R Programming , Logistic and Linear regression, Integrals

Fundamentals of R , Logistic and Linear regression , Variables selection using mallows'CP , Integrals approximation

Deal Score+5

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


  • The only prerequisite for this class is the willingness to learn
  • A basic knowledge of programing will be helpful but not necessary


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 )


  • 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

How to use the mallows’CP to select the best subset of parameters for the linear regression model

confidence intervals of parameters

model performance , prediction and interpretation du result

  • Integral approximation

By the end of this course , you will be able to effectively code in R. You will be able to summarize your data , visualize your data , detect and eliminate outliers and missing values form your data ,construct and interpret a logistic or linear regression model using the best subset of parameters, predict , approximate complex integral and more .

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