R Programming from Beginner to Advanced then prediction
4.5 (4 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
86 students enrolled
Wishlisted Wishlist

Please confirm that you want to add R Programming from Beginner to Advanced then prediction to your Wishlist.

Add to Wishlist

R Programming from Beginner to Advanced then prediction

Case Study : Prediction using both a logistic regression and a linear regression model then integral approximation
4.5 (4 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
86 students enrolled
Created by Modeste Atsague
Last updated 7/2017
English
Curiosity Sale
Current price: $10 Original price: $20 Discount: 50% off
30-Day Money-Back Guarantee
Includes:
  • 5.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • R-Programing *****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
View Curriculum
Requirements
  • The only prerequisite for this class is the willingness to learn
  • A basic knowledge of programing will be helpful but not necessary
Description

This course is intended for Students who wish to be familiar with some basic and advanced concepts of R, Students seeking a broad range of application on which  prediction using a logistic regression model or a linear regression model, integral approximation, describtive statistics and more.

By the end of this cours , you will be able to elfeectively code in R. You will be able to summarize your data , visualize your data , detect and eliminate outliers form your data ,construct and interpret a logistic or linear regression model, predict , approximate complex integral and more .


Who is the target audience?
  • Students seeking a career as statistician, mathematician, Engineer, data analyst or simply a change of career
Students Who Viewed This Course Also Viewed
Curriculum For This Course
21 Lectures
05:26:13
+
Introduction
1 Lecture 01:49
+
Getting Started
20 Lectures 05:24:24
welcome
00:19

How to Download R
01:48

Assignment And Arithmetic
11:59

Vector List and matrixes
01:13:43

Functions in R
12:00

For loop
08:22

If Statement
07:11

Ifelse Statement
06:27

Repeat Statement
03:43

While Statement
07:54


Introduction to Application
00:22

Introduction to Powerpoint
00:26

Powerpoint Presentation on Linear and Logistic Regression
18:19

Introduction on Logistic Regression
00:13

Logistic Regression
37:46

Introduction to linear regression and integral Computation
00:15

Simple Linear Regression
20:04

Multiple Linear Regression
40:52

Integral Computation
36:49
About the Instructor
Modeste Atsague
4.5 Average rating
4 Reviews
86 Students
1 Course
Statistician

Hi, My Name is Modeste. Currently a Lectures at Central Connecticut State University, I invest a lot of time on learning and teaching. Covering a wide range of topics in Mathematics, Statistics and Computer Science , Some of my main interests include machine learning, data reduction techniques, Statistical Computing, regression analysis and a wide range of mathematical Statistics topics including parameter estimate.

With my background, which combines Mathematics ,Statistics and computer science, I have a very strong interest in computational Statistics and Statistical computing.

You might not or have less background in Computer Science     and Statistics but i will do my best so that you benefit from my experience