HOW IS IT USEFUL IN FIELD OF FORENSIC SCIENCE AND IN THIS I HAVE SHOWN THE TYPES OF CORRELATION, SIGNIFICANCE , METHODS AND KARL PEARSON'S METHOD OF CORRELATION
Correlation analysis measures the relationship between two or more variables. The correlation coefficient ranges from -1 to 1, indicating the strength and direction of the linear relationship. A positive correlation means the variables increase together, while a negative correlation means they change in opposite directions. Correlation only measures association and does not imply causation. Common methods for calculating correlation include Pearson's correlation coefficient, Spearman's rank correlation coefficient, and scatter plots.
Multiple regression analysis allows researchers to examine the relationship between one dependent or outcome variable and two or more independent or predictor variables. It extends simple linear regression to model more complex relationships. Stepwise regression is a technique that automates the process of building regression models by sequentially adding or removing variables based on statistical criteria. It begins with no variables in the model and adds variables one at a time based on their contribution to the model until none improve it significantly.
This document discusses the meaning and types of correlation. It defines correlation as a statistical tool that measures the relationship between two variables. The degree of relationship is measured by the correlation coefficient, which ranges from -1 to 1. A positive correlation means the variables change in the same direction, while a negative correlation means they change in opposite directions. Common methods for studying correlation include scatter plots, Karl Pearson's coefficient, and Spearman's rank correlation coefficient. The coefficient of correlation, denoted by r, measures the strength and direction of the linear relationship between variables.
1. Correlation analysis measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where -1 is a perfect negative correlation, 0 is no correlation, and 1 is a perfect positive correlation.
2. Scatter diagrams provide a visual representation of the relationship between two variables but do not provide a precise measure of correlation. Pearson's correlation coefficient (r) calculates the numerical strength of the linear relationship.
3. Correlation is widely used in fields like agriculture, genetics, and physiology to study relationships between variables like crop yield and fertilizer use, gene linkage, and organism growth and environmental factors.
This document introduces various types of correlation. Correlation refers to the relationship between two or more variables. There are positive and negative correlations. Positive correlation means that as one variable increases, the other also increases, while negative correlation means that one variable increases as the other decreases. Other types discussed include simple, partial, and multiple correlation. Linear correlation means the ratio of change between variables is constant, while non-linear correlation means the ratio of change is not constant. Examples are provided for each type of correlation.
The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population.
This document discusses correlation and the Pearson's coefficient of correlation. It defines correlation as the relationship between two variables, which can be positive, negative, or zero. The Pearson's coefficient of correlation, represented by r, measures the strength and direction of this relationship. The document provides examples of calculating r using the product-moment method for different sets of data. It interprets the resulting r values and discusses advantages and limitations of the product-moment correlation method.
The document discusses regression analysis and its key concepts. Regression analysis is used to understand the relationship between two or more variables and make predictions. There are two main types: simple linear regression, which involves two variables, and multiple regression, which involves more than two variables. Regression lines show the average relationship between the variables and can be used to predict outcomes. The regression coefficients measure the change in the dependent variable for a unit change in the independent variable. The standard error of the estimate indicates how close the data points are to the regression line.
This document discusses correlation and provides examples of its applications. It defines correlation as a linear relationship between two variables and describes types of correlation including positive, negative, simple, partial and multiple correlations. Simple correlation coefficient (r) is explained, which measures the strength and nature of a relationship between two quantitative variables. An example of calculating r using age and weight data is shown. Several real-life examples of positive and negative correlations are given such as the relationships between study time and test scores, age and clothing size, and temperature and sales.
The document discusses probability theory and provides definitions and examples of key concepts like conditional probability and Bayes' theorem. It defines probability as the ratio of favorable events to total possible events. Conditional probability is the probability of an event given that another event has occurred. Bayes' theorem provides a way to update or revise beliefs based on new evidence and relates conditional probabilities. Examples are provided to illustrate concepts like conditional probability calculations.
Partial Correlation, Multiple Correlation And Multiple Regression AnalysisSundar B N
This document discusses correlation and regression analysis. It defines partial correlation as assessing the relationship between two variables while controlling for the effect of a third variable. Multiple correlation is defined as measuring the strength of the relationship between a single dependent variable and two or more independent variables. Formulas are provided for partial correlation coefficients measuring the correlation between different pairs of variables while controlling for others. Multiple correlation coefficients are also defined as measuring the correlation between a dependent variable and the combination of multiple independent variables.
This document presents information about regression analysis. It defines regression as the dependence of one variable on another and lists the objectives as defining regression, describing its types (simple, multiple, linear), assumptions, models (deterministic, probabilistic), and the method of least squares. Examples are provided to illustrate simple regression of computer speed on processor speed. Formulas are given to calculate the regression coefficients and lines for predicting y from x and x from y.
Bar Diagram (chart) in Statistics presentationsheiblu
This document discusses bar diagrams and their components. It defines a bar diagram as a chart that uses rectangular bars to present qualitative data, with the bar lengths proportional to the values. It notes that qualitative data deals with descriptions that can be observed but not measured, such as colors, textures, smells, tastes, and appearances. The key components of a bar diagram are collecting qualitative data, drawing and labeling the x- and y- axes, and drawing the bars. An example bar diagram and table show the numbers of children who favorite different cartoons. Finally, it lists different types of bar diagrams like horizontal, grouped, and stacked bar charts.
Correlation and regression are statistical techniques used to analyze relationships between variables. Correlation determines the strength and direction of a relationship, while regression describes the linear relationship to predict changes in one variable based on changes in another. There are different types of correlation including simple, multiple, and partial correlation. Regression analysis determines the regression line that best fits the data to estimate values of one variable based on the other. The correlation coefficient measures the strength of linear correlation from -1 to 1, while regression coefficients are used to predict changes in the variables.
Statistics is the study of collecting, analyzing, presenting, and organizing quantitative data. It involves developing techniques to gather, display, and evaluate numerical data to assist with decision-making. Statistics has many applications across various fields like planning, economics, business, industry, science, education, and warfare. It is widely used in business and management functions such as marketing, production, finance, banking, investment, purchasing, accounting, and management control.
This document is a presentation by Dwaiti Roy on partial correlation. It begins with an acknowledgement section thanking various professors and resources that helped in preparing the presentation. It then provides definitions and explanations of key concepts related to partial correlation such as correlation, assumptions of correlation, coefficient of correlation, coefficient of determination, variates, partial correlation, assumptions and hypothesis of partial correlation, order and formula of partial correlation. Examples are provided to illustrate partial correlation. The document concludes with references and suggestions for further reading.
The document discusses standard deviation and its properties. Standard deviation is a measure of how spread out numbers are from the average (mean) value. It is always non-negative and can be impacted by outliers. A low standard deviation means values are close to the mean, while a high standard deviation means values are more spread out. Standard deviation can be used to calculate what percentage of data falls within certain intervals from the mean when data is normally distributed.
The document discusses correlation analysis and different types of correlation. It defines correlation as the linear association between two random variables. There are three main types of correlation:
1) Positive vs negative vs no correlation based on the relationship between two variables as one increases or decreases.
2) Linear vs non-linear correlation based on the shape of the relationship when plotted on a graph.
3) Simple vs multiple vs partial correlation based on the number of variables.
The document also discusses methods for studying correlation including scatter plots, Karl Pearson's coefficient of correlation r, and Spearman's rank correlation coefficient. It provides interpretations of the correlation coefficient r and coefficient of determination r2.
The document discusses properties of the correlation coefficient. It states that the correlation coefficient ranges from -1 to 1, with -1 indicating perfect negative correlation, 1 indicating perfect positive correlation, and 0 indicating no correlation. It also explains that the correlation coefficient is a measure of the strength and linear relationship between two variables and is symmetric, meaning the correlation between variables x and y is the same as between y and x. Additionally, the document defines probable error of the correlation coefficient as a way to determine the limits within which the population correlation coefficient is expected to fall.
This document discusses various measures of dispersion used to quantify how spread out or variable a data set is. It defines dispersion and explains the purposes of measuring it. The key measures of dispersion discussed are range, quartile deviation, mean deviation, variance, standard deviation, and coefficient of variation. Formulas are provided for calculating each measure along with their merits and limitations. The conclusion emphasizes that measures of dispersion are useful for comparing distributions and further statistical analysis.
This document discusses correlation coefficient and different types of correlation. It defines correlation coefficient as the measure of the degree of relationship between two variables. It explains different types of correlation such as perfect positive correlation, perfect negative correlation, moderately positive correlation, moderately negative correlation, and no correlation. It also discusses different methods to study correlation including scatter diagram method, graphic method, Karl Pearson's coefficient of correlation method, and Spearman's rank correlation method. It provides examples and steps to calculate correlation coefficient using these different methods.
This document discusses correlation analysis and different methods of studying correlation. It begins by defining correlation as the association between two or more variables. There are different types of correlation such as positive, negative, linear, and curvilinear correlation. The degree of correlation can be determined using the correlation coefficient, with values ranging from -1 to 1. Common methods for studying correlation discussed include scatter diagrams, Karl Pearson's coefficient, Spearman's rank correlation, and concurrent deviation method. The properties and interpretation of the correlation coefficient are also outlined.
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This document discusses correlation and regression analysis techniques used in physical geography to examine relationships between variables. Correlation determines the degree of relationship between two variables and is represented by the correlation coefficient r, which ranges from -1 to 1. Regression identifies relationships between a dependent variable and one or more independent variables by calculating a best-fit line that minimizes residuals. The document provides examples of calculating the correlation coefficient r and estimating the regression equation between variables.
This document defines and explains different types of correlation. It begins by defining correlation as a statistical tool to measure the relationship between two variables. There are three main types of correlation discussed: positive correlation where both variables move in the same direction, negative correlation where the variables move in opposite directions, and zero correlation where a change in one variable does not affect the other. The document also discusses linear and non-linear correlation, as well as simple, partial, and multiple correlation. Different methods for measuring correlation are presented, including graphical methods like scatter diagrams and algebraic methods like Pearson's correlation coefficient.
This presentation covered the following topics:
1. Definition of Correlation and Regression
2. Meaning of Correlation and Regression
3. Types of Correlation and Regression
4. Karl Pearson's methods of correlation
5. Bivariate Grouped data method
6. Spearman's Rank correlation Method
7. Scattered diagram method
8. Interpretation of correlation coefficient
9. Lines of Regression
10. regression Equations
11. Difference between correlation and regression
12. Related examples
Correlation analysis measures the strength and direction of association between two or more variables. It is represented by the coefficient of correlation (r), which ranges from -1 to 1. A value of 0 indicates no association, 1 indicates perfect positive association, and -1 indicates perfect negative association. The scatter diagram is a graphical method to visualize the association between variables by plotting their values. Karl Pearson's coefficient is a commonly used algebraic method to calculate the coefficient of correlation from sample data.
This document discusses correlation and defines it as the statistical relationship between two variables, where a change in one variable results in a corresponding change in the other. It describes different types of correlation including positive, negative, simple, partial and multiple. Methods for studying correlation are also outlined, including scatter diagrams and Karl Pearson's coefficient of correlation (represented by r), which quantifies the strength and direction of the linear relationship between two variables from -1 to 1. The coefficient of determination (r2) is also introduced, which expresses the proportion of variance in one variable that is predictable from the other.
This document discusses correlation analysis in agriculture. It begins by defining correlation as the relationship between two or more variables. Some key points:
- Correlation can be positive (variables move in the same direction), negative (variables move in opposite directions), linear, nonlinear, simple, multiple, partial or total.
- Common types analyzed in agriculture include the relationship between yield and rainfall, price and supply, height and weight.
- Methods for measuring correlation are discussed, including Karl Pearson's coefficient of correlation (denoted by r), Spearman's rank correlation, and scatter diagrams.
- The value of r ranges from -1 to 1, with higher positive or negative values indicating a stronger linear relationship between variables
This document discusses correlation and regression analysis. It defines correlation as a statistical measure of how two variables are related. A correlation coefficient between -1 and 1 indicates the strength and direction of the linear relationship between variables. A scatterplot can show this graphically. Regression analysis involves using one variable to predict scores on another variable. Simple linear regression uses one independent variable to predict a dependent variable, while multiple regression uses two or more independent variables. The goal is to identify the regression line that best fits the data with the least error. The coefficient of determination, R2, indicates how much variance in the dependent variable is explained by the independent variables.
This document provides an overview of correlation and linear regression analysis. It defines correlation as a statistical measure of the relationship between two variables. Pearson's correlation coefficient (r) ranges from -1 to 1, with values farther from 0 indicating a stronger linear relationship. Positive values indicate an increasing relationship, while negative values indicate a decreasing relationship. The coefficient of determination (r2) represents the proportion of shared variance between variables. While correlation indicates linear association, it does not imply causation. Multiple regression allows predicting a continuous dependent variable from two or more independent variables.
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This document discusses correlation analysis and different types of correlation. It begins by defining correlation as the association between two or more variables. Correlation can be positive, negative, linear, or curvilinear depending on how the variables move in relation to each other. The degree of correlation is determined by calculating the coefficient of correlation, with values ranging from +1 to -1. Several methods are described for studying correlation including scatter diagrams, Karl Pearson's coefficient, Spearman's rank correlation, and concurrent deviation method. The document also outlines how to interpret the coefficient of correlation and test its significance.
This document discusses correlation and regression analysis. It defines correlation as a statistical measure of how strongly two variables are related. A correlation coefficient between -1 and 1 indicates the strength and direction of the linear relationship between variables. Regression analysis allows us to predict the value of a dependent variable based on the value of one or more independent variables. Simple linear regression involves one independent variable, while multiple regression involves two or more independent variables to predict the dependent variable. The document provides examples and formulas for calculating correlation, regression lines, explained and unexplained variance, and the coefficient of determination.
This document discusses correlation analysis and different types of correlation. It defines correlation as a statistical analysis of the relationship between two or more variables. There are three main types of correlation discussed:
1. Positive correlation means that as one variable increases, the other also tends to increase. Negative correlation means that as one variable increases, the other tends to decrease.
2. Simple correlation analyzes the relationship between two variables, while multiple correlation analyzes three or more variables simultaneously. Partial correlation holds the effect of other variables constant.
3. Methods for measuring correlation include scatter diagrams, which graphically show the relationship, and algebraic formulas that calculate a correlation coefficient to quantify the strength and direction of the relationship.
Correlation and linear regression are fundamental statistical concepts used to explore relationships between variables and make predictions. Correlation measures the strength and direction of association between variables, while linear regression models relationships to enable predictions. Understanding these concepts equips analysts to draw meaningful conclusions from data and make informed recommendations. Mastery of correlation and linear regression techniques allows for discoveries of patterns within datasets across various domains like finance, economics, psychology, and more.
This document discusses correlation and regression analysis. It defines correlation as a relationship between two variables where a change in one variable is accompanied by a change in the other. There are three types of correlation: positive, negative, and no correlation. It also discusses various methods to calculate the coefficient of correlation, including Pearson's correlation coefficient, Spearman's rank correlation coefficient, and concurrent deviation method. Regression is defined as a functional relationship between two or more related variables that is represented by a curve or line of best fit called the regression line.
The document discusses various statistical techniques for analyzing the relationship between two variables, including scatter plots, covariance, correlation coefficients, linear regression, and curvilinear regression. It provides formulas and assumptions for each method, and explains how to interpret the results to determine if variables are related and the strength and direction of their relationship.
Power point presentationCORRELATION.pptxSimran Kaur
This document discusses different types of correlation and methods for determining correlation between variables. It defines correlation as the relationship between two or more variables, and describes positive correlation as variables changing in the same direction and negative correlation as changing in opposite directions. It also distinguishes between simple correlation of two variables and multiple correlation of more than two variables. Additionally, it introduces several methods for measuring correlation, including scatter plots, Karl Pearson's coefficient of correlation, and Spearman's rank correlation coefficient.
Regression analysis allows researchers to identify an equation that best fits paired data and predict the relationship between two quantitative variables. Linear regression assumes a linear relationship and finds the line that best describes how the dependent variable changes with the independent variable. The regression line equation takes the form Y = bX + a, where b is the slope and a is the intercept. Researchers can use linear regression to predict new Y values based on X and assess how well the linear model fits the data.
what is it , use of face recognition in terms of Biometrics. types of bio metrics in which category is it placed . what are its applications and used in terms of future prespective
what things are visible which instruments are used, what are the major functions of the instrument used and which is the best technique used by the scientific officer to compare whether two soil samples are from same area or different area.
in terms of Forensic Science, how iris recognition is done and what are the key factors that should be kept in mind. It can be its Advantages, Disadvantages, Approaches and very importantly the working process.
whenever and wherever a Disaster takes place in the form of Tsunami, Earthquake, Terrorist attack or Bomb blast the bodies which we get at the crime scene are either damaged or sometimes face cannot be identified.
Whenever there is a crime, the culprit leaves some type of evidence. Bitemark is a very peculiar and main evidence for a Forensic Odontologist who studies and tells whether the mark is superficial or cutaneous
The document describes Weibel's lung model from 1963, which was a symmetrical tree lung model for adults with 35 degree branching angles. It featured symmetrical tubes of the same generation with identical geometric parameters. This was the simplest model of the human lung and is widely used. The document also describes the different generations that make up the airways in the lungs, starting from the trachea and branching all the way to the terminal bronchioles and alveoli. Finally, it discusses the tracheobronchial tree, which includes the branching structure of the airways that supply air to the lungs, starting from the trachea down to the segmental bronchi.
Open Source and AI - ByWater Closing Keynote Presentation.pdfJessica Zairo
ByWater Solutions, a leader in open-source library software, will discuss the future of open-source AI Models and Retrieval-Augmented Generation (RAGs). Discover how these cutting-edge technologies can transform information access and management in special libraries. Dive into the open-source world, where transparency and collaboration drive innovation, and learn how these can enhance the precision and efficiency of information retrieval.
This session will highlight practical applications and showcase how open-source solutions can empower your library's growth.
How to Use Pre Init hook in Odoo 17 -Odoo 17 SlidesCeline George
In Odoo, Hooks are Python methods or functions that are invoked at specific points during the execution of Odoo's processing cycle. The pre-init hook is a method provided by the Odoo framework to execute custom code before the initialization of the module's data. ie, it works before the module installation.
Dr. Nasir Mustafa CERTIFICATE OF APPRECIATION "NEUROANATOMY"Dr. Nasir Mustafa
CERTIFICATE OF APPRECIATION
"NEUROANATOMY"
DURING THE JOINT ONLINE LECTURE SERIES HELD BY
KUTAISI UNIVERSITY (GEORGIA) AND ISTANBUL GELISIM UNIVERSITY (TURKEY)
FROM JUNE 10TH TO JUNE 14TH, 2024
APM event held on 9 July in Bristol.
Speaker: Roy Millard
The SWWE Regional Network were very pleased to welcome back to Bristol Roy Millard, of APM’s Assurance Interest Group on 9 July 2024, to talk about project reviews and hopefully answer all your questions.
Roy outlined his extensive career and his experience in setting up the APM’s Assurance Specific Interest Group, as they were known then.
Using Mentimeter, he asked a number of questions of the audience about their experience of project reviews and what they wanted to know.
Roy discussed what a project review was and examined a number of definitions, including APM’s Bok: “Project reviews take place throughout the project life cycle to check the likely or actual achievement of the objectives specified in the project management plan”
Why do we do project reviews? Different stakeholders will have different views about this, but usually it is about providing confidence that the project will deliver the expected outputs and benefits, that it is under control.
There are many types of project reviews, including peer reviews, internal audit, National Audit Office, IPA, etc.
Roy discussed the principles behind the Three Lines of Defence Model:, First line looks at management controls, policies, procedures, Second line at compliance, such as Gate reviews, QA, to check that controls are being followed, and third Line is independent external reviews for the organisations Board, such as Internal Audit or NAO audit.
Factors which affect project reviews include the scope, level of independence, customer of the review, team composition and time.
Project Audits are a special type of project review. They are generally more independent, formal with clear processes and audit trails, with a greater emphasis on compliance. Project reviews are generally more flexible and informal, but should be evidence based and have some level of independence.
Roy looked at 2 examples of where reviews went wrong, London Underground Sub-Surface Upgrade signalling contract, and London’s Garden Bridge. The former had poor 3 lines of defence, no internal audit and weak procurement skills, the latter was a Boris Johnson vanity project with no proper governance due to Johnson’s pressure and interference.
Roy discussed the principles of assurance reviews from APM’s Guide to Integrated Assurance (Free to Members), which include: independence, accountability, risk based, and impact, etc
Human factors are important in project reviews. The skills and knowledge of the review team, building trust with the project team to avoid defensiveness, body language, and team dynamics, which can only be assessed face to face, active listening, flexibility and objectively.
Click here for further content: https://www.apm.org.uk/news/a-beginner-s-guide-to-project-reviews-everything-you-wanted-to-know-but-were-too-afraid-to-ask/
Topics to be Covered
Beginning of Pedagogy
What is Pedagogy?
Definition of Pedagogy
Features of Pedagogy
What Is Pedagogy In Teaching?
What Is Teacher Pedagogy?
What Is The Pedagogy Approach?
What are Pedagogy Approaches?
Teaching and Learning Pedagogical approaches?
Importance of Pedagogy in Teaching & Learning
Role of Pedagogy in Effective Learning
Pedagogy Impact on Learner
Pedagogical Skills
10 Innovative Learning Strategies For Modern Pedagogy
Types of Pedagogy
2. CORRELATION
Correlation is an analysis used to determine the relationship between two or
more variables.
The measure of correlation is called coefficient of correlation and is denoted
by the symbol ‘r’.
It helps us in finding the degree or extent of quantitative relationship between
two variables.
It does not say anything about the cause and effect relationship between the
two variables.
3. SIGNIFICANCE OF CORRELATION
It is used to determine the relationship between two variables.
It reduces the range of uncertainty. The predictions based on correlation analysis
are more precise and reliable.
It helps us to estimate the value of dependent variable for the given value of
independent variable.
4. TYPES OF CORRELATION
Correlation is described or classified in several different ways such as:
1. Positive and Negative Correlation: Whether correlation is positive (direct) or negative
(inverse) would depend upon the direction of change of the variables. If both the variables
are varying in the same direction i.e., if as one variable is increasing the other, on an
average is also increasing or, if as one variable is decreasing the other, on an average, is
also decreasing, correlation is said to be positive.
If on the other hand, the variable are varying in positive direction, i.e. as one variable is
increasing the other is decreasing or vise versa, and correlation is said to be negative.
5. 2. Liner and Curvilinear (Non-Linear) Correlation.
Linear Correlation: Correlation is said to be linear when the amount of change in one
variable tends to bear a constant ratio to the amount of change in the other.
Non-Linear Correlation: The correlation would be non-linear if the amount of change in one
variable does not bear a constant ratio to the amount of change in the other variable.
6. METHODS OF STUDYING CORRELATION
Correlation can be studied by any of the following method.
1. Scatter diagram method.
2. Karl Pearson’s coefficient of correlation.
3. Spearman’s coefficient of rank correlation and
4. Concurrent deviation method.
7. Scatter diagram method
Scatter diagram or dot diagram is the simplest graphical device of showing the correlation
between the two variables (x and y). Such diagrammatic representation of bivariate data is
known as scatter diagram.
Observations:
Positive Correlation: When the x and y values increases together there will be a
positive correlation. (r= +1)
Negative Correlation: When the x value gets bigger and the y value gets smaller there
will be a negative correlation. (r= -1)
No Correlation: When the points do not show a pattern there is no correlation. (r= 0)
8. It is simple and non-
mathematical method
of studying
correlation
It is easy to
understand
Merit of
Scatter
Diagram
Method It gives only a rough
idea of how the two
variable are related.
Exact degree of
correlation between
the two variables can
not be established
by applying this
method.
Demerit
of Scatter
Diagram
Method
9. Karl Pearson’s Coefficient of Correlation
It is used universally for describing the degree of correlation between two series .
Formula of computing Pearson’s r is:
Here, x = ( X- X ) ; y= ( Y- Y )
Sx = Standard deviation of x series
Sy = Standard deviation of y series
N = Number of pairs of observation
Modified version:
Where, x = ( X- X ) ; y= ( Y- Y )
10. Procedure for computing the correlation coefficient
Calculate the mean of the two series ‘x’ &’y’
Calculate the deviations ‘x’ &’y’ in two series from their respective mean.
Square each deviation of ‘x’ &’y’ then obtain the sum of the squared deviation
i.e.
Multiply each deviation under x with each deviation under y & obtain the product of ‘xy’.
Then obtain the sum of the product of x , y i.e. Σxy
Substitute the value in the formula.
12. It is most
important and
precise
method of
measuring the
relationship of
two variables.
It measures the
direction as
well as the
relationship
between the
two variables.
Merit of
Karl
Pearson’s
Method
The computational
procedure of this
method is difficult
as compared to
other method.
The value of the
coefficient is
affected by
extreme items.
Demerit
of Karl
Pearson’s
Method