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Lovely Lawns, Inc., intends to use sales of lawn fertilizer to predict lawn mower sales. The store manager estimates a probable six-week lag between fertilizer sales and mower sales. The pertinent data are:

| Period | Fertilizer Sales (tons) | Number of Mowers Sold |
|--------|-------------------------|-----------------------|
| 1 | 1.6 | 12.0 |
| 2 | 1.3 | 10.0 |
| 3 | 1.8 | 13.0 |
| 4 | 2.0 | 15.0 |
| 5 | 2.2 | 15.0 |
| 6 | 1.5 | 11.0 |
| 7 | 1.6 | 12.0 |
| 8 | 1.4 | 10.0 |
| 9 | 1.8 | 13.0 |
| 10 | 1.4 | 10.0 |
| 11 | 1.9 | 15.0 |
| 12 | 1.4 | 11.0 |
| 13 | 1.7 | 14.0 |
| 14 | 1.4 | 13.0 |

a. Determine the correlation between the two variables. Does it appear that a relationship between these variables will yield reasonable predictions? Explain.

b. Obtain a linear regression line for the data.

c. Predict expected lawn mower sales for the first week in August, given fertilizer sales six weeks earlier of 2 tons.

Answer :

Answer:

R = 0.8815 ;

y = 6.14679X + 2.33028 ;

14.624

Step-by-step explanation:

Using an online correlation Coefficient calculator, the correlation Coefficient, R between the number of lawn fertilizer sold and the number of lawn mowers sold is 0.8815. This R value depicts a strong positive correlation between both variables.

The regression line equation obtained by Plotting the values to generate a linear model is :

y = 6.147X + 2.330

With ;

Slope or gradient being 6.147

Intercept = 2.330

C.)

x = 2

y = 6.147X + 2.330

y = 6.147(2) + 2.330

y = 14.624

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Rewritten by : Barada