It is important to look at data in a graphical form. Patterns are the essence of data exploration, and the eye’s ability to discern forms and patterns makes visual display integral to the process. The visual display of quantitative information can help us see connections and relationships in the data, which are oftentimes difficult to detect in tables of numbers. We should look at data in a graphical form, and not rely solely on computational or statistical metrics.

 In this discussion, we will explore graphs in linear regression. Our data 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 are eight 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, and Y4 on X4, and compare the results (summary statistics). Explain what, if anything, 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 the X’s and the Y’s.

•Explain what lessons you draw from this exercise.

Place the summary statistics and the plots in a separate Word document and attach that document to your initial post. Address the questions in the body of your initial discussion post.

Guided Response: Respond to at least two of your peers by Day 7, 11:59PM. Do your summary statistics and plots agree with those of your colleague? If not, how and why do they differ? Did your colleague’s conclusions broaden your perspective on linear regression? All initial and peer postings should be at least 250-500 words in APA format supported by scholarly sources.