Top 5 statistical data analysis techniques that a data scientist should know
by Satavisa Pati
July 14, 2021
The best statistical data analysis techniques for a data scientist to know.
Statistical data analysis is a procedure of performing various statistical operations. It’s a kind of quantitative research, which seeks to quantify the data, and generally, applies some form of statistical analysis. Quantitative data involves descriptive data, such as survey data and observational data. Analyzing statistical data usually involves some form of statistical tools, which a layman cannot use without having any statistical knowledge. Here are the best techniques for analyzing statistical data
Linear regression is the technique used to predict a target variable by providing the best linear relationship between the dependent and independent variables where the best fit indicates the sum of all distances to the middle of the shape and the actual observations at each data point is as minimal as it is achievable. There are mainly two types of linear regression, namely;
Simple linear regression: It deploys a single independent variable to predict a dependent variable by providing the most appropriate linear correlation. To understand simple linear regression in detail, click on the link.
Multiple linear regression: It takes more than one independent variable to predict the dependent variable by providing the most appropriate linear relationship. There is much more to explore on Multiple Linear Regression, learn with this guide.
Being a data mining technique, classification allows specific categories of a collection of data to make more meticulous predictions and analysis. The types of classification techniques are;
Logistic regression: A regression analysis technique to be performed when the dependent variable is dichotomous or binary. It is a predictive analysis used to explain the data and the connection between a binary dependent variable and other nominal independent variables.
Discriminant analysis: In this analysis, two or more groups (populations) are called a priori and the new set of observations is grouped into one of the known groups based on the calculated characteristics. It displays the distribution of the “X” predictors distinctly in each of the response classes and uses Bayes’ theorem to present these classes in terms of estimates of the probability of the response class, given the value of “X”.
The approach of extracting chunks of repeated samples from the actual data samples is known as resampling, which is a nonparametric method of statistical inference. Moreover, based on the original data, it produces a new sampling distribution and uses experimental methods instead of analytical methods to generate a specific sampling distribution. To understand the resampling method, the techniques below should also include;
Priming: From the validation of a predictive model and its performance, to overall methods, from the estimation of the bias to the variance of the model, the Bootstrapping technique is used under these conditions. It works by sampling with replacement from the actual data and considers “unselected” data points as test samples.
Cross-validation: This technique is used to validate the performance of the model and can be performed by dividing the training data into K parts. When performing cross-validation, part K-1 can be considered as a training ser and the remaining part made up acts as a test set. Up to K times, the process is repeated, then the average of the K scores is accepted as the performance estimate.
Tree-based methods are the most commonly used techniques for regression and classification problems. They incorporate the superposition or detachment of the predictor space in terms of several manageable sections and are also known as decision tree methods because the particular division rules are applied to fragment the predictor space that can be examined. in a tree.
Bagging: It decreases the variance of the prediction by producing additional data for training from an actual data set by implementing “combinations with repetitions” to create multiple steps of the size equivalent to that of the data from origin. In reality, the predictive strength of the model cannot be improved by increasing the size of the training set, but the variance can be reduced by tightly fitting the prediction to an expected outcome.
Booster: This approach is used to calculate the result through various models and after that the average of the result is calculated by applying a weighted average approach. By integrating the advantages and disadvantages of this approach and a varied weighting formula, appropriate predictive efficiency can be achieved for an extended chain of input data.
Unsupervised learning techniques come into play and can be applied when groups or categories across the data are not known. Clustering and association rules are common approaches (examples) of unsupervised learning in which various sets of data are assembled into groups (categories) of strictly related elements.
Analysis of the main components: PCA supports the generation of a low dimensional illustration of the dataset by recognizing a linear set of the mixture of mutually uncorrelated characteristics having maximum variance. In addition, it helps to acquire latent interaction between variables in an unsupervised setting.
K-Means group: Based on the distance from the cluster to the centroid, it separates the data into k dissimilar clusters.
Hierarchical classification: By developing a tree structure of clusters, hierarchical clustering helps to develop a hierarchy of clusters at several levels.
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