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What is statistical analysis?

What is statistical analysis?

Statistical analysis can be defined as the organized process through which many quantitative data related to a specific topic are collected from several different sources in order to verify, classify and summarize these data in order to provide some information or logical explanations for the topic related to the statistical analysis process. Statistical analysis in all directions, patterns and nature of relationships for all variables related to the subject of the study, as all of this contributes to providing a comprehensive view on the subject of the study, and contributes to shedding light on all its aspects, and all of this helps to provide qualitative information to the beneficiaries of the statistical analysis process in the future.
What is statistical analysis?
What is statistical analysis?

statistics Science

 Statistics can be defined as that science that is concerned with collecting, analyzing and interpreting data in order to use the information that results from analyzing this data in a group of economic activities. Qualitative, and with the passage of time the need for applications of statistics increased due to the increase in the population, and the need to provide a wide database containing extensive information on the various segments of society for the benefit of government agencies later on, and the concept of statistical analysis emerges from the concept of general statistics, and in this article will be The answer to the question What is statistical analysis?

Statistical analysis and statistical significance

The concept of statistical significance refers to that result emanating from the statistical analysis process towards a specific topic, and the strength of statistical significance varies in terms of the probability of occurrence or non-occurrence of that result, which, if it occurs, will be attributed to a specific reason, and a set of methods are used in order to determine the statistical significance in the analysis process. Statistical, including testing a particular probability or random experimentation, as this can be done by chance, and statistical data related to many scientific, medical or economic topics are collected and analyzed and the contents of these data are researched through accurate statistical analyzes, and then the investigation is carried out on Statistical indications associated with those sciences, which give some explanations for what is happening in them.

Statistical significance also depends on a set of different variables, the presence of which helps to support the results of the statistical significance that are reached, and to increase the percentage of its reliability to the maximum extent possible. The objectivity of this sample selected for the study and its lack of bias or inclinations to any of the parties helps to make the results of the statistical analysis more realistic, and if the statistical data has a specific inclination for a particular group of the total study population, it will reflect the reality related to those selected samples only, because of course it is It does not represent all the elements of society, but rather is limited to a category that has mostly common characteristics.

Bayesian Theorem in Statistical Analysis

One of the ways that in some cases is relied upon in the process of statistical inference, drawing conclusions and estimating their value is what is known as Bayesian estimation, which is called in English Bayes's theorem, where the results of statistical analysis are expected based on the presence of previous experiences, studies or information that can be built upon. The results are compared to what happened previously, especially in the presence of some similar conditions. In addition to these expected results, some information from the experimental environment accompanying the statistical analysis process.

In Bayesian theory, some actual information from the experimental reality is added to what was expected based on previous studies and information in order to reach the final statistical inference in the statistical analysis process. With specific iterations in the input stage or the initial stages of the statistical analysis process, where many specialized mathematical concepts and formulas are subsequently applied to them in order to analyze and explain the phenomena associated with them in practice.

Path analysis and statistical analysis

Path analysis can be defined as one of the methods used in the statistical analysis process, and in the process of path analysis, causal models are evaluated by studying the courting among the established variable and  or extra unbiased variables. In the path analysis method, researchers also make charts that show the relationship between the various variables studied by path analysis, and some statistical analysts use some statistical programs to make comparisons between the statistical analyzes that they put and the resulting relationships between these variables in reality, and among the most prominent specialized programs In the statistical analysis processes, the SPSS program and the STATA program.

Path analysis requirements in statistical analysis

To apply path analysis in the statistical analysis process, there must be a set of requirements that pave the way for the study of causal relationships between variables, and give results that benefit statistical analysts in understanding the links that combine these variables, and the most prominent of these requirements are the following:
  • Unidirectional: This means that all the causal relationships studied by the path analysis between variables take one direction so that the process of their analysis is possible, as this type of analysis cannot be applied between pairs of changes whose presence of one contributes to the emergence of the other variable.
  • Chronological order: This requirement means that there be a specific temporal arrangement of the variables, so that there is no conflict in the presence of the variables due to the overlap of timing and the absence of an appropriate time interval.

Markov Statistical Analysis

This method is called Markov analysis after the scientist Andrei Markov who formulated it, and Markov statistical analysis is one of the special statistical analysis methods, which are applied in some fields such as accounting and marketing, where this type of statistical analysis is used in order to anticipate some future variables that are not It is related to the historical behavior of some accounting items, such as predicting the amount of bad debts in organizations out of the total public debt. As for marketing, this statistical analysis is applied in some areas, such as knowing the extent to which some customers adhere to a specific brand.

The importance of samples in statistical analysis

The importance of the samples used in the statistical analysis varies depending on the beneficiary of these samples and what these samples represent for the beneficiaries, and these samples may be chosen regularly or randomly, but in the end they aim to reflect the reality of the study community, covering all characteristics of the study community, and can Statement of the importance of samples from the following areas:
  • Auditing of accounts: auditors use samples in order to verify the fairness of the records and financial statements in order to express an opinion on the validity of the financial statements contained therein and the conformity of what was recorded in the financial records and what has been implemented on the ground.
  • Marketing: The sampling method is one of the most important methods used in evaluating the marketing process, and benefiting from the feedback on sales operations, as a sample of potential consumers or consumers is targeted in order to know aspects of satisfaction or dissatisfaction with specific commodity specifications, and the results of The study and statistical analysis on this sample find ways to make the goods or services more desirable.
  • Polling operations: In polling operations, a sample is taken from the community concerned with polling or voting operations to form opinion polls, and the comprehensiveness of the sample taken from the entire community contributes to creating a state of possible anticipation of what will be voted on by voters in the future based on their orientations and subjective decisions related to the behavior or specific interest.
  • Quality study: In quality studies, samples of specific products are taken to see if these samples conform to the approved specifications and standards for this type of product, whether in terms of design, efficiency or otherwise.

Standards of Measurement in Statistical Analysis

The concept of measurement in statistical analysis is related to the quality of the statistical data that is provided in order to conduct the statistical study on it, as the standard process in statistical analysis requires that these data be within tabs that are consistent, diverse and appropriate to the subject of the study. Providing these tabs contributes to statistical comparison processes, and the formation of a case of Comprehensive understanding of the results of statistical analysis, and the measurement criteria in statistical analysis can be summarized through the following:

nominal measurement standard

The nominal measurement standard aims to describe things, people or events accurately in order to distinguish the data from each other, and there are many statistical techniques that are used in the accurate description of the data such as the use of special symbols or serial numbers that cannot be repeated or matched, for example Driver's license number, product serial number, or vehicle coding systems.

ordinal measurement standard

In the ordinal measurement standard, the data is described with specific numbers that mean a specific arrangement, and the ordinal measurement standard is used to separate groups that do not have similar characteristics from each other, and this standard can also be used in preferential operations so that the numbers are arranged ascending or descending depending on the degree of quality or promotion In a specific field, for example, the number 1 is a symbol of a class that contains characteristics that the number does not have, and the number 2 is a symbol of a class that contains characteristics that the number 3 does not have, and so on.

cut-off criterion

In the boundary criterion, a specific point is taken to be a reliable reference in the measurement, and a continuous series of numbers is formed that contains the amount of difference between one number and another. The most prominent examples of this type of measurement standards in statistical analysis are units of temperature measurement, where each A scale of a reference point has specific characteristics, but these characteristics differ for the same number in another unit of measure, for example a temperature of 5 °C for a specific substance is significantly different from a temperature of 5 °F.

Relative Standard

The relative criterion is similar to the cut-off criterion in terms of content, but it lags behind in the presence of a reference that represents the zero point for the characteristic that describes the statistical data, and one of the most prominent examples that use the relative criterion with regard to the physical properties of specific materials, the zero in the relative standard is a real zero and when the substance is in The degree of zero for this means the absence of the characteristic, as if it is said that the length is equal to zero or that the weight is equal to zero, and this criterion was called the relative standard, because it depends on the description of things or people with a percentage of a specific characteristic that is specific to it, as if we say that the size of a solid is equal to twice Another stereoscopic size, where the size characteristic between the two bodies was taken in a relative manner.

Statistical analysis and investment

Statistical analysis is widely used in the process of managing investment portfolios and determining the foundations of effective investment, by calculating quantitative statistical measures and some statistics on investment operations, it can be expected what will happen to investments in the future, and investment statistical analysis helps to anticipate some future financial investment risks in order to avoid falling and incurring losses, and this leaves the investor with space for the calculated risk in which he bears some risk, but it does not cause him serious investment losses in the future, and the investor who uses statistical analysis must have some knowledge regarding taxes, expenses and market movements to understand what is going on in investment portfolios To make as much profit as possible.

Asymptotic Variance in Statistical Analysis

Asymptotic analysis can be defined as one of the methods used to describe behavior, and one of the most important applications associated with asymptotic variance is its use in statistical mechanics, applied mathematics, and computer science. Statistical Numerical mechanisms and mathematical patterns are built in in a way that facilitates the creation of a realistic model of phenomena that occur in reality through applied mathematics using a set of mathematical formulas and specialized differential equations.

In asymptotic variance, a set of statistical values ​​is estimated based on the observed data and the associated estimation characteristics. In statistics, specialists differentiate between these capabilities based on the presence of a set of different characteristics according to the following:
  • Sample characteristics: It means those characteristics associated with the sample that can be accurately described regardless of the size of that sample.
  • Asymptotic properties: These are properties associated with infinite samples, which may reach infinity.
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