Wednesday, July 6

# What is statistics and uses?

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214 Statistics is an especially important branch of Mathematics. Statistics is the branch that deals with the organization, collection, interpretation, analysis, as well as presentation of data. The application of statistics can be found in every field of the modern world. The use of statistics can be also observed to solve various industrial, scientific and social problems. In data collection, statics are proved to be very helpful. For instance, statistical experts collect data by modeling a specific experiment design with the help of survey samples when census data cannot be collected.

Data is a particularly important part of statistics. Data are the figures and facts which are collected, analyzed, and summarized for interpretation and presentation. Data can be broadly classified into two categories. i.e., quantitative or qualitative. Quantitative data involves the value of data in the form of numbers or counts where a unique numerical value is usually associated with each data set. This data is any quantifiable information which can be utilized for statistical analysis and mathematical calculations. This type of data helps to take the real-life discussions. Quantitative data helps to answer various questions such as “How often?”, “How many?”, “How much?”. Using mathematical procedures this data can be verified, evaluated, and analyzed. In simple words, these data are generally used to quantify a problem. On the other hand, qualitative data defines qualities or characteristics which are usually collected by using questionnaires, observation, or interviews, and frequently appear in narrative form. Sample collection also plays a significant role. Researchers usually follow different standard methods for sample collection. Observational studies involve simple survey methods for sample collection. Whereas, experimental studies involve some experimentally designed methods for sample collection.

The statistical methods which are used to analyze the data can be broadly categorized into two types, descriptive statistics, and inferential statistics. Descriptive statistics are graphical, tabular, and numerical data summaries. The descriptive statistics provide the interpretation as well as the presentation of data. For example, the statistics which are commonly observed in magazines and newspapers are descriptive in nature. Univariate approaches of descriptive statistics usage data to improve the understanding of a single variable. Similarly, multivariate approaches of descriptive statistics focus on utilizing statistics to understand the relationships between two or more variables. Statistical inference is basically utilized to understand the underlying distribution of probability using data. Inferential statistical analysis properties of a population, for instance by testing hypotheses and deriving estimates. It is assumed that the collected data set is sampled from a larger group of population.

Now, where we use statistics?

The use of statistics can be observed in various real-life applications. Here, I have listed a few practical applications of statics.

• Government

Government use statistics for various applications to estimate the population growth, making observations in education and health, and many more. The government takes the help of the statistical data to develop policies.

• In weather forecasting

Yes! the weather forecast notifications that you receive daily on your mobile or television are based on the statistical models only. The weather conditions are compared with a pre-recorded statistical model of the weather seasons and conditions.

• In mathematical modeling

Applied statistics include inferential statistics and the application of descriptive statistics. Theoretical statistics involves logical arguments which are related to statistical inference and encompassing mathematical statistics. Mathematical statistics comprises not only the manipulation of probability distributions essential for deriving results related to methods of estimation and inference but also several aspects of the design of experiments and computational statistics.

• In data mining and machine learning

Data mining and machine learning are two of the most promising field of computational. Modern technologies such as artificial intelligence, automation, etc are based on these two technologies. statistics is a very important tool for data mining and machine learning. Statistical data are used in machine learning to develop a probabilistic model for various purposes.