A production plant cost-control engineer is responsible for cost reduction. One of the costly items in his plant is the amount of water used by the production facilities each month. He decided to investigate water usage by collecting seventeen observations on his plant's water usage and other variables.
Variable Description
Temperature Average monthly temperate (F)
Production Amount of production (M pounds)
Days Number of plant operating days in the month
Persons Number of persons on the monthly plant payroll
Water Monthly water usage (gallons)
Temperature |
Production |
Days |
Persons |
Water |
58.8 |
7107 |
21 |
129 |
3067 |
65.2 |
6373 |
22 |
141 |
2828 |
70.9 |
6796 |
22 |
153 |
2891 |
77.4 |
9208 |
20 |
166 |
2994 |
79.3 |
14792 |
25 |
193 |
3082 |
81.0 |
14564 |
23 |
189 |
3898 |
71.9 |
11964 |
20 |
175 |
3502 |
63.9 |
13526 |
23 |
186 |
3060 |
54.5 |
12656 |
20 |
190 |
3211 |
39.5 |
14119 |
20 |
187 |
3286 |
44.5 |
16691 |
22 |
195 |
3542 |
43.6 |
14571 |
19 |
206 |
3125 |
56.0 |
13619 |
22 |
198 |
3022 |
64.7 |
14575 |
22 |
192 |
2922 |
73.0 |
14556 |
21 |
191 |
3950 |
78.9 |
18573 |
21 |
200 |
4488 |
79.4 |
15618 |
22 |
200 |
3295 |
i. Create the Scatter Plot graph between “water” and “production”
ii. Use the appropriate routine of Data Analysis in MS Excel or any other statistical software in order to estimate the correlation matrix of all variables
iii. Compute 95% confidence interval for the values of the correlation coefficient between and determine which ones are statistically significant”
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