Save The Spreadsheet To Your Computer With Your Mouse Highli

Save The Spreadsheet To Your Computerwith Your Mouse Highlight All O

Save the spreadsheet to your computer. With your mouse, highlight all of the data on the spreadsheet in columns A and B. In the tabs at the top of the page, click Insert. In the Insert ribbon, in the Charts section, click Scatter. Be sure to select the option where it will just plot dots, it will be called Scatter with only Markers. If you do this right, then you'll see a chart on the page. Now, on the chart, right-click on one of the data points (dots). Just pick a dot somewhere near the middle of the distribution. Select Add Trendline from the drop-down menu that appears when you right-click on a dot. A new menu will appear. Select Linear, select Automatic, and click the boxes next to Display Equation on chart and Display r-squared value on chart. Click Close. Now, you should see a line drawn through the dots. It will roughly cut through the middle of the dot distribution. You'll also see the linear regression equation and r2 value displayed next to the line. To see an example spreadsheet containing a completed analysis click here. Now that you’ve completed your analysis and determined the linear regression formula and r2, it is now time to report on the results of your study and examine your findings. In a Microsoft Word document, respond to the following: Report the sample you selected and the question that was explored in the study. Report the r2 linear correlation coefficient and the linear regression equation produced in the Excel spreadsheet. What would be the value of Pearson’s r (simply the square root of r2)? Would Pearson’s r be positive or negative? What does this imply about the relationship between the factors in this study? What is the implication of any correlation found between the variables in the study you picked? Does this correlation imply a causal relationship? Explain. Are there other variables that you think should have been examined that would have improved this study or helped to pinpoint what factors are causal? For this assignment, you will submit a spreadsheet and a report. The spreadsheet will be the Microsoft Excel file containing your scatterplot and analysis. Name your Microsoft Excel file as follows: LastnameFirstInitial_M3_A2.xls. The report will be a Microsoft Word document in which you will address all of the questions in this assignment in the form of a narrative. Name your Microsoft Word document as follows: LastnameFirstInitial_M3_A2.docx. Submit both files to the M3: Assignment 2 Dropbox.

Paper For Above instruction

The task involves conducting a simple linear regression analysis using Microsoft Excel to explore the relationship between two variables, followed by a comprehensive written report. The process begins with selecting and highlighting data in columns A and B of an Excel spreadsheet, then creating a scatterplot chart with markers only to visualize the data distribution. After generating the scatterplot, a trendline is added by right-clicking a data point, choosing 'Add Trendline,' and selecting the 'Linear' option. Display options are selected to include the regression equation and the r-squared value, providing key statistical measures of the relationship.

The regression analysis yields important details: the linear regression equation, which predicts one variable based on the other, and the r-squared (r2) value, indicating the proportion of variance in the dependent variable explained by the independent variable. From the r2 value, Pearson’s correlation coefficient (r) can be derived by taking the square root of r2, with the sign (positive or negative) determined based on the slope of the regression line and the expected relationship between the variables. A positive Pearson’s r suggests a direct relationship, whereas a negative r indicates an inverse relationship; both imply correlation but do not confirm causation.

In the accompanying report, the researcher must describe the sample and research question addressed. They should interpret the regression equation, report and explain the r2 and Pearson’s r, and discuss the direction of the relationship indicated by r. The report should also analyze the implications of the correlation: whether it suggests causality or merely an association. It must acknowledge that correlation does not imply causation and consider additional variables that might improve the study’s design to clarify causal factors.

Overall, this exercise demonstrates how regression analysis can quantify relationships between variables, revealing correlations that are essential for understanding underlying patterns. However, the report underscores the importance of cautious interpretation, recognizing that correlation alone cannot establish cause-and-effect. Suggestions for further research or inclusion of extra variables highlight the ongoing nature of scientific inquiry.

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