Healthcare Analytics: Regression in R Assignment Help

R, an open-source statistical computing software, can be used to create linear and logistic regression models. Our expert, a biotech expert and epidemiologist, demonstrates how to perform a forward stepwise modelling process using the publicly available Behavioral Risk Factor Surveillance Survey (BRFSS) dataset in this course. We demonstrate how to design your research by selecting a hypothesis based on scientific plausibility. Our experts then walk you through the steps of preparing, developing, and finalising a linear regression model as well as a logistic regression model. We also discuss how to interpret diagnostic plots, improve model fit, and compare models, among other things. Following are the topics covered by us:

KEY FEATURE

Matching healthcare cost models to analytic objectives and data characteristics in a study necessitates caution.

The study results and interpretation can be heavily influenced by the model chosen, with a real risk of erroneous results and conclusions.

Cost analysis in clinical and public health care is now a standard component of both experimental and epidemiological research.

Regression analyses are used by researchers from a variety of disciplines to understand the mathematical relationships between variables for purposes of description.

Healthcare Analytics: Regression in R Assignment Help

Our qualified experts are ready to provide their expertise and assist you with all your Healthcare Analytics: Regression in R and queries. We are available 24×7! Reach us at any time to get your queries solved.
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Key Topics
    • An Extensive Guide for Health Care Data Analysis using R(Machine Learning Algorithms, GLM)
    • Data exploration (Data distribution inferences, Univariate Data analysis, Two-sample t-test)
    • Data Correlation Analysis
    • Feature Selection (using Logistic regression)
    • Outlier Detection (using principal component graph)
    • Basic Parameter Tuning(CV, complexity parameter)
    • Data modeling
    • Basic GLM (With all Features and eliminating few features based on AIC)
    • Logistic Regression
    • Decision Tree
    • Naïve Bayes

    What You Will Learn From This Course

    1. As a first step toward advanced analysis with R software, describe a data set from scratch using descriptive statistics and simple graphical methods.
    2. Interpret the results of your analysis and consider the role of chance and bias as possible explanations.
    3. In R, run multiple logistic regression analyses and interpret the results.
    4. In R, assess the model assumptions for multiple logistic regression.

    Multiple Linear Regression For Public Health

    The goal of Data Carpentry is to teach researchers basic concepts, tools and skills for working with data so that they can get more done in less time and with less pain. This lesson was created for researchers interested in working with public health data in R, but it may also be useful for researchers in other fields.
    This lesson provides an overview of linear regression with multiple explanatory variables. Because the model contains multiple explanatory variables, this is known as multiple linear regression. This lesson’s first episode explains how to fit and interpret models with one continuous and one categorical explanatory variable. These models are expanded in the second episode by including an interaction between the two explanatory variables. Predictions of the mean are covered in the third episode. The final episode discusses model fit and model assumption evaluation.

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