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Statistics for Data Science and Business Analysis
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Data Science Courses
Statistics for Data Science and Business Analysis
06 Distributions
Introduction to inferential statistics
Introduction to inferential statistics
Sửa lần cuối: Thứ bảy, 11 Tháng tám 2018, 2:31 PM
◄ Practical example
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What does the course cover?
Diễn đàn tin tức
Understanding the difference between a population and a sample
The various types of data we can work with
Levels of measurement
Categorical variables_ Visualization techniques for categorical variables
Numerical variables_ Using a frequency distribution table
Histogram charts
Cross tables and scatter plots
Measures ofThe main measures of central tendency_ mean_ median_ mode
Measuring skewness
Measuring how data is spread out_ calculating variance
Standard deviation and coefficient of variation
Calculating and understanding covariance
The correlation coefficient
Practical example
What is a distribution
The Normal distribution
The standard normal distribution
Understanding the central limit theorem
Standard error
Working with estimators and estimates
Confidence intervals - an invaluable tool for decision making
Calculating confidence intervals within a population with a known variance
Student's T distribution
Calculating confidence intervals within a population with an unknown variance
What is a margin of error and why is it important in Statistics
Calculating confidence intervals for two means with dependent samples
Calculating confidence intervals for two means with independent samples (part 1)
Calculating confidence intervals for two means with independent samples (part 2)
Calculating confidence intervals for two means with independent samples (part 3)
Practical example_ inferential statistics
The null and the alternative hypothesis
Establishing a rejection region and a significance level
Type I error vs Type II error
Test for the mean_ Population variance known
What is the p-value and why is it one of the most useful tool for statisticians
Test for the mean_ Population variance unknown
Test for the mean_ Dependent samples
Test for the mean_ Independent samples (Part 1)
Test for the mean_ Independent samples (Part 2)
Practical example_ hypothesis testing
Introduction to regression analysis
Correlation and causation
The linear regression model made easy
What is the difference between correlation and regression
A geometrical representation of the linear regression model
A practical example - Reinforced learning
Decomposing the linear regression model - understanding its nuts and bolts
What is R-squared and how does it help us
The ordinary least squares setting and its practical applications
Studying regression tables
The multivariate linear regression models
Adjusted R-squared
What does the F-statistic show us and why we need to understand it
OLS assumptions
A1_ Linearity
A2_ No endogeneity
A3_ Normality and homoscedasticity
A4_ No autocorrelation
A5_ No multicollinearity
Dummy variables
Practical example_ regression analysis
What is a distribution ►
Statistics for Data Science and Business Analysis
1. Introduction
2. Sample or population data_
03 The fundamentals of descriptive statistics
04 Measures of central tendency_ asymmetry_ and variability
05 Practical example_ descriptive statistics
06 Distributions
07 Estimators and estimates
08 Confidence intervals_ advanced topics
09 Practical example_ inferential statistics
10 Hypothesis testing_ Introduction
11 Hypothesis testing_ Let's start testing!
12 Practical example_ hypothesis testing
13 The fundamentals of regression analysis
14 Subtleties of regression analysis
15 Assumptions for linear regression analysis
16 Dealing with categorical data
17 Practical example_ regression analysis
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