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Running marketing spend linear regression analysis...
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Analyze another fileStandard multiple linear regression — predicts one numeric variable from one or more predictors. Shows coefficients, R-squared, residual diagnostics, and prediction intervals.
Use this when you have a clear outcome variable and a few predictors, and want interpretable coefficients.
If you have many correlated predictors, use Elastic Net. If the outcome is binary (yes/no), use Logistic Regression.
Built for: Marketing analyst, business analyst, student, researcher
Typical data source: CSV with one outcome column and 1-10 predictor columns, all numeric
Ad spend and sales data
Minimum 20 rows · Best with 50-5000 rows
Multiple linear regression to predict sales revenue from multi-channel advertising spend (TikTok, Facebook, Google Ads). Estimates ROI per channel and optimizes budget allocation.
Actual vs predicted sales values
Sales return per dollar spent on each advertising channel
Residuals vs fitted values to check homoscedasticity
Distribution of residuals for normality assessment
Variance Inflation Factors for each predictor
Leverage vs Cook's distance to identify influential observations
K-fold cross-validation performance metrics
Point predictions with confidence bands
Standardized coefficients showing relative predictor importance
Scale-location plot for constant variance assessment
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Correlation — Just want correlations, not a predictive model
Need more power? Elastic Net — Many predictors, need regularization
Similar: Ridge
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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