Correlation In Data Analytics & Business Statistics
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.70 GB | Duration: 2h 18m
Statistical Analysis using Correlation
What you'll learn

Correlation Analysis
Requirements
No
Description
Correlation is a process of finding out the degree of relationship between two variables. Correlation is a great statistical technique and a very interesting one. The correlation is one of the easiest descriptive statistics to understand and possibly one of the most widely used. The term correlation refers to the measurement of a relationship between two or more variables. A correlational coefficient is used to represent this relationship and is often abbreviated with the letter 'r.' A correlational coefficient typically ranges between –1.0 and +1.0 and provides two important pieces of information regarding the relationship: Intensity and Direction. The value -1 indicates a perfect negative correlation, while a +1 indicates a perfect positive correlation. A correlation of zero means there is no relationship between the two variables. When there is a negative correlation between two variables, as the value of one variable increases, the value of the other variable decreases, and vise versa. In other words, for a negative correlation, the variables work opposite each other.This course will give insights on:Calculation of Coefficient of Correlation using Karl Pearson's method, Spearman's Rank Difference Method & Method of Concurrent Deviations. Here , several important techniques like Direct Method and Assumed Mean Method in Karl Pearson have also been discussed in detail. Correlation in Grouped Series has also been explained in detail. In Spearman's Method both approaches having different ranks and cases having same ranks have been explained along with Method of Concurrent deviations.Overall, the students will have a great learning time and will be studying all the major tools and techniques of Correlation Analysis.
Who this course is for
Graduation Students, Management Students, MBA, BBA, Commerce students, Data Analytics students
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