Reference no: EM132385788
Business Data Analysis Excel Project - Climate Change
Climate change is an issue of global concern. Although climate scientists have collected a large amount of data unambiguously showing rising global temperatures, there are still people (mostly with economic interests in coal and fossil fuels) who deny that the climate is changing. In this project you will work with data from NASA to draw conclusions for yourself.
The accompanying data file contains global temperature anomalies, as well as the temperature anomalies for the Northern and the Southern hemispheres separately, all from year 1880 until 2018. Temperature anomalies indicate how much warmer or colder it is than normal for a particular place and time. For the accompanying data, \normal" always means the average over the 30-year period 1951-1980 for that place and time of year.
Starting from the data file posted on Omnivox, implement the following steps using Excel:
1. Insert a scatter plot of the global temperature anomalies against the year. On the scatter plot insert the equation of the linear regression line and the coefficient of determination. To ensure the accuracy of subsequent computations, the values in the regression line equation should be displayed with precision of 10 decimals.
2. Citing the coefficient of determination, comment on the quality of the model.
3. Interpret the slope of the regression line.
4. Using the regression line make a prediction for 2018 and compare to the actual value by computing the residual.
5. Make a prediction for 2050.
6. Make a prediction for 2100.
7. Insert scatter plots for the temperature anomalies for the Northern and the Southern hemi-spheres including the regression lines.
8. Comparing the two slopes, comment on which hemisphere warms up faster.
9. Insert another scatter plot of the global temperature anomalies against the year. On this scatter plot, insert the equation of a quadratic regression function and the coefficient of de-termination. To ensure the accuracy of subsequent computations, the values in the regression equation should be displayed with precision of 10 decimals.
10. Compare the quality of the quadratic model against the quality of the linear model.
11. Using the quadratic function make a prediction for 2018 and compare with the actual value by computing the residual.
12. Make a prediction for 2050 using the quadratic regression model.
13. Make a prediction for 2100 using the quadratic regression model.
Using your computations from the Excel file write a report in Word/PDF. The report should include: title, your name, abstract, all the models you have built with the relevant scatter plots and equations, sentences on the quality of the models and interpretations of the results. Your report should also include conclusions and recommendations for action (e.g. why you would recommend to people around you to replace their gasoline-powered car with an electric vehicle).
Attachment:- Business Data Analysis Excel Project.rar