Numerous aspects of the field of statistics are relevant to our daily lives, and in the business of running our largest cities, statistics are everywhere around us…

What is statistical analysis?

It’s the science of collecting, analyzing and presenting large amounts of data to find patterns and trends. Research, industry and government use statistics daily to make better decisions that are based on the latest data.

Consider, for instance,

  • To produce beautiful fabrics, to boost the airline industry, and to help guitarists make beautiful music, manufacturers use statistics.
  • Children remain healthy because scientists analyze data when they produce viral vaccines, which ensures consistency and safety.
  • By gaining greater insight into subscriber needs, communication companies are able to optimize network resources, improve customer service, and reduce churn.
  • Statistics are relied upon by governments around the world to better understand their countries, businesses, and citizens.

Take a moment to look around. Statistical methods have been used to improve hundreds of products and processes every day. From the tube of toothpaste in your bathroom to the planes flying overhead, you are inundated with statistics every day.

Statistical analysis software

By including additional tools for organization and interpretation of data sets, as well as for presenting that data, statistical analysis software typically allows users to perform more complex analyses. RMP and Stata are examples of statistical analysis software. For example, IBM SPSS Statistics covers the whole analytical process, from data preparation to data management and analysis to reporting. There is a customizable interface, and even though it may not be easy to use for someone unfamiliar with it, it is relatively simple for those who are familiar with its uses.

Five types of statistical analysis - ppt video online download

Statistical analysis process

  1. Data collection – A statistical analysis begins with collecting data. You can gather data through primary or secondary sources, such as surveys, customer relationship management software, online quizzes, financial reports, and marketing automation tools. If you want to ensure your data is reliable, you can choose data from a sample that represents the population. For example, a company might study previous customer data to learn about buyer behavior.

  2. Data organization – After data collection, you need to organize data. Also known as cleaning, this step involves identifying and removing duplicate data and inconsistencies that may impede accurate analysis. Taking this step can help businesses ensure the data they have and the conclusions they draw from it are correct.

  3. Data presentation – This step extends data cleaning by presenting data for easy analysis. You can use descriptive statistics tools to summarize the data and determine how to display it based on its arrangement.

  4. Data analysis – Statistical methods, such as inferential and associational statistical analysis, can be used to identify patterns, trends, and relationships in data sets. Software such as spreadsheets can automate this process and reduce the likelihood of human error in the statistical analysis process. This can allow you to analyze data efficiently.

  5. Data interpretation – Data interpretation provides conclusive results regarding the purposes of the analysis. In order to make the results more accessible to nonprofessionals, data can be presented in the form of charts, reports, scorecards, and dashboards. An analysis of the impact of a 6,000-person factory on crime in a town with 13,000 residents can show a declining rate of criminal activities. Use an appropriate line graph to show the decline.

Statistics is so unique because it can go from health outcomes research to marketing analysis to the longevity of a light bulb. It’s a fun field because you really can do so many different things with it.