It can be divided according to the type of information studied into: quantitative and qualitative analysis. The first examines the relationship between quantifiable information; The second examines the relationship of non-numerical information in situational and structural contexts.[16]​.

The analysis of the information in this study adopts a correlational and transversal approach, since it aims to identify the relationship between the FOB value of copper exports and various macroeconomic indicators of Peru, without intervening in said variables and considering a previously delimited time period.

It can also be divided according to the objective into: exploratory analysis (ADE) and confirmatory analysis (CDA). The first, looks for links and relationships between the data; The second focuses on verifying the hypotheses formulated regarding a set of information.[17]​.

Contenido

Son varios los enfoques presentados, sin pretender ser una metodología estricta sino más bien inicial o para exploración de campo, podemos mencionar las siguientes.[18]​.

Nonlinear data analysis

Mathematical formulas or models (known as algorithms) can be applied to data to identify relationships between variables; for example, using correlation or causation.[19]​[20]​ Generally speaking, models can be developed to evaluate a specific variable based on other variables contained in the data set, with some residual error depending on the accuracy of the model implemented (e.g., Data = Model + Error).[21]​

[22]​.

Inferential statistics includes the use of techniques that measure relationships between particular variables. For example, regression analysis can be used to model whether a change in advertising (independent variable X) provides an explanation for the variation in sales (dependent variable Y). In mathematical terms, Y (sales) is a function of X (advertising). It can be described as:

where the model is designed such that and minimize the error when the model predicts for a given range of values ​​of . Analysts can also try to build models that are descriptive of the data, with the goal of simplifying the analysis and communicating the results.[23]​.

Smart buildings

A data analytics approach can be used to predict energy consumption in buildings.[24]​ To implement smart buildings, several steps are carried out in the data analysis process, in which building management operations, such as heating, ventilation, air conditioning, lighting and security, are performed automatically by simulating the needs of building users and optimizing resources such as energy and time.[25]​.

Analytics and business intelligence

Analytics is "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to make decisions and take action."[26]​[27]​ It is a subset of business analytics, which is a set of technologies and processes that use data to understand and analyze business performance for the purpose of making decisions.[28]​.

For example, social media analytics or social media monitoring is the process of collecting and analyzing data from social networks.[29]​[30][31]​ It is often used by marketers to track online conversations about products and companies. Companies specify how they will use this personal data in their privacy notices.[32]​.

Education

In education, most educators have access to a data system to analyze student data.[33]​[34]​ These data systems provide data to educators in a non-recipe data format (tag embedding, additional documentation and help system, and key packaging/display and content decision making) to improve the accuracy of educators' data analysis.

It can be divided according to the type of information studied into: quantitative and qualitative analysis. The first examines the relationship between quantifiable information; The second examines the relationship of non-numerical information in situational and structural contexts.[16]​.

The analysis of the information in this study adopts a correlational and transversal approach, since it aims to identify the relationship between the FOB value of copper exports and various macroeconomic indicators of Peru, without intervening in said variables and considering a previously delimited time period.

It can also be divided according to the objective into: exploratory analysis (ADE) and confirmatory analysis (CDA). The first, looks for links and relationships between the data; The second focuses on verifying the hypotheses formulated regarding a set of information.[17]​.

Contenido

Son varios los enfoques presentados, sin pretender ser una metodología estricta sino más bien inicial o para exploración de campo, podemos mencionar las siguientes.[18]​.

Nonlinear data analysis

Mathematical formulas or models (known as algorithms) can be applied to data to identify relationships between variables; for example, using correlation or causation.[19]​[20]​ Generally speaking, models can be developed to evaluate a specific variable based on other variables contained in the data set, with some residual error depending on the accuracy of the model implemented (e.g., Data = Model + Error).[21]​

[22]​.

Inferential statistics includes the use of techniques that measure relationships between particular variables. For example, regression analysis can be used to model whether a change in advertising (independent variable X) provides an explanation for the variation in sales (dependent variable Y). In mathematical terms, Y (sales) is a function of X (advertising). It can be described as:

where the model is designed such that and minimize the error when the model predicts for a given range of values ​​of . Analysts can also try to build models that are descriptive of the data, with the goal of simplifying the analysis and communicating the results.[23]​.

Smart buildings

A data analytics approach can be used to predict energy consumption in buildings.[24]​ To implement smart buildings, several steps are carried out in the data analysis process, in which building management operations, such as heating, ventilation, air conditioning, lighting and security, are performed automatically by simulating the needs of building users and optimizing resources such as energy and time.[25]​.

Analytics and business intelligence

Analytics is "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to make decisions and take action."[26]​[27]​ It is a subset of business analytics, which is a set of technologies and processes that use data to understand and analyze business performance for the purpose of making decisions.[28]​.

For example, social media analytics or social media monitoring is the process of collecting and analyzing data from social networks.[29]​[30][31]​ It is often used by marketers to track online conversations about products and companies. Companies specify how they will use this personal data in their privacy notices.[32]​.

Education

In education, most educators have access to a data system to analyze student data.[33]​[34]​ These data systems provide data to educators in a non-recipe data format (tag embedding, additional documentation and help system, and key packaging/display and content decision making) to improve the accuracy of educators' data analysis.