Best Quick Linear Regression Apps

159+ iOS apps curated and reviewed

4.2 avg rating
17 free apps
Updated September 2026

Every iPhone needs a few good quick linear regression apps. We benchmark 159+ of them on App Store rating, freshness, and pricing, then surface the standouts so you don't have to scroll the App Store yourself.

Compare top Quick Linear Regression Apps (September 2026)

#AppPriceRatingAdsIn-App PurchasesUpdated
1Quick Linear RegressionQuick Linear Regression$0.991.0★ (2)NoNoAug 2026
2Art of Stat: RegressionArt of Stat: RegressionFree4.7★ (20)NoYesJul 2026
3LineRegressionLineRegressionFreeNoNoMay 2026
4Line-FitLine-FitFree3.7★ (3)NoNoSep 2026
5Quick ML - Pocket Data StudioQuick ML - Pocket Data StudioFree was $9.99NoYesSep 2026
6Polynomial RegressionPolynomial RegressionFreeNoNoMay 2026
7Matrix Solver Step by StepMatrix Solver Step by StepFree was $0.994.8★ (62)NoNoSep 2026
8Linear Algebra WorldLinear Algebra WorldFree5.0★ (1)NoNoJul 2026

Top Picks for Quick Linear Regression

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FAQ about Quick Linear Regression

What are the best quick linear regression apps for iPhone?
Quick Linear Regression tops our quick linear regression apps ranking as of September 2026, with a 1.0-star average from 2 App Store ratings, and it's priced at $0.99.
Yes — 17 of the top quick linear regression apps we rank are free to download, including Art of Stat: Regression and LineRegression.
Quick Linear Regression has no ads and has no in-app purchases, according to its App Store listing as of September 2026.
We track 159+ quick linear regression apps on the iOS App Store as of September 2026, ranked by rating, recency, and analysis of real user reviews.
Quick Linear Regression is used to estimate the relationship between an independent variable (X) and a dependent variable (Y) using statistical methods like ordinary least squares. It helps in understanding the 'goodness of fit' of the data to a straight line.
It fits a straight line to a scatter plot of X-Y coordinates by minimizing the sum of squared differences between actual outcomes and the line's predicted values. It also calculates the R-squared value to indicate how well the line represents the data.