Probability & Statistical Foundations
Random variables, probability distributions, expectation, variance, sampling and asymptotic concepts.
E(X), Var(X)
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Discover how statistical theory and econometric methods transform economic observations into interpretable evidence. Learn regression, estimation, hypothesis testing, time-series analysis and the assumptions behind reliable inference.
Econometrics combines economic theory, mathematics and statistical methods to estimate relationships, evaluate hypotheses and investigate economic questions using data.
A regression coefficient is not automatically a causal effect. Credible interpretation requires attention to the research design, identifying assumptions, omitted variables, measurement, sampling uncertainty and model specification.
At Mind Guru Gyan Academy, the objective is to connect equations and statistical output with economic reasoning so that results can be interpreted responsibly.
Explore the statistical foundations, regression methods, diagnostic tools and advanced empirical models used in modern economic research.
Random variables, probability distributions, expectation, variance, sampling and asymptotic concepts.
OLS estimation, regression coefficients, fitted values, residuals and ceteris paribus interpretation.
Properties of estimators, standard errors, confidence intervals, t-tests and F-tests.
Error variance, serial dependence, diagnostic tests, robust standard errors and remedies.
Correlated regressors, omitted-variable bias, functional form and specification errors.
Binary indicators, reference categories, interactions and interpreting categorical effects.
Stationarity, autocorrelation, unit roots, cointegration and dynamic models.
Fixed and random effects, difference-in-differences, instrumental variables and identification.
Research questions, data preparation, reproducible analysis, interpretation and reporting.
Explore a simulated relationship between education and earnings. Change the underlying slope and the strength of the simulated noise to see how the fitted regression responds.
All observations are synthetic and generated in the browser. They do not represent actual wage data or an empirical study.
Adjust the settings or generate a new synthetic sample.
Econometric analysis involves more than estimating coefficients. Model assumptions, uncertainty and research design determine what the evidence can legitimately support.
Investigate heteroskedasticity, autocorrelation, residual patterns and possible model misspecification.
Evaluate hypotheses using appropriate standard errors, confidence intervals, test statistics and p-values.
Assess endogeneity, confounding and identification assumptions before interpreting estimates causally.
Follow a coherent sequence that connects probability, estimation, diagnostics and applied economic analysis.
Probability, distributions, sampling and estimation.
OLS, assumptions, standard errors and hypothesis tests.
Robust inference, specification, time series and panel data.
Design empirical questions, analyse evidence and report limitations.
Mark each area when you have studied it and can explain its assumptions, methods and economic interpretation.
This checklist is session-only. Student accounts and persistent progress can be added separately.
Learn to estimate relationships, test hypotheses, question assumptions and communicate empirical results with precision.