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R Programming , Logistic and Linear regression, Integrals

 R Programming , Logistic and Linear regression, Integrals
 Udemy Course R Programming , Logistic and Linear regression, Integrals | NED
R Programming , Logistic and Linear Regression. With the help of this course you can Case Study : Implementation of a logistic regression
 by Modeste Atsague

What you'll learn
  • R-Programming *****Objective By the end of this course, a student should be able to do the following: -Install R and R Studio -Be familiar with the R interface -Be familiar with the R syntax -Be able to define a list, a vector, a data frame, a time variable, a string, a matrix and more. -Describe some build in function necessary for computation -Be able to apply numerical operations (- , +, / ,*) on list, matrix and vectors - Be familiar with a if, for and while loops which are necessary to be an effective programmer. -Be able to implement and use Functions Note: Functions are use to avoid all form of repetition and to save time. Therefore, be more productive. -Be familiar with some plotting methods for data visualization -Be able test for the existence of missing values and the elimination process in the data set -Be able to produce a summary statistic of the data and interpret the result -Be able to implement a simple and multiple linear modeling and interpret your findings -Be able to implement some sampling method (This is extremely important for Applied stochastic process) -Be able use R programing for integral approximation. This part is very useful in engineering. Engineers can rely on these methods to solve complex integrals
 Description

In this class, we cover the following :

    Fundamentals of R

Assignment and Arithmetic with R

Vectors, list and matrix

loops (for, if ,if else, while, repeat )

functions
  • Some nice Plots for data visualization (outlier detection, correlation coefficients )
  • How to detect and eliminate outliers and missing values from the data
  • How to use the mallows'CP to select the best subset of parameters for the linear regression model
  • logistic and linear regression  models

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