Statistics
•Download as PPT, PDF•
1 like•126 views
Correlation measures the relationship between two variables. It can be positive, meaning the variables increase or decrease together, or negative, meaning one variable increases as the other decreases. The Pearson correlation coefficient ranges from -1 to 1, with -1 indicating a perfect negative correlation, 0 indicating no correlation, and 1 indicating a perfect positive correlation. Values between -1 and 0 represent negative correlation, while values between 0 and 1 represent positive correlation. Graphically, correlation strength can be classified as small, medium, or large depending on how close the coefficient is to -1 or 1.
Report
Share
Report
Share
Recommended
Wm 10 portfolio valuation correlation
Correlation is a statistical technique used to determine the strength and direction of the relationship between two variables. The coefficient of correlation (r) measures the statistical correlation and ranges from +1 to -1, where +1 is a perfect positive correlation, -1 is a perfect negative correlation, and 0 is no correlation. Common guidelines for interpreting the coefficient define strong, moderate, and weak correlations based on the absolute value of r. While correlation can examine relationships between quantifiable data, it does not prove causation between variables due to potential confounding factors.
Lecture 3 ba 3 statistics for business analytics
Correlation is a statistical technique used to determine the strength and direction of the relationship between two variables. The coefficient of correlation (r) ranges from +1 to -1, indicating the strength of either a positive or negative relationship, or no relationship if r equals 0. Guidelines are provided to interpret the strength of correlation based on the r value. Correlation is appropriate for examining relationships between quantifiable data but not categorical data, and should not be used alone to determine causation.
procedure for finding correlation coefficient
The coefficient of correlation is a number between -1 and 1 that indicates the strength and direction of the linear relationship between two variables. A value of 0 means no correlation, 1 means perfect positive correlation, and -1 means perfect negative correlation. The coefficient of correlation is calculated using Karl Pearson's method and is denoted by r. It is a dimensionless number that is unaffected by changes in scale or origin of the variables.
Pearson Product Moment Correlation - Thiyagu
The coefficient of correlation computed by product moment coefficient of correlation or Pearson's correlation coefficient and symbolically represented by r. This presentation explains the concept, computation, merits and demerits of Pearson Product Moment Correlation.
Correlation coefficient
This document discusses correlation and the correlation coefficient. It defines correlation as a measure of the relationship between two variables. It explains that the correlation coefficient indicates the magnitude and direction of the relationship as well as the strength of the relationship and percentage of variance explained. Values of the correlation coefficient are interpreted as showing no relationship, a low relationship, or a strong relationship. Conditions for interpreting the correlation coefficient include data forming a linear pattern and equal variances.
Correlation
This document defines and explains different types of correlation between two variables: positive correlation, negative correlation, no correlation, perfect correlation, strong correlation, and weak correlation. It provides examples of scatter plots that illustrate positive and negative correlation, and includes formulas to calculate the correlation coefficient (r) between two variables from their data values.
Multiple Correlation - Thiyagu
Multiple Correlation Coefficient denoting a correlation of one variable with multiple other variables. The Multiple Correlation Coefficient, R, is a measure of the strength of the association between the independent (explanatory) variables and the one dependent (prediction) variable. This presentation explains the concept of multiple correlation and its computation process.
Correlation Computations Thiyagu
This document discusses various types of correlation coefficients used in statistics. It begins by describing Pearson's product-moment correlation coefficient (r) which measures the strength and direction of the linear relationship between two continuous variables. It then provides background on Karl Pearson who developed r. The rest of the document defines and provides formulas for other correlation coefficients including Spearman's rank correlation coefficient, point-biserial correlation, biserial correlation, tetrachoric correlation, and phi coefficient; and discusses their uses based on the scale types (nominal, ordinal, interval/ratio) of the variables.
Recommended
Wm 10 portfolio valuation correlation
Correlation is a statistical technique used to determine the strength and direction of the relationship between two variables. The coefficient of correlation (r) measures the statistical correlation and ranges from +1 to -1, where +1 is a perfect positive correlation, -1 is a perfect negative correlation, and 0 is no correlation. Common guidelines for interpreting the coefficient define strong, moderate, and weak correlations based on the absolute value of r. While correlation can examine relationships between quantifiable data, it does not prove causation between variables due to potential confounding factors.
Lecture 3 ba 3 statistics for business analytics
Correlation is a statistical technique used to determine the strength and direction of the relationship between two variables. The coefficient of correlation (r) ranges from +1 to -1, indicating the strength of either a positive or negative relationship, or no relationship if r equals 0. Guidelines are provided to interpret the strength of correlation based on the r value. Correlation is appropriate for examining relationships between quantifiable data but not categorical data, and should not be used alone to determine causation.
procedure for finding correlation coefficient
The coefficient of correlation is a number between -1 and 1 that indicates the strength and direction of the linear relationship between two variables. A value of 0 means no correlation, 1 means perfect positive correlation, and -1 means perfect negative correlation. The coefficient of correlation is calculated using Karl Pearson's method and is denoted by r. It is a dimensionless number that is unaffected by changes in scale or origin of the variables.
Pearson Product Moment Correlation - Thiyagu
The coefficient of correlation computed by product moment coefficient of correlation or Pearson's correlation coefficient and symbolically represented by r. This presentation explains the concept, computation, merits and demerits of Pearson Product Moment Correlation.
Correlation coefficient
This document discusses correlation and the correlation coefficient. It defines correlation as a measure of the relationship between two variables. It explains that the correlation coefficient indicates the magnitude and direction of the relationship as well as the strength of the relationship and percentage of variance explained. Values of the correlation coefficient are interpreted as showing no relationship, a low relationship, or a strong relationship. Conditions for interpreting the correlation coefficient include data forming a linear pattern and equal variances.
Correlation
This document defines and explains different types of correlation between two variables: positive correlation, negative correlation, no correlation, perfect correlation, strong correlation, and weak correlation. It provides examples of scatter plots that illustrate positive and negative correlation, and includes formulas to calculate the correlation coefficient (r) between two variables from their data values.
Multiple Correlation - Thiyagu
Multiple Correlation Coefficient denoting a correlation of one variable with multiple other variables. The Multiple Correlation Coefficient, R, is a measure of the strength of the association between the independent (explanatory) variables and the one dependent (prediction) variable. This presentation explains the concept of multiple correlation and its computation process.
Correlation Computations Thiyagu
This document discusses various types of correlation coefficients used in statistics. It begins by describing Pearson's product-moment correlation coefficient (r) which measures the strength and direction of the linear relationship between two continuous variables. It then provides background on Karl Pearson who developed r. The rest of the document defines and provides formulas for other correlation coefficients including Spearman's rank correlation coefficient, point-biserial correlation, biserial correlation, tetrachoric correlation, and phi coefficient; and discusses their uses based on the scale types (nominal, ordinal, interval/ratio) of the variables.
Spearman Rank Correlation - Thiyagu
The Spearman’s Rank Correlation Coefficient is the non-parametric statistical measure used to study the strength of association between the two ranked variables. This method is applied to the ordinal set of numbers, which can be arranged in order, i.e. one after the other so that ranks can be given to each. This presentation slides explains the procedure to find out the Rank Difference correlation and its applications.
Correlation
Correlation measures the strength and direction of association between two variables. It ranges from -1 to 1, where 0 indicates no association and values closer to 1 or -1 indicate stronger positive or negative associations, respectively. The document provides examples of positive correlation between variables such as height and weight that increase together, and negative correlation where variables such as TV time and grades move in opposite directions. Formulas and different types of correlation including partial and multiple correlation are also defined in the document.
WF ED 540, Class Meeting13, 19 November 2015
This document discusses Pearson's product-moment correlation and its assumptions. It describes the range of correlation values and the null hypothesis of no correlation. It explains that correlation measures the strength of a linear association between two interval or ratio-scaled variables and is not a measure of causation. Hypothesis testing is used to determine if a correlation is statistically significant different than zero.
Statisticalrelationships
This document discusses statistical relationships and various ways to measure the strength and direction of relationships between variables. It defines different types of relationships such as general association, monotonic relationships, and linear relationships. It also describes how to evaluate the strength of relationships and introduces common measures of association used to numerically summarize relationships, noting that the interpretation depends on the level of measurement and type of relationship being measured. Important properties of coefficients are outlined, including how their values indicate the null relationship and strength of association.
Educ eval ppt correlation
This document discusses correlation and correlation coefficients. It defines correlation as the strength and direction of relationship between two variables. The correlation coefficient (r) ranges from -1 to 1, with values closer to these extremes indicating a stronger relationship. Positive correlations occur when high scores in one variable are associated with high scores in the other, and vice versa for low scores. Negative correlations are the opposite. Scatterplots can graphically show the correlation. While correlation does not prove causation, it indicates an association between variables.
Partial Correlation - Thiyagu
Partial Correlation measures the Correlation between X and Y Controlling for Z. Partial correlation is a measure of the strength and direction of a linear relationship between two continuous variables whilst controlling for the effect of one or more other continuous variables (also known as 'covariates' or 'control' variables). This presentation explains the concept of Partial correlation and its computation process.
Measuring relationships
This document discusses the Pearson r correlation coefficient and how to calculate, interpret, and evaluate the significance of relationships between variables. The Pearson r ranges from -1 to 1, with values closer to these extremes indicating a stronger linear relationship and values near 0 a weaker relationship. A correlation of -0.67 represents the strongest relationship among the options given. The correlation coefficient is calculated using the formula presented. An example calculates the correlation between number of hospitals and maternal mortality rate in various cities. Interpretation of the correlation involves evaluating the magnitude, sign, strength of relationship, and coefficient of determination. Statistical significance can be determined by comparing the correlation to critical values from tables.
Phi Coefficient of Correlation - Thiyagu
The phi coefficient is that system of correlation which is computed between two variables, where neither of them is available in a continuous measures and both of them are expressed in the form of natural or genuine dichotomies. This presentation slides describes the concept and procedures to do the computation of phi coefficient of correlation.
Scatterplots - LSRLs - RESIDs
This document discusses scatterplots and correlation. Scatterplots can be used to display the relationship between two quantitative variables, known as the explanatory and response variables. Correlation measures the strength and direction of a linear relationship between two variables on a scale of -1 to 1. A correlation near -1 or 1 indicates a strong linear relationship, while a value closer to 0 indicates a weaker linear relationship or no relationship. Scatterplots can be interpreted by describing the direction, form, and strength of the relationship between variables as well as identifying any outliers.
Introduction to simple linear regression and correlation in spss
1. The document discusses simple linear regression and correlation. It defines simple correlation and describes positive, negative, and no correlation between two variables.
2. It explains that the correlation coefficient shows the degree of relation between two variables and can range from -1 to 1.
3. Simple linear regression predicts an outcome variable (y) as a function of a predictor variable (x) using the linear regression formula y = B0 + B1x, where B0 is the intercept and B1 is the regression coefficient.
Practice post
This document discusses measuring linear association between variables through scatterplots and correlation. It defines correlation as a measure of the strength and direction of linear association between two quantitative variables. Correlation values range from -1 to 1, with -1 indicating a perfect negative linear relationship and 1 indicating a perfect positive linear relationship. The document cautions that correlation only measures linear association and provides an example where data sets with identical correlations show different relationships.
2.5 a correlation & best fitting lines
The document discusses finding the line of best fit for a data set using linear regression. It explains that the line of best fit should have approximately the same number of points above and below the line. It also describes how the slope of the line indicates whether the correlation is positive, negative, or relatively no correlation. Finally, it introduces the correlation coefficient r as a measure of the strength of correlation, with values closer to 1 or -1 indicating a stronger correlation.
Matching algorithm for online dating
Matching is important part of online dating. Here are some tips of making algorithms for online dating. Learn more about COGXIO & DateIITians Algorithm used for matching online for some parts. COGXIO mostly used Machine Learning further to improve total consumer experience.
Love equation
The document presents a mathematical equation for love based on percentages of positive and negative traits. It states that for a lover, one's positive traits will be 10% more in the lover, while one's negative traits will be 10% less. This 10% threshold provides maximum satisfaction and fulfillment for both parties. The lover enhances one's strengths and reduces weaknesses according to this model.
Association analysis
1. Association analysis includes correlation coefficient analysis and path coefficient analysis to study the relationship between two or more variables.
2. Correlation coefficient analysis measures the degree and direction of association between variables on a scale of -1 to 1, where 1 is total positive correlation and -1 is total negative correlation.
3. Path coefficient analysis splits the correlation coefficients into measures of direct and indirect effects to determine the direct and indirect contribution of independent variables to a dependent variable like yield.
Coefficient of correlation...ppt
The document defines and describes the coefficient of correlation, which is a statistic that measures the strength and direction of the linear relationship between two variables on a scale of -1 to +1. It explains that a correlation of -1 is a perfect negative correlation, 0 is no correlation, and +1 is a perfect positive correlation. The document also differentiates between types of correlation coefficients and methods for computing correlation coefficients, including Karl Pearson's and Spearman's rank correlation coefficients.
Correlation analysis
Correlation analysis examines the relationship between two or more variables. Positive correlation means the variables increase together, while negative correlation means they change in opposite directions. The Pearson correlation coefficient, r, quantifies the strength of linear correlation between -1 and 1. Multiple correlation analysis extends this to measure the correlation between one dependent variable and multiple independent variables. It is useful but assumes linear relationships and can be complex to calculate.
G5-Statistical-Relationship-Among-Variables.pptx
The document discusses statistical relationships among variables and correlation. It defines correlation as a measure of the strength and direction of the linear relationship between two variables. Correlation values range from -1 to 1, where values closer to -1 or 1 indicate a stronger relationship, and a value of 0 indicates no relationship. The document also explains how to evaluate the strength of a correlation based on its value, computes correlations using the Pearson's r formula, and discusses assessing the statistical significance of a correlation through hypothesis testing.
Correlation IN STATISTICS
The document discusses different types of correlation and methods for studying correlation. It describes Karl Pearson's coefficient of correlation, which measures the strength and direction of a linear relationship between two variables. The coefficient ranges from -1 to 1, where -1 is a perfect negative correlation, 0 is no correlation, and 1 is a perfect positive correlation. The document also discusses other types of correlation coefficients like Spearman's rank correlation coefficient and methods for analyzing correlation like scatter plots.
Biostatistics - Correlation explanation.pptx
This document discusses correlation and methods for studying correlation. Correlation refers to the relationship between two variables, where a positive correlation means the variables change in the same direction and a negative correlation means they change in opposite directions. Methods for studying correlation include scatter plots, Karl Pearson's coefficient of correlation (denoted r), and the coefficient of determination (denoted r2). The coefficient of correlation quantifies the strength and direction of the linear relationship between variables while the coefficient of determination indicates how much variability in one variable can be predicted from the other variable.
Correlationanalysis
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.
CORRELATION ANALYSIS NOTES.pdf
STATISTICS FOR DECISION MAKING - CORRELATION ANALYSIS, MEANING, TYPES, METHODS OF CALCULATION OF CORRELATION
More Related Content
What's hot
Spearman Rank Correlation - Thiyagu
The Spearman’s Rank Correlation Coefficient is the non-parametric statistical measure used to study the strength of association between the two ranked variables. This method is applied to the ordinal set of numbers, which can be arranged in order, i.e. one after the other so that ranks can be given to each. This presentation slides explains the procedure to find out the Rank Difference correlation and its applications.
Correlation
Correlation measures the strength and direction of association between two variables. It ranges from -1 to 1, where 0 indicates no association and values closer to 1 or -1 indicate stronger positive or negative associations, respectively. The document provides examples of positive correlation between variables such as height and weight that increase together, and negative correlation where variables such as TV time and grades move in opposite directions. Formulas and different types of correlation including partial and multiple correlation are also defined in the document.
WF ED 540, Class Meeting13, 19 November 2015
This document discusses Pearson's product-moment correlation and its assumptions. It describes the range of correlation values and the null hypothesis of no correlation. It explains that correlation measures the strength of a linear association between two interval or ratio-scaled variables and is not a measure of causation. Hypothesis testing is used to determine if a correlation is statistically significant different than zero.
Statisticalrelationships
This document discusses statistical relationships and various ways to measure the strength and direction of relationships between variables. It defines different types of relationships such as general association, monotonic relationships, and linear relationships. It also describes how to evaluate the strength of relationships and introduces common measures of association used to numerically summarize relationships, noting that the interpretation depends on the level of measurement and type of relationship being measured. Important properties of coefficients are outlined, including how their values indicate the null relationship and strength of association.
Educ eval ppt correlation
This document discusses correlation and correlation coefficients. It defines correlation as the strength and direction of relationship between two variables. The correlation coefficient (r) ranges from -1 to 1, with values closer to these extremes indicating a stronger relationship. Positive correlations occur when high scores in one variable are associated with high scores in the other, and vice versa for low scores. Negative correlations are the opposite. Scatterplots can graphically show the correlation. While correlation does not prove causation, it indicates an association between variables.
Partial Correlation - Thiyagu
Partial Correlation measures the Correlation between X and Y Controlling for Z. Partial correlation is a measure of the strength and direction of a linear relationship between two continuous variables whilst controlling for the effect of one or more other continuous variables (also known as 'covariates' or 'control' variables). This presentation explains the concept of Partial correlation and its computation process.
Measuring relationships
This document discusses the Pearson r correlation coefficient and how to calculate, interpret, and evaluate the significance of relationships between variables. The Pearson r ranges from -1 to 1, with values closer to these extremes indicating a stronger linear relationship and values near 0 a weaker relationship. A correlation of -0.67 represents the strongest relationship among the options given. The correlation coefficient is calculated using the formula presented. An example calculates the correlation between number of hospitals and maternal mortality rate in various cities. Interpretation of the correlation involves evaluating the magnitude, sign, strength of relationship, and coefficient of determination. Statistical significance can be determined by comparing the correlation to critical values from tables.
Phi Coefficient of Correlation - Thiyagu
The phi coefficient is that system of correlation which is computed between two variables, where neither of them is available in a continuous measures and both of them are expressed in the form of natural or genuine dichotomies. This presentation slides describes the concept and procedures to do the computation of phi coefficient of correlation.
Scatterplots - LSRLs - RESIDs
This document discusses scatterplots and correlation. Scatterplots can be used to display the relationship between two quantitative variables, known as the explanatory and response variables. Correlation measures the strength and direction of a linear relationship between two variables on a scale of -1 to 1. A correlation near -1 or 1 indicates a strong linear relationship, while a value closer to 0 indicates a weaker linear relationship or no relationship. Scatterplots can be interpreted by describing the direction, form, and strength of the relationship between variables as well as identifying any outliers.
Introduction to simple linear regression and correlation in spss
1. The document discusses simple linear regression and correlation. It defines simple correlation and describes positive, negative, and no correlation between two variables.
2. It explains that the correlation coefficient shows the degree of relation between two variables and can range from -1 to 1.
3. Simple linear regression predicts an outcome variable (y) as a function of a predictor variable (x) using the linear regression formula y = B0 + B1x, where B0 is the intercept and B1 is the regression coefficient.
Practice post
This document discusses measuring linear association between variables through scatterplots and correlation. It defines correlation as a measure of the strength and direction of linear association between two quantitative variables. Correlation values range from -1 to 1, with -1 indicating a perfect negative linear relationship and 1 indicating a perfect positive linear relationship. The document cautions that correlation only measures linear association and provides an example where data sets with identical correlations show different relationships.
2.5 a correlation & best fitting lines
The document discusses finding the line of best fit for a data set using linear regression. It explains that the line of best fit should have approximately the same number of points above and below the line. It also describes how the slope of the line indicates whether the correlation is positive, negative, or relatively no correlation. Finally, it introduces the correlation coefficient r as a measure of the strength of correlation, with values closer to 1 or -1 indicating a stronger correlation.
Matching algorithm for online dating
Matching is important part of online dating. Here are some tips of making algorithms for online dating. Learn more about COGXIO & DateIITians Algorithm used for matching online for some parts. COGXIO mostly used Machine Learning further to improve total consumer experience.
Love equation
The document presents a mathematical equation for love based on percentages of positive and negative traits. It states that for a lover, one's positive traits will be 10% more in the lover, while one's negative traits will be 10% less. This 10% threshold provides maximum satisfaction and fulfillment for both parties. The lover enhances one's strengths and reduces weaknesses according to this model.
Association analysis
1. Association analysis includes correlation coefficient analysis and path coefficient analysis to study the relationship between two or more variables.
2. Correlation coefficient analysis measures the degree and direction of association between variables on a scale of -1 to 1, where 1 is total positive correlation and -1 is total negative correlation.
3. Path coefficient analysis splits the correlation coefficients into measures of direct and indirect effects to determine the direct and indirect contribution of independent variables to a dependent variable like yield.
What's hot (15)
Introduction to simple linear regression and correlation in spss
Introduction to simple linear regression and correlation in spss
Similar to Statistics
Coefficient of correlation...ppt
The document defines and describes the coefficient of correlation, which is a statistic that measures the strength and direction of the linear relationship between two variables on a scale of -1 to +1. It explains that a correlation of -1 is a perfect negative correlation, 0 is no correlation, and +1 is a perfect positive correlation. The document also differentiates between types of correlation coefficients and methods for computing correlation coefficients, including Karl Pearson's and Spearman's rank correlation coefficients.
Correlation analysis
Correlation analysis examines the relationship between two or more variables. Positive correlation means the variables increase together, while negative correlation means they change in opposite directions. The Pearson correlation coefficient, r, quantifies the strength of linear correlation between -1 and 1. Multiple correlation analysis extends this to measure the correlation between one dependent variable and multiple independent variables. It is useful but assumes linear relationships and can be complex to calculate.
G5-Statistical-Relationship-Among-Variables.pptx
The document discusses statistical relationships among variables and correlation. It defines correlation as a measure of the strength and direction of the linear relationship between two variables. Correlation values range from -1 to 1, where values closer to -1 or 1 indicate a stronger relationship, and a value of 0 indicates no relationship. The document also explains how to evaluate the strength of a correlation based on its value, computes correlations using the Pearson's r formula, and discusses assessing the statistical significance of a correlation through hypothesis testing.
Correlation IN STATISTICS
The document discusses different types of correlation and methods for studying correlation. It describes Karl Pearson's coefficient of correlation, which measures the strength and direction of a linear relationship between two variables. The coefficient ranges from -1 to 1, where -1 is a perfect negative correlation, 0 is no correlation, and 1 is a perfect positive correlation. The document also discusses other types of correlation coefficients like Spearman's rank correlation coefficient and methods for analyzing correlation like scatter plots.
Biostatistics - Correlation explanation.pptx
This document discusses correlation and methods for studying correlation. Correlation refers to the relationship between two variables, where a positive correlation means the variables change in the same direction and a negative correlation means they change in opposite directions. Methods for studying correlation include scatter plots, Karl Pearson's coefficient of correlation (denoted r), and the coefficient of determination (denoted r2). The coefficient of correlation quantifies the strength and direction of the linear relationship between variables while the coefficient of determination indicates how much variability in one variable can be predicted from the other variable.
Correlationanalysis
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.
CORRELATION ANALYSIS NOTES.pdf
STATISTICS FOR DECISION MAKING - CORRELATION ANALYSIS, MEANING, TYPES, METHODS OF CALCULATION OF CORRELATION
correlation-ppt [Autosaved].pptx statistics in BBA from parul University
This document discusses correlation and different correlation methods. It defines correlation as a statistical measure of how two variables change together. There are three types of correlation: positive, negative, and zero. Positive correlation means the variables increase or decrease together, while negative correlation means one variable increases as the other decreases. Scatter diagram, Pearson's coefficient, and rank correlation are three methods discussed to study correlation between variables. Pearson's coefficient ranges from -1 to 1 to indicate the strength and direction of linear correlation between two variables.
CORRELATION.ppt
This document discusses correlation and regression analysis. It defines correlation as a statistical measure of how two variables are related or associated. A correlation coefficient between -1 and 1 indicates the strength and direction of the linear relationship between variables. Scatterplots can show these relationships visually. The Pearson correlation coefficient is the most common measure used, where 0 indicates no correlation and 1 or -1 indicates perfect correlation. Correlation only measures linear relationships and does not necessarily imply causation between variables.
BS_5Correlation.pptx
This document discusses different types and methods of studying correlation between variables. It defines correlation as the relationship between two or more variables. There can be positive correlation, where variables change in the same direction, negative correlation, where they change in opposite directions, and no correlation. Methods of studying correlation include scatter diagrams, which plot the variables to show their relationship, and Karl Pearson's coefficient of correlation, which measures the strength of the linear relationship between variables mathematically. The coefficient r ranges from -1 to 1, indicating the degree of negative or positive correlation.
Correlation (1)
This document discusses correlation analysis and different correlation techniques. It defines correlation as the relationship between two variables and how a change in one causes a change in the other. Correlation can be positive, negative, or no correlation. Methods for studying correlation include scatter diagrams, Pearson's coefficient, and Spearman's rank correlation. Pearson's coefficient represents the strength and direction of correlation between -1 and 1. Spearman's rank correlation determines correlation when data is in rank form rather than numerical values. Various types of correlation and formulas for calculating correlation coefficients are also outlined.
Meaning and types of correlation
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.
Correlation Analysis
This document discusses correlation analysis. It defines correlation as the degree of relationship between two random variables. Correlation can be positive, negative, simple, partial, or multiple depending on the direction and number of variables. Linear correlation means changes in one variable are proportionally related to changes in the other. Non-linear correlation means changes are not proportional. Correlation is measured using the correlation coefficient r, which ranges from -1 to 1. A higher absolute r value means stronger correlation. Correlation only indicates relationship and not causation. The document also covers probable error and coefficient of determination in interpreting correlation results.
Correlation by ramesh kumar
This power point presentation is regarding Correlation , Kinds of correlation, Positive and Negative Correlation, Linear and Non - linear correlation.
cor2-161031090555.pptxgfgfgfdgfdgfdgfdgfdgfdgdfgf
This document defines correlation and discusses different types of correlation. It states that correlation refers to the relationship between two variables, where their values change together. There can be positive correlation, where variables change in the same direction, or negative correlation, where they change in opposite directions. Correlation can also be linear, nonlinear, simple, multiple, or partial. The degree of correlation is measured using a coefficient of correlation between -1 and 1, indicating no, perfect, or limited correlation. Correlation is studied using scatter diagrams or graphs and calculated using formulas to find the coefficient.
Correlation
This document defines correlation and discusses different types of correlation. It states that correlation refers to the relationship between two variables, where their values change together. There can be positive correlation, where variables change in the same direction, or negative correlation, where they change in opposite directions. Correlation can also be linear, nonlinear, simple, multiple, or partial. The degree of correlation is measured by the coefficient of correlation, which ranges from -1 to 1. Graphic and algebraic methods like scatter diagrams and calculating the coefficient can be used to study correlation.
correlation ;.pptx
1. Correlation measures the strength and direction of association between two variables. It ranges from -1 to 1, where -1 is perfect negative correlation, 0 is no correlation, and 1 is perfect positive correlation.
2. There are different types of correlation based on the direction, number of variables, and constancy of relationships. Common types include positive, negative, simple, multiple, and partial correlation.
3. Correlation coefficients like Pearson's r and Spearman's rho are used to calculate correlation. Pearson's r assumes linear relationships while Spearman's rho assumes monotonic relationships between variables.
correlation.pptx
1. Correlation measures the strength and direction of association between two variables. It ranges from -1 to 1, where -1 is perfect negative correlation, 0 is no correlation, and 1 is perfect positive correlation.
2. There are different types of correlation based on the direction, number of variables, and constancy of relationships. Common types include positive, negative, simple, multiple, and partial correlation.
3. Correlation coefficients like Pearson's r and Spearman's rho are used to calculate correlation. Pearson's r assumes linear relationships while Spearman's rho assumes monotonic relationships between variables.
Correlation analysis
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.
correlationandregression1-200905162711.pdf
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.
Similar to Statistics (20)
correlation-ppt [Autosaved].pptx statistics in BBA from parul University
correlation-ppt [Autosaved].pptx statistics in BBA from parul University
More from nooremah
ESSAY TYPE TEST
This document discusses essay type questions and how to construct them. It defines essay questions as requiring detailed, multi-sentence answers that demonstrate critical thinking across different dimensions. It outlines Bloom's Taxonomy of cognitive skills that can be assessed through essay questions, from lower-order skills like knowledge and comprehension to higher-order skills like analysis, synthesis and evaluation. The document also discusses developing a scoring scheme for essay questions that evaluates various features of answers, and provides examples of different types of essay questions and their advantages and disadvantages.
CLASSROOM ENVIRONMENT
The document discusses effective classroom delivery techniques. It emphasizes establishing good relationships with students, communicating information interactively, and maintaining student interest through a variety of engagement strategies. Some key techniques include introducing topics through stories, activities, pictures and questions; using simple, clear language; speaking with expression and gestures; creating a positive learning environment; and modeling expectations while keeping students actively engaged.
Teacher as role model
This document discusses teachers as role models. It states that role models inspire and encourage striving for greatness and potential. Teachers are especially important role models after parents. Good teachers inspire students to learn through their teaching style and interaction. They motivate and encourage students through praise, recognition, involvement, and incentives. The teacher's entire personality reflects on students' minds, so every teacher must have a good personality with pleasing appearance, manners, enthusiasm, initiative, open-mindedness, and professional ethics.
Aims goals and objectives.
This document discusses the goals, aims, and objectives of curriculum. It defines curriculum as planned activities inside or outside of school. The main aims of education are to provide education and develop students' moral character. Goals reflect broader purposes and outcomes, while objectives are more specific levels of educational achievement. Objectives can target cognitive, affective, and psychomotor skills. The document outlines the aims of curriculum at different education levels and provides examples of goals and taxonomies of educational objectives.
Types of attention (1)
This document discusses two types of attention: involuntary attention, which arises without conscious effort due to instinctual reactions or personal interests, and voluntary attention, which is aroused through willpower. Involuntary attention includes enforced involuntary attention from instincts like fear or curiosity, and spontaneous involuntary attention from sentiments or interests. Voluntary attention contains implicit voluntary attention motivated by rewards or punishments, and explicit attention obtained through repeated willful acts.
Time table (1)
This document discusses the importance and principles of developing school timetables. It defines a timetable as a schedule that arranges classes, subjects, and other school activities according to time. Developing a proper timetable is important as it establishes a routine for students and teachers, promotes punctuality, and helps distribute time equitably across subjects. Key principles for making timetables include considering the school type, fixing appropriate class times and lengths, allocating sufficient time per class, and addressing fatigue and variety to prevent boredom. The document also describes different types of timetables like master, class-wise, and teacher-wise timetables.
Multiple intelligence (1)
Howard Gardner introduced the theory of multiple intelligences which claims that intelligence is not a single general ability but rather composed of multiple abilities. Gardner identified 8 types of intelligence: linguistic, logical-mathematical, spatial, bodily-kinesthetic, musical, interpersonal, intrapersonal, and naturalist. The theory argues that individuals have different strengths in these intelligences and learn best through activities tailored to their preferred intelligence types. Teachers should design lessons incorporating a variety of activities to engage students with different intelligences.
Presentation1
This document discusses two types of learning: classical and operant conditioning. Classical conditioning, proposed by Ivan Pavlov, involves learning through association, such as a dog salivating when it hears a bell due to being conditioned to associate the bell with food. Operant conditioning, proposed by B.F. Skinner, involves learning through reinforcement and punishment, such as a rat learning to press a lever for food pellets. Both types of learning involve changes in behavior through conditioning and consequences.
More from nooremah (8)
Recently uploaded
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
Main Java[All of the Base Concepts}.docx
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
DRUGS AND ITS classification slide share
Any substance (other than food) that is used to prevent, diagnose, treat, or relieve symptoms of a
disease or abnormal condition
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Fix the Import Error in the Odoo 17
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Recently uploaded (20)
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Digital Artefact 1 - Tiny Home Environmental Design
Digital Artefact 1 - Tiny Home Environmental Design
Pride Month Slides 2024 David Douglas School District
Pride Month Slides 2024 David Douglas School District
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Statistics
- 3. Correlation • Correlation is the relationship or connection between two or more things. • Correlation is the study of relationship of one variable to another. • Interdependence of variable
- 4. Correlation • Correlation is positive when both variables increase or decrease together. e.g: demand has increased and prices of colors increase. • Correlation is consider negative when one increase and other variable decrease. e.g: supply of hair colors has increased then demand of color has decreased.
- 5. Pearson correlation • It refers to the Pearson R correlation, a statistical formula which is used to measure strength between variable and relationship. • To determine the strong relation between variables, its range from -1.00 to 1.00 • Positive • Negative
- 6. Coefficient of correlation • Correlation coefficient are expressed as value between +1 to -1. • When both variable increase or decrease together, it would consider positive correlation. • The change of variable in opposite direction would consider negative correlation • if value come in zero then no realtion.
- 7. Coefficient of correlation • -1 0 +1 perfect negative perfect reliable correlation -0.39 poor adverse 0—0.39poor reliable -0.40 -- -0.69 moderate 0.40—0.69 moderate -0.70 -- -0.99 high o.70—0.99 high
- 8. Graphically
- 9. Graphically Strength of Association Positive Negative Small .1 to .3 -0.1 to -0.3 Medium .3 to .5 -0.3 to -0.5 Large .5 to 1.0 -0.5 to -1.0
- 11. Example
- 12. Example x y x2 y2 XY 6 7 36 49 42 5 5 25 25 25 9 2 81 4 18 4 6 16 36 24 24 20 158 114 109
- 13. solution X= 24 Y= 20 X2= 158 Y2= 114 XY= 109 PUT THE VALUE GET THE ANSWER !