Statistics

ARIMA (AutoRegressive Integrated Moving Average) is a popular time series forecasting method that models a time series as a combination of autoregressive (AR) and moving average (MA) components, with the added ability to handle non-stationary time series through differencing. Let’s consider an example of forecasting the monthly sales of a particular product over the past […]

Suppose we want to determine whether there is a difference in the average test scores of students in three different schools: School A, School B, and School C. To test this hypothesis, we randomly select a sample of students from each school and administer a test. The data we collect is as follows: To perform

Learning statistics is essential for becoming proficient in data science. Here are some guidelines for learning statistics for data science: In summary, learning statistics for data science requires a solid foundation in basic statistical concepts, practical application of these concepts, and an understanding of statistical models. Seeking help when needed and practicing regularly are also

Statistics is essential for data analytics because it provides a framework for collecting, organizing, analyzing, and interpreting data. Without statistical methods, it would be difficult to draw meaningful insights from data and make informed decisions based on those insights. Here are some specific reasons why statistics is important for data analytics: In short, statistics is