Do rain days really cost you money?

Eighteen completed jobs and four things a site diary already records. Rain days correlate with final cost at 0.003, and the strongest correlation in the matrix belongs to a column that drives nothing at all.

Construction Intermediate Statistics free

After you install, this is the model to open.

What Moves With Cost on This Job?

  1. In your spreadsheet, click the Sortia icon in the strip of icons down the right-hand edge. No strip? Click the arrow at the bottom-right to open it. You can also use Extensions, then Sortia, then Open Sortia.
  2. Click Start from a template and put that name in the search box.
  3. Pick the card with that name and click Load this template. It arrives on a new tab with real numbers already in it.

The answer

Rain days and cost
r = 0.003 zero, across 18 finished jobs
Crew size and cost
r = 0.98 but r = 0.94 with site area: same thing twice
Design changes
r = 0.83 only 0.65 explained by size: the one to act on

Eighteen finished jobs and four things the office already records about each one. Click Run: the matrix comes back with final cost correlating 0.965 with site area, 0.979 with crew size, 0.825 with design changes and 0.003 with rain days. Start with the last one, because it is the finding. Rain days, the thing named in more delay notices than anything else on a site, has no relationship with what a job finally costs.

Three thousandths, which is zero with a rounding error attached. The weather causes plenty of pain and none of it survives to the final account, at least across these eighteen jobs. Now the trap. Crew size has the highest correlation in the matrix at 0.979, and it drives nothing. Read the crew size row against site area: 0.945. Bigger jobs get bigger crews, so crew size is a restatement of job size, and putting fewer people on a job would not move the cost, it would move the programme.

A correlation matrix ranks companionship, not causation, and the two columns that travel together most closely here are the two that measure the same underlying thing. That leaves design changes as the one variable that both correlates strongly with cost, at 0.825, and is only moderately explained by job size, at 0.651. It is the candidate a contractor can actually act on.

To take it further, run Regression with final cost as the Y range and site area plus design changes as the X range: it will tell you what one change is worth in dollars once job size is already accounted for, which correlation cannot. Two limits worth stating before anyone quotes a number from here. The matrix sees straight-line relationships only, so a driver that bites hard past a threshold and not at all below it can come back near zero, and rain days may be exactly that.

And nothing here fixes the direction of cause: expensive jobs may attract changes rather than the other way round. To use your own book, paste one job per row with numeric columns only, keep the job reference outside the range, and widen it.

The model

It arrives on a tab called Template: What Moves With Cost, carrying these columns:

  • Final cost ($K)
  • Site area (m2)
  • Design changes (count)
  • Rain days
  • Crew size (people)
  • Job reference

Once it is in your sheet

  1. The model arrives with real numbers in it and runs as it stands, so you can press the button first and understand it second.
  2. Change the numbers to yours. The sheet marks which cells are inputs and which hold formulas, and most labels carry a note explaining the row.
  3. Press the run button at the bottom of the panel. It is labeled for the tool you are in, and the result lands on its own tab, with a written reading of it beside the figures.

Never used Google Sheets? Start here goes the whole way, in seven steps, and assumes nothing.