Part 1: Correlation Analysis In this project, you will use MS Excel to perform correlation analysis on the HR analytics dataset. Itis multivariate data, and for this part of the assignment, we will use Monthly Income as the dependent variable. 1. Find out how employee's Monthly Income is correlated to: Age Education, Distance from Home Job Involvement Performance Rating Total Working Years 2. Create tables and scatterplots and paste all of them here. 3. Explain your findings in detail (the strength of correlation of each variable with monthly income and the direction of linear relationship - positive or negative correlation). 4. Based on your learnings from this activity, explain variables that show a significant linear relationship with employee attrition. The strength of correlation can be interpreted as: Perfect: near + 1 High degree: between * 0.50 and + 1, strong correlation. Moderate degree: between + 0.30 and 0.49, medium correlation. Low degree: below + .29, small correlation. Mo correlation: zero Part 2: Predictive Analytics - Simple Linear Regression You will perform simple linear regression analysis on the HR analytics dataset. Use MS Excel for all these tasks. a. Dependent variable: Monthly Income b. Independent variable: Age Develop hypothesis Create scatterplots to show the relationship and write down the regression equation. Test the hypothesis (is the value of p higher or less than alpha? What about critical value testing?) Repeat the steps above using Total Working Years as the dependent variable and then using Education as the independent variable. Which model is the strongest model that explains the strongest linear relationship between the dependent and independent variable? Include all the tables, graphics (scatterplots), and a summary of your findings in this document