QUESTION
1 answer:
1. For this type of problem you can choose anything you want and the process will still be the same. For the purposes of this answer let us choose coffee as the drink that we are going to analyze.
2. We can find the equation of the line of best fit by inputting the data given in the problem on a calculator/software. For this item I chose to use SPSS. Inputting the data to the software, we get the value of the slope at -22.7 and the y-intercept at 130.3.
EQUATION:
In this equation x is the price and y represents the sales.
3. The slope tells us how many units the sales would change if we were to increase the price by 1 unit. Since our slope is -22.7, this will tell us that we would see a decrease of 22.7 in sales if we were to increase our price by 1 unit.
4. I have attached the graph as an image below. The scatter plot contained all points from the data that has been given. The line of best fit from the equation calculated in question #2 is also shown in the graph.
5. As we can see, the line of best fit hits all the points in the scatterplot. The other points fall closely from the line. Therefore, we can say that the line of best fit is a good model of the data. For added evidence, we can also compute for the Pearson correlation coefficient which gives us -0.998. This indicates a strong negative relationship.
6. For this item we will just need the equation for the line of best fit and the data that has been given in the problem. For the actual sales, we will just refer to the given while we will compute for the predicted sales using the line of best fit.
PRICE ACTUAL SALES PREDICTED SALES
1.5 96 96
1.6 94 94
1.8 91 89
2.0 85 85
2.1 83 83
2.2 79 80
2.5 73 74
2.7 70 69
2.9 64 64
3.0 63 62
2. We can find the equation of the line of best fit by inputting the data given in the problem on a calculator/software. For this item I chose to use SPSS. Inputting the data to the software, we get the value of the slope at -22.7 and the y-intercept at 130.3.
EQUATION:
In this equation x is the price and y represents the sales.
3. The slope tells us how many units the sales would change if we were to increase the price by 1 unit. Since our slope is -22.7, this will tell us that we would see a decrease of 22.7 in sales if we were to increase our price by 1 unit.
4. I have attached the graph as an image below. The scatter plot contained all points from the data that has been given. The line of best fit from the equation calculated in question #2 is also shown in the graph.
5. As we can see, the line of best fit hits all the points in the scatterplot. The other points fall closely from the line. Therefore, we can say that the line of best fit is a good model of the data. For added evidence, we can also compute for the Pearson correlation coefficient which gives us -0.998. This indicates a strong negative relationship.
6. For this item we will just need the equation for the line of best fit and the data that has been given in the problem. For the actual sales, we will just refer to the given while we will compute for the predicted sales using the line of best fit.
PRICE ACTUAL SALES PREDICTED SALES
1.5 96 96
1.6 94 94
1.8 91 89
2.0 85 85
2.1 83 83
2.2 79 80
2.5 73 74
2.7 70 69
2.9 64 64
3.0 63 62
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