Simple Linear Regression with Plain-English Interpretation

Usage

reg_interpret(formula, data, conf.level = 0.95, context = NULL)

Arguments

formula: A formula of the form outcome ~ predictor
data: A data frame containing the variables
conf.level: Confidence level. Default 0.95.
context: Optional description of the study design or sampling method, echoed back in the printed report. Default NULL.

Value

An object of class statease_reg containing regression results and interpretation. Use print() to display the formatted report.

Examples

df <- data.frame(
  exam_score = c(23,45,12,67,34,89,56,43,78,90),
  study_hours = c(2,5,1,7,3,9,6,4,8,10)
)
result <- reg_interpret(exam_score ~ study_hours, data = df)
print(result)
#> 
#> -- statease Simple Linear Regression Report ---------------------
#>   Outcome      : exam_score
#>   Predictor    : study_hours
#>   N            : 10
#> -----------------------------------------------------------------
#>   Model Equation:
#>   exam_score = 4.800 + 8.891 * study_hours
#> -----------------------------------------------------------------
#>   Coefficients:
#>   Intercept    : 4.800
#>   Slope        : 8.891  (SE = 0.336)
#>   t-statistic  : 26.442
#>   p-value      : 0.0000
#>   95% CI      : [8.116, 9.666]
#> -----------------------------------------------------------------
#>   Model Fit:
#>   R-squared    : 0.9887
#>   Adj R-squared: 0.9873
#>   F-statistic  : 699.184 (df = 1, 8)  p = 0.0000
#> -----------------------------------------------------------------
#>   Assumption Checks:
#>     Normality (residuals)   : PASSED   (Shapiro-Wilk p = 0.642)
#>     Homoscedasticity        : PASSED   (non-constant variance test p = 0.363)
#>     Residual independence   : PASSED   (Durbin-Watson DW = 1.84, p = 0.682)
#> 
#>   NOTE: Assumption checks are diagnostic tools and may be
#>   influenced by sample size and other characteristics of the
#>   data. Passing a check does not prove that an assumption is
#>   satisfied, and a warning does not automatically invalidate
#>   the analysis. Interpret these results alongside your
#>   knowledge of the data.
#> -----------------------------------------------------------------
#>   Interpretation:
#>   The predictor study_hours is statistically significant (p = 0.0000 < alpha 0.05).
#>   The slope is positive - as study_hours increases by 1 unit, exam_score increases by 8.891 units.
#>   R-squared = 0.9887: study_hours explains 98.9% of the
#>   variance in exam_score (large effect).
#> -----------------------------------------------------------------
#>