This paper brings out the effect of suppressed negative emotions on human behavior. Past negative experiences of different personalities were taken into consideration to relate with FRM and conclusions are derived on it to state the effects.
A Study of Impact of Westernization on Indian Culture Using Fuzzy Relational ...ijbuiiir
Over the years the civilizations of the world have
adopted many of the Wests styles and
ways of life. This Westernization has started a downward spiral
in destroying the cultural
diversity of the world. Westernization has caused many people
to reject their traditional style of
clothing and alter their daily life to conform with the styles of
the Western part of the world. In this paper an attempt is made
to study the impact of westernization on Culturally Rich Indian
by using Fuzzy Relational Maps (FRMs).
Study on the Impact of Malnutrition and Fruits Using Fuzzy Relational Mapsijcnes
we find the relation between the nutritious content in fruits using Fuzzy Relational Maps. We are using a linguistic questionnaire and using this interview we have constructed the FRM model, relating the nutritious content in fruits. Thus we use FRM models to study and analyze this problem and our study strongly reveals that the fruits intake by the pregnant women minimizes the rate of malnutrition
Study on Impact of Media on Education Using Fuzzy Relational MapsMangaiK4
Abstract- In this paper we bring out the depth of impact of media upon the growth of education. Education moulds an individual to take firm decision on issues. It makes to feel independent and leads to a more exposed world. Media is a very powerful tool to explore the world and have access to the world. Internet, Mobile phones etc, helps for an easy access to any part of the world at out finger tips. Media may lead us to both constructive and destructive mechanism depending the way we deal with it. Here we use FRM model to study and analyze the impact of media on education.
On the Health Hazards of the Sugarcane Using IFRM ModelMangaiK4
Abstract- In this paper, we use Induced Fuzzy Relational Mappings (IFRM) to analyze the problem of health hazards faced by the sugarcane cultivators due to chemical pollution. Based on our study, we made conclusion and suggest some remedial measures.
A Study of Personality Influence in Building Work Life Balance Using Fuzzy Re...ijdmtaiir
Personality plays an important role in work life
balance irrespective of the organizational setups and other
factors. It has become a subject of concern in terms of
technological, market and organizational changes associated
with an individual’s personality. Here in this study an attempt is
made to study about the holistic picture of personality influence
in work-life balance on the basis of experts’ opinion. The
influence of personality is studied from the big five factors of
personality traits. The data were analyzed using Fuzzy
Relational mapping (FRM) model and conclusions arrived for
which personality has more influence in building work life
balance and which one is more vulnerable for work life
imbalance
A Study of the effects of emotions and Personality on Physical Health using I...ijdmtaiir
Emotions have a significant influence on the human
performance and intelligent behavior.As a negative emotion,
anger is the main cause in destroying one’s happiness. Also the
effects of anger are stress, fear etc., and they play a major role
in building a negative personality. The personality plays a
vital role in affecting states of emotions in any specific
situations. In this paper, we analyzethe emotion‘anger’which
affects physical health by relating with the dimensions of
personality using Induced Neutrosophic Relational Maps.
Section one describes problem of study. Section two gives the
information on the development of Induced Linked
Neutrosophic Relational Maps. Section three, the adaption of
the problem using Induced Linked Neutrosophic Relational
Maps (ILNRMs). Section four,conclusion and scope for
futurestudy.
A Study of Pervasive Computing Environments in Improving the Quality of Life ...ijdmtaiir
This document discusses using induced linked fuzzy relational maps (ILFRMs) to analyze the impact of computer education on job opportunities and quality of life. It defines FRMs and LFRMs and the process of finding hidden patterns in ILFRMs. Attributes related to computer education impact, available jobs, and quality of life dimensions are identified. Expert opinions are used to construct the relational matrices between these attribute groups. The hidden pattern analysis process is described, taking the "increasing creative/tolerance power" attribute as the initial ON state. The analysis shows computer education can increase job opportunities which in turn improve quality of life dimensions.
To Analyze Stress in Education Using Bam ModelMangaiK4
This document summarizes a research paper that analyzes stress in education using Bidirectional Associative Memory (BAM) models. The paper:
1) Interviews 50 people using a linguistic questionnaire about stress and its impact on education.
2) Develops a BAM model with stress attributes like time stress and emotional stress as the input, and types of education like technical education as the output.
3) Analyzes initial input vectors representing stress and shows the model converges to a fixed point, representing how different stress types relate to education types.
A Study of Impact of Westernization on Indian Culture Using Fuzzy Relational ...ijbuiiir
Over the years the civilizations of the world have
adopted many of the Wests styles and
ways of life. This Westernization has started a downward spiral
in destroying the cultural
diversity of the world. Westernization has caused many people
to reject their traditional style of
clothing and alter their daily life to conform with the styles of
the Western part of the world. In this paper an attempt is made
to study the impact of westernization on Culturally Rich Indian
by using Fuzzy Relational Maps (FRMs).
Study on the Impact of Malnutrition and Fruits Using Fuzzy Relational Mapsijcnes
we find the relation between the nutritious content in fruits using Fuzzy Relational Maps. We are using a linguistic questionnaire and using this interview we have constructed the FRM model, relating the nutritious content in fruits. Thus we use FRM models to study and analyze this problem and our study strongly reveals that the fruits intake by the pregnant women minimizes the rate of malnutrition
Study on Impact of Media on Education Using Fuzzy Relational MapsMangaiK4
Abstract- In this paper we bring out the depth of impact of media upon the growth of education. Education moulds an individual to take firm decision on issues. It makes to feel independent and leads to a more exposed world. Media is a very powerful tool to explore the world and have access to the world. Internet, Mobile phones etc, helps for an easy access to any part of the world at out finger tips. Media may lead us to both constructive and destructive mechanism depending the way we deal with it. Here we use FRM model to study and analyze the impact of media on education.
On the Health Hazards of the Sugarcane Using IFRM ModelMangaiK4
Abstract- In this paper, we use Induced Fuzzy Relational Mappings (IFRM) to analyze the problem of health hazards faced by the sugarcane cultivators due to chemical pollution. Based on our study, we made conclusion and suggest some remedial measures.
A Study of Personality Influence in Building Work Life Balance Using Fuzzy Re...ijdmtaiir
Personality plays an important role in work life
balance irrespective of the organizational setups and other
factors. It has become a subject of concern in terms of
technological, market and organizational changes associated
with an individual’s personality. Here in this study an attempt is
made to study about the holistic picture of personality influence
in work-life balance on the basis of experts’ opinion. The
influence of personality is studied from the big five factors of
personality traits. The data were analyzed using Fuzzy
Relational mapping (FRM) model and conclusions arrived for
which personality has more influence in building work life
balance and which one is more vulnerable for work life
imbalance
A Study of the effects of emotions and Personality on Physical Health using I...ijdmtaiir
Emotions have a significant influence on the human
performance and intelligent behavior.As a negative emotion,
anger is the main cause in destroying one’s happiness. Also the
effects of anger are stress, fear etc., and they play a major role
in building a negative personality. The personality plays a
vital role in affecting states of emotions in any specific
situations. In this paper, we analyzethe emotion‘anger’which
affects physical health by relating with the dimensions of
personality using Induced Neutrosophic Relational Maps.
Section one describes problem of study. Section two gives the
information on the development of Induced Linked
Neutrosophic Relational Maps. Section three, the adaption of
the problem using Induced Linked Neutrosophic Relational
Maps (ILNRMs). Section four,conclusion and scope for
futurestudy.
A Study of Pervasive Computing Environments in Improving the Quality of Life ...ijdmtaiir
This document discusses using induced linked fuzzy relational maps (ILFRMs) to analyze the impact of computer education on job opportunities and quality of life. It defines FRMs and LFRMs and the process of finding hidden patterns in ILFRMs. Attributes related to computer education impact, available jobs, and quality of life dimensions are identified. Expert opinions are used to construct the relational matrices between these attribute groups. The hidden pattern analysis process is described, taking the "increasing creative/tolerance power" attribute as the initial ON state. The analysis shows computer education can increase job opportunities which in turn improve quality of life dimensions.
To Analyze Stress in Education Using Bam ModelMangaiK4
This document summarizes a research paper that analyzes stress in education using Bidirectional Associative Memory (BAM) models. The paper:
1) Interviews 50 people using a linguistic questionnaire about stress and its impact on education.
2) Develops a BAM model with stress attributes like time stress and emotional stress as the input, and types of education like technical education as the output.
3) Analyzes initial input vectors representing stress and shows the model converges to a fixed point, representing how different stress types relate to education types.
The Pearman Personality Integrator (The PearmanTM) sets a new standard for assessing personality. With an innovative assessment experience coupled with a deeper look into the individuality of personality type, The Pearman provides a measure of personality in one’s natural state (i.e., what your clients feel most comfortable doing) and in one’s every day environment (i.e., how your clients are required to act in their profession). It also explores any disconnects your clients have by providing insight into their level of flexibility. Going back to the roots of personality type theory while incorporating new insights into the way individuals operate in the 21st century, The Pearman is perfectly suited for use with your talent development initiatives.
The Pearman Personality Integrator (The PearmanTM) sets a new standard for assessing personality. With an innovative assessment experience coupled with a deeper look into the individuality of personality type, The Pearman provides a measure of personality in one’s natural state (i.e., what your clients feel most comfortable doing) and in one’s every day environment (i.e., how your clients are required to act in their profession). It also explores any disconnects your clients have by providing insight into their level of flexibility. Going back to the roots of personality type theory while incorporating new insights into the way individuals operate in the 21st century, The Pearman is perfectly suited for use with your talent development initiatives.
This document discusses dummy variables and hypothesis testing. It provides examples of regressions using dummy variables to represent categories like gender and political orientation. It asks multiple choice questions about interpreting coefficients in regressions with dummy variables. Regarding hypothesis testing, it discusses using F-tests and t-tests to test hypotheses about whether regression coefficients or entire relationships are equal across groups.
1. The document discusses establishing a method to quantify and enhance the value of people in an organization based on their productivity, ethics, and behaviors.
2. It proposes measuring individuals based on factors like their motives, attitudes, behaviors, and ability to follow standard procedures. This "value" measurement could then be improved through learning and developing individuals.
3. Several models and principles are defined to link individuals' characteristics like personality, response to stimuli, and character to their value through factors like energy, learning, and containing distractions. The goal is optimizing individuals' value when their response equals the appropriate response.
This document defines correlation and discusses the relationship between two variables or events. It introduces the Pearson correlation coefficient r, which ranges from -1 to 1 and measures the strength and direction of association between two variables. Strong positive correlations near 1 indicate that as one variable increases, so does the other. The document also discusses how correlation does not necessarily imply causation and provides examples of calculating r from sample data.
This document discusses attitude scales and their components. It defines attitude and lists its three components: affective, behavioral, and cognitive. It explains what attitude scales are and their two main dimensions: direction and intensity. It then describes the Thurstone and Likert scales, which are the two primary methods used to measure attitudes. The Thurstone scale uses judges to rate statements, while the Likert scale uses a rating system without judges. In less than 3 sentences, this document provides an overview of attitude measurement using scales.
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.
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.
T H E B E H A V I O R A N A L Y S T T O D A Y .docxdeanmtaylor1545
This document provides an introduction to Relational Frame Theory (RFT) by comparing it to Lang's cognitive model of a fear network. It summarizes RFT's key principles of relational responding and framing relationships between stimuli. The document introduces an RFT account of Lang's fear network model to highlight how RFT analyzes explicit and implicit relationships between thoughts, emotions, behaviors, and physiological responses. It explains how discriminating relational frames allows one to glean more information from stimuli than looking at them individually, but can also lead to psychological problems if relational responding gets out of control.
International Journal of Mathematics and Statistics Invention (IJMSI)inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
There are several statistical tests that can be used to investigate correlations between variables based on the type of data and study design:
- A z-test can compare a sample proportion to a population proportion to see if they are significantly different, as when comparing PKU rates.
- Spearman's rank correlation or Pearson's correlation can measure the strength and direction of relationships between ordinal or interval/ratio variables.
- A t-test can analyze differences between repeated measures before and after an intervention to see if they are statistically significant.
- A chi-square test can determine if there is a relationship between categorical variables, such as student responses on a Likert scale. The appropriate test depends on the data
This document discusses different methods of measuring attitudes, specifically the Thurstone and Likert scales. It explains that an attitude scale aims to provide a valid measure of an individual's social attitudes. The Thurstone scale requires judges to sort statements into categories representing different degrees of favorability, and then computes median values to assign scale weights. The Likert scale presents statements with rating options like agree/disagree and sums responses. The document outlines the development process for each type of scale.
Mathematics is considered the mother of all sciences because it provides tools to solve problems in other sciences like biology, chemistry, and physics. Other subjects are based on mathematical concepts like structure, quantity, and change. Calculus is fundamental to modern science, and fields like game theory and operations research use mathematics and have significant applications. A math graduate would be a good fit for the Bangladesh Bank because banking relies heavily on mathematics and data analysis. Those with a background in math are also strong problem solvers able to work with complex models.
The document discusses regression and correlation analysis between BMI (Kg/m2) of pregnant mothers and birth weight (kg) of their newborns using data from 15 mothers. A scatter plot showed a positive linear relationship between BMI and birth weight. Linear regression was used to calculate the regression line as y=1.775351+0.0330817x, which can be used to predict birth weight based on a mother's BMI. The correlation coefficient (R) between BMI and birth weight was 0.94, indicating a strong positive correlation.
Unit 1 - Mean Median Mode - 18MAB303T - PPT - Part 1.pdfAravindS199
Sir Francis Galton was a prominent English statistician, anthropologist, eugenicist, and psychometrician in the 19th century. He produced over 340 papers and books, and created the statistical concepts of correlation and regression. As a pioneer in meteorology and differential psychology, he devised early weather maps, proposed theories of weather patterns, and developed questionnaires to study human communities and intelligence. The document discusses Galton's background and contributions to statistics, anthropology, meteorology, and psychometrics.
Discriminant analysis (DA) is a statistical technique used to predict group membership when the dependent variable is categorical and the independent variables are continuous. It identifies which variables discriminate between two or more naturally occurring groups. DA develops a linear equation to predict group membership based on weighted combinations of predictor variables. It aims to maximize the distance between group means to achieve strong discriminatory power. Like regression, DA assumes variables are normally distributed, cases are randomly sampled, and groups are mutually exclusive and collectively exhaustive. It requires at least two groups with minimal overlap and similar group sizes of at least five cases. DA can classify new cases into groups based on the discriminant functions derived from existing data.
This document provides an overview of key concepts in psychology including:
- The scientific method and how psychologists ask and answer questions through description, correlation, and experimentation.
- Common research methods like surveys, interviews, and longitudinal studies.
- The importance of control groups, random assignment, and double-blind studies in experiments.
- Statistical analysis and making inferences from data through measures like mean, median, standard deviation, and statistical significance.
- Frequently asked questions about the field address topics like laboratory research, cross-cultural comparisons, animal research ethics, and the value-laden nature of psychology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses key concepts in psychological science research methods. It covers the limits of intuition and common sense, the need for the scientific method in psychology, and various research techniques used including case studies, surveys, naturalistic observation, experiments, and statistical analysis. Experimental research involves manipulating independent variables, measuring dependent variables, and controlling for other factors. Statistical analysis allows researchers to describe patterns in data and make inferences about populations.
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.
A Survey of Ontology-based Information Extraction for Social Media Content An...ijcnes
The amount of information generated in the Web has grown enormously over the years. This information is significant to individuals, businesses and organizations. If analyzed, understood and utilized, it will provide a valuable insight to its stakeholders. However, many of these information are semi-structured or unstructured which makes it difficult to draw in-depth understanding of the implications behind those information. This is where Ontology-based Information Extraction (OBIE) and social media content analysis come into play. OBIE has now become a popular way to extract information coming from machine-readable sources. This paper presents a survey of OBIE, Ontology languages and tools and the process to build an ontology model and framework. The author made a comparison of two ontology building frameworks and identified which framework is complete.
Economic Growth of Information Technology (It) Industry on the Indian Economyijcnes
Information Technology (IT) is an important emerging sector of the Indian Economy. IT in India is an industry comprising of two noteworthy segments IT administrations and business process outsourcing (BPO).The segment has expanded its commitment to Indias GDP from 1.2% in 1998 to 9.3% in 2015. According to NASSCOM, the segment amassed incomes of US$147 billion out of 2015, with send out income remaining at US$99 billion and household income at US$48 billion, developing by more than 13%.Indias present Prime Minister Narendra Modi has begun a venture called �DIGITAL INDIA i.e., Computerized India to help secure IT a position both inside and outside of India. The IT sector has served as a fertile ground for the growth of a new entrepreneurial class with innovative corporate practices and has been instrumental in reversing the brain drain, raising Indias brand equity and attracting foreign direct investment (FDI) leading to other associated benefits. The Size of this sector has increased at a tremendous rate of 35% per year during the last 10 years. This Paper examines the India�s growth in IT industry and also studied the impact of IT on the Indian Economy.
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Similar to Effetcs of Suppressed Negative Emotions on Human Behaviour
The Pearman Personality Integrator (The PearmanTM) sets a new standard for assessing personality. With an innovative assessment experience coupled with a deeper look into the individuality of personality type, The Pearman provides a measure of personality in one’s natural state (i.e., what your clients feel most comfortable doing) and in one’s every day environment (i.e., how your clients are required to act in their profession). It also explores any disconnects your clients have by providing insight into their level of flexibility. Going back to the roots of personality type theory while incorporating new insights into the way individuals operate in the 21st century, The Pearman is perfectly suited for use with your talent development initiatives.
The Pearman Personality Integrator (The PearmanTM) sets a new standard for assessing personality. With an innovative assessment experience coupled with a deeper look into the individuality of personality type, The Pearman provides a measure of personality in one’s natural state (i.e., what your clients feel most comfortable doing) and in one’s every day environment (i.e., how your clients are required to act in their profession). It also explores any disconnects your clients have by providing insight into their level of flexibility. Going back to the roots of personality type theory while incorporating new insights into the way individuals operate in the 21st century, The Pearman is perfectly suited for use with your talent development initiatives.
This document discusses dummy variables and hypothesis testing. It provides examples of regressions using dummy variables to represent categories like gender and political orientation. It asks multiple choice questions about interpreting coefficients in regressions with dummy variables. Regarding hypothesis testing, it discusses using F-tests and t-tests to test hypotheses about whether regression coefficients or entire relationships are equal across groups.
1. The document discusses establishing a method to quantify and enhance the value of people in an organization based on their productivity, ethics, and behaviors.
2. It proposes measuring individuals based on factors like their motives, attitudes, behaviors, and ability to follow standard procedures. This "value" measurement could then be improved through learning and developing individuals.
3. Several models and principles are defined to link individuals' characteristics like personality, response to stimuli, and character to their value through factors like energy, learning, and containing distractions. The goal is optimizing individuals' value when their response equals the appropriate response.
This document defines correlation and discusses the relationship between two variables or events. It introduces the Pearson correlation coefficient r, which ranges from -1 to 1 and measures the strength and direction of association between two variables. Strong positive correlations near 1 indicate that as one variable increases, so does the other. The document also discusses how correlation does not necessarily imply causation and provides examples of calculating r from sample data.
This document discusses attitude scales and their components. It defines attitude and lists its three components: affective, behavioral, and cognitive. It explains what attitude scales are and their two main dimensions: direction and intensity. It then describes the Thurstone and Likert scales, which are the two primary methods used to measure attitudes. The Thurstone scale uses judges to rate statements, while the Likert scale uses a rating system without judges. In less than 3 sentences, this document provides an overview of attitude measurement using scales.
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.
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.
T H E B E H A V I O R A N A L Y S T T O D A Y .docxdeanmtaylor1545
This document provides an introduction to Relational Frame Theory (RFT) by comparing it to Lang's cognitive model of a fear network. It summarizes RFT's key principles of relational responding and framing relationships between stimuli. The document introduces an RFT account of Lang's fear network model to highlight how RFT analyzes explicit and implicit relationships between thoughts, emotions, behaviors, and physiological responses. It explains how discriminating relational frames allows one to glean more information from stimuli than looking at them individually, but can also lead to psychological problems if relational responding gets out of control.
International Journal of Mathematics and Statistics Invention (IJMSI)inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
There are several statistical tests that can be used to investigate correlations between variables based on the type of data and study design:
- A z-test can compare a sample proportion to a population proportion to see if they are significantly different, as when comparing PKU rates.
- Spearman's rank correlation or Pearson's correlation can measure the strength and direction of relationships between ordinal or interval/ratio variables.
- A t-test can analyze differences between repeated measures before and after an intervention to see if they are statistically significant.
- A chi-square test can determine if there is a relationship between categorical variables, such as student responses on a Likert scale. The appropriate test depends on the data
This document discusses different methods of measuring attitudes, specifically the Thurstone and Likert scales. It explains that an attitude scale aims to provide a valid measure of an individual's social attitudes. The Thurstone scale requires judges to sort statements into categories representing different degrees of favorability, and then computes median values to assign scale weights. The Likert scale presents statements with rating options like agree/disagree and sums responses. The document outlines the development process for each type of scale.
Mathematics is considered the mother of all sciences because it provides tools to solve problems in other sciences like biology, chemistry, and physics. Other subjects are based on mathematical concepts like structure, quantity, and change. Calculus is fundamental to modern science, and fields like game theory and operations research use mathematics and have significant applications. A math graduate would be a good fit for the Bangladesh Bank because banking relies heavily on mathematics and data analysis. Those with a background in math are also strong problem solvers able to work with complex models.
The document discusses regression and correlation analysis between BMI (Kg/m2) of pregnant mothers and birth weight (kg) of their newborns using data from 15 mothers. A scatter plot showed a positive linear relationship between BMI and birth weight. Linear regression was used to calculate the regression line as y=1.775351+0.0330817x, which can be used to predict birth weight based on a mother's BMI. The correlation coefficient (R) between BMI and birth weight was 0.94, indicating a strong positive correlation.
Unit 1 - Mean Median Mode - 18MAB303T - PPT - Part 1.pdfAravindS199
Sir Francis Galton was a prominent English statistician, anthropologist, eugenicist, and psychometrician in the 19th century. He produced over 340 papers and books, and created the statistical concepts of correlation and regression. As a pioneer in meteorology and differential psychology, he devised early weather maps, proposed theories of weather patterns, and developed questionnaires to study human communities and intelligence. The document discusses Galton's background and contributions to statistics, anthropology, meteorology, and psychometrics.
Discriminant analysis (DA) is a statistical technique used to predict group membership when the dependent variable is categorical and the independent variables are continuous. It identifies which variables discriminate between two or more naturally occurring groups. DA develops a linear equation to predict group membership based on weighted combinations of predictor variables. It aims to maximize the distance between group means to achieve strong discriminatory power. Like regression, DA assumes variables are normally distributed, cases are randomly sampled, and groups are mutually exclusive and collectively exhaustive. It requires at least two groups with minimal overlap and similar group sizes of at least five cases. DA can classify new cases into groups based on the discriminant functions derived from existing data.
This document provides an overview of key concepts in psychology including:
- The scientific method and how psychologists ask and answer questions through description, correlation, and experimentation.
- Common research methods like surveys, interviews, and longitudinal studies.
- The importance of control groups, random assignment, and double-blind studies in experiments.
- Statistical analysis and making inferences from data through measures like mean, median, standard deviation, and statistical significance.
- Frequently asked questions about the field address topics like laboratory research, cross-cultural comparisons, animal research ethics, and the value-laden nature of psychology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses key concepts in psychological science research methods. It covers the limits of intuition and common sense, the need for the scientific method in psychology, and various research techniques used including case studies, surveys, naturalistic observation, experiments, and statistical analysis. Experimental research involves manipulating independent variables, measuring dependent variables, and controlling for other factors. Statistical analysis allows researchers to describe patterns in data and make inferences about populations.
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.
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A Survey of Ontology-based Information Extraction for Social Media Content An...ijcnes
The amount of information generated in the Web has grown enormously over the years. This information is significant to individuals, businesses and organizations. If analyzed, understood and utilized, it will provide a valuable insight to its stakeholders. However, many of these information are semi-structured or unstructured which makes it difficult to draw in-depth understanding of the implications behind those information. This is where Ontology-based Information Extraction (OBIE) and social media content analysis come into play. OBIE has now become a popular way to extract information coming from machine-readable sources. This paper presents a survey of OBIE, Ontology languages and tools and the process to build an ontology model and framework. The author made a comparison of two ontology building frameworks and identified which framework is complete.
Economic Growth of Information Technology (It) Industry on the Indian Economyijcnes
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Effetcs of Suppressed Negative Emotions on Human Behaviour
1. Integrated Intelligent Research(IIR) International Journal of Business Intelligent
Volume: 04 Issue: 01 June 2015,Pages No.44- 46
ISSN: 2278-2400
44
Effetcs of Suppressed Negative Emotions on
Human Behaviour
A. Victor Devadoss1
, A.Felix2
, M. Elizabeth Suganya3
Department of Mathematics, Loyola College, Chennai
Abstract-This paper brings out the effect of suppressed
negative emotions on human behavior. Past negative
experiences of different personalities were taken into
consideration to relate with FRM and conclusions are derived
on it to state the effects.
Keywords: Fuzzy Relational Map (FRM), emotions, primary
and secondary emotions.
I. INTRODUCTION
Emotions have been described as discrete and consistent
responses to internal or external events which have a particular
significance for the organism. Human emotions may be
positive or negative .it depends on the event experienced.
Negative emotions: fear, anger, guilt, depression, pride,
jealous, self –pity, anxiety, envy, frustration, shame, denial,
offended, negative, regret sad, worried and grief.
Positive emotions: love, appreciation, happiness, hope,
enthusiasm, vitality, confidence, gratitude, patient, trust,
vulnerable and optimistic.
Researches have been done on emotions for long years on
various criteria.In recent years a growing body of research has
shown that emotion can profoundly influence a variety of
cognitive functions (Cytowic, 1996;Damasio, 1994[1];
Johnson & Tversky, 1983)[2]. Among these investigations one
area that receives increasing attention from both theoretical and
applied fields is decision making in risky situations. The
experimental study of the emotional influences requires the
induction of emotions in order to determine their effects
(Martin, 1990)[8]. Specifically, positive emotions and negative
emotions have been found to influence risky decisions in
different ways, resulting in diverse choice behaviors in terms
of risk-aversion or risk-seeking (Isen, 2001[4]; Kahn &
Isen[5], 1993. However, the research findings are quite
contradictory regarding the effects of emotions, largely due to
the fact that various studies adopt different operationalization
of positive emotions and negative emotions, as well as of risky
decision making (Hockey, Maule, Clough & Bdzola,
2000)[3].The Effects of Induced Positive and Negative
Emotions on Risky Decision Making(Jiaying
Zhao,2006)[7].the literature shows the so far works done on
induced emotions and risky decisions based on induced
emotions both positive and negative.This paper gives us a
picture of how negative emotions, when suppressed leads a
person to take risky decisions based on their life events. First
section deals with FRM preliminaries, the second section deals
with description of the problem, third section with adaptation
to the FRM model and at last with conclusion and suggestions.
II. FUZZY RELATIONAL MAPS (FRMA)
The new notion called Fuzzy Relational Maps (FRMs) was
introduced by Dr. W.B.Vasantha and Yasmin Sultana in the
year 2000. In FRMs we divide the very casual associations into
two disjoint units, like for example the relation between a
teacher and a student or relation; between an employee and an
employer or a relation; between the parent and the child in the
case of school dropouts and so on. In these situations we see
that we can bring out the casual relations existing between an
employee and employer or parent and child and so on. Thus for
us to define a FRM we need a domain space and a range space
which are disjoint in the sense of concepts. We further assume
no intermediate relations exist within the domain and the range
space. The number of elements in the range space need not in
general be equal to the number of elements in the domain
space.In our discussion the elements of the domain space are
taken from the real vector space of dimension n and that of the
range space are real vectors from the vector space of dimension
m (m in general need not be equal to n). We denote by R the
set of nodes R1, … , Rm of the range space, where Ri = {(x1, x2,
…, xm) / xj = 0 or 1} for i = 1, … ,m. If xi = 1 it means that the
node Ri is in the ON state and if xi = 0 it means that the node Ri
is in the OFF state.Similarly D denotes the nodes D1,…,Dn of
the domain space where Di = {(x1,…, xn) / xj = 0 or 1} for i = 1,
…, n. If xi = 1, it means that the node Di is in the on state and if
xi = 0 it means that the node Di is in the off state.A FRM is a
directed graph or a map from D to R with concepts like
policies or events etc. as nodes and causalities as edges. It
represents casual relations between spaces D and R. Let Di and
Rj denote the two nodes of an FRM. The directed edge from D
to R denotes the casuality of D on R , called relations. Every
edge in the FRM is weighted with a number in the set {0,
1}.Let ei j be the weight of the edge Di Rj, e i j {0.1}. The
weight of the edge DiRj is positive if increase in Di implies
increase in Rj or decrease in Di implies decrease in Rj. i.e.
casuality of Di on Rj is 1. If e i j = 0 then Di does not have any
effect on Rj. We do not discuss the cases when increase in Di
2. Integrated Intelligent Research(IIR) International Journal of Business Intelligent
Volume: 04 Issue: 01 June 2015,Pages No.44- 46
ISSN: 2278-2400
45
implies decrease in Rj or decrease in Di implies increase in
Rj.When the nodes of the FRM are fuzzy sets, then they are
called fuzzy nodes, FRMs with edge weights
{0, 1) are called simple FRMs.
Let D1, …, Dn be the nodes of the domain space D of an FRM
and R1, …, Rm be the nodes of the range space R of an FRM.
Let the matrix E be defined as E = (ei j ) where ei j {0, 1}; is
the weight of the directed edge DiRj (or RjDi), E is called the
relational matrix of the FRM.
It is pertinent to mention here that unlike the FCMs, the FRMs
can be a rectangular matrix; with rows corresponding to the
domain space and columns corresponding to the range space.
This is one of the marked difference between FRMs and
FCMs.
Let D1, …, Dn and R1,…,Rm be the nodes of an FRM. Let DiRj
(or Rj Di) be the edges of an FRM, j = 1, …, m, i = 1, …, n.
The edges form a directed cycle if it possesses a directed cycle.
An FRM is said to be acycle if it does not posses any directed
cycle.
An FRM with cycles is said to have a feed back when there is a
feed back in the FRM, i.e. when the casual relations flow
through a cycle in a revolutionary manner the FRM is called a
dynamical system.
Let DiRj ( or RjDi), 1 j m, 1 i n. When Rj ( or Di) is
switched on and if casuality flows through edges of the cycle
and if it again causes Ri(Dj), we say that the dynamical system
goes round and round. This is true for any node Ri (or Dj) for 1
i m, ( or 1 j n).
The equilibrium state of this dynamical system is called the
hidden pattern. If the equilibrium state of the dynamical system
is a unique state vector, then it is called a fixed point. Consider
an FRM with R1, …, Rm and D1, …, Dn as nodes. For example
let us start the dynamical system by switching on R1 or D1. Let
us assume that the FRM settles down with R1 and Rm ( or D1
and Dn) on i.e. the state vector remains as (10…01) in R [ or
(10…01) in D], this state vector is called the fixed point.
1.1.1 If the FRM settles down with a state vector repeating in
the form A1 A2 …. Ai A1 or ( B1 B2 …
Bi B1 ) then this equilibrium is called a limit cycle.
Methods of determination of hidden pattern.
Let R1, …, Rm and D1, …, Dn be the nodes of a FRM with feed
back. Let E be the n m relational matrix. Let us find a hidden
pattern when D1 is switched on i.e. when an input is given as
vector A1= (1000...0) in D the data should pass through the
relational matrix E. This is done by multiplying A1 with the
relational matrix E. Let A1E = (r1, … , rm) after thresholding
and updating the resultant vector (say B) belongs to R. Now we
pass on B into ET
and obtain BET
. After thresholding and
updating BET
we see the resultant vector say A2 belongs to D.
This procedure is repeated till we get a limit cycle or a fixed
point.
III. DESCRIPTION OF THE PROBLEM
Emotions control our thinking, behavior and actions. Emotions
affect your physical bodies as much as our body affects our
feelings and thinking. People, who ignore, dismiss, repress or
just ventilate their emotions, are setting themselves up for
physical illness. Emotions that are not felt and released but
buried within the body can cause serious problems. When a
person is subjected to extreme negative thought he is prone to
the secondary negative thoughts over it. This is a slow process
that creeps into human mind unknowingly. When we have an
experience that we find painful or difficult, and are either
unable to cope with the pain, or just afraid of it, we often
dismiss this emotion and either get busy, exercise more, drink
or eat a bit more, or just pretend it has not happened. When we
do this we do not feel the emotion and this results in what is
called repressed, suppressed or buried emotions. These feelings
stay in our muscles, ligaments, stomach. These emotions
remain buried within us until we bring that emotion up and feel
the emotion, thus releasing it. Emotions that are buried on the
long-term are the emotions that normally cause serious
problems in behavior. This leads him to take risky decisions
such as suicide, alcoholism, sex addiction, drug addiction,
divorce etc. To analyse the effect of negative emotions on
human behavior we have interviewed more than 50 people in
Chennai. What they spell out are chosen as attributes of cause
group and effect group.
IV. ADAPTATION TO FRM MODEL
The types of negative emotions are taken as the domain space
and the expressed form of negative feelings is taken as the range
space of the FRM. In choosing the attributes there is no hard and
fast rule. It is left to the choice of any researcher to include or
exclude any of the attributes.
Attributes Related to the Domain space M given by M = {M1,
…,M5}
M1 - stress
M2 - anger
M3 - depression
M4 - fear
M5 - guilt
Attributes Related to the Range space Y given by Y = {Y1, …,Y7}
Y1 - suicide
Y2 - divorce
Y3 - alcohol addiction
Y4 - drug addiction
Y5 - sex addiction
Now using the expert's opinion we have the following relation
matrix A. We have M1.M2, M3,M4, M5 as the rows and
Y12,Y2,Y3,Y4,Y5 are the columns.
3. Integrated Intelligent Research(IIR) International Journal of Business Intelligent
Volume: 04 Issue: 01 June 2015,Pages No.44- 46
ISSN: 2278-2400
46
1 1 1 0 1
0 1 0 0 0
1 0 1 0 1
0 0 0 1 1
1 0 1 0 1
A
hidden pattern of the state vector X=(1 0 0 0 0 ) is obtained by the
following method:
XA ↪(1 0 0 0 0) = Y
YAT
↪(1 1 1 0 1) =X1
X1A ↪(1 1 1 1 1) =Y1
Y1AT
↪(1 1 1 1 1) =X2
X2A ↪(1 1 1 1 1) =Y2
Y2AT
↪(1 1 1 1 1) =X3 (say)
X3A ↪(1 1 1 1 1) =Y3 (say)
(Where ↪ denotes the resultant vector after thresholding and
updating) When we take M1 in the ON state (stress) and all other
attributes to be in the off state.We see the effect of X on the
dynamical system A.Thus we see the effect of the suppressed
emotions as exression of risky decisions wherein stress and
depression plays a vital part .
V. CONCLUSION AND SUGGESTION
Emotions cannot be prohibited but they can be managed by
realizing and understanding oneself . Government can project
programs on how to manage oneself when caught in stress,
depression ect.recent survey says that most suicide cases come
police department which is schocking . citizens should be
provided with educational classes on how to tackle with emotions
.Psychological well-being leads to desirable outcomes, even
economic ones, and does not necessarily follow from them.
People who score high in psychological well-being later earn
high income and perform better at work then people who score
low in well-being. It is also found to be related to physical
health. In addition, it is often noticed that what a society
measures will in turn influence the things that it seeks. If a
society takes great effort to measure productivity, people in the
society are likely to focus more on it and sometimes even to
the detriment of other values. If a society regularly assesses
well-being, people will provide their attention on it and learn
more about its causes. Psychological well-being is therefore
valuable not only because it assesses well-being more directly
but it has beneficial consequences. Thus it leads to GNH.
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