Numerical Methods \ 1-1
Although there are publications on verbal financial management in Turkey, there is a great deficiency in digital financial management. This study, which is prepared to compensate for this deficiency to some extent, includes more than 1,300 solved problems and tries to ensure that finance is experienced live in the company. Today, financial management education in our universities should be numerical, not verbal, as a requirement of the job. It is also clear that a financial education that is not pedagogically involved in company activities, cannot analyze and evaluate financial events, and is not effective in determining financial policies is not sufficient. The purpose of this book; It is to show the student and the reader how to solve simplified financial problems and to give sufficient financial knowledge on the relevant subject. While the theoretical information in the book was aimed to be at the basic level, it was thought to be at a very advanced level and the theoretical knowledge was concentrated in the book. The second purpose of the book is to prepare for the Financial Management Case Studies book. At the end of the book, the problems were emphasized and after the questions were solved, different orientation questions were asked for the student to test himself.
"Multi-Criteria Decision Making" methods, which support decision makers at all levels in decision-making processes to make more effective and correct decisions in solving the problems encountered, are discussed in detail in this book.
In the second edition of the book; 20 different methods, which are widely used in the literature, are included with their theoretical explanation and current application examples. In the solution of sample applications, package programs such as Super Decisions, M-MACBETH, VISUAL PROMETHEE, QM for Windows, EMS and MS Excel were used. By explaining 28 different sample applications, each step of the solution is supported with screenshots, it is aimed to understand the methods in the best way by the reader.
It is thought that the book will be an important resource especially for researchers studying at graduate and doctorate level, academics working in the field of quantitative decision making, and all decision makers who use scientific methods in decision-making processes.
In the second edition of the book; 20 different methods, which are widely used in the literature, are included with their theoretical explanation and current application examples. In the solution of sample applications, package programs such as Super Decisions, M-MACBETH, VISUAL PROMETHEE, QM for Windows, EMS and MS Excel were used. By explaining 28 different sample applications, each step of the solution is supported with screenshots, it is aimed to understand the methods in the best way by the reader.
It is thought that the book will be an important resource especially for researchers studying at graduate and doctorate level, academics working in the field of quantitative decision making, and all decision makers who use scientific methods in decision-making processes.
In order to produce an effective and rational solution to decision-making problems, the purpose of the task should be clearly formulated, a set of criteria should be defined, and the selected criteria should be weighted with the most appropriate method. It should be analyzed with scientific-based multi-criteria decision-making methods that allow researchers to choose the most effective and most appropriate solution among the available alternatives.
This book has several features. First, it will help researchers or decision makers find the necessary methods for the problems they need to analyze, by categorizing the methods as those that weight criteria and those that rank alternatives. Secondly, those who want to gain in-depth information about a particular method can access the "general information" section of each method. Third, a total of 27 methods used for weighting and ranking were solved with the developed MCDM software. It is thought that this will have two great benefits for the reader. By combining information and theoretical content, readers can easily see how the method works in practice through the example. They can also facilitate the decision-making processes they have to solve by seeing how the method can be used in practice. Fourth, they can benefit from the answers of the exercises solved by signing up on the website established as an online platform, and they can reach the solution of any decision problem within seconds by entering a new problem and using 27 methods defined in the system.
This book has several features. First, it will help researchers or decision makers find the necessary methods for the problems they need to analyze, by categorizing the methods as those that weight criteria and those that rank alternatives. Secondly, those who want to gain in-depth information about a particular method can access the "general information" section of each method. Third, a total of 27 methods used for weighting and ranking were solved with the developed MCDM software. It is thought that this will have two great benefits for the reader. By combining information and theoretical content, readers can easily see how the method works in practice through the example. They can also facilitate the decision-making processes they have to solve by seeing how the method can be used in practice. Fourth, they can benefit from the answers of the exercises solved by signing up on the website established as an online platform, and they can reach the solution of any decision problem within seconds by entering a new problem and using 27 methods defined in the system.
In this book, 21 of the Multi-Attribute Decision Making (MCDM) methods are presented in a specific systematic, with both their mathematical background and managerial applications. Examined methods: AHP, ANP, DEMATEL, ELECTRE, PROMETHEE, TOPSIS, VIKOR, MOORA, ENTROPI, COPRAS, SWARA, WASPAS, SMAA, GRA, MABAC, ORESTE, TODIM, ARAS, EDAS, CODAS, MACBETH. In the book, the theoretical part of these methods, which are widely used in MLKV, are explained in detail and their applications with MS Excel solutions for real problems are given. Applications can be easily accessed from the publisher's website. Although there are free/paid software for some MLKV methods, it is beneficial for the readers to examine the applications with MS Excel solutions after absorbing the theoretical part. Applications with MS Excel solutions, which are presented with the book, can be developed specifically for the problem, for applications to be made with methods that do not currently have existing software. For methods with software, researchers/managers who have completed this process can easily interpret and analyze the results of the software.
“Multi-Criteria Decision Making Methods: MS Excel® Solutions for Applications I consider one of the most comprehensive books ever published in this field. There are 22 chapters in the book, which are covered in a wide range from the most known and used methods to many new methods developed in recent years and prepared by experts in the subject. The presence of solved problems prepared on current issues in each chapter adds a special value to the book. I am really pleased that such a qualified study has been prepared in the Turkish language of science.
The editor of the book, Prof. Dr. Mehmet Kabak and Assoc. Dr. I congratulate Yetkin Çınar for his success in bringing together experts working on multi-criteria decision making at different universities. I congratulate the authors of the chapters, each of which has been prepared successfully, for their contributions to the Turkish scientific world. I would like to thank all the referees who contributed to the preparation of the book.”
Prof. Dr. Cengiz Kahraman
ITU Industrial Engineering Department
“Decision making is a process of selection and qualification that people face at every moment. There is a serious need for a systematic analytical flow, depending on the size of the decision under consideration. In addition, considering the decision problem together with the criteria in the ecosystem and evaluating it in terms of these criteria is of great importance in terms of the accuracy and validity of the decision. Within the scope of this book; The systematic handling of the decision process, the determination of the most appropriate method and its evaluation with a systematic analytical flow have been successfully handled with real-life applications. In particular, analytical decision-making approaches are explained in a detailed and understandable way. I think that the book will be an important reference source for every evaluator who will take part in the decision process, and it will be a masterpiece for students who want to examine this process.”
Prof. Dr. İhsan Kaya
Yıldız Teknik University
“Multi-Criteria Decision Making Methods: MS Excel® Solutions for Applications I consider one of the most comprehensive books ever published in this field. There are 22 chapters in the book, which are covered in a wide range from the most known and used methods to many new methods developed in recent years and prepared by experts in the subject. The presence of solved problems prepared on current issues in each chapter adds a special value to the book. I am really pleased that such a qualified study has been prepared in the Turkish language of science.
The editor of the book, Prof. Dr. Mehmet Kabak and Assoc. Dr. I congratulate Yetkin Çınar for his success in bringing together experts working on multi-criteria decision making at different universities. I congratulate the authors of the chapters, each of which has been prepared successfully, for their contributions to the Turkish scientific world. I would like to thank all the referees who contributed to the preparation of the book.”
Prof. Dr. Cengiz Kahraman
ITU Industrial Engineering Department
“Decision making is a process of selection and qualification that people face at every moment. There is a serious need for a systematic analytical flow, depending on the size of the decision under consideration. In addition, considering the decision problem together with the criteria in the ecosystem and evaluating it in terms of these criteria is of great importance in terms of the accuracy and validity of the decision. Within the scope of this book; The systematic handling of the decision process, the determination of the most appropriate method and its evaluation with a systematic analytical flow have been successfully handled with real-life applications. In particular, analytical decision-making approaches are explained in a detailed and understandable way. I think that the book will be an important reference source for every evaluator who will take part in the decision process, and it will be a masterpiece for students who want to examine this process.”
Prof. Dr. İhsan Kaya
Yıldız Teknik University
The fields of probability and statistics, which are widely used in many fields today, contain theorems that are difficult to understand and are complicated by students. Therefore, the aim of this book is; The aim is to teach probability and statistics fields more easily to students studying in all departments of universities, especially science, social and health, with an easy-to-understand expression and using a plain language. In order to achieve this, many solved examples from daily life are given in the chapters and at the end of the chapters of the book.
Since the book is prepared in accordance with the contents of Probability and Mathematical Statistics courses, which are generally taught in two semesters in many departments of universities, the textbook at the undergraduate level can also be used as a supplementary resource in graduate programs.
Since the book is prepared in accordance with the contents of Probability and Mathematical Statistics courses, which are generally taught in two semesters in many departments of universities, the textbook at the undergraduate level can also be used as a supplementary resource in graduate programs.
This book was created by blending the lecture notes used for years for the Differential Equations course taught in the Department of Mathematics, Faculty of Science, Ankara University, with the current literature, and rearranging them in the required width and depth in an original way. Consisting of fifteen chapters and 554 pages, this book is designed to be taught in undergraduate mathematics programs for two semesters.
In this book, basic concepts, theorems and solution methods, which are indispensable for differential equations, are emphasized and attention is drawn to the real-life application areas of differential equations. Graphs were drawn, tables were set up, sufficient number of examples were solved and many exercises were given so that the concepts, theorems and solution methods covered could be easily understood. In addition, sample problems for different application areas of differential equations were examined and a series of exercises were prepared in this context. Finally, the answers to most of the exercises left to the reader are shared at the end of the book for self-control. In this respect, this book has general norms that can be followed and taught as a textbook not only in mathematics departments, but also in physics, astronomy and space sciences and engineering departments. In summary, anyone who has undergraduate or graduate education in basic sciences, engineering, economics and health and needs differential equations can easily benefit from this book as much as they want.
In this book, basic concepts, theorems and solution methods, which are indispensable for differential equations, are emphasized and attention is drawn to the real-life application areas of differential equations. Graphs were drawn, tables were set up, sufficient number of examples were solved and many exercises were given so that the concepts, theorems and solution methods covered could be easily understood. In addition, sample problems for different application areas of differential equations were examined and a series of exercises were prepared in this context. Finally, the answers to most of the exercises left to the reader are shared at the end of the book for self-control. In this respect, this book has general norms that can be followed and taught as a textbook not only in mathematics departments, but also in physics, astronomy and space sciences and engineering departments. In summary, anyone who has undergraduate or graduate education in basic sciences, engineering, economics and health and needs differential equations can easily benefit from this book as much as they want.
Probability and Statistics teaching aims to develop data collection tools for students to analyze real-life problems, to acquire data, to visualize these collected data or to interpret them through various calculations. When the studies are examined, it is seen that both students and teachers experience various difficulties in the teaching and learning of probability and statistics. The reasons for these difficulties are that most of the students try to memorize the formula instead of understanding the subject, cannot understand the question and cannot use appropriate teaching materials. This work, which was prepared to establish a solid bridge between theory and practice in the teaching of probability and statistics, The First Step to Data Science: Teaching Probability and Statistics, Basic Concepts and Teaching Related to Probability, Use of Simulation in Teaching Probability, Teaching Probability Distributions, Statistical Problem Solving Process: Research Question It consists of 9 main sections under the headings of Creating, Data Collection, Organizing and Analyzing, Distribution Concept and Teaching Frequency Distributions, Teaching Central Tendency Measures, Teaching Central Dispersion (Change) Measures, Difficulties Experienced by Students, Causes and Solution Suggestions.
Each of the sections in the work; It has been written by academics who are experts in their fields and continue their academic studies in different universities in Turkey. We hope that the prepared work will be beneficial to academicians, teachers, students studying in undergraduate and graduate programs with its rich content and application examples and will contribute to the field of mathematics education.
Each of the sections in the work; It has been written by academics who are experts in their fields and continue their academic studies in different universities in Turkey. We hope that the prepared work will be beneficial to academicians, teachers, students studying in undergraduate and graduate programs with its rich content and application examples and will contribute to the field of mathematics education.
Statistics; It is an interdisciplinary science that facilitates collecting data, classifying data, making sense of it by analyzing, explaining, evaluating and interpreting data on a particular subject, making it easier to draw conclusions, obtain information and/or make decisions. Statistical methods, which are used in studies carried out in many fields from biology to chemistry, from environmental sciences to economics, from sociology to space sciences, from medicine to actuarial, are frequently applied in the business world besides academic life. At this point, it is seen that the importance of statistical literacy has increased.
In the Business Statistics book; basic statistical concepts, subjects and analyzes were tried to be exemplified by associating them with the field of business administration. At the end of the chapter, it is aimed to use Excel and SPSS applications and the theoretical information about statistics by real users more quickly, effectively and widely.
The book can be used as a textbook in departments within the faculties of economics and administrative sciences of universities, or as a bedside book for working life.
“Knowledge is knowing what we know and what we do not know. With what we know we discover what we don't know, so knowledge expands. With more knowledge, we begin to know that we do not know more. Thus, knowledge expands without stopping.
In the Business Statistics book; basic statistical concepts, subjects and analyzes were tried to be exemplified by associating them with the field of business administration. At the end of the chapter, it is aimed to use Excel and SPSS applications and the theoretical information about statistics by real users more quickly, effectively and widely.
The book can be used as a textbook in departments within the faculties of economics and administrative sciences of universities, or as a bedside book for working life.
“Knowledge is knowing what we know and what we do not know. With what we know we discover what we don't know, so knowledge expands. With more knowledge, we begin to know that we do not know more. Thus, knowledge expands without stopping.
Finance is a branch that examines money management, as well as financial systems consisting of banks, loans, assets, investments and debts, and represents the working process of these systems. Today, finance has become an important part of sustainability as well as its role as an intermediary in the economic system. Therefore, due to the increase in the importance of investment in the realization of sustainable development and the provision of information technology for new and environmentally friendly energy systems, the computation of many criteria and alternatives, and the complexity of risk, and the use of programs that make faster and easier calculations, studies in the field of finance are now in the world. is of great importance. For this reason, this book, with the efforts of thirteen scientists in the field of Finance; Quantitative decision methods, current analyzes from the perspectives of economics, business and econometrics, and extensive literature links, as well as original studies consisting of eleven chapters that bring together theory and practice.
Although statistics is a very easy field to learn, the learner's own affective barriers can prevent learning. When we look at those who have problems in learning statistics, it is seen that most of these individuals have had negative experiences in numerical or statistics courses in the past.
This work, named Statistical, provides a practical learning resource for researchers, academicians and students working in both social sciences and other fields of science, without overwhelming the learner with statistical formulas. A self-resource has been created for all the information needed for introduction to statistics and the application of fundamental analysis.
The first part of the book has a structure that is explained simply and reinforced with real-life examples. The statistical information in the first part forms the basis of the subjects in the second part. In the second part, the introduction of the SPSS program, the explanation for all statistical analysis, the visual processing steps of the SPSS application of the relevant subject, the introduction of the SPSS results, the tabulation and interpretation parts in accordance with the APA format, which is frequently used in social sciences. In addition to these, the most common mistakes in the relevant statistics at the end of each topic and the special situations that the researcher may encounter and the solution proposals, which consist of their solution suggestions, are in the nature of answers to the questions that most researchers cannot find answers to. The book aims to provide permanent learning as a whole and systematically.
This work, named Statistical, provides a practical learning resource for researchers, academicians and students working in both social sciences and other fields of science, without overwhelming the learner with statistical formulas. A self-resource has been created for all the information needed for introduction to statistics and the application of fundamental analysis.
The first part of the book has a structure that is explained simply and reinforced with real-life examples. The statistical information in the first part forms the basis of the subjects in the second part. In the second part, the introduction of the SPSS program, the explanation for all statistical analysis, the visual processing steps of the SPSS application of the relevant subject, the introduction of the SPSS results, the tabulation and interpretation parts in accordance with the APA format, which is frequently used in social sciences. In addition to these, the most common mistakes in the relevant statistics at the end of each topic and the special situations that the researcher may encounter and the solution proposals, which consist of their solution suggestions, are in the nature of answers to the questions that most researchers cannot find answers to. The book aims to provide permanent learning as a whole and systematically.
This book is designed to cover the two-semester content of the general mathematics course taught in the Business Administration Departments of universities.
In the book, the basic subjects of calculus, linear algebra, probability and differential equations are tried to be dealt with in an intuitive approach by associating them with reality as much as possible, in order to prepare the ground for the students of business administration to solve the problems they will encounter in the real world by using the concepts and symbolism of mathematics.
At the end of each chapter, a sufficient number of examples are discussed to reinforce the subject, and the solutions of many of them are also included.
In the book, the basic subjects of calculus, linear algebra, probability and differential equations are tried to be dealt with in an intuitive approach by associating them with reality as much as possible, in order to prepare the ground for the students of business administration to solve the problems they will encounter in the real world by using the concepts and symbolism of mathematics.
At the end of each chapter, a sufficient number of examples are discussed to reinforce the subject, and the solutions of many of them are also included.
This book, which is the first of two volumes prepared for the general mathematics courses taught in the Business Administration and Economics Departments of our universities, has plenty of questions with solutions at the end of each section. The solution to each problem is explained broadly and clearly. Even if proofs of some theorems are not given while the subjects are being treated, it has been tried to geometrically imply why they are so. Most of the theorems and fundamental formulas have been proved.
There are enough examples in the book to explain the theorems and basic information. While introducing a new concept, the relation of this concept with previous concepts was established, and it was tried for our students to learn by understanding that information in the easiest way.
There are enough examples in the book to explain the theorems and basic information. While introducing a new concept, the relation of this concept with previous concepts was established, and it was tried for our students to learn by understanding that information in the easiest way.
This book, which is the second of two volumes prepared for the general mathematics courses taught in the Business Administration and Economics departments of our universities, has plenty of questions at the end of each section. The solution to each problem is explained broadly and clearly. Even if proofs of some theorems are not given while the subjects are being studied, it has been tried to intuit why this is the case. Some of the theorems and basic formulas have been proved.
There are enough examples in the book to explain the theorems and basic information. While introducing a new concept, the relation of this concept with previous concepts was established, and it was tried for our students to learn by understanding that information in the easiest way.
There are enough examples in the book to explain the theorems and basic information. While introducing a new concept, the relation of this concept with previous concepts was established, and it was tried for our students to learn by understanding that information in the easiest way.
Statistics is widely used in all fields of science and many business fields because software package programs have developed by following theoretical developments. In an environment dominated by uncertainty, it is extremely important to ensure that statistics, which are used as a tool for making the right decision, are learned and comprehended both theoretically and practically. In our opinion, the most important feature of this book is that it has been prepared in such a way as to support the distance education system and self-learning effort, which has become widespread with the Covid-19 epidemic, both theoretically and practically. Therefore, in the book; first, the theoretical explanation about each section was made comprehensively, interpreted in detail with solved examples, and then the examples related to the subjects were applied using Excel, SPSS, Matlab or R software packages. Thus, it is aimed to minimize the need for teachers and classroom environment as in traditional formal education. With this book, it is expected that the deficiency in the self-learning, application and interpretation of statistics will be eliminated.
Machine learning, which is expressed as a sub-field of artificial intelligence; It is widely used in many fields led by engineering, finance and bioinformatics. In order to develop machine learning applications, it is important to understand some algorithms that contain calculus, linear algebra and statistics theoretically. After learning the theoretical aspects of these algorithms, an application can be developed by coding it with a programming language with an easy and rich library structure such as Python. The theoretical aspects of machine learning algorithms in the book were meticulously examined, and the necessary linear algebra and statistics were also briefly examined. Python applications were developed for each algorithm using problems containing original datasets. People who want to develop applications with Deep Learning, which is a sub-field of machine learning, especially learn the basic information in this book, will provide an important infrastructure. Deep learning architectures will be easier to understand after reading this book.
Who is this book for?
• Those who want to start developing a Machine Learning application but do not know exactly where to start,
• Those who are already developing Machine Learning applications,
• Those who prepare dissertations involving Machine Learning and conduct scientific studies in the fields of Science, Engineering and Social Sciences
Who is this book for?
• Those who want to start developing a Machine Learning application but do not know exactly where to start,
• Those who are already developing Machine Learning applications,
• Those who prepare dissertations involving Machine Learning and conduct scientific studies in the fields of Science, Engineering and Social Sciences
This book, titled Modern Time Series and Methods, which was prepared with the motivation created by the lack and need of Turkish resources on the subject of contemporary time series methods, consists of nine chapters. The book, which is prepared in a way that can be used by both graduate students and anyone interested in the subject, includes the most up-to-date topics related to modeling and prediction of time series methods, and a simple and plain language is used in the narration.
This textbook, which was written for undergraduate and graduate students, covers the second part of the "Probability and Statistics" course taught at universities, statistics and applications.
Book; It has been prepared by taking into account the concepts, basic theoretical issues and sample problems for application, which are suitable for the content of statistics courses. Basic topics of statistics such as sampling distributions, rank statistics and distributions, point and interval estimations, hypothesis testing are discussed in detail by making many sample solutions.
This textbook; It is designed as a resource for undergraduate and graduate students studying in Science, Engineering, Medicine, Technology and Economics and Administrative Sciences, among the faculties where statistics are used extensively. This book can also be used as a reference book for lecturers who teach outside the statistics departments.
In the book, 177 solved examples and 316 end-of-chapter exercise questions are included in terms of practice. These questions are based on typical mistakes and misconceptions that students can make.
Book; It has been prepared by taking into account the concepts, basic theoretical issues and sample problems for application, which are suitable for the content of statistics courses. Basic topics of statistics such as sampling distributions, rank statistics and distributions, point and interval estimations, hypothesis testing are discussed in detail by making many sample solutions.
This textbook; It is designed as a resource for undergraduate and graduate students studying in Science, Engineering, Medicine, Technology and Economics and Administrative Sciences, among the faculties where statistics are used extensively. This book can also be used as a reference book for lecturers who teach outside the statistics departments.
In the book, 177 solved examples and 316 end-of-chapter exercise questions are included in terms of practice. These questions are based on typical mistakes and misconceptions that students can make.
In this book, after giving explanatory information about why numerical methods are needed, the issue of numerical errors encountered in all methods is examined.
In the book, which was studied with the MATLAB software package, first of all, basic information about the subject was given in the introduction part of the chapters on numerical methods.
Throughout the other chapters;
• linear equations and systems
• nonlinear equations and systems
• eigenvalue
• interpolation
• curve fitting
• derivative
• integral
• initial value
• limits
Numerical methods used for solving problems are explained and how these methods can be applied to related problems in solving different engineering problems.
In the book, which was studied with the MATLAB software package, first of all, basic information about the subject was given in the introduction part of the chapters on numerical methods.
Throughout the other chapters;
• linear equations and systems
• nonlinear equations and systems
• eigenvalue
• interpolation
• curve fitting
• derivative
• integral
• initial value
• limits
Numerical methods used for solving problems are explained and how these methods can be applied to related problems in solving different engineering problems.
Since the units examined in the studies in the field of social sciences have various characteristics, the data is defined by more than one variable. In the case of a large number of variables, multivariate statistical analysis techniques are needed to analyze them together.
In this book, which is prepared as an undergraduate and graduate level textbook, multivariate statistical analysis techniques are discussed both theoretically and practically. In addition to the main multivariate statistical analysis techniques, advanced multivariate statistical analysis techniques are also included in the book. Analysis techniques in the content of the book were explained within the framework of basic conceptual information and applications were made with Stata, SPSS and R programs.
In order to guide students and researchers who will apply multivariate statistical analysis techniques, the book includes applications on various subjects in the field of social sciences, and attention has been paid to the detailed interpretation of the analysis results. Since these applications seek answers to various research questions by using real data, the book also has the feature of a research book, so it can be used by sectoral researchers, undergraduate and graduate students in all areas where multivariate analysis techniques are applied.
This book, which consists of twenty-one chapters, has been prepared by academics working at various universities in Turkey, whose fields of expertise are statistics, econometrics and operations research, and includes the following topics.
• Basic Assumptions of Multivariate Statistical Analysis Techniques
• Analysis of Data Entry and Multivariate Analysis Assumptions with Stata, SPSS and R Programs
• Multiple Linear Regression Models
• Logistic Regression Analysis
• Multivariate Analysis of Variance
• Factor Analysis
• Cluster Analysis
• Discriminant Analysis
• Canonical Correlation Analysis
• Multidimensional Scaling Analysis
• Reliability Analysis
• Conjoint Analysis
• Compliance Analysis
• Classification Decision Trees
• Artificial neural networks
• Survival Analysis
• System Simulation
• Hidden Class Analysis
• Text Mining
In this book, which is prepared as an undergraduate and graduate level textbook, multivariate statistical analysis techniques are discussed both theoretically and practically. In addition to the main multivariate statistical analysis techniques, advanced multivariate statistical analysis techniques are also included in the book. Analysis techniques in the content of the book were explained within the framework of basic conceptual information and applications were made with Stata, SPSS and R programs.
In order to guide students and researchers who will apply multivariate statistical analysis techniques, the book includes applications on various subjects in the field of social sciences, and attention has been paid to the detailed interpretation of the analysis results. Since these applications seek answers to various research questions by using real data, the book also has the feature of a research book, so it can be used by sectoral researchers, undergraduate and graduate students in all areas where multivariate analysis techniques are applied.
This book, which consists of twenty-one chapters, has been prepared by academics working at various universities in Turkey, whose fields of expertise are statistics, econometrics and operations research, and includes the following topics.
• Basic Assumptions of Multivariate Statistical Analysis Techniques
• Analysis of Data Entry and Multivariate Analysis Assumptions with Stata, SPSS and R Programs
• Multiple Linear Regression Models
• Logistic Regression Analysis
• Multivariate Analysis of Variance
• Factor Analysis
• Cluster Analysis
• Discriminant Analysis
• Canonical Correlation Analysis
• Multidimensional Scaling Analysis
• Reliability Analysis
• Conjoint Analysis
• Compliance Analysis
• Classification Decision Trees
• Artificial neural networks
• Survival Analysis
• System Simulation
• Hidden Class Analysis
• Text Mining
Today, time series or similar courses are taught in economics, business, health and engineering departments of universities. Researchers doing postgraduate education in these departments are much more interested in time series. The most important reason for this is observing how a series of variables change over time, wondering how these variables will take shape in the future, and being the subject of academic studies.
This book does not go into much of the theory or mathematics of time series. Only mandatory equations are included. Most of the time series employees see the problem of how to create a suitable model with a more suitable program, not the mathematics. We also planned to use a methodology that can be easily understood by every researcher, by following a simpler, more understandable and more easy-to-understand way. You will notice this easily when you start working.
This book does not go into much of the theory or mathematics of time series. Only mandatory equations are included. Most of the time series employees see the problem of how to create a suitable model with a more suitable program, not the mathematics. We also planned to use a methodology that can be easily understood by every researcher, by following a simpler, more understandable and more easy-to-understand way. You will notice this easily when you start working.
The book presented by the author to the members of the jury as a “Professor Presentation” is the first of the 6-book Statistics Series. "Regression and Correlation Analysis", which is one of the most fundamental subjects of "statistics", which in a sense forms the basis of "Multivariate Statistical Methods", is discussed in detail in the book, which was published for the first time in 1996, for the second edition in 2002 and for the third edition in 2017.
As it is known, this analysis, which is one of the most frequently used methods in determining "Forecast and Policy" especially in Business Administration, is examined theoretically by distinguishing between population and sample in the book, the validity of the assumptions is examined, and how to find solutions for deviations from the assumptions, if any, is included. Applications are presented both with detailed solutions and as computer (SPSS Program) outputs.
As it is known, this analysis, which is one of the most frequently used methods in determining "Forecast and Policy" especially in Business Administration, is examined theoretically by distinguishing between population and sample in the book, the validity of the assumptions is examined, and how to find solutions for deviations from the assumptions, if any, is included. Applications are presented both with detailed solutions and as computer (SPSS Program) outputs.
Many useful methods for data analysis have been developed in the field of statistics, and most of these methods are ready and waiting to be used in R. Interest in R language(m) is increasing day by day as it makes it possible to access many resources and data on the internet. The data to be used in practice is also very important, especially for understanding a statistical analysis. In addition to the datasets built in the R language, every web page describing statistical data analysis using the R language also includes application data. This leads to an easy comprehension of the R language. Another advantage of the R language is that high quality graphics can be obtained by easily installing various graphics software packages into the R environment. It is also free and flexible since it is open-source. Authors of new statistical methods contribute regularly to many libraries in R, so R is a living language.
With these features, it is a unique software for undergraduate, graduate or doctoral students. It is a very enjoyable, attractive and indispensable language for those who work in the fields of statistics and econometrics, as it has a wide range of resources and is easily accessible. This book aims to spread, popularize and support the use of the R programming language within the framework of the above-mentioned motives and is designed for all scientists who need to analyze data on their own. Intended for practitioners faced with data analysis, this book is practice-oriented in this respect. I hope that the book, in which the writing of mathematics is reduced as much as possible, and more attention is drawn to examples and intuition, will be useful...
With these features, it is a unique software for undergraduate, graduate or doctoral students. It is a very enjoyable, attractive and indispensable language for those who work in the fields of statistics and econometrics, as it has a wide range of resources and is easily accessible. This book aims to spread, popularize and support the use of the R programming language within the framework of the above-mentioned motives and is designed for all scientists who need to analyze data on their own. Intended for practitioners faced with data analysis, this book is practice-oriented in this respect. I hope that the book, in which the writing of mathematics is reduced as much as possible, and more attention is drawn to examples and intuition, will be useful...
In today's world, billions of data are produced every second. However, due to the abundance of data produced, today's age is called the "Big Data Age" or "Information Age". Transforming data into information in a qualified sense is of great importance in terms of both macro and micro aspects.
Data mining is the name given to the whole process of compiling, analyzing and converting data into qualified information output. Data mining applications in the literature; clustering, regression, classification or association rule inferences. In this respect, after the book titled "Clustering Algorithms Used in Data Mining and Applied Examples with R" published in 2020, this book on regression models was presented to your benefit. In this book, the concept of data mining has been defined in general, and then the models have been examined one by one. Hoping that this important resource will contribute to readers and researchers...
Topics covered in this book are as follows:
1. Introduction to Data Science and Machine Learning
2. Linear and Curvilinear (Polinomial) Regression Analysis
3. Decision Tree and Random Forest Regression Model
4. MARS Method
5. Support Vector Machines
6. XGBOOST Method
7. LightGBM and Catboost Algorithms
8. Artificial Neural Networks
9. ARIMA and LSTM Model
10. Convolutional Neural Networks (CNN)
Data mining is the name given to the whole process of compiling, analyzing and converting data into qualified information output. Data mining applications in the literature; clustering, regression, classification or association rule inferences. In this respect, after the book titled "Clustering Algorithms Used in Data Mining and Applied Examples with R" published in 2020, this book on regression models was presented to your benefit. In this book, the concept of data mining has been defined in general, and then the models have been examined one by one. Hoping that this important resource will contribute to readers and researchers...
Topics covered in this book are as follows:
1. Introduction to Data Science and Machine Learning
2. Linear and Curvilinear (Polinomial) Regression Analysis
3. Decision Tree and Random Forest Regression Model
4. MARS Method
5. Support Vector Machines
6. XGBOOST Method
7. LightGBM and Catboost Algorithms
8. Artificial Neural Networks
9. ARIMA and LSTM Model
10. Convolutional Neural Networks (CNN)
In the book named Time Series and Forecasting Methods; All forecasting methods, from basic to advanced, are introduced with R applications. The book has been prepared with the intention of being a bedside book for researchers who want to train themselves as predictors and practitioners who need foresight methods. In the book, subject introductions are tried to be given by creating a good balance of theory and practice. In addition to the classical prediction methods, popular methods such as artificial neural networks and deep learning methods as artificial intelligence prediction methods have also found their place in the content of the book. R applications of all the methods given in the book were carried out. Practitioners benefiting from the book will have the ability to use the automatic prediction methods included in the R program. It is a book for undergraduate, graduate and doctoral students in Statistics, Business Administration, Industrial Engineering, Data Science, Computer Engineering and Economics.