David Eugene Booth

Kent State University | KSU · Department of Management and Information Systems

Classification is one of the fundamental goals of science and is basic to the diagnosis of disease. Unfortunately, classifying objects (e.g., patients) on the basis of clinical and/or laboratory experimental observations into various groups can be difficult when the groups overlap or contain outlying points. Recently, Broffitt, Randles, and co-work...

We begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then propose a protocol for the discovery and use of risk factors us...

Inspired by Mishkin (2011), we study a sample of high-frequency Eurex trading data from the peak of economic expansion preceding the global financial crisis of 2007-2009. Trading volume measures and bid�ask spread in DAX futures market explain DAX equity returns. Advanced variable selection and multinomial model with survey analysis identify most...

Outlier detection can be very important in analyzing data from Scatchard plots. In this study, a robust (outlier-resistant) regression procedure was used in conjunction with a Scatchard plot to study the binding of the methylphenazinium cation with double-stranded DNA. The procedures, their results, and their advantages are discussed.

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these software organizations can govern the life cycles of their inquiries and be strategic in their in their future financ...

This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these software packages, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

Estimating the banking failures within the US is a priority in this research. We try to determine the independent variables that could be used to determine the banking failures. However, we try to use robust estimation in order to predict failing banks. We use R and SAS to determine the bank failures, therefore, R and SAS are utilized in predicting...

Current revision of this paper

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future fina...

Two major computer processor security bugs, dubbed Meltdown and Spectre, affect nearly every device made in the last 20 years. The ramifications of how much these bugs will impact computing is still playing out, but it could lead to compromised servers for cloud platforms and other farther-reaching effects. The Meltdown and Spectre bugs affect a v...

This paper shows the advantages and disadvantages of using Python and R in teaching various types of students based on the latest data. We also compare Python and R in solving business problems for actual companies. We give some examples of how to utilize both Python and R. For example, we provide examples of teaching correlation coefficient both w...

The selection of predictor variables is very important in prediction of the value of the dependent variable. There has been many of these selection methods. In the old days, we used backward elimination & forward addition and stepwise regression. However, these methods were found to be finding unstable predictors. More recently the more stable meth...

The selection of predictor variables is very important in prediction of the value of the dependent variable. There has been many of these selection methods. In the old days, we used backward elimination & forward addition and stepwise regression. However, these methods were found to be finding unstable predictors. More recently the more stable meth...

When it comes to data science one of the most common points of debate is R vs SAS vs Python vs SPSS. It is a well-known fact that R, Python, SPSS and SAS are the most important four programming languages to be learned for data analysis by data scientists.

This paper uses the selection of predictor variables in a very important determination in prediction of the value of the dependent variable. There has been many of these selection methods., The selection of predictor variables is very important in prediction of the value of the dependent variable. There has been many of these selection methods. In...

This paper shows the advantages and disadvantages of using Python and R in teaching various types of students based on the latest data. We also compare Python and R in solving business problems for actual companies. We give some examples of how to utilize both Python and R. For example, we provide examples of teaching correlation coefficient both w...

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future fina...

Prediction models are an important part of financial research. In this paper, we review statistical prediction models; their application to finance, introduce Machine Learning methods for financial prediction, and investigate their use in banking research. By using stable statistical prediction, investors and regulators can hope to predict future b...

Prediction models are an important part of financial research. In this paper, we review statistical prediction models, their application to finance, introduce Machine Learning methods for financial prediction, and investigate their use in banking research. By using stable statistical prediction, investors and regulators can hope to predict future b...

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future fina...

predicting bank failures and selection of independent variables in an ordinary multiple regression model

This paper discusses how R Python and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these software, organizations can govern the life cycles of their inquiries and be strategic in their in their future finan...

This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these software packages, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

Prediction models are an important part of financial research. In this paper, we review statistical prediction models, their application to finance, introduce Machine Learning methods for financial prediction, and investigate their use in banking research. By using stable statistical prediction, investors and regulators can hope to predict future b...

This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these software packages, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

There are many sofware packages and programs thatt can help researchers. These programs inclufe open source packages such as Python and R.There are two types of programs. 1. Less costly programs such as EXCEL, smd MINITAB thst iis good for smaller companies or organizations 2.Programs that are mote expensive that are usrgil for lsrget pojects or bi...

This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these software packages, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these software packages, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these software packages, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

“Analytics Software to Solve Business Research Problems” Discussion of SAS,SPSS, R, Python, Excel & MINITAB in business research settings.This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By usin...

EWSA2 Final Check Fit model var. select with current capsize then test with 1 year following CS. Did I do that? Maybe Take abs(CS-capsize) as prediction residual and get mean, variance, standard deviation for prediction residual P.e.=abs(CS(21,hat)-CS(21)) Abstract Prediction models are an important part of financial research. In this paper, we rev...

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future fina...

This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these software packages, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

We begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then propose a protocol for the discovery and use of risk factors us...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenents of this dichotomy is that variable selection methods should only be applied to predictive models. In this paper, we consider comparing the effectiveness of the acquisition strategies implemented by Googl...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenents of this dichotomy is that variable selection methods should only be applied to predictive models. In this paper, we consider comparing the effectiveness of the acquisition strategies implemented by Googl...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenets of this dichotomy is that variable selection methods should be applied only to predictive models. In this paper, we compare the effectiveness of the acquisition strategies implemented by Google and Yahoo...

Part 1 of current CV

This paper shows the advantages of using Python and R in teaching various types of students. We also compare Python and R in solving business problems for a variety of companies. We give some examples of how to utilize both Python and R, such as teaching correlation coefficient with both Python and R. We provide three teaching goals for Python and...

This paper discusses how R and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial stabil...

This paper discusses how R and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial stabil...

Estimating the banking failures within the US is a priority in this research. We try to determine the independent variables that could be used to determine the banking failures. However, we try to use robust estimation in order to predict failing banks. We use R and SAS to determine the bank failures, therefore, R and SAS are utilized in predicting...

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future fina...

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future fina...

This paper discusses how R and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these software, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial stabili...

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future fina...

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future fina...

Prediction models are an important part of financial research. In this paper, we review statistical prediction models, their application to finance, introduce Machine Learning methods for financial prediction, and investigate their use in banking research. By using stable statistical prediction, investors and regulators can hope to predict future b...

Prediction models are an important part of financial research. In this paper, we review statistical prediction models, their application to finance, introduce Machine Learning methods for financial prediction, and investigate their use in banking research. In this paper we discuss how R is the best software programing language in predicting the ban...

This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these software packages, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

In this chapter, we include a discussion of two analytic models first Hadoop and second Microsoft Cloud, and how they can help or assist the IT manager in companies. Hadoop uses both Distributed File-System and MapReduce in performing Analytics. On the other hand Microsoft Cloud makes use of OpenShift. What makes this chapter unique is that it comb...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenents of this dichotomy is that variable selection methods should only be applied to predictive models. In this paper, we consider comparing the effectiveness of the acquisition strategies implemented by Googl...

This paper discusses how R Python SPSS and SAS can be used in research and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these software packages, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial...

This paper discusses how R Python SPSS and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future fina...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenents of this dichotomy is that variable selection methods should only be applied to predictive models. Because it is well-known that explanatory models and predictive models are different. In this paper, we c...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenents of this dichotomy is that variable selection methods should only be applied to predictive models. In this paper, we consider comparing the effectiveness of the acquisition strategies implemented by Googl...

Estimating the banking failures within the US is a priority in this research. We try to determine the independent variables that could be used to determine the banking failures. However, we try to use robust estimation in order to predict failing banks. We use R and SAS to determine the bank failures, therefore, R and SAS are utilized in predicting...

This paper discusses how R and SAS can be used to predict bank failures and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these software, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial stabili...

According to Mansaf andKashish (2016), big data growth is very high. It is very difficult to manage due to various characteristics, which is why Hadoop and neo4j are both critical to using large datasets and applying knowledge from them to real-world scenarios. Hadoop was developed from Google’s techniques in analyzing large datasets whereas neo4j...

R and SAS, two software designed to run statistical analyses and output graphics can be used for banking research. R can run on any operating system, is open-source, and reflects many of the changing field preferences. It is also highly standardized. SAS is a paid software system that provides high performance analytics for banking research. Organi...

This paper discusses how R and SAS can be used to predict bank failures and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial stabil...

This paper discusses how R and SAS can be used to predict bank failures and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial stabil...

This paper discusses how R and SAS can be used to predict bank failures and the benefit that it would have to company’s futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organizations can govern the life cycles of their inquiries and be strategic in their in their future financial stabil...

This paper examines data analytics and how changes in the field have given rise to several new programs. This paper also provides discussion on these programs and how the programs approach large data analytics and allay the concerns of ever-growing data files. After examining the basics, (Hadoop and Cloud services) further exploration develops by l...

Identifying banking failures using R & SAS programming languages

R and SAS, two software designed to run statistical analyses and output graphics can be used for banking research. R can run on any operating system, is open-source, and reflects many of the changing field preferences. It is also highly standardized. SAS is a paid software system that provides high performance analytics for banking research. Organi...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenents of this dichotomy is that variable selection methods should only be applied to predictive models. In this paper, we consider comparing the effectiveness of the acquisition strategies implemented by Googl...

According to Mansaf and Kashish (2016), big data growth is very high. It is very difficult to manage due to various characteristics, which is why Hadoop and neo4j are both critical to using large datasets and applying knowledge from them to real-world scenarios. Hadoop was developed from Google’s techniques in analyzing large datasets whereas neo4j...

Banking research contains many examples of use of R and SAS, two of the most popular software packages. The most important part of banking research is identification of bank failures. R and SAS, two software designed to run statistical analyses and output graphics can be used for banking research. R can run on any operating system, is open-source,...

This paper examines data analytics and how changes in the field have given rise to several new programs. This paper also provides discussion on these programs and how the programs approach large data analytics and allay the concerns of ever-growing data files. After examining the basics, (Hadoop and Cloud services) further exploration develops by l...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenents of this dichotomy is that variable selection methods should only be applied to predictive models. In this paper, we consider comparing the effectiveness of the acquisition strategies implemented by Googl...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenents of this dichotomy is that variable selection methods should only be applied to predictive models. In this paper, we consider comparing the effectiveness of the acquisition strategies implemented by Googl...

We begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then propose a protocol for the discovery and use of risk factors us...

This paper examines analytics and how changes have given rise to several new programs. This paper also provides discussion on these programs and how the programs approach large analytics and allay the concerns of ever-growing data files. After examining the basics, (Hadoop and Cloud services) further exploration develops by looking at complementary...

In the recent statistical literature, the difference between explanatory and predictive statistical models has been emphasized. One of the tenents of this dichotomy is that variable selection methods should only be applied to predictive models. In this paper, we consider comparing the effectiveness of the acquisition strategies implemented by Googl...

We begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then propose a protocol for the discovery and use of risk factors us...

Current CV 7/18/2018

This article compares and contrasts a visual analytics system in neo4j with a predictive analytics system of Hadoop in the context of a relational database like Oracle , SQL or, MySQL.

Both neo4j and Hadoop are analytics techniques. Neo4j is a visual analytics measure, while Hadoop is a predictive analytics measure They both exist within the paradigm of network modeling relational database similar toi Oracle