Explain on the meaning of each of the followingiBest linear unbiased estimatorii R-squared and Adjusted R-squarediii Cross-sectional data and time-series data

i. Best linear unbiased estimator (BLUE) refers to a statistical estimator that is both linear and unbiased, and has the minimum variance among all unbiased linear estimators. In other words, it is a method of estimating a population parameter that is both unbiased (on average, the estimator produces estimates that are equal to the true parameter value) and efficient (the estimator has the smallest variance compared to other unbiased estimators).

ii. R-squared and Adjusted R-squared are statistical measures used to evaluate the goodness-of-fit of a regression model. R-squared represents the proportion of the variance in the dependent variable that can be explained by the independent variables in the model. It ranges from 0 to 1, where a value closer to 1 indicates a better fit.

Adjusted R-squared is an extension of R-squared that accounts for the number of independent variables in the model. It penalizes the addition of unnecessary variables that do not significantly contribute to explaining the dependent variable. Adjusted R-squared will always be lower or equal to R-squared, and it is generally preferred when comparing models with different numbers of independent variables.

iii. Cross-sectional data refers to data collected at a particular point in time, typically from different individuals, entities, or units. It provides information on multiple variables for a single point in time. For example, a survey conducted on a specific day in which individuals' income, age, and education level are collected is an example of cross-sectional data.

Time-series data, on the other hand, refers to data collected over a series of regular time intervals. It tracks the values of a variable over time, allowing for the analysis of trends, patterns, and seasonality. Examples of time-series data include monthly sales figures, daily stock prices, or annual GDP growth rates. Time-series data is often used to make predictions or forecast future values based on historical patterns