In the technologically driven 21st century, gathering vast amounts of data to analyze and identify patterns and trends is a common phenomenon to make better business decisions. In fact, the US Bureau of Labor Statistics predicts that data analyst jobs will see a 20% increase in growth from 2018 to 2028, proving the ever-increasing need for market research across the market.
What Is Statistical Analysis?
Statistical analysis is the procedure where you perform various statistical operations to consolidate and analyze large quantities of data and find trends and patterns that can help you interpret the data correctly. The entire process is crucial to remove all bias when reviewing the information to make the right decisions.
It is a component of data analysis that, according to TechTarget, can be “used in situations like gathering research interpretations, statistical modeling or designing surveys and studies.”
Now, you can break down the process of statistical analysis into 5 simple steps:
• Describe the nature of the data that you have to analyze
• Explore the relation of the data to the population
• Create a model that best explains how the data relates to the underlying population
• Evaluate the validity of the model
• Employ predictive analysis that can help you run scenarios for future actions
Through these simple processes, you’ll be able to identify the possibility of any trends or similarities that enables you to make informed decisions for the future. Moreover, the two types of statistical analysis that you should be aware of are:
1. Descriptive statistics
The role of descriptive statistics is to summarize the observed data with the help of charts, tables and other visual mediums without bothering to draw a conclusion from the same. The main intention here is to make it easier for people to understand and visualize raw data.
For example, you could gather data on the number of searches for plagiarism checker tools in the UK over the past decade. Once you have the information, you could divide it into monthly, weekly, or daily searches. Since all the numbers can be highly confusing, descriptive statistics can help you visualize it properly through bar graphs, pie charts, etc.
2. Inference statistics
Once you have a visual summary of the data, you can further interpret and draw conclusions from it. Inference statistics helps organizations test hypotheses and apply the results to the sample group.
Suppose you have gathered the number of searches for proofreading tools online. According to your data, the number has significantly increased by 200% in the past three years. Through this, you can conclude that:
• The number of people with access to the internet has increased in the past three years
• The number of people looking for quick proofreading solutions has increased
• The quality of overall final drafts have decreased significantly due to which people have to depend on online tools
What Is R Programming?
R is a free, open-source programming language developed by Roass Ihaka and Robert Gentleman in August 1993. Statisticians and data miners worldwide use this programming language for statistical computing and graphics, statistical software and data analysis polls, and data mining surveys.
Thanks to its easy-to-use interface, R is the most popular programming language that professionals use to retrieve, clean, and analyze data. In fact, some of the significant stalwarts in the industry that have actively used R Programming are:
• Twitter – They use R programming to monitor the user experience
• Human Rights Data Analysis Group – Measures the impact of war
• Ford – Uses R programming to analyze social media and develop car designs
• Microsoft – Released Microsoft R Open
Why Do People Use R Programming For Statistical Analysis?
R programming language is a natural choice whenever data analysts want to conduct statistical analysis. Since a statistician developed the programming language to import and analyze data without wasting a second, the programming language has the solution for all data analysis needs. Hence, it's no surprise that the simplicity and power of R programming execute makes it the #1 choice for professionals worldwide.
Diving into greater detail,
R programming is free and open-source
R is licensed under the GNU General Public license. Therefore, it is free to download, and you get to pick the R package of your choosing from the same license without worrying about breaking the law.
R is platform-friendly
R programming is compatible with Windows, Linux and Mac. So, you can run the program anywhere without added problems with the platform. Furthermore, when you code on one platform, you can quickly move it to another platform without any issues.
R has a rich collection of more than 12,000 packages covering every statistical function you can count. A few worth mentioning are:
• Familial
• MARSS
• LOMAR
• ManifoldOptim
• MFSIS
• Sigminer
• RMySQL
• TSANN
• BIOMASS
Since R programming was strictly designed for statistical analysis, it is easier for R Programmers to gain detailed insights into how the data is structured. As a result, they're also more equipped to understand the best ways to apply the data science techniques for better results.
R Programming’s Contribution To Statistical Analysis
R programming has gained popularity for its role in bridging the gap between software development and data analysis. Thanks to mathematicians and statisticians developing R for over two decades, it currently has the richest ecosystem to perform statistical analysis. With its vast library of 12,000 packages in the open-source repository and excellent tools to communicate results, R has made its impact felt across the globe.
R is a free, open-source code that allows any programmer to access the underlying code and add in their contribution for free. This ensures that R can always perform the latest statistical operations, fix bugs quickly and open up the opportunity to connect to a community of programmers who you can turn to for help at any point.
If you’ve never used R, you might wonder whether it's truly as great as it seems. Well, what if you could perform any statistical function with this programming language?
You can use R programming for:
• Statistical testing
• Prescriptive analytics
• Predictive analytics
• (M)AN(C)OVA
• Time-series analysis
• Regression equations
• What-if analysis
• Regression models
• Data exploration
• Forecasting
• Hierarchical linear modeling
• Structural equation modeling
• Text mining
• Data mining
• Merging data sets
• Visual analytics
• Web analytics
• Create 3D and interactive plots
• Social media analytics
• Sentiment analysis
• Bayesian statistics
And many more…
Furthermore, you can also use R programming for several real-world business problems by integrating R analytics into the platform. Through this process, you'll be able to apply all kinds of statistical models to your analysis and improve your understanding of the emerging trends from the data. Thus, there’s no doubt that R’s contribution towards statistical analysis is nothing to scoff at.
Summing it up,
R is a programming language that you use for statistical computing, data visualization, cleaning, and interpretation. Scientists and statisticians have developed this programming language for two decades to democratize analytics and enable people to use interactive data visualization and reporting tools. Its vast library allows you to fulfil every statistical need you can have. Furthermore, its ability to provide accurate insights make R the best programming language for statistical analysis.
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