Data Analysis and Statistical Modeling in R


Summary, Description

It is still good practise to consider the true essence of your data before implementing any data science model. In this course, we will discuss mathematical modelling fundamentals and implementations. To run this review, we are going to use R Programming Language. We’re going to start with math, data distribution and statistical principles, and then we’re going to interpret our data by using plots and maps. To prove our arguments and use hypothesis checking to draw inferences safely, we can use statistical modelling.

This course is split into three parts:

We will discuss the following principles in the 1st segment.

1. Usual Delivery

2. Distribution Binomial

3. Distribution of Chi-Square

4. Densities Inside

5. Cumulative CDF Function for Delivery

6. The Quantiles

7. Numbers at random

8. CLT Central Limit Theorem

9. R Distribution of statistics

Oh. 10. Functions of Delivery

11. 11. Mean for

12. 12. The Median

13. 13. Scope

Oh. 14. Standard Deviation

A fifteen. Discrepancy

16. 16. Amount of quadrates

17. 17. Skewness The Skewness

Eighteen. Of kurtosis

2nd Division

1. Bar Packets

2. A histogram

3. Pie diagrams

4. Plots by box

5. Plots Disperse

6. Charts with Dot

7. Plots Mat

8. For groups plots

9. Datasets Plotting

The following principles will be developed in Section 3 of this course.

1. Parametric assays

2. Non-Parametric Reviews

3. What does it entail to be statistically significant?

4. P-The Value

5. Checking Hypothesis

6. Two-Tailed Analysis

7. Tailed One Test

8. Real Population implies

9. Checking Hypothesis

10. Proportional Problem

For whom this course is intended:

University and graduate students in computer science
Aspirants in Computer Science
Beginners who want mathematical modelling to be done and read about its applications
In order to conduct mathematical analysis, individuals who choose to switch from SPSS and EXCEL to R


Course Link

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