The document discusses the concept of correlation, specifically linear correlation. It provides definitions of correlation from various sources and explains that correlation refers to the relationship between two or more variables. The degree of this relationship is measured by the correlation coefficient. Common types of correlation are discussed such as positive and negative correlation. Methods for studying correlation are also outlined, including scatter diagrams and Karl Pearson's coefficient of correlation.
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 provides an overview of regression analysis including:
- Regression analysis is a statistical process used to estimate relationships between variables and predict unknown values.
- The document outlines different types of regression like simple, multiple, linear, and nonlinear regression.
- Key aspects of regression like scatter diagrams, regression lines, and the method of least squares are explained.
- An example problem is worked through demonstrating how to calculate the slope and y-intercept of a regression line using the least squares method.
This document discusses multiple regression analysis and its use in predicting relationships between variables. Multiple regression allows prediction of a criterion variable from two or more predictor variables. Key aspects covered include the multiple correlation coefficient (R), squared correlation coefficient (R2), adjusted R2, regression coefficients, significance testing using t-tests and F-tests, and considerations for using multiple regression such as sample size and normality assumptions.
- Regression analysis is a statistical technique used to measure the relationship between two quantitative variables and make causal inferences.
- A regression model graphs the relationship between a dependent variable (Y axis) and one or more independent variables (X axis). The goal is to find the linear equation that best fits the data.
- The regression equation takes the form Y = a + bX, where a is the intercept, b is the slope coefficient, and X and Y are the variables. The coefficient b indicates the strength and direction of the relationship.
This document provides information about statistical tests and data analysis presented by Dr. Muhammedirfan H. Momin. It discusses the different types of statistical data, such as qualitative vs quantitative and continuous vs discrete data. It also covers topics like sample data sets, frequency distributions, risk factors for diseases, hypothesis testing, and tests for comparing proportions and means. Specific statistical tests discussed include the z-test and how to calculate test statistics and compare them to critical values to determine statistical significance. Examples are provided to illustrate how to perform these tests to analyze differences between data sets.
Overviews non-parametric and parametric approaches to (bivariate) linear correlation. See also: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/Correlation
This document discusses correlation and regression. Correlation describes the strength and direction of a linear relationship between two variables, while regression allows predicting a dependent variable from an independent variable. It provides examples of calculating the correlation coefficient r to determine the strength and direction of relationships between variables like education and self-esteem or family income and number of children. The regression equation describes the linear regression line and can be used to predict values of the dependent variable from known values of the independent variable.
The document discusses the concept of correlation, specifically linear correlation. It provides definitions of correlation from various sources and explains that correlation refers to the relationship between two or more variables. The degree of this relationship is measured by the correlation coefficient. Common types of correlation are discussed such as positive and negative correlation. Methods for studying correlation are also outlined, including scatter diagrams and Karl Pearson's coefficient of correlation.
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 provides an overview of regression analysis including:
- Regression analysis is a statistical process used to estimate relationships between variables and predict unknown values.
- The document outlines different types of regression like simple, multiple, linear, and nonlinear regression.
- Key aspects of regression like scatter diagrams, regression lines, and the method of least squares are explained.
- An example problem is worked through demonstrating how to calculate the slope and y-intercept of a regression line using the least squares method.
This document discusses multiple regression analysis and its use in predicting relationships between variables. Multiple regression allows prediction of a criterion variable from two or more predictor variables. Key aspects covered include the multiple correlation coefficient (R), squared correlation coefficient (R2), adjusted R2, regression coefficients, significance testing using t-tests and F-tests, and considerations for using multiple regression such as sample size and normality assumptions.
- Regression analysis is a statistical technique used to measure the relationship between two quantitative variables and make causal inferences.
- A regression model graphs the relationship between a dependent variable (Y axis) and one or more independent variables (X axis). The goal is to find the linear equation that best fits the data.
- The regression equation takes the form Y = a + bX, where a is the intercept, b is the slope coefficient, and X and Y are the variables. The coefficient b indicates the strength and direction of the relationship.
This document provides information about statistical tests and data analysis presented by Dr. Muhammedirfan H. Momin. It discusses the different types of statistical data, such as qualitative vs quantitative and continuous vs discrete data. It also covers topics like sample data sets, frequency distributions, risk factors for diseases, hypothesis testing, and tests for comparing proportions and means. Specific statistical tests discussed include the z-test and how to calculate test statistics and compare them to critical values to determine statistical significance. Examples are provided to illustrate how to perform these tests to analyze differences between data sets.
Overviews non-parametric and parametric approaches to (bivariate) linear correlation. See also: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/Correlation
This document discusses correlation and regression. Correlation describes the strength and direction of a linear relationship between two variables, while regression allows predicting a dependent variable from an independent variable. It provides examples of calculating the correlation coefficient r to determine the strength and direction of relationships between variables like education and self-esteem or family income and number of children. The regression equation describes the linear regression line and can be used to predict values of the dependent variable from known values of the independent variable.
Partial correlation estimates the relationship between two variables while removing the influence of a third variable. It is a way to determine the correlation between two variables when controlling for a third. For example, a researcher may want to know the correlation between height and weight but also wants to control for gender, which can influence bone and muscle structure. Using the data sample provided, the correlation between height and weight was 0.825 but decreased to 0.770 when controlling for gender, showing gender partially explains the relationship between height and weight.
Correlation analysis measures the strength and direction of association between two or more variables. It is represented by the coefficient of correlation (r), which ranges from -1 to 1. A value of 0 indicates no association, 1 indicates perfect positive association, and -1 indicates perfect negative association. The scatter diagram is a graphical method to visualize the association between variables by plotting their values. Karl Pearson's coefficient is a commonly used algebraic method to calculate the coefficient of correlation from sample data.
This document is a presentation by Dwaiti Roy on partial correlation. It begins with an acknowledgement section thanking various professors and resources that helped in preparing the presentation. It then provides definitions and explanations of key concepts related to partial correlation such as correlation, assumptions of correlation, coefficient of correlation, coefficient of determination, variates, partial correlation, assumptions and hypothesis of partial correlation, order and formula of partial correlation. Examples are provided to illustrate partial correlation. The document concludes with references and suggestions for further reading.
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.
Regression Analysis presentation by Al Arizmendez and Cathryn LottierAl Arizmendez
We present an overview of regression analysis, theoretical construct, then provide a graphic representation before performing multiple regression analysis step by step using SPSS (audio files accompany the tutorial).
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
The document discusses correlation and linear regression. It defines Pearson and Spearman correlation as statistical techniques to measure the relationship between two variables. Pearson correlation measures the linear association between interval variables, while Spearman correlation measures statistical dependence between two variables using their rank order. Linear regression finds the best fit linear relationship between a dependent and independent variable to predict changes in one based on the other. The key assumptions and interpretations of correlation coefficients and regression lines are also covered.
Correlation and regression analysis are statistical methods used to determine relationships between variables. Correlation determines if a linear relationship exists between variables but does not imply causation. While correlation between age and height in children suggests a causal relationship, correlation between mood and health is less clear on causality. Regression analysis helps understand how changes in independent variables impact a dependent variable when other independent variables are held fixed. Linear regression models the dependent variable as a linear combination of parameters, while non-linear regression uses iterative procedures when the model is non-linear in parameters.
The document provides an introduction to regression analysis and performing regression using SPSS. It discusses key concepts like dependent and independent variables, assumptions of regression like linearity and homoscedasticity. It explains how to calculate regression coefficients using the method of least squares and how to perform regression analysis in SPSS, including selecting variables and interpreting the output.
This document discusses correlation and different types of correlation analysis. It defines correlation as a statistical analysis that measures the relationship between two variables. There are three main types of correlation: (1) simple and multiple correlation based on the number of variables, (2) linear and non-linear correlation based on the relationship between variables, and (3) positive and negative correlation based on the direction of change between variables. The degree of correlation is measured using correlation coefficients that range from -1 to +1. Common methods to study correlation include scatter diagrams and Karl Pearson's coefficient of correlation.
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.
Regression analysis is a statistical technique used to estimate the relationships between variables. It allows one to predict the value of a dependent variable based on the value of one or more independent variables. The document discusses simple linear regression, where there is one independent variable, as well as multiple linear regression which involves two or more independent variables. Examples of linear relationships that can be modeled using regression analysis include price vs. quantity, sales vs. advertising, and crop yield vs. fertilizer usage. The key methods for performing regression analysis covered in the document are least squares regression and regressions based on deviations from the mean.
Correlation analysis is used to find the degree of relationship between two or more variables by applying statistical tools. It produces a correlation coefficient that describes the strength and direction of the relationship. There are different types of correlation, including positive correlation where variables move in the same direction, negative correlation where they move in opposite directions, simple correlation between two variables, partial correlation controlling for other variables, and multiple correlation between three or more variables. Correlation analysis is important for measuring the degree of relationship between variables, estimating their values, and understanding economic behavior.
This document discusses correlation and regression analysis. It defines correlation analysis as examining the relationship between two or more variables, and regression analysis as examining how one variable changes when another specific variable changes in volume. It covers positive and negative correlation, linear and non-linear correlation, and how to calculate the coefficient of correlation. Regression analysis and regression equations are introduced for using a known variable to predict an unknown variable. Examples are provided to illustrate key concepts.
This document discusses Pearson's product-moment correlation coefficient, which is a statistical measure of the linear correlation between two variables introduced by Karl Pearson. It defines Pearson's r as the covariance of two variables divided by the product of their standard deviations. The document provides examples of calculating r and interpreting the resulting correlation coefficient between -1 and +1. It outlines the procedure, merits and demerits of Pearson's r and discusses its applications in pharmaceutical analysis and drug development.
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.
This document discusses hypothesis testing and the t-test. It covers:
1) The basics of hypothesis testing including null and alternative hypotheses, types of hypotheses, and types of errors.
2) The t-test, which is used for small samples from a normally distributed population. It relies on the t-distribution and the degree of freedom.
3) Applications of the t-test including testing the significance of a single mean, difference between two means, and paired t-tests.
4) When sample sizes are large, the normal distribution can be used instead in Z-tests for similar applications.
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.
HOW IS IT USEFUL IN FIELD OF FORENSIC SCIENCE AND IN THIS I HAVE SHOWN THE TYPES OF CORRELATION, SIGNIFICANCE , METHODS AND KARL PEARSON'S METHOD OF CORRELATION
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 research methods and data collection techniques. It describes different research designs including case studies, surveys, experiments, and other types. It then focuses on surveys, explaining what they are, that they collect quantitative data, and can be descriptive or explanatory. The document also discusses experiments, case studies, and other key elements of research design such as independent and dependent variables. It explores various data collection methods like interviews, focus groups, questionnaires, and the advantages and disadvantages of different question types.
The document discusses various data processing and analysis techniques including:
1. Editing of raw data to detect and correct errors through field editing by investigators and central editing by a team.
2. Coding of responses by assigning numerals or symbols to classify answers into categories for analysis.
3. Classification of data by grouping into classes based on common attributes or class intervals.
4. Tabulation by summarizing data into statistical tables for further analysis according to accepted principles.
Partial correlation estimates the relationship between two variables while removing the influence of a third variable. It is a way to determine the correlation between two variables when controlling for a third. For example, a researcher may want to know the correlation between height and weight but also wants to control for gender, which can influence bone and muscle structure. Using the data sample provided, the correlation between height and weight was 0.825 but decreased to 0.770 when controlling for gender, showing gender partially explains the relationship between height and weight.
Correlation analysis measures the strength and direction of association between two or more variables. It is represented by the coefficient of correlation (r), which ranges from -1 to 1. A value of 0 indicates no association, 1 indicates perfect positive association, and -1 indicates perfect negative association. The scatter diagram is a graphical method to visualize the association between variables by plotting their values. Karl Pearson's coefficient is a commonly used algebraic method to calculate the coefficient of correlation from sample data.
This document is a presentation by Dwaiti Roy on partial correlation. It begins with an acknowledgement section thanking various professors and resources that helped in preparing the presentation. It then provides definitions and explanations of key concepts related to partial correlation such as correlation, assumptions of correlation, coefficient of correlation, coefficient of determination, variates, partial correlation, assumptions and hypothesis of partial correlation, order and formula of partial correlation. Examples are provided to illustrate partial correlation. The document concludes with references and suggestions for further reading.
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.
Regression Analysis presentation by Al Arizmendez and Cathryn LottierAl Arizmendez
We present an overview of regression analysis, theoretical construct, then provide a graphic representation before performing multiple regression analysis step by step using SPSS (audio files accompany the tutorial).
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
The document discusses correlation and linear regression. It defines Pearson and Spearman correlation as statistical techniques to measure the relationship between two variables. Pearson correlation measures the linear association between interval variables, while Spearman correlation measures statistical dependence between two variables using their rank order. Linear regression finds the best fit linear relationship between a dependent and independent variable to predict changes in one based on the other. The key assumptions and interpretations of correlation coefficients and regression lines are also covered.
Correlation and regression analysis are statistical methods used to determine relationships between variables. Correlation determines if a linear relationship exists between variables but does not imply causation. While correlation between age and height in children suggests a causal relationship, correlation between mood and health is less clear on causality. Regression analysis helps understand how changes in independent variables impact a dependent variable when other independent variables are held fixed. Linear regression models the dependent variable as a linear combination of parameters, while non-linear regression uses iterative procedures when the model is non-linear in parameters.
The document provides an introduction to regression analysis and performing regression using SPSS. It discusses key concepts like dependent and independent variables, assumptions of regression like linearity and homoscedasticity. It explains how to calculate regression coefficients using the method of least squares and how to perform regression analysis in SPSS, including selecting variables and interpreting the output.
This document discusses correlation and different types of correlation analysis. It defines correlation as a statistical analysis that measures the relationship between two variables. There are three main types of correlation: (1) simple and multiple correlation based on the number of variables, (2) linear and non-linear correlation based on the relationship between variables, and (3) positive and negative correlation based on the direction of change between variables. The degree of correlation is measured using correlation coefficients that range from -1 to +1. Common methods to study correlation include scatter diagrams and Karl Pearson's coefficient of correlation.
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.
Regression analysis is a statistical technique used to estimate the relationships between variables. It allows one to predict the value of a dependent variable based on the value of one or more independent variables. The document discusses simple linear regression, where there is one independent variable, as well as multiple linear regression which involves two or more independent variables. Examples of linear relationships that can be modeled using regression analysis include price vs. quantity, sales vs. advertising, and crop yield vs. fertilizer usage. The key methods for performing regression analysis covered in the document are least squares regression and regressions based on deviations from the mean.
Correlation analysis is used to find the degree of relationship between two or more variables by applying statistical tools. It produces a correlation coefficient that describes the strength and direction of the relationship. There are different types of correlation, including positive correlation where variables move in the same direction, negative correlation where they move in opposite directions, simple correlation between two variables, partial correlation controlling for other variables, and multiple correlation between three or more variables. Correlation analysis is important for measuring the degree of relationship between variables, estimating their values, and understanding economic behavior.
This document discusses correlation and regression analysis. It defines correlation analysis as examining the relationship between two or more variables, and regression analysis as examining how one variable changes when another specific variable changes in volume. It covers positive and negative correlation, linear and non-linear correlation, and how to calculate the coefficient of correlation. Regression analysis and regression equations are introduced for using a known variable to predict an unknown variable. Examples are provided to illustrate key concepts.
This document discusses Pearson's product-moment correlation coefficient, which is a statistical measure of the linear correlation between two variables introduced by Karl Pearson. It defines Pearson's r as the covariance of two variables divided by the product of their standard deviations. The document provides examples of calculating r and interpreting the resulting correlation coefficient between -1 and +1. It outlines the procedure, merits and demerits of Pearson's r and discusses its applications in pharmaceutical analysis and drug development.
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.
This document discusses hypothesis testing and the t-test. It covers:
1) The basics of hypothesis testing including null and alternative hypotheses, types of hypotheses, and types of errors.
2) The t-test, which is used for small samples from a normally distributed population. It relies on the t-distribution and the degree of freedom.
3) Applications of the t-test including testing the significance of a single mean, difference between two means, and paired t-tests.
4) When sample sizes are large, the normal distribution can be used instead in Z-tests for similar applications.
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.
HOW IS IT USEFUL IN FIELD OF FORENSIC SCIENCE AND IN THIS I HAVE SHOWN THE TYPES OF CORRELATION, SIGNIFICANCE , METHODS AND KARL PEARSON'S METHOD OF CORRELATION
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 research methods and data collection techniques. It describes different research designs including case studies, surveys, experiments, and other types. It then focuses on surveys, explaining what they are, that they collect quantitative data, and can be descriptive or explanatory. The document also discusses experiments, case studies, and other key elements of research design such as independent and dependent variables. It explores various data collection methods like interviews, focus groups, questionnaires, and the advantages and disadvantages of different question types.
The document discusses various data processing and analysis techniques including:
1. Editing of raw data to detect and correct errors through field editing by investigators and central editing by a team.
2. Coding of responses by assigning numerals or symbols to classify answers into categories for analysis.
3. Classification of data by grouping into classes based on common attributes or class intervals.
4. Tabulation by summarizing data into statistical tables for further analysis according to accepted principles.
This document provides instructions for analyzing the relationship between two variables using cross tabulation in SPSS. It discusses identifying independent and dependent variables, creating cross tabs by moving variables to column and row positions, calculating column percentages to compare groups, and determining if a relationship exists based on differences in percentages between groups. Examples are provided to demonstrate how to interpret cross tabs and determine if one group is more or less likely than another to have a characteristic.
Sampling techniques allow researchers to gather data from a subset of a population rather than measuring the entire population due to constraints of time, resources, and access. There are different sampling methods including random sampling, which gives each member of the population an equal chance of being selected; systematic sampling, which selects samples at regular intervals; and stratified sampling, which divides the population into subgroups and samples proportionally from each subgroup to ensure representativeness. Sampling provides a time- and cost-effective way to make inferences about the whole population.
This document discusses correlation and linear regression. It defines correlation as the analysis of the relationship between two quantitative variables. Pearson's r is used to calculate correlation and can range from -1 to 1, with values closer to those extremes indicating a stronger linear relationship. A positive r represents a positive correlation while a negative r represents an inverse relationship. The coefficient of determination (r-squared) provides the proportion of variance shared between variables. However, correlation does not imply causation. Linear regression finds the best fitting straight line through data points to predict the value of one variable based on the other.
This document discusses data processing and analysis. It defines key terms like data, information, variables, and cases. It explains that data processing involves collecting, organizing, and analyzing raw data to produce useful information. The main steps in data processing are editing, coding, classification, data entry, validation, and tabulation. Types of data processing include manual, electronic data processing (EDP), real-time processing, and batch processing. The data processing cycle involves input, processing, and output stages to convert data into accurate and useful information.
This document discusses different types of research designs used in marketing research. It describes exploratory research as research conducted when little is known about a problem to gain insights and hypotheses. Descriptive research aims to describe important variables like who, what, where, and how. The goal is to determine frequencies and relationships between variables. Longitudinal research involves repeated measurements of a panel over time, while cross-sectional research measures a sample only once to generate summary statistics. Exploratory and descriptive designs use methods like secondary data analysis, interviews, and focus groups to collect data.
The document outlines the basic data processing cycle which involves 4 key operations - input, process, storage, and output. Data is entered through input devices, manipulated and processed by the processor, stored on storage devices like hard disks, and finally output through output devices to present the useful information to the user.
This document discusses discriminant analysis, which is a statistical technique used to classify observations into predefined groups based on independent variables. It can be used to predict the likelihood an entity belongs to a particular class. The document outlines the objectives, purposes, assumptions, and steps of discriminant analysis. It provides examples of using it to classify individuals as basketball vs volleyball players or high vs low performers based on variables.
The document discusses the manager-researcher relationship in business research projects. It defines research and explains why managers need better information through research. Some key points covered include the different styles of research, what constitutes good research, the roles and obligations of managers and researchers, and types of studies commonly used in research like reporting studies and practical studies.
Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components to research including the research design, methodology, instrumentation, sampling, data analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
This document discusses the definition and purpose of research. It defines research as the systematic process of collecting and analyzing information to increase understanding of a topic or to solve a problem. The purpose of research is to gain new knowledge, correct perceptions, and find solutions to problems. Some key characteristics of good research include careful planning and analysis, accurate observation, and openness to new ideas. Nursing research specifically aims to improve patient care and develop effective solutions to health issues.
This document provides an overview of computers and data processing. It defines key terms like data, information, and data processing. It describes the basic functional units and components of a computer system, including input, output, central processing, and memory units. It also distinguishes between computer hardware and software. Common hardware components are described along with system software and application software categories. The document provides examples of commonly used application software packages like word processors, spreadsheets, and database management systems. It explains the concepts of data, information, and how data is processed into useful information through various data processing methods and cycles.
Secondary data refers to data that has already been collected by someone else, while primary data is data that is collected by the researcher themselves. Some advantages of using secondary data include not having to reinvent the wheel, saving time and money, and the data may be very accurate if collected by a government agency. However, secondary data can be limited by being outdated, incomplete, or inconsistent over time. Primary data collection allows customizing data to specific research questions but is more time consuming and expensive. Researchers must determine if their question can be answered by existing secondary data or requires new primary data collection.
This document discusses various methods for collecting research data, including primary and secondary sources. It describes different types of self-report methods like interviews, questionnaires, and scales. Interviews can be structured, unstructured, or semi-structured. Questionnaires contain different types of questions in various formats. Scales discussed include Likert scales, semantic differential scales, and visual analog scales. The document provides advantages and disadvantages of each method.
Analysis of covariance (ANCOVA) is a statistical test that assesses whether the means of a dependent variable are equal across levels of a categorical independent variable while statistically controlling for the effects of other continuous variables known as covariates. ANCOVA works by adjusting the sums of squares for the independent variable to remove the influence of the covariate. This allows ANCOVA to test for differences between groups while controlling for the influence of other continuous variables. The assumptions of ANCOVA include those of ANOVA as well as the assumptions that the relationship between the dependent variable and covariate is linear and the same across all groups.
This document contains notes from a sermon series on fulfilment. It discusses three parts: (1) The kings against the King from Psalm 2:2, (2) The victory of the King from Psalm 2:6, and (3) The kiss and the King from Psalm 2:12. It also discusses a sermon on the coming of the crucified King, referencing Romans 8:17 about being heirs with Christ and sharing in his glory, and Acts 4:29-30 about speaking God's word with boldness through Jesus.
The document is about a girl who faces many challenges including balancing school and work without proper shoes or school supplies, taking outdoor baths in the cold, and living in a leaky house. Despite these difficulties, she remains determined to succeed in school, get her family a better home, help her siblings get an education, and inspire others through her example of hard work and perseverance.
To get a copy of the slides for free Email me at: japhethmuthama@gmail.com
You can also support my PhD studies by donating a 1 dollar to my PayPal.
PayPal ID is japhethmuthama@gmail.com
Correlation Analysis for MSc in Development Finance .pdfErnestNgehTingum
• Correlation is another way of assessing the relationship between variables.
– it measures the extent of correspondence between the ordering of two random variables.
• There is a large amount of resemblance between regression and correlation but for their methods of interpretation of the relationship.
– For example, a scatter diagram is of tremendous help when trying to describe the type of relationship existing between two variables.
This presentation covered the following topics:
1. Definition of Correlation and Regression
2. Meaning of Correlation and Regression
3. Types of Correlation and Regression
4. Karl Pearson's methods of correlation
5. Bivariate Grouped data method
6. Spearman's Rank correlation Method
7. Scattered diagram method
8. Interpretation of correlation coefficient
9. Lines of Regression
10. regression Equations
11. Difference between correlation and regression
12. Related examples
Power point presentationCORRELATION.pptxSimran Kaur
This document discusses different types of correlation and methods for determining correlation between variables. It defines correlation as the relationship between two or more variables, and describes positive correlation as variables changing in the same direction and negative correlation as changing in opposite directions. It also distinguishes between simple correlation of two variables and multiple correlation of more than two variables. Additionally, it introduces several methods for measuring correlation, including scatter plots, Karl Pearson's coefficient of correlation, and Spearman's rank correlation coefficient.
The document discusses chi-square test and its properties. It defines chi-square as a non-parametric statistical test used for discrete data to test for independence and goodness of fit between observed and expected frequencies. The chi-square test has some key assumptions including independent random samples, nominal or ordinal level data, and no expected cell counts below 5. It is calculated by subtracting expected from observed frequencies, squaring the differences, and dividing by expected counts. The chi-square test can identify if there is a significant association between variables but does not measure the strength of the association.
The document discusses various statistical methods for analyzing relationships between variables, including chi-square tests, measures of association like lambda and gamma, and rank correlation. Chi-square tests can be used to test for independence and goodness of fit between nominal or ordinal variables. Measures like lambda and gamma range from 0 to 1 and indicate the strength of association while controlling for errors. Rank correlation assesses relationships between variables when only ordinal data is available by analyzing the agreement between ranks. Cross tabulation allows investigating patterns of bivariate association through distribution analysis.
Correlation measures the strength and direction of association between two variables. Positive correlation means both variables increase or decrease together, while negative correlation means one variable increases as the other decreases. Correlation does not imply causation. The correlation coefficient r ranges from -1 to 1, where -1 is total negative correlation, 0 is no correlation, and 1 is total positive correlation. Common types of correlation coefficients include Pearson's correlation coefficient, used with normally distributed interval or ratio data, and Spearman's rank correlation coefficient, used with ordinal or non-normally distributed data. Regression analysis can be used to predict the value of a dependent variable from the value of an independent variable when they are linearly correlated.
This document provides an overview of simple linear regression and correlation analysis. It defines regression as estimating the relationship between two variables and correlation as measuring the strength and direction of that relationship. The key points covered include:
- Regression finds an estimating equation to relate known and unknown variables. Correlation determines how well that equation fits the data.
- Pearson's correlation coefficient r measures the linear relationship between two variables on a scale from -1 to 1.
- The coefficient of determination r2 indicates what percentage of variation in the dependent variable is explained by the independent variable.
- Statistical tests can evaluate whether a correlation is statistically significant or could be due to chance.
This document discusses correlation and regression analysis techniques for examining relationships between variables. Correlation analysis measures the strength and direction of relationships, while regression analysis helps determine the form of relationships to predict or estimate variable values. Simple linear regression analyzes relationships between two variables, with one independent variable potentially controlling the other dependent variable. A scatter diagram plots variable values to visualize their relationship. Correlation coefficients range from -1 to 1 to indicate perfect negative or positive correlation.
Correlational research describes the linear relationship between two or more variables without attributing cause and effect. The correlation coefficient is used to measure the strength of this relationship on a scale from -1 to 1. Positive correlations indicate variables increase or decrease together, while negative correlations mean they change in opposite directions. Scatterplots visually depict the correlation by showing how paired values of different variables relate on a graph. The Pearson's r formula is commonly used to calculate correlation coefficients from sample data.
This document provides an overview of correlation and linear regression analysis. It defines correlation as a statistical measure of the relationship between two variables. Pearson's correlation coefficient (r) ranges from -1 to 1, with values farther from 0 indicating a stronger linear relationship. Positive values indicate an increasing relationship, while negative values indicate a decreasing relationship. The coefficient of determination (r2) represents the proportion of shared variance between variables. While correlation indicates linear association, it does not imply causation. Multiple regression allows predicting a continuous dependent variable from two or more independent variables.
The document discusses various methods of correlation analysis. It begins by defining correlation as a statistical technique used to measure the strength and direction of association between two quantitative variables. Some key points made in the document include:
- Correlation can be positive (variables move in the same direction), negative (variables move in opposite directions), or zero (no relationship).
- Methods for measuring correlation discussed include scatter diagrams, Karl Pearson's coefficient, and Spearman's rank correlation coefficient.
- Correlation can be simple, partial, or multiple depending on the number of variables studied. It can also be linear or non-linear based on the relationship between the variables.
- Correlation only measures association but does not determine
Correlation is a technique used to determine the co-occurrence of two or more variables. Correlation coefficients range from -1 to 1, with values closer to these extremes indicating a stronger linear relationship between variables. Positive correlations indicate that high values in one variable are associated with high values in the other, while negative correlations show that high values in one variable are associated with low values in the other. Scatter plots can be used to display the relationship between two variables and identify if the correlation is positive, negative, or zero. The size of the correlation coefficient corresponds to the width of an imaginary ellipse that can be drawn around the points in a scatter plot.
Unit-I, BP801T. BIOSTATISITCS AND RESEARCH METHODOLOGY (Theory)
Correlation: Definition, Karl Pearson’s coefficient of correlation, Multiple correlations -
Pharmaceuticals examples.
Correlation: is there a relationship between 2
variables.
Multivariate Analysis Degree of association between two variable- Test of Ho...NiezelPertimos
The document discusses multivariate analysis and correlation. It defines correlation as a measure of the degree of association between two variables. A correlation coefficient between -1 and 1 indicates the strength and direction of the linear relationship, with values closer to 1 or -1 being stronger. Positive correlation means the variables move in the same direction, while negative correlation means they move in opposite directions. The document provides examples and methods for calculating and interpreting correlation coefficients, including using scatter plots and the Pearson product-moment formula. Excel functions for finding correlation across multiple data sets are also described.
This document discusses correlation and linear regression analysis. It begins by outlining the learning objectives which are to describe relationships between variables using correlation, estimate effects of independent variables on dependents with regression, and perform and interpret different types of regression analyses. It then provides examples of how correlation calculates the strength and direction of relationships between interval variables and how regression finds the best fitting linear equation to estimate relationships between variables. It emphasizes that regression minimizes the sum of squared errors to find the line of best fit for the data.
The document provides an overview of correlation, regression, and other statistical methods. It defines correlation as measuring the association between two variables, while regression finds the best fitting line to predict a dependent variable from an independent variable. Simple linear regression uses one predictor variable, while multiple linear regression uses two or more. Logistic regression is used for nominal dependent variables. Nonlinear regression fits curved lines to nonlinear data. The document provides examples and guidelines for choosing the appropriate statistical test based on the type of variables.
This document discusses correlation and regression analysis. It defines correlation as a statistical measure of how two variables are related. A correlation coefficient between -1 and 1 indicates the strength and direction of the linear relationship between variables. A scatterplot can show this graphically. Regression analysis involves using one variable to predict scores on another variable. Simple linear regression uses one independent variable to predict a dependent variable, while multiple regression uses two or more independent variables. The goal is to identify the regression line that best fits the data with the least error. The coefficient of determination, R2, indicates how much variance in the dependent variable is explained by the independent variables.
Covariance and correlation(Dereje JIMA)Dereje Jima
The document discusses covariance and correlation, which are mathematical models used to assess relationships between variables. Covariance measures how two variables change together, while correlation measures both the strength and direction of the linear relationship between variables. Correlation coefficients range from -1 to 1, where values closer to 1 or -1 indicate a strong linear relationship and values closer to 0 indicate no linear relationship. The document also discusses partial correlation and multiple correlation, which measure relationships while controlling for additional variables. Factors that can affect correlation analyses include sample size and outliers.
This document provides an outline for a training session on publishing research in international scholarly journals. The objectives of the training are to teach research coordinators about the publishing process, how to select journals, write cover letters and manage submissions, understand open access options and predatory journals, and how to deal with reviewers and editorial comments. The methodology will include interactive lectures, group and individual work, internet/web sessions, and assignments. The contents and plan lists the session titles, durations, methods, and activities. Topics that will be covered include the publishing process, selecting journals and writing cover letters, ethical issues in publishing, the submission process, and dealing with reviewers and editors.
1) Blinding in clinical trials refers to keeping trial participants, investigators, and assessors unaware of treatment assignments to prevent bias.
2) Potential benefits of blinding include less psychological or physical bias in participants, better compliance, and less bias in outcome assessments.
3) Types of blinding include non-blinded (where all know assignments), single-blinded (one group remains unaware), and double-blinded (participants, investigators, and assessors remain unaware). Placebos are often used to maintain blinding.
1) The document describes the phases of clinical trials, from Phase I to Phase III. Phase I trials involve small numbers of patients and evaluate safety, Phase II evaluates efficacy and identifies groups likely to benefit, and Phase III further evaluates efficacy and safety in large randomized controlled trials.
2) The document provides examples of Phase I, II, and III clinical trial designs and goals. Phase III trials are typically multicenter, randomized controlled trials used to generate evidence for marketing approval of new drugs. Control groups are important to account for factors like natural disease progression.
3) Clinical trials progress from exploratory Phase I safety studies to definitive Phase III trials evaluating efficacy versus a control as the standard for regulatory approval of new interventions.
Here are the designs I would recommend for each case:
Case 1: N-of-1 design. This design is well-suited for testing the efficacy of a treatment for an individual patient, as in this case assessing L-arginine for a carrier of OTCD.
Case 2: Randomized withdrawal design. This minimizes time on placebo by giving all patients open-label treatment initially to identify responders, who are then randomized to continue treatment or placebo. This is appropriate given the reversible but relatively slow outcome.
Case 3: Delayed start design. This can distinguish treatment effects on symptoms from effects on disease progression, which is important given the primary endpoint of changes on the UPDRS scale for Parkinson
Here are the components I predict each phrase came from:
1. We hypothesized that... Introduction
2. The sample size was 50 patients. Materials and methods
3. As shown in Figure 1,... Results
4. These findings have important implications... Discussion
5. In conclusion,... Conclusions
This document discusses various types of bias, confounding, and causation that can occur in epidemiological studies. It defines a confounder as a variable that is associated with the exposure and affects the outcome but is not in the causal pathway. Three main types of bias are described: selection bias, information bias, and confounding. Specific biases like recall bias, observer bias, and non-respondent bias are explained. Methods for controlling confounding like matching, stratification, and multivariate analysis are also outlined. The document concludes by discussing Hill's criteria for determining a causal association and threats to the internal and external validity of experimental studies.
This document discusses various methods and instruments for collecting data in research studies. It begins by defining data and explaining why data collection is important. It then covers primary and secondary sources of data, as well as internal and external sources. The main methods of collecting primary data discussed are direct personal investigation through interviews, indirect oral investigation, case studies, measurements, and observation. Secondary data sources include published and unpublished sources. The document also discusses self-reported data collection methods like surveys, interviews, and questionnaires. Other methods covered include document review, focus groups, and observation. Mixed methods are also briefly discussed.
The document discusses various topics related to cancer epidemiology. It provides statistics showing that lung, breast, colon, stomach, prostate, liver and cervix cancers are among the most common types of cancer. It also discusses factors contributing to cancer deaths, finding that tobacco use accounts for 30%, diet 35%, infections 10%, and other factors like occupation, pollution and genetics account for smaller percentages. The document also discusses associations between specific cancers and factors like infections, radiation, chemicals, diet, obesity and geography.
This document provides information about cancer epidemiology and burden globally and in Egypt. It discusses that cancer is characterized by uncontrolled growth and spread of abnormal cells, which is caused by external and internal factors. It then provides details on cancer rates, definitions, and statistics for assessing cancer burden for different populations. Specifically for Egypt, it shares data on estimated new cancer cases and cancer deaths in 2012 and projected for 2020, finding increases expected due to demographic changes. Overall, the document analyzes global and local cancer incidence, mortality and prevalence data.
Community diagnosis is vital in health planning, evaluation and needs assessment, several types of indicators are valid to be used for community diagnosis including Socio-economic, demographics, health system, and living arrangements.
This document discusses infection prevention procedures for Ebola virus disease outbreaks. It provides objectives of recognizing epidemiological features of EVD and the role of infection control. It summarizes the causative agents of EVD, including the five species and the 2014 West African outbreak being caused by the Zaire species. It also discusses transmission occurring through contact with bodily fluids, with human-to-human transmission driving the 2014 outbreak. The document outlines safety precautions for health care settings, including strict standard precautions, personal protective equipment, and patient placement in isolation rooms.
The document provides information about plagiarism, including its definition, why it is considered fraud, how copyright laws relate to intellectual property, what constitutes plagiarism, different types of plagiarism, excuses commonly used to justify plagiarism, and examples of plagiarized work. It notes that plagiarism involves presenting someone else's ideas or work as your own without giving them proper credit or attribution.
Diagnostic, screening tests, differences and applications and their characteristics, four pillars of screening tests, sensitivity, specificity, predictive values and accuracy
This document discusses concepts related to public awareness and health education. It begins by outlining objectives of recognizing the concept of public awareness, basic components of communication and education processes, and health education theories. It then provides details on raising public awareness, including that the process must meet mutual needs and influence community attitudes/behaviors. Key approaches to awareness raising are discussed, including personal communication, mass communication, education, and advocacy. Several behavioral models are examined at the individual, interpersonal, and community levels, including the health belief model, stages of change model, diffusion of innovations theory, and community organization approach. Communication challenges and factors influencing complexity are also reviewed.
This document discusses different types of sampling methods used in research. It begins by defining key terms like target population, sample, and sampling frame. It then covers different sampling techniques including probability sampling methods like simple random sampling, stratified random sampling, and cluster sampling as well as non-probability sampling methods. For each method, it provides examples and discusses their advantages and disadvantages for representing populations. The document aims to help medical students understand how to select appropriate sampling methods based on their research questions.
Competency-based education in Public Health, a model of employing Hybrid-PBL educational method in building core Public Health competencies at the undergraduate medical education.
Euro 2024 Predictions - Group Stage Results
How did we get on?
We predicted 10 of the 16 teams to progress to the last 16, just one less than we targeted
A fantastic win last night by Georgia against Portugal 2 - 0
See our results for each group and some surprises we didnt expect
https://www.selectdistinct.co.uk/2024/06/27/euro-2024-prediction-results
#EURO2024 #Germany2024 #England #EURO2024predictions
[Metaisach.com] Bồi Dưỡng Học Sinh Giỏi Vật Lý Lớp 11 - Tập 1 - Nguyễn Phú Đồ...truongngocyennhi9120
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[D2T2S04] SageMaker를 활용한 Generative AI Foundation Model Training and TuningDonghwan Lee
이 세션에서는 SageMaker Training Jobs / SageMaker Jumpstart를 사용하여 Foundation Model 을 Pre-Triaining 하거나 Fine Tuing 하는 방안을 제시합니다. 이 세션을 통해 아래 3가지가 소개됩니다.
1. 파운데이션 모델을 처음부터 Training
2. 오픈 소스 모델을 사용하여 파운데이션 모델을 Pre-Training
3. 도메인에 맞게 모델을 Fine Tuning하는 방안
발표자:
Miron Perel, Principal ML GTM Specialist, AWS
Kristine Pearce, Principal ML BD, AWS
Amazon Aurora 클러스터를 초당 수백만 건의 쓰기 트랜잭션으로 확장하고 페타바이트 규모의 데이터를 관리할 수 있으며, 사용자 지정 애플리케이션 로직을 생성하거나 여러 데이터베이스를 관리할 필요 없이 Aurora에서 관계형 데이터베이스 워크로드를 단일 Aurora 라이터 인스턴스의 한도 이상으로 확장할 수 있는 Amazon Aurora Limitless Database를 소개합니다.
Amazon DocumentDB(MongoDB와 호환됨)는 빠르고 안정적이며 완전 관리형 데이터베이스 서비스입니다. Amazon DocumentDB를 사용하면 클라우드에서 MongoDB 호환 데이터베이스를 쉽게 설치, 운영 및 규모를 조정할 수 있습니다. Amazon DocumentDB를 사용하면 MongoDB에서 사용하는 것과 동일한 애플리케이션 코드를 실행하고 동일한 드라이버와 도구를 사용하는 것을 실습합니다.
Applications of Data Science in Various IndustriesIABAC
The wide-ranging applications of data science across industries.
From healthcare to finance, data science drives innovation and efficiency by transforming raw data into actionable insights.
Learn how data science enhances decision-making, boosts productivity, and fosters new advancements in technology and business. Explore real-world examples of data science applications today.
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...ThinkInnovation
Objective
To identify the impact of speed limit restrictions in different constituencies over the years with the help of DID technique to conclude whether having strict speed limit restrictions can help to reduce the increasing number of road accidents on weekends.
Context*
Generally, on weekends people tend to spend time with their family and friends and go for outings, parties, shopping, etc. which results in an increased number of vehicles and crowds on the roads.
Over the years a rapid increase in road casualties was observed on weekends by the Government.
In the year 2005, the Government wanted to identify the impact of road safety laws, especially the speed limit restrictions in different states with the help of government records for the past 10 years (1995-2004), the objective was to introduce/revive road safety laws accordingly for all the states to reduce the increasing number of road casualties on weekends
* The Speed limit restriction can be observed before 2000 year as well, but the strict speed limit restriction rule was implemented from 2000 year to understand the impact
Strategies
Observe the Difference in Differences between ‘year’ >= 2000 & ‘year’ <2000
Observe the outcome from multiple linear regression by considering all the independent variables & the interaction term
❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT MATKA GUESSING KALYAN CHART FINAL ANK SATTAMATAK KALYAN MAKTA SATTAMATAK KALYAN MAKTA
Oracle PaaS and IaaS Universal Credits Service Descriptions.pdf
Linear Correlation
1. 05/04/14 Dr Tarek Amin 1
Investigating the Relationship
between Two orMore Variables
(Correlation)
Professor Tarek Tawfik Amin
Public Health, Faculty of Medicine
Cairo University
amin55@myway.com
2. The Relationship Between Variables
Variables can be categorized into two types when investigating
their relationship:
Dependent:
A dependent variable is explained oraffected
by an independent variable. Age and height
Independent :
Two variables are independent if the pattern of
variation in the scores forone variable is not
related orassociated with variation in the scores
forthe othervariable.
The level of education in Ecuadorand the infant
mortality in Mali
3. Techniques used to Analyze the Relationship between Two
Variables
Method Examples
I- Tabularand graphical methods:
These present data in way that reveals a
possible relationship between two
variables.
II-Numerical methods:
Mathematical operations used to quantify,
in a single number, the strength of a
relationship (measures of association).
When both variables are measured at least
at the ordinal level they also indicate the
direction of the relationship.
Bivariate table for categorical data
(nominal/ordinal data)
Scatter plot for interval/ratio.
Lambda, Cramer’s V (nominal)
Gamma, Somer’s d, Kendall’s tau-b/c
(ordinal with few values)
Spearman’s rank order Co/Co.
(ordinal scales with many values)
Pearson’s product moment correlation
(Interval/ratio)
These techniques are called collectively as
Bi-variate descriptive statistics
4. Correlation: indications
o Correlational techniques are used to study
relationships.
o They may be used in exploratory studies in
which one to intent to determine whether
relationships exist,
o And in hypothesis testing about a particular
relationship.
5. Correlations techniques used to
assess
the existence,
the direction
and the strength
of association between
variables.
6. Pearson Correlation (Numeric, interval/ratio)
The Pearson product moment correlation coefficient (rorrho)
is the usual method by which the relation between two
variables is quantified.
Type of data required:
Interval/ratio sometimes ordinal data.
At least two measures on each subjects at the
interval/ratio level.
Assumptions:
The sample must be representative of the population.
The variables that are being correlated must be normally
distributed.
The relationship between variables must be LINEAR.
8. 05/04/14 Dr Tarek Amin 8
Relationships Measured with Correlation Coefficient
The correlation coefficient is the cross products
of the Z-scores.
[ ]( )nzXzYr ∑=
Where:
ZX= the z-score of variable X
ZY= the z-score of variable Y
N= number of observations
9. Because the means and standard deviations
of any given two sets of variables are
different, we cannot directly compare the
two scores.
However, we can, transform them from the
ordinary absolute figures to Z-scores with a
mean of 0 and SDof 1.
The correlation is the mean of the cross-
products of the Z-score foreach value
included, a measure of how much each pair
of observations (scores) varies together.
Tips
10. Correlation Coefficient (r)
The correlation coefficient r allows us to
state mathematically the relationship that
exists between two variables. The correlation
coefficient may range from +1.00 through 0.00 to – 1.00.
A + 1.00 indicates a perfect positive
relationship,
0.00 indicates no relationship,
and -1.00 indicates a perfect negative
relationship.
11. I-Strength of the Correlation Coefficient
How large r should forit to be useful?
In decision making at least 0.95 while those concerning
human behaviors 0.5 is fair.
The strengths of r are as follow:
0.00-0.25 little if any.
0.26 -0.49 LOW
0.50- 0.69 Moderate
0.70 - 0.89 High
0.90 – 1.00 Very high .
12. II-Significance of the Correlation
The level of statistical significance is greatly
affected by the sample size n.
If r is based on a sample of 1,000, there is much
greaterlikelihood that it represents the r of the
population than if it were based on 10 subjects.
13. ‘ With large sample sizes rs that are described as
demonstrating (little if any) relationship are
statistically significant’
Statistical significance implies that r
did not occurby chance, the
relationship is greaterthan zero.
14. - The correlation coefficient also tell us the type
of relation that exists; that is, whetheris
positive ornegative.
- The relationship between job satisfaction and job
turnoverhas been shown to be negative; an
inverse relationship exists between them.
When one variable increases, the other decreases.
- Those with highergrades have lowerdropout rates
(a positive relationship).
Increases in the score of one variable is accompanied by
increase in the other.
III- Direction of correlation
15. Relationships Measured by Correlation
Coefficients:
When using the formula with Z-scores, ris the
average of the corss-products of the Z-scores.
[ ]( )nzXzYr ∑=
A five subjects took a quiz X, on which the scores ranged from
6to 10 and an examination Y, on which the scores ranged form
82to 98.
Calculate r and determine the pattern of correlation?
16. 05/04/14 Dr Tarek Amin 16
Formula forcalculating correlation coefficient r.
[ ]( )nzXzYr ∑=
17. A perfect positive relationship between two variables.
Subjects X (quiz) Y
(examination
)
zX zY zX*zY
1
2
3
4
5
6
7
8
9
10
82
86
90
94
98
-1.42
-0.71
0.00
0.71
1.42
-1.42
0.71
0.00
0.71
1.42
2.0
0.5
0.0
0.5
2.0
mean X= 8, SD=1.41 mean Y= 90 sd=5.66 ∑zXzY= 5.00
r= ∑zXzY/n =
5.00/5 = +1
23. The following table is SPSS output describing the correlation between age, education in years,
smoking history, satisfaction with the current weight, and the overall state of health fora randomly
selected subjects.
Overall state
of health
Satisfaction
with current
weight
Smoking
history
Education in
years
Subject's
age
1.000
.
434
Subject's age
Pearson Correlation
Sig.(2 tailed)
N
.022
.649
419
Education in years
Pearson Correlation
Sig.(2 tailed)
N
-.108*
.026
423
.143**
.003
432
Smoking history
Pearson Correlation
Sig.(2 tailed)
N
-.009
.849
440
.033
.493
424
-.077
.109
432
Satisfaction with current
weight
Pearson Correlation
Sig.(2 tailed)
N
1.000
.
444
.370*
.000
443
-.200*
.000
441
.149**
.000
425
-.126**
.009
433
Overall state of health
Pearson Correlation
Sig.(2 tailed)
N
*Correlation is significant at the 0.05 level (2-tailed(.
** Correlation is significant at the 0.01 level (2-tailed).
24. Figure (1): Insulin resistance (HOMA-IR) in relation to
serum ferritin level among cases and controls.
Ferritin (log)
2.82.62.42.22.01.8
HOMA-RI
8
7
6
5
4
3
2
Controls
Sickle
Total Population
r=0.804, P=0.0001
25. Figure (2): 1,25 (OH) vitamin D in relation to body mass
index among obese and lean controls.
Body mass index
5040302010
VitaminDlevel
100
80
60
40
20
0
Lean
Obese
Total Population
r= -.166, P=0.036