Luminary Learner

 Which regression model should you use that depends on the character of the dependent variable. In economics, the choice of regression model is driven by the specific nature of the economic decision or outcome being measured. Here are the same models:  1. Linear Regression Economic DV:  Continuous, unbounded, often in logs (e.g., log wages, log prices). Example (Labor): Predicting an individual’s  log hourly wage  (USD) based on years of education, experience, and gender. Why linear?  Wages are continuous and roughly normal after log transformation. Linear regression gives the familiar Mincer equation. 2. Tobit Regression Economic DV:  Continuous but  corner solution  (many observations at a boundary, usually zero). Example (Consumer demand): Household  expenditure on alcohol  per month. Many households spend zero (abstainers), but among spenders, the amount is continuous. Why not linear?  Linear would predict negative spending...

Tutorial on Quantitative Analysis in STATA - Part 6 from where you can learn how to create "Log File" and "Do File" for saving all the commands in Stata and executing all the commands at a time in Stata

Tutorial on how to create automatic output table in word file from the Quantitative Panel Data Analysis using Stata

Tutorial on Quantitative Analysis (Panel Data Analysis) in STATA Part 4, Fixed Effect Model, Random Effect Model, Hausman specification Test How to apply Fixed Effect Model, How to apply Random Effect Model, How to apply Hausman specification Test, Fixed Effect vs Random Effect, How to store the Fixed Effect, How to store the random effect, How to choose between Fixed Effect and Random Effect

This tutorial covers the following issues:  How to test Multicollinearity, How to test Heteroscedasticity, How to control Heteroscedasticity issue, How to control Autocorrelation issue

Tutorial on Quantitative Data Analysis in Stata Part 2 (Data Normality Test, Generate New Variable, Ensuring Normal Distribution)

This tutorial covers the following issues:  How to import or input data in STATA, How to save data in STATA, How to create “Log File” in STATA, How to “declare” the panel data in STATA, How to see the “types of data” structure in STATA, How to get the “Descriptive Statistics” in STATA, How to get the “Correlation Matrix” of the variables in STATA, Quantitative Research, Quantitative Analysis, Pairwise Correlation, Mean Median Standard Deviation Minimum Maximum,

In Normality Test, the hypothesises are: H0: The data is normally distributed. H1: The data is not normally distributed. If low p-value (less than 0.05 ): Rejecting the null hypothesis = Data significantly deviates from a normal distribution = Data does not follow a normal distribution. If high p-value (greater than 0.05): Do not reject the null hypothesis = Data to be normally distributed . Comment: If the data is not normally distributed, parameters, such as means, variances, and regression coefficients, may be biased or inaccurate , affecting the validity of the conclusions. From this tutorial, you will learn how to test the normality of data . It also explains how to transform non-normal data into normal data if the normality test indicates that the data is not normally distributed.

Ontology ( Belief ) Epistemology ( How knowledge is acquired ) Research Paradigm Research Approach (How theories are developed and tested) Methodology (Process of data collection and analysis) Realism ( Objective Reality) →→→→→→→   Positivism (Knowledge is gained through measurement and observation) →→→→ Positivist →→→→ Deductive (Testing Hypothesis) →→→→ Quantitative (Surveys, Experiments) Relativism ( Subjective Reality) →→→→→→→ Constructivism (Knowledge is constructed through experiences ) →→→ Interpretivist/ Constructivist →→→→ Inductive (Building Theory) →→→→→ Qualitative (Interviews, Case Studies) Combination of both Objective and Subjective→→→→→→ Knowledge is Practical→→→→→→ Pragmatism →→ Abductive (Flexible, Iterative) →→→→ Mixed-Method...

Selecting Appropriate Test in Quantitative Analysis A. Tests for Comparing Means (Differences) Independent Samples T-Test •          Purpose: Compares the means of two independent groups . •          Data: Continuous outcome, 2 groups.   (If normality assumption met). Paired T-Test •          Purpose: Compares the means of two related groups ( before and after ). •          Data: Continuous outcome, same group measured twice.   (If data Is normally distributed). ANOVA (Analysis of Variance) •          Purpose: Compares means across three or more groups. •          Data: Continuous outcome, multiple groups. ( Normal data) •          One-Way ANOVA: Compares one independen...