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# Time for action – the arbitrary choice of parameters

1. We begin with reasonable guesses of the slope and intercept terms for a simple linear regression model. The idea is to inspect the difference between the fitted line and the actual observations. Invoke the graphics windows using` par(mfrow=c(1,3))`.
2. Obtain the scatter plot of the `CPU_Time` against `No_of_IO` with:
`plot(No_of_IO,CPU_Time,xlab="Number of Processes",ylab="CPU Time",ylim=c(0,0.6),xlim=c(0,11))`
3. For the guessed regression line with the values of being (0.05, 0.05), plot a line on the scatter plot with `abline(a=0.05,b=0.05,col= "blue")`.
4. Define a function which will find the y value for the ...

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