R Studio Data Analysis & Modeling

I'm in urgent need of a data analyst proficient in using R Studio for a data analysis and modeling assignment. This task involves creating algorithms using Linear Regression models. I already have my specific dataset for the project which is less than 10,000 records. Ideal Skills for the Job: Data Access and download the study dataset: [login to view URL] This dataset is a survey of 2033 residents of Canada was conducted to determine the key factors associated with political engagement. A variety of variables were measured and recorded including some tests they were asked to complete. Below is the data dictionary for the data set. One group of respondents (“Treat”) were given additional education on political matters while the other (“Control”) were not. Assignment Tasks Your task will be to used multiple linear regression to determine the factors which contribution to Political Awareness (variable: Pol). As always, imagine this analysis will be generating a report that will be presented to a client. Q1: Reduce Dimensionality 1.1. Apply the Missing Value Filter to remove appropriate columns of data. 1.2. Apply the Low Variance Filter to remove appropriate columns of data. 1.3 Apply the High Correlation Filter to remove appropriate columns of data. Q2: Variable transform 2.1 any variables that are required to conduct the regression analysis, e.g. categorical variables to dummies. Q3: Outliers 3.1. Create boxplots of all relevant variables (i.e. numeric, non-binary) to determine outliers. 3.2. Comment on any outliers you see and deal with them appropriately. Q4: Exploratory Analysis 4.1. Correlations: Create both numeric and graphical correlations 4.2. Comment on noteworthy correlations you observe. Are these surprising? Do they make sense? Q5: Simple Linear Regression 5.1. Create a simple linear regression model using Pol as the dependent variable and score as the independent. Create a scatter plot of the two variables and overlay the regression line. 5.2. Create a simple linear regression model using Pol as the dependent variable and scr as the independent. Create a scatter plot of the two variables and overlay the regression line. 5.3. Compare the models. Which model is superior? Why? 6: Model Development 6.1. Multivariate linear regression- create two models using two automatic variable selection techniques discussed in class(Full(baseline), Backward). 6.2. For each model interpret and comment on the five main measures (Your commentary should be yours, not simply copied from example): 1. F-Stat 2. R-Squared value 3. Residuals 4. Significant variables 5. Variable Coefficient 6.3. Model evaluation: For both models evaluate the main assumptions of regression and evaluate model performance 6.4. Based on your preceding analysis, recommend which of the models should be used. 7: Professionalism and Clarity Deadline: with in 4 hours