The discussion last August 17 started with the topic of the Contingency Table. The use of this kind of table can help us to understand what's the relationship between categorical variables. It will also allow you to organize your data and answer some diverse questions.
The next topic is Measures of Distribution and Variation. For the distribution, it tells the difference between sample data values and distribution means. While the measures of variation, it is to describe the distribution or dispersion of data.
Next is the visualization distribution, you can easily understand how individual data points are distributed within the broader data set. It is essential to easily know what data means so that you can create solutions based on what is presented.
For the distribution, one method is the normal distribution where it can analyze data when there is an equally likely chance of being above or below the mean for continuous data. Second is skewness and kurtosis distribution, its essential to use so that it can be measured in descriptive statistics that characterizes the asymmetry of a data distribution.
Business analytics seeks to analyze and report data in order to forecast future business success and analyze past business performance. On the other hand, the business analysis concentrates on operations and processes, identifying business requirements and making recommendations.
When working with small sample sizes or when the population standard deviation is unknown, the t-distribution, commonly referred to as Student's t-distribution, is a utilized probability distribution.
A statistical technique called logistic regression is used to model the likelihood of a binary outcome. It is frequently employed when estimating the likelihood that a given incident falls under one of several categories. A key idea in statistics is hypothesis testing, which is used to draw conclusions about populations from sample data.
Understanding these principles, which are basic to statistics and data analysis, will greatly improve your capacity to make sensible judgments and derive conclusions from data.
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