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Multiple Linear Regression Calculator

Estimate the unique contribution of several predictors to one outcome. Text predictors are dummy coded automatically, and every coefficient comes with β, confidence intervals and VIF.

Free · No sign-upReplaces SPSS · StataWorks with your data
Updated · By Bibin Xavier

Data 90 × 12

Paste from Excel, or drop a CSV, TSV or Excel file here.
Example data 90 rows, 12 variables. Data stays in this browser tab.
IDtext · 90 levelsGrouptext · 3 levelsGendertext · 2 levelsAgenumericHours_StudynumericAnxietynumericMotivationnumericPre_ScorenumericPost_ScorenumericExam_ScorenumericPassednumeric · codedSatisfactionnumeric · coded
S001ControlMale2311.2294.660626712
S002WorkshopFemale233.9283.554705402
S003OnlineFemale1810.132455576603
S004ControlMale2613.6413.552555903
S005WorkshopFemale229.7273.656636504
S006OnlineFemale207.9323.865826502
S007ControlFemale206.328372836103
S008WorkshopMale228293.164726213

Variables and options

Predictors 4 selected · min 1
Text variables become dummy variables, using the first category as the reference.
Results update automatically when you change an option.

Results

90 rows · example data
R².375
Adjusted R².338
Model F test< .001F(5, 84) = 10.09
Std. error of estimate7.58
Table 1

Coefficients

PredictorBSEβtp95% CIVIF
(Intercept)54.1425.8919.19< .001[42.43, 65.86]
Hours_Study1.1440.408.3292.80.006[0.33, 1.96]1.85
Motivation2.7661.632.1961.69.094[-0.48, 6.01]1.80
Anxiety-0.5330.111-.428-4.80< .001[-0.75, -0.31]1.07
Group [Online]2.0551.975.1051.04.301[-1.87, 5.98]1.36
Group [Workshop]4.1581.970.2112.11.038[0.24, 8.08]1.35

Note. VIF above 5 suggests problematic multicollinearity. Durbin-Watson = 1.96. N = 90.

Table 2

ANOVA

SourceSSdfMSFp
Regression2903.535580.7110.09< .001
Residual4832.538457.53
Total7736.0689

Residuals against fitted values

Normal Q-Q plot of residuals

Interpretation

Together the predictors explain 37.5% of the variance in Exam_Score (adjusted R² = .338), and the model is statistically significant. Significant unique predictors: Hours_Study, Anxiety, Group [Workshop]. Residual normality: Shapiro-Wilk p = .752.

The model explained 37.5% of the variance in Exam_Score, R² = .375, F(5, 84) = 10.09, p < .001.

How to cite this tool

Using these results in a thesis, journal article, report or class assignment? Please cite this free tool so others can find it. It keeps the site free.

APA 7

Xavier, B. (2026). Multiple Linear Regression Calculator [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/multiple-regression-calculator/

BibTeX 

@software{xavier_multipleregression_2026,
  author = {Xavier, Bibin},
  title = {Multiple Linear Regression Calculator},
  publisher = {FreeStatisticalTools.com},
  year = {2026},
  url = {https://freestatisticaltools.com/multiple-regression-calculator/},
  note = {Web application}
}

Suggested sentence for your methods section: “The analysis was carried out with the free Multiple Linear Regression Calculator tool on FreeStatisticalTools.com (Xavier, 2026).”

What is multiple regression?

Multiple regression estimates the unique effect of several predictors on one continuous outcome. Text predictors are dummy coded automatically, and collinearity is checked with variance inflation factors.

When to use multiple regression

Use it to predict a continuous outcome from two or more predictors, or to test one predictor while controlling for others.

Assumptions

  • Linear relationships.
  • Independent, normally distributed residuals with constant variance.
  • No severe multicollinearity (VIF below 5–10).
  • About 10–20 cases per predictor.

How to use this tool

  1. Enter your data. Paste columns from Excel, Google Sheets or SPSS with variable names in the first row, or upload a CSV or Excel file. The example data shows the expected layout.
  2. Choose the variables. Select the outcome and predictors.
  3. Read the results. Results update automatically. Read the plain-English interpretation, then use Copy table to paste APA-style tables into Word and Copy for the results sentence.

How to report multiple regression in APA style

Report R², F and its p for the model, then B, SE, β and p for each predictor in a table.

Frequently asked questions

How many cases do I need for multiple regression?

A common rule is at least 10 to 20 cases per predictor; Green (1991) suggests N ≥ 50 + 8k for testing the overall model.

What VIF indicates multicollinearity?

VIF above 5 suggests problematic collinearity and above 10 indicates a serious problem.

How are categorical predictors handled?

A text variable with k categories becomes k − 1 dummy variables, using the first category in alphabetical order as the reference group.

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