Simple Linear Regression: It deploys a sole independent variable to predict a dependent variable by providing the most suitable linear correlation. There are two types of linear regression mainly, that are Linear Regression is the technique that is used to predict a target variable by providing the best linear relationship among the dependent and independent variable where best fit indicates the sum of all the distances amidst the shape and actual observations at each data point is as minimum as achievable. When a data expert implements different statistical models to the data to examine, understand and decipher the information more imperatively, through this approach, the data expert identifies connections among variables, makes prophecies, and visualizes data that can be used and leveraged even by any non-analyst.ĥ Statistical Techniques for Data Analysis Some common types of statistical models are Correlation Test, Regression model, Analysis of Variance, Analysis of Covariance, Chi-square, etc. And since there are different types of variables, correspondingly, different statistical models are there. In other words, for recognizing relationships between two or more variables, statistical models exist. "Modern statisticians are familiar with the notion that any finite body of data contains only a limited amount of information on any point under examination that this limit is set by the nature of the data themselves, and cannot be increased by any amount of ingenuity expended in their statistical examination: that the statistician's task, in fact, is limited to the extraction of the whole of the available information on any particular issue." - R. Therefore, a statistical model could be an equation or a visual portrayal of the information on the basis of thorough research conducted over the years. The statistical model can be expressed as a combination of results depending on consolidated data and population's understanding that are deployed to foretell information in a generalized form.
“Statistical Modelling is simply the method of implementing statistical analysis to a dataset where a Statistical Model is a mathematical representation of observed data.”Īlso Read | Types of Statistical Analysis And here, the statistical model is the mathematical expression that is being deployed. In the more modest words, Statistical Modeling is an interpreted, mathematically-prescribed method to approximate truth which is being generated by the data and for making forecasts out of this approximation.įor example, depicting a quantity through an average and a standard deviation is the simple form of statistical modelling. “Primarily, the main purpose of statistics is to explain and anticipate information.”
In order to understand statistics properly, it demands one of the most important aspects as understanding statistical modelling. Forwarding the discussion a step ahead, we will discuss the concept of statistical modelling, some general statistical terms, common statistical techniques, and a brief note on statistical modelling vs machine learning. As Josh Wills put it, “A data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”įrom the previous blog, you must have acquired a brief note about Statistical Data Analysis.
0 kommentar(er)
