Call:
lm(formula = data$Costs ~ data$Arrays)
Residuals:
Min 1Q Median 3Q Max
-10.268 -4.862 1.335 3.214 14.595
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.96281 12.87567 3.570 0.0051 **
data$Arrays 0.20673 0.06706 3.083 0.0116 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.963 on 10 degrees of freedom
Multiple R-squared: 0.4873, Adjusted R-squared: 0.436
F-statistic: 9.503 on 1 and 10 DF, p-value: 0.01159
a) Use linear regression to estimate the cost of processing a single array.
cost(n) = 45.96281 + n * 0.2067253
b) Interpret each component of the regression equation. What does the y-intercept mean in the
context of this problem? What does the slope mean in the context of this problem? How can you
use this information to get a more complete picture of the cost of microarry processing?
intercept: fixed costs, regardless of the amount of processed arrays
slope: slope is lower than 1, so if more arrays get processed then it is less expensive for each array.
c) How much will it cost to process 643 arrays in one month? What error do you expect
for your prediction?
cost(643) = 179 ( +/- 8 [= standard deviation])