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Understanding statistical analysis

What is statistical analysis and how is it different from data analysis?
(clicks) Statistical analysis is the process of collecting, exploring, and presenting data to uncover underlying patterns and trends. It is used to explore data sets and create models using advanced techniques, data analytics projects and exploratory data analysis, rely heavily on statistical analysis. So having a good understanding of the basics of statistics, will help you to use it effectively. Remember, the type of statistical analysis you choose, will depend on the type of data you have and the business problem at hand. In turn, this will determine how you construct your research design, the types of variables chosen and the distribution of the data.
Let’s look at how the types of data are classified and how this impacts what you can do with it from a statistical perspective. The first thing to do is to distinguish whether your data is quantitative or categorical. Quantitative data have numeric values, which are measures or counts that allow us to perform mathematical operations. Categorical data on the other hand, helps us to group different non-numerical data and can be represented by a name, a symbol or a numeric code. Quantitative and categorical data can be further subdivided. Quantitative data can be continuous or discrete and categorical data can be ordinal or nominal. Discrete data variables take on fixed values within a range. For example, think of rolling dice.
Since dice have fixed values, you couldn’t roll the dice with a value of 3.5. Discrete data includes only those values that can be counted in whole numbers or integers and are separate. This means that data can’t be broken down into fractions or decimals. However, continuous data variables can take on any value within a range. Continuous data can be repeatedly divided into finer levels and is measured on a scale or continuum taking any value between two points. Nominal variables are categorical data that don’t have any order or ranking while ordinal data is associated with a ranking system.
Sometimes analysts will code ordinal data that can be used like quantitative data to do certain statistical analysis that may be relevant to a business problem. So be aware of the data you have and how you can best use it to transform it to address the question you are trying to answer or the hypothesis you are testing. Now it’s over to you. What type of data are you working with and how will you capture and transform it?

In this video, you learned about basic statistical analysis and how to use it with different types of data.

Data analysis and statistical analysis

Data analysis and statistical analysis are two terms that are used interchangeably in the field of data science and data analytics. Both these involve processing data to cull patterns and trends and so, for the purpose of this short course, could be considered to have the same set of objectives. However, data analysis also involves data scientists using data visualisation techniques for identifying trends and patterns that statistical analysis does now. You will learn more about data visualisation for data analysis in Week 4 of this short course.

Next, you will learn about descriptive statistics through a video.

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Introduction to Data Science for Business

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