# regr

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Percentile

##### regr: a simple regression analysis wrapper

The regr function wraps a number of linear regression functions into one convenient interface that provides similar output to the regression function in SPSS. It automatically provides confidence intervals and standardized coefficients. Note that this function is meant for teaching purposes, and therefore it's only for very basic regression analyses.

Keywords
utilities
##### Usage
regr(formula, dat = NULL, conf.level = .95, digits = 2, pvalueDigits = 3, coefficients = c("raw", "scaled"), plot = FALSE, ci.method = c("widest", "r.con", "olkinfinn"), ci.method.note = FALSE, env = parent.frame())
##### Arguments
formula
The formula of the regression analysis, of the form y ~ x1 + x2, where y is the dependent variable and x1 and x2 are the predictors.
dat
If the terms in the formula aren't vectors but variable names, this should be the dataframe where those variables are stored.
conf.level
The confidence of the confidence interval around the regression coefficients.
digits
Number of digits to round the output to.
pvalueDigits
The number of digits to show for p-values; smaller p-values will be shown as
coefficients
Which coefficients to show; can be "raw" to only show the raw (unstandardized) coefficients; "scaled" to only show the scaled (standardized) coefficients), or c("raw", "scaled') to show both.
plot
For regression analyses with only one predictor (also sometimes confusingly referred to as 'univariate' regression analyses), scatterplots with regression lines and their standard errors can be produced.
ci.method, ci.method.note
Which method to use for the confidence interval around R squared, and whether to display a note about this choice.
env
The enviroment where to evaluate the formula.
##### Value

A list of three elements: A list of three elements:

• regr
##### Examples
### Do a simple regression analysis
regr(age ~ circumference, dat=Orange);

regr(Orange$age ~ Orange$circumference, pvalueDigits=18);