Linear Regression in Excel with LINEST, SLOPE and FORECAST — No Add-ins

Linear Regression in Excel with LINEST, SLOPE and FORECAST — No Add-ins featured illustration

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⏱ 2 min readUpdated 28 September 2026

The simplest machine-learning model is a straight line through your data. Excel can fit it with plain formulas — no Analysis ToolPak needed.

In this article
  1. Example: ad spend vs sales
  2. LINEST: everything at once
  3. Reading the result sensibly
  4. Continue learning

Example: ad spend vs sales

Put monthly ad spend in B2:B25 (x) and sales in C2:C25 (y).

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=SLOPE(C2:C25, B2:B25)          ' extra sales for each extra ₹1 of ads
=INTERCEPT(C2:C25, B2:B25)      ' predicted sales with zero ad spend
=RSQ(C2:C25, B2:B25)            ' 0-1: how much of the variation the line explains
=FORECAST.LINEAR(50000, C2:C25, B2:B25)   ' predicted sales at ₹50,000 spend

LINEST: everything at once

=LINEST(C2:C25, B2:B25, TRUE, TRUE)

In Excel 365 this spills a 5×2 block of statistics: slope and intercept in the first row, their standard errors in the second, R² in the third, then the F statistic and sums of squares. For several inputs (ad spend and discount %), pass a multi-column x range: =LINEST(D2:D25, B2:C25, TRUE, TRUE). Note that LINEST returns the coefficients in reverse column order.

Reading the result sensibly

  • R² of 0.8 means the line explains 80% of the ups and downs — it does not prove ads cause sales.
  • Predict only inside the range you have data for. A line fitted on ₹10k–₹60k spends says nothing reliable about ₹5 lakh.
  • Always chart it: Insert → Scatter, then Add Trendline → Display equation and R². One outlier can drag the whole line.
💡 TREND returns fitted values for a whole column at once — =TREND(C2:C25, B2:B25) — handy for a “predicted vs actual” chart.

Want to see how the computer actually finds that line? Read linear regression from scratch in Python.

Continue learning

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