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The attached data from the Dutchess County, NY, Recorder of Deeds office provides data on homes sold in 2006. Your task is to analyze this data and determine the dependency of Selling Price on three variables: Living Area, Number of Bedrooms and Number of Bathrooms. The dependent variable is Selling Price and the other three are independent variables. Analyze each one by plotting and determining the least squares regression line, coefficient of variation and correlation coefficient for each.1. Plot the data (Selling Price vs Living Area, Selling Price vs Number of Bedrooms, and Selling Price vs Number of Bathrooms) as a set of scatter diagrams. Be sure to include title, labels and appropriate units for the axes. Use a software program like Excel to produce the charts. Include the least squares regression equation and the coefficient of variation and correlation coefficient for each plotIf there seem to be correlations, do they appear to be linear or non-linear? Do they appear to be strong correlations or weak? Are they positive or negative? Describe from a mathematical (statistical) point of view.3. Determine the least squares trend line for each graph, including coefficient of determination and correlation coefficient. Excel will determine a trend line and coefficient of determination (“Add Trend Line” option), but you can use any tool for this that you wish, including manual calculation or Excel’s Data Analysis Tools. You will have to compute the correlation coefficient from the coefficient of determination. Make sure the trend line, least squares equation and both coefficients are printed on each graph.4. Based on the computed coefficients (coefficient of determination and correlation coefficient), are the correlations strong, weak or nonexistent?5. Give at least TWO examples of confounding variables that might skew some or all of this data.6. Considering Regression Analysis in general:•
