36.1 One summary table

Packages for reporting:

Summary Statistics Table:

Regression Table

library(jtools)
data(movies)
fit <- lm(metascore ~ budget + us_gross + year, data = movies)
summ(fit)
Observations 831 (10 missing obs. deleted)
Dependent variable metascore
Type OLS linear regression
F(3,827) 26.23
0.09
Adj. R² 0.08
Est. S.E. t val. p
(Intercept) 52.06 139.67 0.37 0.71
budget -0.00 0.00 -5.89 0.00
us_gross 0.00 0.00 7.61 0.00
year 0.01 0.07 0.08 0.94
Standard errors: OLS
summ(
    fit,
    scale = TRUE,
    vifs = TRUE,
    part.corr = TRUE,
    confint = TRUE,
    pvals = FALSE
) # notice that scale here is TRUE
Observations 831 (10 missing obs. deleted)
Dependent variable metascore
Type OLS linear regression
F(3,827) 26.23
0.09
Adj. R² 0.08
Est. 2.5% 97.5% t val. VIF partial.r part.r
(Intercept) 63.01 61.91 64.11 112.23 NA NA NA
budget -3.78 -5.05 -2.52 -5.89 1.31 -0.20 -0.20
us_gross 5.28 3.92 6.64 7.61 1.52 0.26 0.25
year 0.05 -1.18 1.28 0.08 1.24 0.00 0.00
Standard errors: OLS; Continuous predictors are mean-centered and scaled by 1 s.d. The outcome variable remains in its original units.

#obtain clsuter-robust SE
data("PetersenCL", package = "sandwich")
fit2 <- lm(y ~ x, data = PetersenCL)
summ(fit2, robust = "HC3", cluster = "firm") 
Observations 5000
Dependent variable y
Type OLS linear regression
F(1,4998) 1310.74
0.21
Adj. R² 0.21
Est. S.E. t val. p
(Intercept) 0.03 0.07 0.44 0.66
x 1.03 0.05 20.36 0.00
Standard errors: Cluster-robust, type = HC3

Model to Equation

# install.packages("equatiomatic") # not available for R 4.2
fit <- lm(metascore ~ budget + us_gross + year, data = movies)
# show the theoretical model
equatiomatic::extract_eq(fit)
# display the actual coefficients
equatiomatic::extract_eq(fit, use_coefs = TRUE)