This research project focuses on analyzing a dataset consisting of independent and dependent variable values, which are stored in separate files. The analysis is conducted using statistical functions provided in RStudio. The primary objectives of the analysis include determining the number of observations, assessing the fraction of missing data in the independent and dependent variables, and performing complete imputation of the missing values.
To begin the analysis, the independent and dependent variable values from the separate files are merged numerically based on their unique IDs. This step ensures that the data is appropriately sorted and combined for further processing. The presence of missing data, denoted by "NA," is then identified throughout the dataset.
A crucial aspect of the analysis involves developing an algorithm to impute the missing values based on the available independent or dependent variable values. This step enables a comprehensive assessment of the dataset, accounting for the missing data points.
The analysis also provides additional information such as specific confidence intervals and an estimated regression line for the dataset. These insights offer deeper understanding and interpretation of the relationships between the independent and dependent variables.