p.16-17 Introduction This part explains the detailed steps of calculating Little’s test statistic to check for the Missing Completely at Random (MCAR) assumption, as described in the provided example. The test is based on comparing the means of observed and missing data patterns, with the computation of the test statistic $T_L$ and its interpretation.
Jul 19, 2025
p.21 Covariance Derivation The covariance between two random variables $X_1$ and $X_2$ is defined as: $$ \text{Cov}(X_1, X_2) = \mathbb{E}\left[(X_1 - \mathbb{E}(X_1))(X_2 - \mathbb{E}(X_2))\right] $$Step 1: Expanding the expression First, expand the product inside the expectation:
Jul 2, 2025
Let’s consider a simple linear regression model: $$ y = \beta_0 + \beta_1 x + \varepsilon $$Where: $y$: outcome variable $x$: predictor $\varepsilon$: error term (disturbance or residual) $\beta_0, \beta_1$: parameters to estimate What OLS Assumes For the OLS (Ordinary Least Squares) estimator to be unbiased, one of the Gauss-Markov assumptions is:
Jun 28, 2025