This document explains how to use Spearman's rank correlation coefficient to determine the strength and significance of the relationship between two variables. It provides steps to calculate the coefficient using birth rate and economic development data from 12 Central and South American countries. These steps are then applied to determine if there is a correlation between life expectancy and economic development in the same countries.
Correlation- an introduction and application of spearman rank correlation by...Gunjan Verma
this presentation contains the types of correlation, uses, limitations, introduction to spearman rank correlation, and its application. a numerical is also given in the presentation
Kendall's Tau is a nonparametric correlation test used with ordinal or ranked data, like ranks in a competition. It measures the relationship between rank orders of two variables. Researchers analyzed rank orders of athletes in biking and running events of an ironman competition using Kendall's Tau due to ties in ranks. Kendall's Tau results range from -1 to 1, with values closer to the extremes indicating a stronger monotonic relationship between variables.
This is about the correlation analysis in statistics. It covers types, importance,Scatter diagram method
Karl pearson correlation coefficient
Spearman rank correlation coefficient
The document discusses the Pearson Product Moment Correlation Coefficient (r), which is a measure of the linear relationship between two variables. It was developed by Karl Pearson in the late 19th century. The r value ranges from -1 to 1, where -1 is a perfect negative linear relationship, 0 is no linear relationship, and 1 is a perfect positive linear relationship. Values above 0.8 or 0.9 are considered strong correlations, while values around 0.2 or 0.3 are weak correlations. The document provides examples of linear relationships at different r values and the formula to calculate r from sample data.
This document discusses Spearman's rank correlation coefficient. It begins with the history of Spearman's rank correlation, proposed by Charles Spearman to measure the strength of association between two variables. It then explains key differences between Spearman's rank correlation and Pearson's correlation, such as Spearman's being a non-parametric measure of monotonic relationships between ranked variables. The document provides details on calculating and interpreting Spearman's rank correlation coefficient and discusses its advantages and uses in genetics and plant breeding applications.
The document discusses different types of correlation between two variables: positive correlation, negative correlation, and no correlation. It defines correlation as a statistical measure of the linear relationship between two variables. Different methods for measuring correlation are described, including scatter diagrams, Karl Pearson's coefficient of correlation, rank correlation, and autocorrelation. Karl Pearson's coefficient yields a numerical value between -1 and 1 to indicate the strength and direction of linear correlation. Rank correlation is used for qualitative variables by assigning ranks and finding the correlation between the ranks.
This document provides an overview of correlation analysis procedures in SPSS, including bivariate correlation, partial correlation, and distance measures. It discusses interpreting correlation coefficients and significance values. Scatterplots are recommended to check assumptions before correlation. Hands-on exercises are included to find correlations between variables while controlling for other variables.
Pearson Correlation, Spearman Correlation &Linear RegressionAzmi Mohd Tamil
This document discusses correlation and linear regression. It defines correlation as a statistic that measures the strength and direction of the linear relationship between two continuous variables. Positive correlation indicates that as one variable increases, so does the other. Negative correlation means the variables are inversely related. Linear regression can be used to predict a continuous outcome variable based on a continuous predictor variable using the regression equation y=a+bx. The regression line minimizes the sum of squared differences between the data points and the line. The slope coefficient b indicates the strength of the linear prediction and can be tested for significance.
This document discusses rank correlation and Spearman's rank correlation coefficient. It defines correlation as a relationship between two variables where a change in one variable corresponds to a change in the other. Rank correlation involves ranking observations from highest to lowest rather than using the original values, which avoids assumptions about the population distribution. Spearman's rank correlation coefficient measures the correspondence between two rankings and is calculated based on the differences between ranks of paired items. It provides a distribution-free measure of correlation.
The document discusses Kendall's tau rank correlation coefficient. It defines tau as a statistic that measures the ordinal association between two measured quantities. An example is shown calculating tau between students' grades and IQ scores. Tau is computed by taking the difference between the number of concordant and discordant pairs, divided by the total number of possible pairs. A value of tau near 1 indicates strong agreement between the rankings, near -1 indicates strong disagreement, and near 0 indicates independence between the variables.
This document discusses correlation analysis and its various types. Correlation is the degree of relationship between two or more variables. There are three stages to solve correlation problems: determining the relationship, measuring significance, and establishing causation. Correlation can be positive, negative, simple, partial, or multiple depending on the direction and number of variables. It is used to understand relationships, reduce uncertainty in predictions, and present average relationships. Conditions like probable error and coefficient of determination help interpret correlation values.
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.
Pearson Product Moment Correlation - ThiyaguThiyagu K
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.
Regression analysis measures the average relationship between two or more variables using their original data units. There are two main types: simple regression involving two variables, and multiple regression involving more than two variables. Regression can be linear, following a straight line, or non-linear/curvilinear. A simple linear regression model relates a dependent variable Y to an independent variable X plus an error term. Estimating the model involves calculating the slope/regression coefficient and intercept. Multiple regression relates a dependent variable to two or more independent variables using a multiple correlation coefficient.
This document defines correlation and correlation analysis. It provides examples of how to construct scatter plots to explore relationships between two variables. Positive correlation is shown by points sloping upwards to the right on a scatter plot, while negative correlation is shown by points sloping downwards to the right. The Pearson correlation coefficient measures the strength and direction of linear relationships between variables and ranges from -1 to 1. A value close to 0 indicates a weak relationship, while values close to 1 or -1 indicate a strong positive or negative relationship, respectively. Hypothesis tests can determine if observed correlation coefficients are statistically significant. Nonparametric methods like the Spearman rank correlation can be used if the data is not interval scaled.
This document discusses simple and multiple regression analysis. Simple regression considers the relationship between one explanatory variable and one response variable, while multiple regression considers the relationship between one dependent variable and multiple independent variables. The document provides the formulas for simple and multiple linear regression. It also presents an example using SPSS to analyze the relationship between firm size, age, and performance. The SPSS output includes measures of model fit like R, R-squared, adjusted R-squared, ANOVA, regression coefficients, and diagnostics for assumptions. Hypothesis testing is conducted on the regression coefficients.
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.
The document discusses different types and methods of measuring correlation between two variables. It describes Karl Pearson's coefficient of correlation (r) which measures the strength and direction of a linear relationship between two variables on a scale of -1 to 1. It also discusses Spearman's rank correlation coefficient (R) which is used when variables can only be ranked rather than measured quantitatively. The key methods covered are scatter diagrams, which graphically depict relationships, and calculating correlation coefficients based on deviations from the mean.
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.
3 examples of PDE, for Laplace, Diffusion of Heat and Wave function. A brief definition of Fouriers Series. Slides created and compiled using LaTeX, beamer package.
First order non-linear partial differential equation & its applicationsJayanshu Gundaniya
There are five types of methods for solving first order non-linear partial differential equations:
I) Equations containing only p and q variables. II) Equations relating z as a function of u. III) Equations that can be separated into functions of single variables. IV) Clairaut's Form where the solution is directly substituted. V) Charpit's Method which is a general method taking integrals of auxiliary equations to solve dz=pdx+qdy and find the solution. These types cover a range of applications including Poisson's, Helmholtz's, and Schrödinger's equations in fields like electrostatics, elasticity, wave theory and quantum mechanics.
This document discusses different types of graphs used to represent frequency distributions: histograms, frequency polygons, and ogives. It provides examples and instructions for constructing each graph type. Histograms use vertical bars to represent frequencies, frequency polygons connect points plotted for class midpoints, and ogives show cumulative frequencies. The document also discusses relative frequency graphs and common distribution shapes like bell-shaped, uniform, and skewed. It assigns practice constructing different graph types from example data.
Standard deviation is a measure of how dispersed data points are from the average value. It is calculated by taking the square root of the variance, which is the average of the squared distances from the mean. For a set of egg weights, the standard deviation is calculated by first finding the mean, then determining the variance by taking the sum of the squared differences from the mean. A low standard deviation means values are close to the mean, while a high standard deviation means values are more spread out. Standard deviation is not affected by adding or subtracting a constant from all values, but is affected by multiplying or dividing all values by a constant.
Skewness is a measure of the asymmetry of a distribution. A perfectly symmetrical distribution has the mean, median and mode equal, while an asymmetrical distribution has these values depart from each other. The greater the skewness, the greater the distance between the mean and mode, with the mean moving furthest from the mode due to its sensitivity to outliers. Positive skewness occurs when the mean is greater than the mode, indicating a distribution skewed to the right, while negative skewness occurs when the mean is less than the mode, indicating a left skew.
The document discusses different methods to calculate arithmetic mean from various types of data series.
It explains the direct and shortcut methods to find the arithmetic mean for individual, discrete and continuous data series.
For individual series, the direct method sums all data points and divides by the total number of data points. The shortcut method assumes a mean, calculates the differences from the assumed mean, and finds the mean as the assumed mean plus the sum of the differences divided by the total number of data points.
This document discusses variance and standard deviation. It defines variance as a measure of how data points differ from the mean. It explains that variance can show how two data sets that have the same mean and median can still be different. The document then provides formulas and examples for calculating variance and standard deviation. It states that standard deviation is a measure of variation from the mean and that a higher standard deviation indicates more spread and less consistency in the data.
Correlation analysis measures the relationship between two or more variables. The sample correlation coefficient r ranges from -1 to 1, indicating the degree of linear relationship between variables. A value of 0 indicates no linear relationship, while values closer to 1 or -1 indicate a strong positive or negative linear relationship. Excel can be used to calculate r using the CORREL function.
The document discusses partial differential equations (PDEs). It defines PDEs and gives their general form involving independent variables, dependent variables, and partial derivatives. It describes methods for obtaining the complete integral, particular solution, singular solution, and general solution of a PDE. It provides examples of types of PDEs and how to solve them by assuming certain forms for the dependent and independent variables and their partial derivatives.
The document discusses the conceptual definition of standard deviation. Standard deviation represents the root average of the squared deviations of scores from the mean. It explains that to calculate standard deviation, each score's deviation from the mean is squared, those squared deviations are averaged, and then the square root of the average is taken to determine the standard deviation in the original units of measurement.
The document discusses how to calculate standard deviation and variance for both ungrouped and grouped data. It provides step-by-step instructions for finding the mean, deviations from the mean, summing the squared deviations, and using these values to calculate standard deviation and variance through standard formulas. Standard deviation measures how spread out numbers are from the mean, while variance is the square of the standard deviation.
The document contains calculations to determine skewness using grouped data. It includes frequency distributions of grouped data with ranges of values for X, frequencies (f), deviations (d), d-squared (d2), and d-cubed (d3). Formulas are provided to calculate the second (m2) and third (m3) moments about the mean. The computations are presented in a table with columns for X, M, f, fM, d, d2, d3, fd2, and fd3.
The document discusses how to calculate variance and standard deviation. It provides the formulas and steps to find variance as the average squared deviation from the mean. Standard deviation is defined as the square root of variance and measures how dispersed data are from the mean, with a larger standard deviation indicating more variation. Examples are worked through to demonstrate calculating variance and standard deviation for different data sets.
This document provides an overview of a data analysis course covering various statistical techniques including correlation, regression, hypothesis testing, clustering, and time series analysis. The course covers descriptive statistics, data exploration, probability distributions, simple and multiple linear regression analysis, logistic regression analysis, and model building for credit risk analysis. Notes are provided on correlation calculation and its properties. Assumptions and interpretations of linear regression are also summarized. The document is intended as a high-level overview of topics covered in the course rather than an in-depth treatment.
This document defines and provides examples for calculating the mean, median, mode, and range of a data set. It explains that the mean is calculated by adding all values and dividing by the number of values, the median involves ordering values and selecting the middle one, the mode is the most frequent value, and the range is the difference between the highest and lowest values. Examples are given for each statistical measure.
This document provides an introduction to correlation and regression analysis. It defines correlation as a measure of the association between two variables and regression as using one variable to predict another. The key aspects covered are:
- Calculating correlation using Pearson's correlation coefficient r to measure the strength and direction of association between variables.
- Performing simple linear regression to find the "line of best fit" to predict a dependent variable from an independent variable.
- Using a TI-83 calculator to graphically display scatter plots of data and calculate the regression equation and correlation coefficient.
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 measures of skewness and interpretation. It defines skewness as the statistical technique to indicate the direction and extent of skewness in a data distribution. There are three types of distributions: symmetrical, positively skewed, and negatively skewed. It provides examples of skewed distribution curves and discusses different methods to calculate and measure skewness, including the mean-mode method, Pearson's coefficient of skewness, and Bowley's coefficient of skewness. Sample calculations are shown to demonstrate how to find these measures of skewness.
Mean, Median, Mode: Measures of Central Tendency Jan Nah
There are three common measures of central tendency: mean, median, and mode. The mean is the average value found by dividing the sum of all values by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently. Each measure provides a single number to represent the central or typical value in a data set.
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Chapter 10: Correlation and Regression
10.1: Correlation
The document defines and provides information about correlation coefficients. It discusses how correlation coefficients measure the strength and direction of linear relationships between two variables. The range of correlation coefficients is from -1 to 1, where values closer to -1 or 1 indicate stronger linear relationships and a value of 0 indicates no linear relationship. It also provides the formula to calculate correlation coefficients and an example of calculating the correlation coefficient for age and blood pressure data.
The document discusses different statistical methods for organizing and summarizing data, including frequency tables, stem-and-leaf plots, histograms, and scatter plots. It provides examples of each method and explains how to interpret the results, such as looking for relationships between variables in scatter plots. Key terms defined include correlation, variables, and linear regression lines.
The document discusses two common measures of the relationship between two sets of scores: Pearson's Product-Moment Correlation and Spearman's Rho. Pearson's correlation measures the linear relationship between metric variables and involves calculating the covariance between the variables and dividing by the product of their standard deviations. Spearman's Rho measures the monotonic relationship between ordinal or ranked variables and involves calculating the difference between the ranks of each variable and finding the average squared difference. Both measures result in a correlation coefficient between -1 and 1, where values closer to 1 or -1 indicate a strong relationship and values near 0 indicate no relationship.
Correlation describes the relationship between two variables that vary together. Positive correlation means both variables increase or decrease together, while negative correlation means one increases as the other decreases. Correlation is useful for comparing relationships precisely, testing if a correlation is statistically significant rather than due to chance, and summarizing the strength of relationships with a correlation coefficient. However, correlation does not prove that one variable causes changes in the other. Spearman's rank correlation calculates a coefficient (rs) to summarize the strength and direction of relationships between variables. It involves ranking paired data, calculating differences between ranks, and using a formula to determine rs and test for statistical significance compared to chance.
Correlation describes the relationship between two variables that vary together. Positive correlation means both variables increase or decrease together, while negative correlation means one increases as the other decreases. Correlation is useful for comparing relationships precisely, testing if correlations are statistically significant rather than due to chance, and summarizing the strength and direction of relationships with a correlation coefficient. However, correlation does not prove that one variable causes changes in the other.
A PRACTICAL POWERFUL ROBUST AND INTERPRETABLE FAMILY OF CORRELATION COEFFICIE...Savas Papadopoulos, Ph.D
If we conducted a competition for which statistical quantity would be the most valuable in exploratory data analysis, the winner would most likely be the correlation coefficient with a significant difference from its first competitor. In addition, most data applications contain non-normal data with outliers without being able to be converted to normal data. Therefore, we search for robust correlation coefficients to nonnormality and/or outliers that could be applied to all applications and detect influenced or hidden correlations not recognized by the most popular correlation coefficients. We introduce a correlation-coefficient family with the Pearson and Spearman coefficients as specific cases. Other family members provide desirable lower p-values than those derived by the standard coefficients in the earlier problems. The proposed family of coefficients, their cut-off points, and p-values, computed by permutation tests, could be applied by all scientists analyzing data. We share simulations, code, and real data by email or the internet.
1Bivariate RegressionStraight Lines¾ Simple way to.docxaulasnilda
1
Bivariate Regression
Straight Lines
¾ Simple way to describe a relationship
¾ Remember the equation for a straight line?
z y = mx + b
¾ What is m? What is b?
¾ How do you compute the equation?
(x1,y1)
(x2,y2)
What if every point is
not on the line?
¾ Straight line may be good description even
if not all points are on the line
Computing the line
when points are scattered
¾ = a + bX
¾ Y-hat means predicted value of Y
¾ Computing the slope:
¾ b = 𝑋−𝑋 𝑌−𝑌
𝑋−𝑋
¾ I ill ri e/r n, b no e al o
consider variability in X and Y
Computing the intercept
¾ a = - bX
¾ Need o pl g in al e of (X, )
¾ Can e j an Y or X!
z Line would be very different depending on
which ones you chose
¾ Must have X and Y that we know are on
the line
z mean of X and mean of Y
2
Computing the intercept
¾ Regression line will always go through the
mean of X and mean of Y
¾ A = 𝑌 - b𝑋
¾ Le r it with our example from before
X
(# of kids)
Y
(hours of
housework) 𝑋 𝑋 𝑌 𝑌 𝑋 𝑋 𝑌 𝑌 𝑋 𝑋
1 1 -1.75 -2.5 4.375 3.063
1 2 -1.75 -1.5 2.625 3.063
1 3 -1.75 -0.5 0.875 3.063
2 6 -0.75 2.5 -1.875 0.563
2 4 -0.75 0.5 -0.375 0.563
2 1 -0.75 -2.5 1.875 0.563
3 5 0.25 1.5 0.375 0.063
3 0 0.25 -3.5 -0.875 0.063
4 6 1.25 2.5 3.125 1.563
4 3 1.25 -0.5 -0.625 1.563
5 7 2.25 3.5 7.875 5.063
5 4 2.25 0.5 1.125 5.063
MX=2.75 MY=3.5 = 0 = 0 = 18.5 = 24.25
Computing the equation
¾ b = .
.
.76
¾ a = 3.5 - .76(2.75)
¾ = 1.41
¾ = 1.41 + .76X
Interpreting the coefficients
¾ Slope
z For a one unit increase in X, we predict a b
unit increase in Y
What does that mean for this study?
¾ Intercept
z The predicted value of Y when X = 0
What does that mean for this study?
Interpreting the coefficients
¾ Slope
z For each additional child, we predict
parents will do an additional .76 hours of
housework per day
¾ Intercept
z For a family with zero kids, we predict they
will do 1.41 hours of housework per day
Drawing the regression line
¾ Need to plot two points
z 𝑋, 𝑌
z Y-intercept
1
Scatterplots and
Correlation
Correlation
¾ Useful tool to assess relationships
¾ Must have two variables measured on one set of
people
¾ Correlation only measures strength of linear
association
Linear relationships are
not perfect lines
¾ Variables have variability (duh)
¾ Relationships may be generally linear
even if all points are not on the line
Magnitude of r Not all relationships are linear
2
Properties of r
¾ X & Y must be quantitative
z Interval or ratio
¾ I doe n ma e hich a iable i edic o
and which is response
z rxy = ryx
Properties of r
¾ Correlation has no units
z So r can be compared for different variables
¾ Value of r is always between -1 and +1
Computing r
¾ Consider deviations around mean of X & Y
¾ (X 𝑋) (Y 𝑌)
Cross-Product
¾ To consider X & Y together, multiply their
deviations
¾ (X 𝑋)(Y 𝑌)
¾ Sign will be positive or negative
¾ Sum of cross-pr
This document discusses various types and methods of measuring correlation between two variables. It describes correlation as a statistical tool to measure the degree of relationship between variables. Some key methods covered include scatter diagrams, Karl Pearson's coefficient of correlation, and Spearman's rank correlation coefficient. Positive and negative correlation examples are provided. The document also differentiates between simple, multiple, partial, and total correlation, as well as linear and non-linear correlation.
This document discusses different types and methods of measuring correlation between variables. It covers:
- Types of correlation including simple, multiple, partial, and total correlation.
- Methods for studying correlation such as scatter diagrams, Karl Pearson's coefficient of correlation, and Spearman's rank correlation coefficient.
- Karl Pearson's coefficient measures the strength and direction of the linear relationship between two quantitative variables. Spearman's rank correlation coefficient is used when variables are qualitative or ranked.
- Positive and negative correlation examples are provided like the relationship between temperature and water consumption.
The document discusses Karl Pearson's coefficient of correlation, which provides a quantitative method for calculating correlation between two variables. It presents the standard formula for Pearson's correlation coefficient r, which measures the strength and direction of the linear relationship between two continuous variables on a scale from -1 to 1. The document also discusses properties of the correlation coefficient r and provides examples of its calculation, including using the rank correlation method to calculate r between marks in two subjects based on students' exam rankings.
Correlation analysis in Biostatistics .pptxHamdiMichaelCC
Correlation analysis measures the strength and direction of association between two variables. There are different types of correlation including simple, multiple, and partial correlation. Scatter diagrams and correlation coefficients like Pearson's r and Spearman's rho are common methods to study correlation. Pearson's r assumes a linear relationship between variables while Spearman's rho can be used when variables are ranked. A correlation coefficient close to 1 or -1 indicates a strong correlation while a value near 0 suggests no correlation.
This document discusses Spearman's rank correlation coefficient (Spearman's rho), which is a nonparametric measure of statistical dependence between two variables. It explains that Spearman's rho measures the strength and direction of association between two ranked variables. The document provides an example of calculating Spearman's rho to explore the correlation between fertilizer amounts and crop yields for 5 different crops. It finds a very strong positive correlation (Spearman's rho = 0.9) between fertilizer and crop yield.
Correlation and regression are statistical methods used to measure relationships between variables. Correlation determines how strongly two variables are related, yielding a correlation coefficient between -1 and 1. Positive correlation means variables increase together, while negative correlation means one variable decreases as the other increases. Regression finds the best-fit line for predicting a dependent variable from an independent variable. It determines whether knowing one variable provides information to predict another variable. The line of best fit minimizes the sum of squared errors between the observed data points and the fitted line.
The document discusses relations and their application to databases in the relational data model. It defines binary and n-ary relations, and explains how databases can be represented as n-ary relations with records as n-tuples consisting of fields. Primary keys are introduced as fields that uniquely identify each record. Common relational operations like projection and join are explained, with examples provided to illustrate how they transform relations.
This document discusses four types of correlation coefficients: Pearson's product-moment correlation, Spearman's rank-order correlation, Phi coefficient, and point-biserial correlation. It provides definitions, formulas, examples and interpretations for each type of correlation. Pearson's correlation is used with interval or ratio scales, while Spearman's correlation is for ordinal scales. Phi coefficient is for nominal scales, and point-biserial is used when one variable is nominal and one is interval.
Correlation and regression are statistical techniques used to describe relationships between variables:
- Correlation determines the strength and direction of relationships between two variables without implying causation.
- Scatter plots show the pattern of relationships as positive, negative, or no correlation.
- Regression predicts the value of an outcome or dependent variable based on the value of an independent variable.
- The regression equation defines the linear relationship between variables as y=mx+b, where m is the slope and b is the y-intercept.
Assessing relative importance using rsp scoring to generateDaniel Koh
This document proposes a new method called Driver's Score (DS) to assess the relative importance of variables in regression models. DS combines measures of a variable's reliability, significance, and power into a single composite score. Reliability is measured using residual errors, significance uses F-ratios of residual errors, and power uses standardized regression coefficients. DS is calculated at the observation level as the geometric mean of these three scores. The document argues DS provides a more intuitive and practical understanding of variable importance than existing methods. An example using industry data demonstrates how to generate DS scores and classify variables by level of importance. The methodology aims to independently measure importance while accounting for interrelationships between variables.
Assessing Relative Importance using RSP Scoring to Generate VIFDaniel Koh
This document proposes a new method called Driver's Score (DS) to assess the relative importance of variables in regression models. DS combines measures of a variable's reliability, significance, and power into a single composite score. Reliability is measured using residual errors, significance uses F-ratios of residual errors, and power uses standardized regression coefficients. DS is calculated at the observation level as the geometric mean of scores for each of these three properties. The document suggests that DS provides a more intuitive and practical understanding of variable importance than existing single-measure methods. An example using industry data demonstrates how to generate DS scores and classify variables by level of importance.
Codeavour 5.0 International Impact Report - The Biggest International AI, Cod...Codeavour International
Unlocking potential across borders! 🌍✨ Discover the transformative journey of Codeavour 5.0 International, where young innovators from over 60 countries converged to pioneer solutions in AI, Coding, Robotics, and AR-VR. Through hands-on learning and mentorship, 57 teams emerged victorious, showcasing projects aligned with UN SDGs. 🚀
Codeavour 5.0 International empowered students from 800 schools worldwide to tackle pressing global challenges, from bustling cities to remote villages. With participation exceeding 5,000 students, this year's competition fostered creativity and critical thinking among the next generation of changemakers. Projects ranged from AI-driven healthcare innovations to sustainable agriculture solutions, each addressing local and global issues with technological prowess.
The journey began with a collective vision to harness technology for social good, as students collaborated across continents, guided by mentors and educators dedicated to nurturing their potential. Witnessing the impact firsthand, teams hailing from diverse backgrounds united to code for a better future, demonstrating the power of innovation in driving positive change.
As Codeavour continues to expand its global footprint, it not only celebrates technological innovation but also cultivates a spirit of collaboration and compassion. These young minds are not just coding; they are reshaping our world with creativity and resilience, laying the groundwork for a sustainable and inclusive future. Together, they inspire us to believe in the limitless possibilities of innovation and the profound impact of young voices united by a common goal.
Read the full impact report to learn more about the Codeavour 5.0 International.
How to Make a Field Storable in Odoo 17 - Odoo SlidesCeline George
Let’s discuss about how to make a field in Odoo model as a storable. For that, a module for College management has been created in which there is a model to store the the Student details.
How To Sell Hamster Kombat Coin In Pre-marketSikandar Ali
How To Sell Hamster Kombat Coin In Pre Market
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How To Sell Hamster Kombat Coin In Pre Market
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How To Sell Hamster Kombat Coin In Pre Market
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Lecture Notes Unit4 Chapter13 users , roles and privilegesMurugan146644
Description:
Welcome to the comprehensive guide on Relational Database Management System (RDBMS) concepts, tailored for final year B.Sc. Computer Science students affiliated with Alagappa University. This document covers fundamental principles and advanced topics in RDBMS, offering a structured approach to understanding databases in the context of modern computing. PDF content is prepared from the text book Learn Oracle 8I by JOSE A RAMALHO.
Key Topics Covered:
Main Topic : USERS, Roles and Privileges
In Oracle databases, users are individuals or applications that interact with the database. Each user is assigned specific roles, which are collections of privileges that define their access levels and capabilities. Privileges are permissions granted to users or roles, allowing actions like creating tables, executing procedures, or querying data. Properly managing users, roles, and privileges is essential for maintaining security and ensuring that users have appropriate access to database resources, thus supporting effective data management and integrity within the Oracle environment.
Sub-Topic :
Definition of User, User Creation Commands, Grant Command, Deleting a user, Privileges, System privileges and object privileges, Grant Object Privileges, Viewing a users, Revoke Object Privileges, Creation of Role, Granting privileges and roles to role, View the roles of a user , Deleting a role
Target Audience:
Final year B.Sc. Computer Science students at Alagappa University seeking a solid foundation in RDBMS principles for academic and practical applications.
URL for previous slides
chapter 8,9 and 10 : https://www.slideshare.net/slideshow/lecture_notes_unit4_chapter_8_9_10_rdbms-for-the-students-affiliated-by-alagappa-university/270123800
Chapter 11 Sequence: https://www.slideshare.net/slideshow/sequnces-lecture_notes_unit4_chapter11_sequence/270134792
Chapter 12 View : https://www.slideshare.net/slideshow/rdbms-lecture-notes-unit4-chapter12-view/270199683
About the Author:
Dr. S. Murugan is Associate Professor at Alagappa Government Arts College, Karaikudi. With 23 years of teaching experience in the field of Computer Science, Dr. S. Murugan has a passion for simplifying complex concepts in database management.
Disclaimer:
This document is intended for educational purposes only. The content presented here reflects the author’s understanding in the field of RDBMS as of 2024.
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2. Is birth rate connected to economic development in Central and South American countries?Data taken from Philip’s International School Atlas (2nd Edition) published 2006 www.i-study.co.uk
3. SPEARMAN’S RANK CORRELATION COEFFICIENTIt says …It allows us to …Compare the RANK ORDER of TWO Data Setswww.i-study.co.uk
4. The total of6 xSpearman’s Rank Correlation CoefficientThe DIFFERENCES between the RANKS assigned to each ‘price’ SQUAREDThe number of different factors in the studywww.i-study.co.uk
5. The MathsFor Argentina:d (rank difference) = 8.5So d2 = 72.25Repeat this calculation for each countryMultiply the sum of all the d2 values by 6 to give the top line of the equationn = the number of countries in the sample =12. n³ - n : 1728 – 12 = 1716 (now we have the bottom line of the equation)Plug in the appropriate numbers to the full equation: your answer for step 4 your answer for step 6R = 1-www.i-study.co.uk
6. What does this R value mean?The closer Ris to +1 or -1, the stronger the likely correlation. Perfect positive correlation is +1 Perfect negative correlation is -1. Therefore, what sort of relationship do your calculations suggest?www.i-study.co.uk
7. What is it showing?Total DisagreementOverall Neither Agree or DisagreeTotal Agreementrs close to -1rs close to 1rs close to 0www.i-study.co.uk
8. The significance of the relationship!The R value must be looked up on the Spearman Rank significance table.
9. 'degrees of freedom' : This is the number of pairs in your sample minus 2 (In this case, 12 – 2 = 10)
10. Using the R value (y axis) the degrees of freedom value (x axis) plot the position of the graph (next page).www.i-study.co.uk