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Statistical analysis is the methodology developed by mathematicians and scientists for data assembling, classifying, analyzing, organizing, presenting, manifesting, and uncovering trends and patterns. It is a branch of mathematics and a major component of data analytics that renders various analytical techniques and tools to provide significance to insignificant values. Statistical analysis is also referred to as the process of removing any bias while reviewing information utilizing numbers. It is also a scientific tool that can help one make informed decisions.
It can be used in situations like statistical modeling, designing surveys, and gathering research interpretations. After data collection, the data is analyzed to:
Statistical analysis is extensively used in science, from social sciences to physics. Statistics is also employed in hypothesis testing where it can provide an approximate value for an unknown that is impossible or difficult to measure.
For example: In the field of social sciences, where measuring choice or consciousness is practically impossible, so statistical analysis is employed in such cases to shed light on what is most likely to happen or least likely to happen. Statistical analysis, an aspect of business intelligence, is also employed by business firms including online technology firms that work with large volumes of data to use the big data optimally for greater success. It involves the collection, scrutinizing, and organizing large amounts of data available in a set of data from where samples can be drawn to draw out some reliable and valid conclusions and also to predict the future trends of information
Descriptive Statistical Analysis: Descriptive statistical analysis is the type of statistical analysis that is employed by business organizations to summarize the available data. The big hunk of data is described with summary tables and charts, without drawing conclusions on the population from which the samples are taken. Using descriptive analysis, understanding and visualizing the raw data becomes very easy as it employs charts, tables, and graphs. In the descriptive analysis, the data is simply described and presented in a meaningful way to make the interpretation of data simple. No conclusion is derived beyond the research conclusion or analyzed data regarding any predetermined hypothesis. Mean, median, mode, range, standard deviation, and variances are some of the useful methods of descriptive statistics.
Inferential Statistical Analysis: This is the second type of statistical analysis. Inferential statistics is a method employed for further studying of data. Inferential statistics is employed by business organizations to test a hypothesis and derive conclusions about the available data. The statistical analysis deals with typically examining the entire data and the results obtained are applied to the entire group.
Predictive Analysis: This type of statistical analysis is implemented to predict the future events or what is likely to happen next based on both the past and present facts and figures. In predictive analysis, machine learning algorithms and statistical techniques are employed to describe the possibilities of future outcomes, trends, and behavior. Data mining, artificial intelligence, data modeling, and machine learning are some of the widely used techniques in predictive analysis to make imperative predictions. Marketing companies, online service providers, insurance organizations, financial corporations, and data-driven marketing agencies adopt this approach for planning for the unpredictable future, to narrow down the risks associated with the unpredictable future and gain a competitive advantage.
Prescriptive Analysis: In this type of analysis the data is examined to ascertain the next action, business organizations employ this method to identify the best possible actions for any impending situation. Other types of statistical analysis methods are generally used for driving exclusions but this one provides the real answer. It is focused on identifying the optimal suggestion for the decision-making process. Simulation, graph analysis, complex event processing, algorithms, machine learning, and business rules are some of the techniques implemented under prescriptive analysis. Though it is similar to predictive and descriptive analysis, but descriptive analysis describes the data, the predictive analysis predicts what could happen and prescriptive analysis provides appropriate suggestions.
Exploratory Data Analysis: It falls under inferential statistics and is generally implemented by data experts. It is basically the first step of the entire data analysis process and is conducted before any other statistical technique. It predicts the data by providing some key insights of the data. It is focused on assessing the patterns in the data in order to reveal potential relationships. It is used for identifying unknown associations between data, inspecting the missing data, examining hypotheses and assumptions, and obtaining the optimum insights.
Causal Analysis: Causal analysis is used to understand and determine the reasons behind why certain things took place or why things are the way they are. The causal analysis identifies the root cause of failures or the reason behind the occurrence of something. It is employed in IT industries to check the quality of a particular software or why a particular software failed and prevents the company from lagging behind due to a bug or breach of data.
Drawing a conclusion from data is the aim of statistical analysis. The analysis of any data involves five discrete steps.
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Population: The large group of objects or individuals from where a research or investigation is started by a researcher or investigator is referred to as a population.
Sample: The randomly selected set of objects or individuals from the population in which the investigation is carried out is referred to as a sample.
Parameters: An unknown numerical briefing of the entire population is referred to as a parameter.
Statistics: An unknown numerical briefing of the sample, that is used in developing inference about the parameter is referred to as statistics. A sample is illustrated by a statistic whereas a population from where the sample is chosen is illustrated by a parameter.
Mean: Mean, also referred to as average, is obtained by dividing the sum of all the examined values or total value by the total number of observations.
Median: Median is the middle value of any ordered set of data.
Mode: The most frequently occurring value in a set of data is referred to as mode.
Range: The difference between the highest value and the lowest value in any set of data is referred to as range.
Standard Deviation: The concept of the closeness of the entire set of data to the average value or the mean is referred to as standard deviation. A small standard deviation indicates that the data is tightly grouped and a large standard deviation indicates that the whole set of data is scattered to a wide range of values.
Variance: The extent to which a particular set of data is spread is referred to as variance. A zero variance indicates that all the values are similar. Variance is the square of the standard deviation; hence it can never be negative. A small variance indicates that the value of data is very close to the mean or average value and a large variance indicate that the value of data is widely spread around the mean or average value.
Probability: Probability refers to the measure of the likelihood that an event might take place in an experiment.
Hypothesis testing: Testing of an assumption or results of an experiment or survey regarding the parameter of a population to ascertain that the results are meaningful.
Statistical analysis helps us in understanding the world a little better using numbers and other quantitative forms of information. It is generally used in everyday life for keeping records, providing knowledge, and calculating the probabilities of any event. Statistical analysis is a fundamental tool also employed by data scientists to gather as well as analyze large volumes of structured and unstructured data. A combination of computer algorithms and statistical formulas are used by data scientists to identify the trends and patterns within the available data, further interpreting the meaning of those patterns and applying them to the real-world situations.
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