CERTIFICATES

This module explains about the various types of data, levels of measurement, categorical variables, visualization techniques, numerical variables, histogram charts, cross tables and scatter plots.

This module provides information about the concepts of main measures of central tendency are mean, median and mode, skewness, variance, standard deviation and coefficient of variation, covariance, and correlation coefficient.

This module provides information about the types of data; levels of measurement; graphs and tables for categorical and numerical variables, and relationship between variables; measures of central tendency, asymmetry, variability, and relationship between variables.

This module explains about the Inferential statistics, what is a distribution, normal distribution, standard normal distribution, central limit theorem and the standard error.

This module explains about the estimators and estimates in decision making, confidence intervals used to calculate confidence intervals within a population with an unknown variance. A margin of error is important in Statistics.

This module explains about the calculating confidence intervals for two means with dependent samples, Confidence intervals, two means, dependent samples.

This module explains about the null and alternative hypotheses and the differences, establishing a rejection region and a significance level, rejection region and significance level and Type I error vs Type II error.

This module makes you to learn about the test for the mean, population variance known, p-value, test for the mean, dependent samples, and test for the population variance unknown.

This module explains about the regression analysis, Correlation and causation, linear regression model made easy, linear regression model, difference between correlation and regression, Correlation vs regression and geometrical representation of the linear regression model.

This module makes you to learn about the decomposing the linear regression model - understanding its nuts and bolts, Decomposition, R-squared, ordinary least squares setting and its practical applications, Studying regression tables, Regression tables. Exercise, multiple linear regression model and adjusted R-squared.

This module provides information about the OLS assumptions require linearity, no endogeneity, normality and homoscedasticity, no autocorrelation, and no multicollinearity.