What is applied statistics?
Applied statistics refers to the application of statistical methods of data analysis in coming up with a problem-solving conclusion and to aid decision-making. Any industry that deals with delivering goods and services to large markets require applied statistics to make strategic business decisions that will yield profitability. This is mainly due to the dynamic nature of markets and the factors influencing the business.
It encompasses the processes of data collection, analysis, interpretation, and presentation. Data collection involves methods of random sampling, experiments, and observational study. Analysis requires the use of statistical algorithms and in some instances software and machine learning techniques. Finally, the presentation is where the data has to be presented in a visual way that is easy to understand. This mainly makes use of statistical plots and graphs.
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Topics under applied statistics
Majorly, applied statistics is categorized into two branches. These are:
This mainly focuses on the quantitative description of statistical data. The major significance of descriptive statistics is that it largely summarizes massive data and gives easy visualization of the data. This makes it very easy to understand and come up with quick decisions.
In descriptive statistics, data can be summarized in two ways. One way is the use of summary graphs and tables. These give a quantitative overview of the data and make it simple to draw quick conclusions by just taking a glance. The other way is to use univariate analysis techniques. These include measures of central tendency and measures of dispersion.
Inferential statistics is all about using data analysis techniques to find out the underlying characteristics of the population. Unlike descriptive statistics which is focused on the observable characteristic, inferential statistics are more of estimates.
From the two branches above, we get the following topics in applied statistics.
|Probability and Probability Distributions||Estimates and sample sizes|
|Hypothesis testing||Analysis of Variance|
|Correlation and Regression||Multinomial experiments and Contingency tables|
|Analysis of Variance||
In addition to the above-mentioned, we also work with software to perform analysis when required. Some of the most common statistical software that we have worked with include:
- R programming
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