This is an example of a project that was assigned to me last year.

I'm going over the same ground again, and I'd appreciate it if someone could help me comprehend the question and provide an explanation.

Multiple Linear Regression Modelling (consider a = 5%) System Cost is an important measure for RACV Solar, as it represents revenue for the company. Build a multiple regression model to predict System Cost. Your model should provide insights into which factors have a significant influence on our revenues, as well as the ability to predict System Cost for various scenarios. For this analysis, you will need to build a multiple regression model using System Cost as the dependent variable. All other variables in the RACV Solar data-set should be included in the model, except Satisfaction Type (Le. exclude Satisfaction Type from your regression model). (b) Using the RACV Solar data set (which now includes transformed dummy variables) as your reference, complete the following steps: iv. Correlation in the section marked "Correlation Table\" [below the Conclusion section on the "Correlation" worksheet) generate a correlation table. Use the \"Correlation\" option in Excel's Data Analysis ToolPak. 0n the correlation table, identify and clearly indicate the Independent Variables which are {virtually} uncorrelated with the Dependent Variable [i.e., all IVs which have a correlation coefficient with the DV of between -D.05 and 0.05). These IVs are to be removed from the model prior to running the first iteration of the regression model. Complete the Uncorrelated Independent Variables summary table which is in the Conclusion section of the Correlation worksheet - a summary of which Independent Variables have been eliminated from the regression model due to being [virtually] uncorrelated with System Cost {DV}. Multicollinearity - review the correlation table for instances of multi-collinearity between Independent Variables IIVI. In cases of multicollinearity, identify and clearly indicate the IVs with the weakest correlation with the Dependent Variable. Vi. These IVs are to be removed from the model prior to running the first iteration of the regression model. Complete the Multi-Collinearity summary table which is in the Conclusion section of the Correlation worksheet - a summary of which Independent Variables have been eliminated from the regression model due to multi-collinearity. Scatter diagrams - in the section marked \"Scatter Diagrams\" {below the Conclusion section on the "Correlation\" worksheet) generate three scatter diagrams, for: 0 System Cost (Dependent Variable, DV) and the Independent Variable (IV) which has the highest correlation with the DV. Format the diagram, and include a linear trendline, - System Cost (DV) and the Independent Variable (IV) which has the lowest [i.e., most negative) correlation with the DV. Format the diagram, and include a linear trendline, and 0 System Cost (DV) and one of the Independent Variables [IV) which are (virtually) uncorrelated with the DV (i.e., correlation coefcient of between {LOB and 0.05). Format the diagram, and include a linear trendline