- Introduction to the course and Instructor
- Data & Code Used in the Course
- Statistics in the Real World
- Designing Studies & Collecting Good Quality Data
- Different Types of Data
- Conclusion to Section 1
Online
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Quick facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
certificate availability
certificate providing authority
The syllabus
Introduction to the Basics of Applied Statistical Modelling
Section 2: The Essentials of the R Programming Language
- Rationale for this section
- Introduction to the R Statistical Software & R Studio
- Different Data Structures in R
- Reading in Data from Different Sources
- Indexing and Subsetting of Data
- Data Cleaning: Removing Missing Values
- Exploratory Data Analysis in R
- Conclusion to Section 2
- Section 2 Quiz
Statistical Tools to Learn More About Your Data
- Summarize Quantitative Data
- Measures of Center
- Measures of Variation
- Charting & Graphing Continuous Data
- Charting & Graphing Discrete Data
- Deriving Insights from Qualitative/Nominal Data
- Conclusion to Section 3
- Section 3 Quiz
Probability Distributions
- Background
- Data Distribution: Normal Distribution
- Checking For Normal Distribution
- Standard Normal Distribution and Z-scores
- Confidence Interval-Theory
- Confidence Interval-Computation in R
- Conclusion to Section 4
- Section 4 Quiz
Statistical Inference
- What is Hypothesis Testing?
- T-tests: Application in R
- Non-Parametric Alternatives to T-Tests
- One-way ANOVA
- Non-parametric version of One-way ANOVA
- Two-way ANOVA
- Power Test for Detecting Effect
- Conclusion to Section 5
- Section 5 Quiz
Relationship Between Two Quantitative Variables
- Explore the Relationship Between Two Quantitative Variables?
- Correlation
- Linear Regression-Theory
- Linear Regression-Implementation in R
- The Conditions of Linear Regression
- Dealing with Multi-collinearity
- What More Does the Regression Model Tell Us?
- Linear Regression and ANOVA
- Linear Regression With Categorical Variables and Interaction Terms
- Analysis of Covariance (ANCOVA)
- Selecting the Most Suitable Regression Model
- Conclusion to Section 6
- Section 6 Quiz
Other Types of Regression
- Violation of Linear Regression Conditions: Transform Variables
- Other Regression Techniques When Conditions of OLS Are Not Met
- Model 2 Regression: Standardized Major Axis (SMA) Regression
- Polynomial and Non-linear regression
- Linear Mixed Effect Models
- Generalized Regression Model (GLM)
- Logistic Regression in R
- Poisson Regression in R
- Goodness of fit testing
- Conclusion to Section 7
- Section 7 Quiz
Multivariate Analysis
- Why do Multivariate Analysis?
- Cluster Analysis/Unsupervised Learning
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Correspondence Analysis
- Similarity & Dissimilarity Across Sites
- Non-metric multi dimensional scaling (NMDS)
- Multivariate Analysis of Variance (MANOVA)
- Conclusion to Section 8
- Section 8 Quiz
Miscellaneous Lectures & Information
- Exploratory Data Analysis With xda
- Read in Data from Online HTML Tables-Part 1
- Read in Data from Online HTML Tables-Part 2
- Use R in Colab
Instructors
Ms Minerva Singh
Data Scientist
Udemy
Other Masters, Ph.D, M.Phil.