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This assignment is due Week 4: Sunday by 11:59 pm ET.
You now know the difference between correlation and causation. Although there are several different ways to make predictions, a regression analysis is one way to do so. The video above explains how to do a simple correlation, regression, and scatterplot in Excel.
Remember, you can always visit your Lifeline for more help. For this assignment, imagine you are looking for a link between soda consumption and weight. Here is your data set:
| Amount of Soda Consumed and Individual’s Weight | ||
| Participant | Number of 12 ounce sodas consumed per day | Weight |
| 1 | 5 | 125 |
| 2 | 10 | 180 |
| 3 | 0 | 110 |
| 4 | 2 | 110 |
| 5 | 5 | 130 |
| 6 | 8 | 205 |
| 7 | 1 | 105 |
| 8 | 3 | 130 |
| 9 | 15 | 200 |
ASSIGNMENT INSTRUCTIONS
Following the instructions in the video, enter your data set on an Excel worksheet. Be sure and use the labels provided in the data set. Then:
- Perform a correlation and regression on the data in Excel.
- Create a scatterplot with a regression line for your data using Excel
- In a separate Word document, discuss your findings (3-4 paragraphs) as if you were explaining to an individual the impact of soda consumption on weight. Is there a significant relationship between soda consumption and weight? Explain how you know this. Using your regression, predict how a person’s soda consumption will impact their weight (how much will drinking additional sodas increase an individual’s weight based upon your findings for this specific data set).
2.4 Excel and Correlation and Regression Rubric
| M2.4 Excel and Correlation and Regression Rubric | ||
| Criteria | Ratings | Pts |
| This criterion is linked to a Learning OutcomeCorrelation | 20 pts Exemplary Your correlation has been run accurately for the data set. Output statistics are clearly identified. 10 pts Progressing Your correlation has been run for the for the data set. Output statistics are identified. There are some errors. 0 pts Not addressed or Not submitted Your correlation and output statistics are inaccurate or missing. | 20 pts |
| This criterion is linked to a Learning OutcomeRegression and Scatterplot | 20 pts Exemplary Your regression has been run accurately for the data set. Scatter plot is present, accurate and includes regression line. 10 pts Progressing Your regression has been run for the for the data set, scatterplot is present, but your regression line is missing. Output statistics are identified. There are some errors. 0 pts Not addressed or Not submitted Your regression, scatterplot, and regression line are inaccurate or missing. | 20 pts |
| This criterion is linked to a Learning OutcomeExplanation of Statistics | 50 pts Exemplary You accurately describe results in common terminology and detail what the correlation, regression and scatterplot tell the reader about the data sets. 42 pts Proficient You describe results in common terminology and detail what your correlation, regression and scatterplot tell the reader about the data set. Minor details need further development. 34 pts Progressing You provide a general description of what your correlation, regression and scatterplot tell the reader about the data set. Some terminology is too technical. Your description needs more information for the reader to understand the data sets. 26 pts Incomplete Your description of what your correlation, regression and scatterplot tell the reader is unclear, too technical, and/or inaccurate. 0 pts Not addressed or Not submitted Does not address this criterion in the assignment. Did not submit assignment. Does not adhere to academic honesty policy. | 50 pts |
| This criterion is linked to a Learning OutcomeDue Date | 10 pts Exemplary Submitted by due date. 8 pts Proficient Submitted one day after due date. 6 pts Progressing Submitted two days after due date. 4 pts Incomplete Submitted three days after due date. 0 pts Not addressed or Not submitted Submitted four or more days after due date. | 10 pts |
| Total Points: 100 |