Each row represents an experimental “run” (in this case, averaged sample of mango wine). Column A shows the run number; Column B shows the percentage of ethanol; Column C shows the amount of glycerol (in g/L); Column D shows the amount of acid (in g/L); Column E shows the temperature of the run (in °C); and Column F shows the pH of the run. Y represents Ethanol of mango wine
Each row represents an experimental “run” (in this case, averaged sample of mango wine). Column A shows the run number; Column B shows the percentage of ethanol; Column C shows the amount of glycerol (in g/L); Column D shows the amount of acid (in g/L); Column E shows the temperature of the run (in °C); and Column F shows the pH of the run. Y represents Ethanol of mango wine.
1. Create a scatterplot of the ethanol data for all 20 runs
a. Draw a graphical representation of the model that you chose to use.
b. Comparing the model to your data, evaluate its fit in one or two sentences. (Not sure what this question is getting at. I've provided all the information given)
c. Replace the generic Yi = β0 + ε with the information you have here.
Please help. I have no idea what this even means or where to start.
HERE is the attached dataset to start with;
a b c d e f
| Run | Ethanol | Glycerol | Acidity | Temp | pH |
| 1 | 4.8 | 3.5 | 0.84 | 24 | 3.8 |
| 2 | 9.6 | 7.3 | 0.27 | 24 | 3.8 |
| 3 | 10.2 | 7.2 | 0.27 | 24 | 3.8 |
| 4 | 8.5 | 5.1 | 0.55 | 24 | 3.8 |
| 5 | 7.3 | 3.2 | 0.68 | 24 | 3.1272 |
| 6 | 4.8 | 1.2 | 1.24 | 30 | 4.2 |
| 7 | 7.9 | 4.4 | 0.65 | 18 | 3.4 |
| 8 | 6.7 | 4.3 | 0.65 | 30 | 3.4 |
| 9 | 9.8 | 6.9 | 0.47 | 24 | 3.8 |
| 10 | 10.2 | 7.2 | 0.32 | 24 | 3.8 |
| 11 | 9.8 | 5.8 | 0.51 | 24 | 3.8 |
| 12 | 10.2 | 7.5 | 0.28 | 24 | 3.8 |
| 13 | 8.2 | 5.1 | 0.49 | 18 | 4.2 |
| 14 | 7.1 | 3.9 | 0.6 | 18 | 3.4 |
| 15 | 8.2 | 4.8 | 0.46 | 13.908 | 3.8 |
| 16 | 5.6 | 3.3 | 0.68 | 18 | 4.2 |
| 17 | 7.5 | 4.5 | 0.58 | 30 | 4.2 |
| 18 | 6.7 | 3.8 | 0.75 | 24 | 4.4728 |
| 19 | 5.5 | 3.8 | 0.45 | 30 | 3.4 |
| 20 | 6.5 | 4 | 0.79 | 34.092 | 3.8 |
THANK YOU so much for your help.
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