How to Run Linear Regression in Google Sheets Guide

1. Manual ways to run linear regression in Google Sheets

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Method 1: Add a trendline to a chart (visual, fast)

1. Put your independent variable (X, e.g. ad spend) in column A and dependent variable (Y, e.g. leads) in column B.
2. Select both columns.
3. Go to Insert → Chart and choose Scatter chart.
4. In the Chart editor, open Customize → Series.
5. Check Trendline and set Type = Linear.
6. Turn on Show R² to see how well the line fits.
   • Pros: Very visual, great for storytelling in decks.
   • Cons: You see slope and fit, but not full regression statistics.

Official help on charts: https://support.google.com/docs/topic/9054603

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Method 2: Use LINEST for full regression statistics

1. Structure data: X in A2:A101, Y in B2:B101.
2. Decide where to place results, e.g. D2.
3. In D2, enter:
   • =LINEST(B2:B101, A2:A101, TRUE, TRUE)
4. Confirm as an array formula:
   • New Google Sheets: just press Enter.
   • Older behavior: you might need Ctrl+Shift+Enter.
5. Google Sheets returns an array: slope(s), intercept, standard errors, R², F-statistic, degrees of freedom, regression sum of squares, and residual sum of squares.
6. Use labels beside each cell to keep track of what’s what.

Official LINEST reference: https://support.google.com/docs/answer/3094249

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Method 3: Force regression through the origin Sometimes you know that when X = 0, Y should be 0 (e.g. zero impressions, zero clicks).

1. Use the same data layout.
2. In D2, use:
   • =LINEST(B2:B101, A2:A101, FALSE, TRUE)
3. Here calculate_b = FALSE forces the intercept to zero.
4. Interpret outputs the same way, but remember the constraint you imposed.

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Method 4: Multiple linear regression (e.g. ad spend + sales headcount)

1. Put X1 in A2:A101 (paid ads), X2 in B2:B101 (sales salaries), Y in C2:C101 (revenue).
2. In a blank block, enter:
   • =LINEST(C2:C101, A2:B101, TRUE, TRUE)
3. The first row of output gives coefficients for each X and the intercept.
4. Later rows give standard errors, R², and sums of squares.
5. Build a prediction cell, e.g.: =$D$2*A2 + $E$2*B2 + $F$2 and drag down.

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Method 5: Use TREND with LINEST for forecasting

1. First run LINEST or directly use TREND:
   • =TREND(B2:B101, A2:A101, A102:A112)
2. This predicts Y values for future X values (e.g. next months’ spend).

TREND help: https://support.google.com/docs/answer/3094263

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2. No-code automation methods

You can go beyond single-use formulas and create simple automation without touching code.

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No-code Method 1: Template-driven dashboards

1. Build a dedicated "Regression" tab with:
   • Raw data range (e.g. Data!A:B).
   • A fixed LINEST block (using ranges like Data!A2:A, Data!B2:B).
   • KPI cells (slope, R², forecasted Y at key X values).
2. Share the Sheet with your team; they only paste new data into the Data tab.
3. The dashboard auto-updates; no one touches formulas.
   • Pros: Zero extra tools, scales fine for small/medium teams.
   • Cons: Still requires people to paste and maintain data.

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No-code Method 2: Connect Sheets to other tools via import features

1. Use File → Import or IMPORTRANGE to pull CRM, ad, or ecommerce exports into your regression Sheet.
2. Point your LINEST and TREND formulas at these imported ranges.
3. Schedule exports from tools like Google Ads or Meta as CSV to a shared Drive folder; teammates just import the latest file.
   • Pros: Reduces manual copying between systems.
   • Cons: Still discrete “report days”, not true continuous automation.

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No-code Method 3: Use Google Sheets add-ons or no-code platforms

1. Use tools like Zapier, Make, or native integrations from your CRM/ad platforms to push rows into Google Sheets when events occur (new lead, closed deal, campaign performance).
2. Keep your LINEST block pointing at an expanding range (e.g. A2:A, B2:B).
3. Set alerts in Sheets when slope, intercept, or R² crosses thresholds (e.g. when a new campaign’s conversion slope outperforms baseline).
   • Pros: Fresh data without touching exports.
   • Cons: Regression still runs “inside” Sheets—no orchestration across multiple files, workspaces, or teams.

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3. Scaling with AI agents (Simular Pro)

Manual and no-code flows still assume a human orchestrator. At scale—a dozen campaigns, multiple markets, weekly tests—you become the bottleneck. This is where an AI computer agent like Simular Pro changes the game.

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AI Method 1: Agent-driven regression runs across many Sheets Imagine every regional marketing manager keeps their own performance Sheet.

1. Configure a Simular Pro agent to:
   • Open each Google Sheet in your browser.
   • Navigate to the “Data” tab and confirm X/Y columns.
   • Insert or update the LINEST formula in a standard "Regression" tab.
   • Read out key metrics (slope, intercept, R²).
   • Paste those metrics into a central master Sheet or dashboard.
2. Trigger the agent via a webhook from your existing pipeline every Monday.
3. Review one consolidated regression summary instead of opening 20 files.
   • Pros: Eliminates repetitive navigation and formula maintenance.
   • Cons: Requires an initial investment in designing and testing the agent workflow.

Learn more about Simular Pro agents: https://www.simular.ai/simular-pro

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AI Method 2: End-to-end reporting workflows Go further and let the agent own the full analytics story.

1. The agent downloads fresh CSV exports from ad platforms or your CRM.
2. It uploads or pastes those into the right tabs in Google Sheets.
3. It verifies that LINEST, TREND, and any summary formulas are present.
4. It generates charts and copies screenshots or values into a slide deck or a summary Sheet.
5. Finally, it posts a link to Slack or email.
   • Pros: Truly end-to-end; humans stay focused on decisions, not clicks.
   • Cons: You must specify guardrails (e.g. where credentials live, which Sheets are in-scope).

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AI Method 3: Scenario testing at scale

1. Create a “Scenarios” tab with different planned X values (budget levels, pricing tiers).
2. Use the agent to:
   • Duplicate that tab per campaign or region.
   • Plug in the current regression coefficients from LINEST.
   • Compute and highlight forecasted outcomes.
3. The agent can then compile a comparison table across all scenarios into a single Sheet.
   • Pros: Lets non-technical teams explore advanced what-if analysis without touching formulas.
   • Cons: Still depends on the quality of your underlying data and regression assumptions.

By combining Google’s robust regression functions with Simular’s production-grade computer-use agents, you transform regression from a one-off spreadsheet chore into a repeatable analytics engine that quietly runs in the background for your sales, marketing, and ops teams.