It is important to look at data in a graphical form. Patterns are theessence of data exploration, and the eye’s ability to discern forms andpatterns makes visual display integral to the process. The visualdisplay of quantitative information can help us see connections andrelationships in the data, which are oftentimes difficult to detect intables of numbers. We should look at data in a graphical form, and notrely solely on computational or statistical metrics. In this discussion, we will explore graphs in linear regression. Ourdata are taken from an article by Frank Anscombe in a 1973 article in The American Statistician, which discusses scatterplots in relation to regression analyses.First, download the dataset MHA610_Week 5_Discussion_Regression_Data.xls.This is a simple Excel workbook, with data on one sheet. There areeight columns of data, with headings X1, Y1, X2, Y2, X3, Y3, X4, Y4.Import the data into Statdisk using the MHA610_Week 5_Discussion_Regression_Data.CSV file, and perform the following analyses.Calculate the regressions of Y1 on X1, Y2 on X2, Y3 on X3, andY4 on X4, and compare the results (summary statistics). Explain what, ifanything, you find unusual about these results.Plot each set of data, along with the fitted regression line.Describe what the graphs tell you about the relationships between theX’s and the Y’s.Explain what lessons you draw from this exercise.Place the summary statistics and the plots in a separate Word documentand attach that document to your initial post. Address the questions inthe body of your initial discussion post. mha610_week_5_discussion_regression_data.xls