Regression Analysis for Statistics & Machine Learning in R Assignment Help

Regression analysis is a key component of both statistical and machine learning-based analysis. This hands-on course will teach you regression analysis in R for both statistical data analysis and machine learning. It takes a practical approach to the relevant concepts, ranging from basic to expert. This course can help you improve your grades, gain new analytical tools for your academic career, apply your knowledge in the workplace, or make business forecasting decisions. All while delving into the knowledge of an Oxford and Cambridge educated researcher.

KEY FEATURE

Regression analysis is a statistical method for determining the structure of a relationship between two variables or three or more variables.

Regression provides insights into the structure of that relationship and measures how well the data fits that relationship.

Multiple regression is a statistical method for determining the relationship between three or more variables: the dependent variable and at least two independent variables.

The relationship between two variables, the independent and dependent, is determined using single variable linear regression.

Regression Analysis for Statistics & Machine Learning in R Assignment Help

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Key Topics
    MINERVA SINGH is an MPhil (Geography and Environment) graduate of Oxford University. I recently completed a PhD programme at Cambridge University (Tropical Ecology and Conservation). I have several years of experience analysing real-world data from various sources using data science techniques and publishing in international peer-reviewed journals. This course is based on my years of experience with regression modelling and implementing various regression models on real-world data. Most statistics and machine learning courses and books cover only the fundamentals of regression analysis. This does not teach students about the various regression analysis techniques that they can use on their own data in both academic and business settings, resulting in inaccurate modelling. This will change as a result of my education. You will progress from implementing and inferring simple OLS (ordinary least squares) regression models to dealing with multicollinearity issues in regression to machine learning-based regression models.

    Become a Regression Analysis Expert and Leverage R's Power for Your Analysis

    • Begin with R and RStudio. Install these on your system, then learn how to load packages and read various types of data into R.
    • Using R, perform data cleaning and visualisation.
    • Use R to perform ordinary least squares regression and learn how to interpret the results.
    • Learn how to deal with multicollinearity using variable selection as well as regularisation techniques like ridge regression.
    • Select variables and regression models using both statistical and machine learning techniques, including cross-validation.
    • Assess the precision of the regression model.
    • Use generalised linear models (GLMs) such as logistic and Poisson regression. To distinguish between male and female voices, use logistic regression as a binary classifier.
    • To work with non-linear and non-parametric data, employ non-parametric techniques such as Generalized Additive Models (GAMs).
    • Use tree-based machine learning models.
    • For improved regression prediction accuracy, use machine learning methods such as random forest regression and gradient boosting machine regression.
    • Model selection should be carried out.

    Key Concepts Taught By Our Online Experts

    • R is used to implement and infer Ordinary Least Squares (OLS) regression.
    • Use statistical and machine learning-based regression models to solve problems like multicollinearity.
    • Perform variable selection and model accuracy testing using techniques such as cross-validation.
    • Use logistic regression as a binary classifier to implement and infer Generalized Linear Models (GLMS).
    • In R, create machine learning-based regression models and test their robustness.
    • Learn when and how to use machine learning models.
    • Compare various machine learning algorithms for regression modelling.

    Develop Your Regression Analysis Skills and Apply Your Knowledge to Real-World Data

    This course is your one-time opportunity to acquire the knowledge of statistical and machine learning analysis that I obtained through rigorous training at two of the world’s best universities, the reading of numerous books, and the publication of statistically rich papers in a renowned international journal such as PLOS One. The course will specifically:

    a) Have students with basic statistical knowledge perform some of the most common advanced regression analysis-based techniques.
    b) Teach students how to use R to perform various statistical and machine learning data analysis and visualisation tasks.
    c) Practically introduce some of the most important statistical and machine learning concepts to students so that they can apply these concepts to practical data analysis and interpretation.
    d) Students will gain a solid understanding of some of the most critical statistical and machine learning concepts for regression analysis.
    e) Students will be able to determine and interpret which regression analysis techniques are best suited to answering their research questions and applicable to their data.

    It is a hands-on, practical course in which we will spend some time dealing with theoretical concepts related to statistical and machine learning regression analysis. The majority of the course, however, will concentrate on applying various techniques to real-world data and interpreting the results. Each video will teach you a new concept or technique that you can apply to your own projects.
    TAKE ACTION RIGHT NOW! We will personally support you and ensure that your course experience is a success.

    Who Can Take Help From Our Regression Analysis For Statistics & Machine Learning In R Experts

    • People who have completed my Statistical Modeling for Data Analysis in R course
    • People with a basic understanding of R-based statistical modelling
    • People who are familiar with linear regression modelling
    • People who want to learn more about regression modelling and apply it to real-world problems.
    • People interested in learning how to use machine learning-based regression models in R.
    • Undergraduates and postgraduates who want to learn more about statistical and machine learning analysis
    • Academic researchers who want to learn new data analysis techniques
    • Business data analysts who want to perform predictive analysis using regression modelling

    Important Topics Studied Under Regression Analysis For Statistics & Machine Learning In R

    Hiring a professional to assist you with a Regression Analysis for Statistics & Machine Learning in R assignment will ensure that you receive a good grade. Based on the guidelines and materials you’ve learned in class, a tutor will write the assignment for you. They will, of course, meet the deadline. If you’re unsure about a subject, this is a good way to supplement your classroom instruction. If you’re still having trouble with Regression Analysis for Statistics & Machine Learning in R, hire someone to help you!

    Get Started With Practical Regression Analysis In R

    • Reading in Data with R
    • Data Cleaning with R
    • Basic Exploratory Data Analysis in R

    Ordinary Last Square Regression Modelling

    • OLS-Implementation
    • Calculate the Confidence Interval in R
    • Confidence Interval and OLS Regressions
    • Linear Regression without Intercept
    • Implement ANOVA on OLS Regression
    • Multiple Linear Regression
    • Multiple Linear regression with Interaction and Dummy Variables
    • Some Basic Conditions that OLS Models Have to Fulfill

    Deal With Multicollinearity In OLS Regression Models

    • Identify Multicollinearity
    • Doing Regression Analyses with Correlated Predictor Variables
    • Principal Component Regression in R
    • Partial Least Square Regression in R
    • Ridge Regression in R
    • LASSO Regression

    Variable and Model Selection

    • Select the Most Suitable OLS Regression Model
    • Select Model Subsets
    • Evaluate Regression Model Performance
    • LASSO Regression for Variable Selection
    • Identify the Contribution of Predictors in Explaining the Variation in Y

    Dealing With Other Violations Of The OLS Regression Models

    • Data Transformations
    • Robust Regression-Deal with Outliers
    • Dealing with Heteroscedasticity

    Generalized Linear Models

    • Logistic regression
    • Logistic Regression for Binary Response Variable
    • Multinomial Logistic Regression
    • Regression for Count Data
    • Goodness of fit testing

    Working With Nonparametric And Non Linear Data

    • Work With Non-Parametric and Non-Linear Data
    • Polynomial and Non-linear regression
    • Generalized Additive Models (GAMs) in R
    • Boosted GAM Regression
    • Multivariate Adaptive Regression Splines (MARS)
    • Machine Learning Regression-Tree Based Methods
    • CART-Regression Trees in R
    • Conditional Inference Trees
    • Random Forest(RF)
    • Gradient Boosting Regression
    • ML Model Selection

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