Getting Data Analysis Help – Spss, Eviews, Stata, Gretl, Minitab, Gauss, R – Data Analysis Consultancy

Best Essay Team is a team specialized on Data Analysis is here to help you in Data Analysis and in all other issues. If you wish, we can help you deal with your assignments and we can be your coach in this regard. You can easily contact with Best Essay Team. Send us your requests on our Whatsapp Support Line or mail address info@bestessayhomework.com, and you can have an idea about the price.

Data Analysis

Nowadays, businesses are in a great competition and the limits of this competition are constantly narrowing. In other words, there is a fine line between winning and losing. It is now crucially important to make creative moves and make the right decisions at the right time to succeed and win in this competitive market. Data analysis provides the potential for innovations that will help businesses make more accurate decisions and create added value with rapidly developing technology and analysis methods in precisely these markets and areas where profit margins decrease. Particularly in recent years, many companies have started to invest more in data analytics in order to get competitive opportunities in important areas such as productivity, profitability and sustainable production processes.

Data analysis enables companies to access the information and tools they need to have this potential by providing the link between the science of statistics and modern numerical computational methods to create business value from high-volume data.

Data Analysis in Schools

Having basic information about statistics helps us to solve the problems we encounter in our daily lives more easily. Statistical learning means solving statistical problems, achieving results and supporting the results by explaining the reasons behind them. Today, many individuals must have statistical knowledge both to understand and evaluate the information that others have revealed and to evaluate the information they collect through research.

Today, due to the importance it has in both normal and business life, statistical issues and data analysis have been included in the curriculum of mathematics lessons recently in many countries since the first school years. In our country, changes have been also implemented to follow these innovations. Especially secondary school mathematics courses (5th, 6,7 and 8th grades) have been revised with these innovations. However, according to the researches carried out with the implementation of these changes, students experience various difficulties in learning concepts in data analysis and statistics. The root causes for these are the inability to interpret graphics, imposing incorrect meanings into concepts, and learning without sufficiently concrete experiences.

When the curricula published by the U.S. Department of Education are analyzed, it is observed that the data analysis issue is available at all grade levels of primary and secondary schools. The main reason for adding data analysis to the curriculum in our country is to help increase students’ level of consciousness and reasoning. On the other hand, it is a fact that students have difficulty in “Data Analysis” added to the curriculum at a new time. If you would like to overcome these difficulties and have detailed information about data analysis, we strongly recommend you to read the rest of the article!

What is Data Analysis?

Data analysis is the process of examining, cleaning, transforming and modelling data with the aim of finding useful information and determining results and helping decision making. In other words, data analysis is a process that includes new findings and results revealed by

collecting and classifying basic data. Data analysis includes different names and types, and it is frequently used in many fields such as business, science and social sciences. With the change and transformation of technology, data analysis tools also differ in size and scope. This new process is referred to as “big data”.

Data analysis is a basic concept that includes different types of data analysis. All kinds of data can be edited with data analytics methods that can be used to correct or improve the situations subject to analysis.

For example; The manufacturing companies record the processes of performing the work, downtime and processing queue in terms of machine types and then analyze the information they have in order to better organize the work process. In this way, it is possible to operate the machines with the highest efficiency.

As a second example; game companies use data analytics to organize in-game events that will give players an active reward in order to keep them more online within the game.

The main function of data analysis is to help businesses make decisions. Thanks to data analysis, decision making processes become more scientific and it is aimed to take the right steps for the future of the business with definitive findings. Thus, it is no doubt that businesses work more effectively. Businesses can benefit from data analysis to make the right decision and to take steps more firmly, especially when making decisions in terms of growth or contraction, making new investments, participating in new projects and tenders. Besides, data analysis plays a major role in determining the supply / demand situation and accordingly draws the road map for the business. If companies reveal that they have not obtained enough efficiency from the goods or services they have presented to market, they could give up based on data analysis.

What is the Purpose of Data Analysis?

Quantitative processing and analysis of data is possible only by statistical means. At this point, data analysis has a great importance especially in business life. It is possible to explain the purpose of data analysis in three parts. The first one is to present the current situation, that is to describe it. The second is to estimate community parameters and correlation. The ultimate goal of data analysis is to control the assumption, that is, compliance with a theoretical distribution and the difference control.

What are the Benefits of Data Analysis?

Data analysis is performed in almost all businesses, and it is a known fact that data analysis has many benefits for business. First of all, businesses can make accurate decisions and shape the future of their business activities with data analysis. They can also have the chance to read feedback from their customers and make various decisions regarding the production of goods and services. In this context, they open the way for them to determine effective marketing methods. Thanks to all these, it is possible for businesses to maintain their commercial activity, increase their profits, productivity and revenues. By making use of various data analysis tools, businesses can make an assessment of the current situation within

their own organization and also evaluate the goods and services they will offer to the market. In addition, they are able to evaluate on a project or tender basis and use the decision making process effectively for them.

What are Data Analysis Tools?

Although there are many data analysis tools and types, it is possible to examine them under 3 categories in general. These are:

· Visual,

· Quantitative,

· Qualitative.

Visual tools are tools that visually offer data analysis. Maps, photos, graphics, etc. are visual data analysis tools. Data analysis results can be revealed with these tools. Tools that offer quantitative data analysis help evaluate results with quantitative data. Furthermore, it is possible to say that there are data analysis tools that offer both quantitative and visual analysis together. Verbal data analysis tools are useful for determining the main problem, making it conceptual, and evaluating methods, policies and practices.

Quantitative Data Analysis

A concept we frequently encounter in research methods is quantitative data analysis. As it is known, it is possible to make mistakes in the analysis of the data collected in the researches and this situation may prevent the achievement of the planned goals.

Scientific researches are classified according to data collection and analysis techniques, degree to which variability can be controlled, data sources, environment, level and time. Scientific researches are divided into two as quantitative and qualitative researches. What needs to be understood from quantitative research is the positivist view that expresses reality separately from the researcher and advocates that reality outside can be expressed, measured and analyzed objectively. On the other hand, what needs to be understood from quantitative research is the positivist view that expresses reality separately from the researcher and that the outside reality can be objectively expressed, measured and analyzed. Quantitative data analysis, in its simplest form of expression, is a study that requires the collection and analysis of quantitative data.

Numerical data obtained by counting or measuring are quantitative data. According to the research, different methods are used for quantitative data analysis. Quantitative data analysis methods to be used should be chosen well in order to reach correct results by research. Quantitative analysis method is selected according to the characteristics of the research conducted and the data collected. The statistical analysis research questions or hypotheses to be made based on the numerical values obtained from a research depend on the number and level of dependent and independent variables in the study, the measurement levels of the collected data, the number of observations and the distribution structure of the dependent variable measurements.

Qualitative Data Analysis

Before explaining qualitative data analysis, it would be appropriate to give information about qualitative research. Qualitative research is one of the processes of obtaining information to understand people’s life stories, attitudes, behaviours and social change. The difference from quantitative research based on statistical data analysis is that qualitative research tries to find answers to questions about how people view events. For this reason, qualitative research is one of the ways of producing information that people develop to solve their own unknowns and to understand the depths of the social systems that they shape with their own efforts.

Qualitative data analysis, on the other hand, refers to a process where the researcher organizes the data, separates it into analysis units, filters it, reveals its shapes, recognizes important variables and decides what information to reflect on the report. In other words, the only thing the researcher does in qualitative data analysis is to reveal and discover the information hidden in the data obtained. As a matter of fact, the most important difference between qualitative and quantitative data analysis is the data analysis process.

Unfortunately, it is not possible to say that there is a common opinion in the literature about the methods, techniques and processes applied in qualitative data analysis. There are many different opinions on this subject. Indeed, one of them is the opinion of Miles and Huberman. According to Miles and Huberman, the qualitative data analysis process consists of three successive stages. The first of these stages is to reduce the data collected by various methods such as observation, interview and document review. The second stage is the visualization of the data. At this stage, after the data is extracted, summarized and converted, it is shaped to draw certain conclusions. It is possible to make use of matrices, graphics and tables while making the data visual. The third stage of the data analysis process is concluding and confirming the result. In other words, the researcher reveals what should be understood from the data obtained at this stage.

Which programs are used in Data Analysis?

While 20-25 years ago, data analysis was done manually, with paper and it was a huge waste of time and difficulty for researchers; today, with the development of technology and the introduction of the Internet in almost all houses, manual methods are a thing of the past and various programs have been developed for data analysis. Below are detailed information about the most used for data analysis:

1) SPSS

SPSS is the abbreviation of “Statistical Package for The Social Sciences”. In other words, it can be defined as a statistical program for social sciences. This program is frequently used in the field of social sciences, educational sciences, health sciences and science. In addition, it is a computer program used by various institutions and organizations to conduct market research. The appearance of this program is similar to Excel and is easily used on Windows and Mac computers.

The SPSS program is frequently used especially in survey analysis Besides, it is also used in the analysis of various measurements in social sciences and science. While “frequency analysis” is used in the numerical distribution of the data obtained in the program; “descriptive statistics” are used to calculate values such as mean, standard deviation, mode, and median of the data obtained. These two statistics are basic analyses, and could be easily done.

Comparison or relationship analyses are generally divided into two subgroups, namely parametric and non-parametric analyses. Which of these analyses will be used depends on various criteria. The primary criteria are whether the data are suitable for normal distribution and whether they are homogeneous. In order to distinguish between normal distribution and homogeneity, having statistical information is a prerequisite. In summary; the distance of the data in the data set should be determined in determining the normal distribution and homogeneity. This is synonymous with clutter. Compared to other statistics, the parametric tests; Independent t-test, One Way ANOVA and their non-parametric counterpart Mann Whitney-U test and Kruskal Wallis-H test require expertise partially. These analyses are used to compare averages or rank averages that belong to two or more groups.

Relationship analysis such as regression and correlation is also possible with the SPSS program. While regression analysis is applied to the normally distributed data; correlation analysis can be applied to both normal and non-normally distributed data.

The important issue in the analysis; is whether the criteria are met or If it is desired to use SPSS program for this, firstly, which analyses should be used in the study should be determined. As a matter of fact, it will be extremely risky to try to determine the analysis after the study. For this, we strongly recommend you to consult a specialist.

2) Eviews

In the scientific world, statistics and data are frequently used to reflect the truth of a researcher and to be provable of a claim. As a matter of fact, when speaking with numbers as a general perception the credibility reflected on the person is always more that has occurred in almost all over the world. Indeed, one way to do this is to analyze. Although there are various programs developed for this, one of them is “Eviews”. Eviews is the abbreviation of “Econometric Views”.

First of all, you need to have the program to do the analysis in Eviews. Then you should have the data you get from reliable sources for the subject you started working or decided on. Later, you need to determine the structure of the data and decide what to do the analysis. Eviews is advantageous for entering data from the keyboard or recorded files by visual means. It is a preferred program to create new series from old series, print out series or make statistical analysis between series. It is a statistical package program for Windows. Eviews is also often used for econometric analysis. Eviews is a software that combines features of traditional statistical software with spreadsheet and relational database infrastructure. It is also an advantageous feature that the outputs can be integrated into programs such as Word and Excel.

Eviews are often used in regression analysis and econometric analysis. However, it is also possible to say that it can be used for general statistical analysis purposes. Panel data, time series and horizontal section analysis can be done with Eviews. It also supports file types like Excel, SPSS, SAS, Stata, Rats and TSP.

In Eviews, advanced techniques such as regression analysis and coefficient statistics, logistic regression, weighted regression, and simultaneous equation systems can be performed with the least squares method. Moreover; time series analysis, vector autoregression, cointegration, granger causality tests and simulations are also possible with Eviews.

3) Stata

Although the Stata program is not a frequently used program in our country, it is one of the popular programs used for data analysis in the world. The Stata program is especially used in social sciences and health sciences and it is possible to say that its usage is increasing. The most important advantage of this program is that it can analyze both in code and window form. Panel data analysis has an important place especially in the recent econometric studies. The Stata program is capable of meeting all needs in the fields of data analysis, data management and graphics. In particular, it is an econometric analysis software that is frequently used due to the advantages it provides in panel data econometrics. Stata is compatible with computer platforms such as Windows, Mac and Unix.

Stata has a point and click interface; There is a powerful intuitive command syntax and an online help system that users can consult at times of difficulty. This makes Stata easier to use. All analyses can be reproduced and documented to publish and review with Stata.

Stata is a program that you can use many features like article modelling, dynamic panel data regressions, generalized prediction equation analysis, multi-level mixed models, sample selection models, multiple loss data replacement with appropriate value, cluster analysis, standardization of ratios, status-control analysis, basic tables and summary statistics.

Data management features of Stata are very strong. Therefore, you can provide full control of all types of data. In this context, you can manage variables and compile statistics based on groups and repeated data.

There are broadcast quality graphics in the Stata program. Stata makes it easy to produce publication-quality separate style graphics, including regression application graphics, distributional drawings, time series graphics, life drawings, and contour graphics. You can also make any changes to your graphic with one click, thanks to the integrated graphic editor, or you can add titles, notes, lines, arrows and text.

Matrix programming in the Stata program “MATA” is both an interactive environment for manipulating matrices and a complete development environment in which compiled and optimized codes can be generated. It contains special features for processing panel data, performs operations on real or complex matrices, provides full support for object oriented programs, and is fully interactive with Stata.

4) Gretl

Gretl is another program that can be used for data analysis. The Gretl program is remarkable because it is open source and free. Gretl program is supported on Linux, Windows, Mac operating systems. Moreover, it is a great advantage that the program language is Turkish. In addition, it also supports English, French, Italian, Spanish, German, Basque, Portuguese and Polish.

There are application datasets and command line scripts in various econometrics tables within Gretl. It is possible to access data sets containing explanations within the program. According to the scientific researches, it is determined that Gretl is the software with the least error among the econometrics software in the market. You can also record sessions with Gretl. For example, you have set up 15 equations, but you want to access them later. It is also possible to save everything you have done so far with a click with the session recording feature. Besides, Gretl is a program that supports many data formats.

Gretl’s features are as follows:

· Wide estimator option: least squares, most likelihood, single equation and system methods

· Time zone methods, unit-root and co-matching tests

· To be able to print model outputs as La Tex charts or equations.

· Integrated scripting language: option to enter commands via graphic interface or scripts

· Command loop structure for Monte Carlo simulations and iterative prediction operations.

· GIU manager to fine-tune Gnuplot drawings.

· GNU R connection for advanced data analysis.

5) Minitab

Nowadays businesses produce a lot of data. The way of successful and effective management involves transforming the data into information and using this information when making decisions on any subject for the business. Although statistical analysis was very difficult in the past, these analyses have become much easier thanks to the software prepared today. Minitab is one of the most widely used statistics software in the world. It is possible to do many operations that package programs cannot do with Minitab. Nonetheless, we strongly recommend that you review the lessons on the website before you start using Minitab.

Minitab is a program originally created in 1972 for educational purposes. In the following years, it was developed to measure product and process capability and to interpret the relationships between process inputs and outputs statistically. Today, Minitab is actively used in many businesses and more than 4000 universities.

Minitab analyzes data quickly and easily, and then transforms it into information. It not only contributes to the correct use of this information, but also makes serious contributions to the easy resolution of the most difficult problems.

It is possible to place multiple graphs in a single window with Minitab. It is also extremely easy to create graphics with this program. For example, you can create graphics with one click. You can view important details by selecting the data notes on the chart. You can update graphics automatically when you enter data. You can easily update your graphics and add titles, footers, reference lines, and more.

There are many statistical methods that you can use in your studies with Minitab. These methods are; comprehensive basic statistics are statistical process control, measurement system analysis, experiment design, reliability analysis and proficiency analysis.

6) Gauss

Gauss is a matrix programming language developed and marketed for mathematics and statistics. Its primary purpose is to solve numerical problems in statistics, econometrics, time series, optimization and 2D 3D visualization. Gauss was originally released for MS-DOS in 1984. Today, this program is available for Linux, Mac and Windows.

Gauss has functions with and without fees. For example; there is no need to pay additional fees to second-order programs for sequential quadratic programming, Qnewton unconstrained optimization and nonlinear equation solution. However, algorithmic derivatives with additional fees are; limited maximum likelihood (It solves the general maximum feasibility problem that is subject to the general restrictions in these parameters.), constrained optimization (It solves non-linear programming problem, subject to general constraints on parameters), curvefit (Nonlinear curve fitting), descriptive statistics sample statistics including averages, frequencies and crosstabs), discrete choice (a statistical package for estimating discrete selection and other models where the dependent variable is somehow qualitative), linear programming (It solves small and large scale linear programming problems.), linear regression (least squares estimation), loglinear analysis (log-linear analysis using analysis categorical data), maximum likelihood (maximum likelihood test of parameters of statistical models), nonlinear equations (It solves nonlinear systems of equations containing as many equations as unknown), optimization (unconstrained optimization), time series (exact estimation of various models subject to general restrictions on parameters).

7) R

Due to the increasing digitalization today, institutions are not satisfied with retrospective reports and increasingly use statistical data analytics methods. Therefore, it becomes an important need for people working on data analytics to learn and use the R statistical programming language.

R is a language and analysis program for mathematics and statistical calculations and graphs. It is slightly different from statistical package programs such as SPSS, SAS. R is a statistical software development environment consisting of many original packages rather than a statistical package program.

The most preferred reasons for R are as follows; it is a very comprehensive statistics program, it is frequently used in econometrics, insurance, finance, medicine, sociology and other fields, it is compatible with all operating systems such as Windows, Mac and Linux, it follows the latest developed methods, it is in the orient structure, almost everything is R object It can be stored as a matrix language, it has many functions and packages developed by users, it can exchange data with other powerful statistical software such as SPSS, Stata, SAS.

R provides a wide range of statistical (linear and nonlinear modelling, classical statistical tests, time series analysis, classification, clustering etc.) and graphical techniques and can be extended. One of R’s strengths is the ease of producing well-designed broadcast quality graphics, including mathematical symbols and formulas as needed. Great attention has been paid to the defaults for small design options in the graphics, but the user retains full control.

R; is an integrated suite of software tools for data processing, computing and graphical display. R includes an efficient data processing and storage facility; a set of operators for calculations in arrays and especially matrices; a large, consistent, integrated middleware collection for data analysis; graphical features for data analysis and display on screen or in print and a well-developed, simple and effective programming language that includes conditions, loops, user-defined recursive functions, and input and output facilities.

Data Analysis Consultancy

One of the most important steps of the scientific research process is undoubtedly the analysis of the data. Because what the collected data means for the research problem can only be revealed by data analysis. For this reason, it can be said that data analysis is a process of giving meaning to data. Data analysis includes the organization of the data set, descriptive statistics, hypothesis testing, correlation and regression and other statistical processes. While these statistical processes were carried out manually in times when the technology did not develop, it has supported the development of package programs with the widespread use of computers and the Internet and entering almost all houses. Many problems related to data analysis appear to be largely resolved with the emergence of packaged programs. As a matter of fact, data analysis that have been carried out as a result of very long periods of time and in such a laborious way have become extremely easy today with the widespread use of computers and intensive use in scientific research. Nevertheless, the most important thing to remember is that the user must have some competencies even in order to benefit from the package programs during the data analysis process.

In this context, researcher must first determine the research problem and decide statistical techniques in accordance with the variables in the study. Then it is necessary to determine whether the assumptions regarding these statistical techniques are met. Furthermore, it is extremely important to know how the results of the analysis can be interpreted and reported when the analysis results are obtained. Although the person who uses packet programs does not have to be a statistician, he/she must have enough knowledge to know what analysis he/she is doing at least and how to interpret it. But ultimately, the correct analysis of the data collected in the study is directly related to the knowledge of statistical techniques. In addition, choosing the right program suitable for the analysis is one of the subjects that the researcher should have known.

The fact that the person does not have enough statistical information or cannot choose the right program ultimately leads her/him to use statistical data which is not suitable for the research problem. As a result, it is not possible to perform the necessary analysis and the results of the analysis are evaluated incorrectly or incompletely. All this undoubtedly affects negatively the reliability and validity of the research. It has been demonstrated in the literature that many studies are lacking in data analysis and it has a negative effect on the quality of scientific research.

Along with all this, data analysis has entered our lives with curriculum changes and adaptation to European countries, and today it has been started to be taught at every level of education starting from secondary education. However, as mentioned at the beginning of the article, the researches stated that students failed in this field. In the same research, it was revealed that the difficulties students experienced about this issue were caused by the inability to interpret graphics, imposing incorrect meanings into concepts, and learning without sufficiently concrete experiences. On the other hand, in spite of all problems, students continue to be given homework on “Data Analysis” in schools, and they are asked to reflect these analyses in various articles in the later stages of education, and ultimately it is seen in business life because of its contributions to the business. So,

· If you could not understand the data analysis at school,

· If you do not have concrete experience in this regard and have difficulty internalizing data analysis,

· If you do not have time to deal with these assignments while preparing for your university exams,

· If you are working and doing a master’s degree at the same time and you have been asked for data analysis,

· If you are in a business life and trained in data analysis, but now you have forgotten and your boss is waiting for data analysis from you, or

· If for any reason you have no time, knowledge, competence, patience,

Don’t worry! We are always here to organize the whole process for you and help you with our expert and experienced staff!

Our team consists of experts in their field and performs statistical data analysis services in response to your academic studies. In statistical data analysis, first of all, hypotheses or sub-problems related to your study are examined. Accordingly, statistical data analysis methods suitable for the desired hypothesis or sub-problems are used. If you do not have hypotheses or sub-problems, these can be completed by our expert team. In addition, the tables revealed by data analysis can be interpr sector with its years of experience and expert staff, providing consultancy services in middle school, high school, university, graduate, doctorate homework, articles, thesis, video, cv, essay, presentation, motivation letter and more. Comments made by our references on the website will also guide you and increase your courage. If you want to get help from an experienced staff with statistical knowledge, a good command of the data analysis process, take action before it’s too late! You can get one step closer to success by contacting our team via the website or WhatsApp.

Best Essay Team is a team specialized on Data Analysis is here to help you in Data Analysis and in all other issues. If you wish, we can help you deal with your assignments and we can be your coach in this regard. You can easily contact with Best Essay Team. Send us your requests on our Whatsapp Support Line or mail address info@bestessayhomework.com, and you can have an idea about the price.

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