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    Applied Statistical Modeling for Data Analysis in R

    BY
    Udemy

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    Mode

    Online

    Fees

    ₹ 3499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 3,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction to the Basics of Applied Statistical Modelling

    • 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

    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

    Ms Minerva Singh
    Data Scientist
    Udemy

    Other Masters, Ph.D, M.Phil.

    Articles

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