Statistics For Bioinformatics I

[LeeAndro]

Published 8/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 208.01 MB | Duration: 0h 35m

Analysis with SPSS

What you'll learn
Basics of Statistics
Analysis by SPSS
Build a predict model of data
Understand some of the common stochastic models encountered in Bioinformatics
This course can be use in Biostatistics, Applied Machine Learning, Computational Drug Designing
This course will be helpful in microarray for linear models, gene expression analysis, clustering, PCA plotting and quality control etc
Requirements
No knowledge of statistics needed.​

You will learn from scratch to advance of Statistics.
Description
Bioinformatics is concerned with the study of inherent structure of biological information and statistical methods are the workhorses in many of these studies. Statistical Bioinformatics acknowledges the inherent variation found in data that are generated as part of the Bioinformatics investigation and attempts to utilize expental structure and design to partition variation in biological and technical components.This course introduces the statistical methods commonly used in Bioinformatics and biological research. Improvements in modern biology have led to a rapid increase in sensitivity and measurability in expents and have reached the point where it is often impossible for a scientist alone to sort through the large volume of data that is collected from just one expent. Students will learn the principles behind statistical methods and how they can be applied to analyses biological sequences and data.There has been a great explosion of biological data and information in recent years, largely due to the advances of various high-throughput biotechnologies such as mass spectrometry, high throughput sequencing, and many genome-wide SNP profiling, RNA gene expression microarray, protein mass spectrometry, and many other recent high-throughput biotechniques. We can tackle all big data with statistics with different software's like R, SPSS, Minitab, SAS and MATLAB. Statistical theory or methods to be introduced in this course include Z-test, t-test, regression, ANOVA, hypothesis testing and multivariate data analysis.

Overview

Section 1: Statistics

Lecture 1 Introduction of Statistics

Lecture 2 Introduction of SPSS

Section 2: Descriptive Statistics

Lecture 3 Mean, Median, Mode and Variance

Lecture 4 Variance and Standard Deviation with calculation

Lecture 5 What is Skewness and Kurtosis

Lecture 6 Quartiles

Lecture 7 Descriptive Analysis by SPSS

Lecture 8 Coefficient of Correlation

Lecture 9 Coefficient of Correlation by SPSS

Section 3: Inferential Statistics

Lecture 10 Introduction of Inferential Statistics

Lecture 11 Least Square Method in Linear Regression

Lecture 12 Methods of Linear Regression

Lecture 13 Linear Regression by SPSS

Section 4: Multiple Linear Regression

Lecture 14 Method of Multiple Linear Regression

Lecture 15 Multiple Linear Regression by SPSS

Section 5: Hypothesis Testing

Lecture 16 Introduction of Hypothesis Testing and its tests

Lecture 17 Hypothesis Testing by SPSS I

Lecture 18 Hypothesis Testing by SPSS II

Section 6: ANOVA

Lecture 19 Introduction of ANOVA and its types

Lecture 20 One way ANOVA by SPSS

Lecture 21 Two way ANOVA by SPSS

Bner of statistics,Bner of Bioinformatics,Bner of Biostatistics

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