Excel Linear Regression Multiple Variables

What this does

Regression fits a straight-line relationship between one outcome column and one or more predictor columns. The ToolPak produces a full report — coefficients, their standard errors, t statistics and p-values, R squared, and the F test for the model as a whole. LINEST returns the same numbers as a spilled array without the report layout. Both require the predictor columns to be contiguous. The same idea underpins a lot of everyday Excel work, so the few minutes spent getting it right here pay back across every sheet you build afterwards. Treat it as a pattern, not a one-off, and it stops being something you look up and starts being something you reach for. Treat “excel linear regression multiple variables” as a small repeatable workflow rather than a one-off click you hope to remember next time. Use a small test block before the live file, so any surprise in the affected cells shows up while it is still harmless. When a formula is involved, keep the inputs labelled beside it, reference cells instead of typing values, and apply number formatting only after the result checks out. That turns a visual that makes the number obvious at a glance into a method you can reuse, explain, and defend when the workbook leaves your screen.

A worked example

Revenue in B2:B50 is modelled on ad spend in C and site visits in D. Data ▸ Data Analysis ▸ Regression with Input Y Range B1:B50, Input X Range C1:D50 and Labels ticked returns R Square 0.83, an ad-spend coefficient of 2.4 with a p-value of 0.004, and a visits coefficient whose p-value of 0.31 says it adds nothing once spend is in the model. The ToolPak output is the difference between an estimated relationship and a quantified one — the p-values are what stop a coincidence being presented as a driver. A practical tip before you scale it up: build it once on a small block of test data, confirm the number against the tool on this page, and only then point it at your real sheet. That one habit catches almost every mistake while it is still cheap to fix, long before a wrong figure reaches a report or a colleague.

In Google Sheets

Google Sheets handles this almost identically to Excel. The formula syntax above is the same, and the menu lives under a slightly different label rather than a ribbon tab. Use the platform toggle at the top of the page to switch every keyboard shortcut between Windows and Mac, and expect at most cosmetic differences in naming. The aim was to get you unstuck fast and leave you a little more capable than a copy-paste would. The answer is at the top, the tool proves it, and the detail above shows why it holds — so the next time a colleague asks, you can answer without reaching for search. Here is the takeaway for “excel linear regression multiple variables”: copy the answer if you are busy, but if you have a spare few minutes, rebuild the example in Excel yourself with the tool above open beside it. That single pass — type it, run it, watch the result move when you change an input — is what turns a formula you found into a technique you trust. Keep your inputs labelled and referenced, never hard-coded, and the same sheet stays correct and auditable as it grows. Done that way, you will not need to look this up again, and you will be the person others ask.

Common mistakes

• Selecting non-adjacent predictor columns; both the ToolPak and LINEST need one contiguous X block, so copy the columns together first.
• Reading LINEST's coefficients left to right in column order — it returns them in reverse, with the last predictor first.
• Reporting a high R squared as proof of causation when the predictors were chosen after seeing the data.
• Leaving strongly correlated predictors in the model, which makes individual coefficients unstable even when the fit looks excellent.

Frequently asked questions