Category: Learning modules
Bootstrap and jackknife are superficially similar statistical techniques that involve re-sampling the data. They are nonparametric and specific resampling techniques that can estimate standard errors and confidence intervals of a population parameter.
estimation in supervised learning, Supervised learningAutoregressive Integrated Moving Average (ARIMA) is popularly known as Box-Jenkins method. The emphasis of this method is on analyzing the probabilistic or stochastic properties of a single time series. Unlike regression models where Y is explained by X1 X2….XN regressor (like the introductory case where GDP is explained by GFC and PFC), ARIMA allows Y (GDP) to be explained by its own past or lagged values.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometricsNeural network, popularly known as Artificial Neural Network (ANN) is an information processing system with a large number of nodes and connections as part of a structure which helps in processing complex information.
regressions in supervised learning, Supervised learning, trend analysisUntil recently, Karl Pearson Correlation analysis was one of the most popular methods to measure linear association between two or more than two variables in a data set. For example, establishing the Karl Pearson Correlation between X variable and Y variable, where both variables belong to a single data set. Canonical Correlation Analysis (CCA), on the other hand, helps measure the correlation among variables which are in different datasets.
correlation, correlation in supervised learning, Supervised learningA decision tree is a graphical representation of possible solutions to a problem based on given conditions. It is called a tree because diagrammatically it starts with a single box (target variable) and ends up in numerous branches and roots (numerous solutions).
classification in supervised learning, Supervised learning, trend discoveryThis article explains the data entry methodology, for performing meta-analysis on the outcomes of studies having a single group. For this purpose, the single group of patients who were administered drug treatment will be taken into consideration.
CMA, CMA analysisThus to assess the model, a common practice in data science is to iterate over various models and select the most appropriate model. In other words it is important to test the same model with different values of parameters.This is called the cross validation method.
estimation in supervised learning, Supervised learningAs seen the relative weighting of studies in the two models are quite different. The random effects model takes into account both the interstudy variance and sample size, resulting in well-distributed weight.
CMA, CMA analysis