Calculate the predicted prices for all of the diameters in the original data using the function $`$compute$\_$all$\_$y$`,$ a slope of $`0\mathrm{.}7`,$ and a y$-$intercept of $`5`.$ Print out your results in a table similar to the one shown below. The table should include a column for the diameter, actual price, predicted price, and error $($actual minus predicted$).$ You should try to use Python's string formatting $($i$.$e$.`$format$`$ method$)$ to assist you with this table. If done correctly, you should be able to do this using a few lines of code and a for loop.

$```$

Diameter Actual Price Predicted Price Error

$6\mathrm{.}07\mathrm{.}009\mathrm{.}20-2\mathrm{.}20$

$8\mathrm{.}09\mathrm{.}0010\mathrm{.}60-1\mathrm{.}60$

$10\mathrm{.}013\mathrm{.}0012\mathrm{.}001\mathrm{.}00$

$14\mathrm{.}017\mathrm{.}5014\mathrm{.}802\mathrm{.}70$

$18\mathrm{.}018\mathrm{.}0017\mathrm{.}600\mathrm{.}40$