1 question
Standard error

Estimators and estimates :
Working with estimators and estimates
Estimators and estimates
1 question
Confidence intervals - an invaluable tool for decision making
Confidence intervals
1 question
Calculating confidence intervals within a population with a known variance
Confidence intervals. Population variance known. Exercise
Student's T distribution
Student's T distribution
1 question
Calculating confidence intervals within a population with an unknown variance
Population variance unknown. T-score. Exercise
What is a margin of error and why is it important in Statistics?
Margin of error
1 question

Confidence intervals: advanced topics :
Calculating confidence intervals for two means with dependent samples
Confidence intervals. Two means. Dependent samples. Exercise
Calculating confidence intervals for two means with independent samples (part 1)
Confidence intervals. Two means. Independent samples (Part 1). Exercise
Calculating confidence intervals for two means with independent samples (part 2)
Confidence intervals. Two means. Independent samples (Part 2). Exercise
Calculating confidence intervals for two means with independent samples (part 3)

Practical example: inferential statistics :
Practical example: inferential statistics
Practical example: inferential statistics

Hypothesis testing: Introduction :
The null and the alternative hypothesis
Null vs alternative
3 questions
Establishing a rejection region and a significance level
Rejection region and significance level
2 questions
Type I error vs Type II error
Type I error vs type II error
4 questions

Hypothesis testing: Let's start testing! :
Test for the mean. Population variance known
Test for the mean. Population variance known. Exercise
What is the p-value and why is it one of the most useful tool for statisticians?
p-value
2 questions
Test for the mean. Population variance unknown
Test for the mean. Population variance uknown. Exercise
Test for the mean. Dependent samples
Test for the mean. Dependent samples. Exercise
Test for the mean. Independent samples (Part 1)
Test for the mean. Independent samples (Part 2)
Test for the mean. Independent samples (Part 2). Exercise

Practical example: hypothesis testing :
Practical example: hypothesis testing
Practical example: hypothesis testing

The fundamentals of regression analysis :
Introduction to regression analysis
Introduction
1 question
Correlation and causation
The linear regression model made easy
The linear regression model
2 questions
What is the difference between correlation and regression?
Correlation vs regression
1 question
A geometrical representation of the linear regression model
A practical example - Reinforced learning

Subtleties of regression analysis :
Decomposing the linear regression model - understanding its nuts and bolts
Decomposition
1 question
What is R-squared and how does it help us?
R-squared
2 questions
The ordinary least squares setting and its practical applications
Studying regression tables
Regression tables. Exercise
The multivariate linear regression model
Adjusted R-squared
The adjusted R-squared
2 questions
What does the F-statistic show us and why we need to understand it?

Assumptions for linear regression analysis :
OLS assumptions
OLS assumptions
1 question
A1. Linearity
A2. No endogeneity
A2. No endogeneity
1 question
A3. Normality and homoscedasticity
A4. No autocorrelation
A4. No autocorrelation
1 question
A5. No multicollinearity
A5. No multicollinearity
1 question

Dealing with categorical data :
Dummy variables

Practical example: regression analysis :
Practical example: regression analysis

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