Applied Statistical Modeling For Data Analysis In R

Statistical modelling is the use of mathematical models and statistical assumptions to generate sample data and make predictions about the real world. A statistical model is a collection of probability distributions for all possible experiment outcomes.
This new four-volume set on Applied Statistical Modeling collects seminal articles in the field that were chosen for their exemplification of the specific model type used, clarity of exposition, and significance to the development of their respective disciplines. The collection is intended to be a master class in how to apply the most commonly used statistical models with the highest level of methodological sophistication. It is essentially a user’s manual for statistical best practises in the social sciences.

KEY FEATURE

The use of mathematical models and statistical assumptions to generate sample data and make predictions about the real world is known as statistical modelling.

Statistical modelling software is a type of computer programme that assists in the collection, organization, analysis, interpretation, and statistical design of data.

The application is to raw data assists data scientists in approaching data analysis strategically by providing intuitive visualisations that aid in identifying relationships.

Internet of Things sensors, census data, public health data, social media data, imagery data, that benefit from real-world predictions are common data sets for statistical analysis.

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Key Topics

    Applied Statistical Modeling For Data Analysis In R

    The most important statistical methods used in sociology, social psychology, political science, management science, media studies, anthropology, and human geography are covered in this truly multidisciplinary collection. Following are the concepts covered by our experts:
    • Apply appropriate statistical techniques to their own data analysis
    • Interpret their statistical analysis results
    • Determine which statistical techniques are most appropriate for their data and questions.
    • Have a solid understanding of fundamental statistical concepts.
    • Execute various statistical analyses in R and interpret the results
    • Create user-friendly data visualisations
    • Formalized hypothesis testing should be carried out.
    • Use linear modelling techniques like multiple regression and GLMs.
    • Apply advanced regression and multivariate analysis.

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    a) The Essentials of the R Language

    • 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

    b) Statistical Tools To Learn More About Your Data

    • Measures of Center
    • Measures of Variation
    • Charting & Graphing Continuous Data
    • Charting & Graphing Discrete Data
    • Deriving Insights from Qualitative/Nominal Data

    c) Probability Distribution

    • Data Distribution: Normal Distribution
    • Checking For Normal Distribution
    • Standard Normal Distribution and Z-scores
    • Confidence Interval-Theory
    • Confidence Interval-Computation in R

    d) Statistical Inference

    • 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

    e) Relationship Between Two Different Quantitative Variables

    • Correlation
    • Linear Regression-Theory
    • Linear Regression-Implementation in R
    • The Conditions of Linear Regression
    • Dealing with Multi-collinearity
    • Linear Regression and ANOVA
    • Linear Regression With Categorical Variables and Interaction Terms
    • Analysis of Covariance (ANCOVA)
    • Selecting the Most Suitable Regression Model

    f) Other Types Of Regression

    • 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

    f) 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)

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