Violent-crime and murder rates together with socioeconomic indicators for the 50 U.S. states and the District of Columbia in 2009. The data are useful for illustrating heteroskedasticity-consistent inference in a cross-sectional design with influential observations.
Format
A tibble with 51 rows and 8 variables:
- state
Name of one of the 50 U.S. states or the District of Columbia.
- violent
Violent-crime rate per 100,000 population.
- murder
Murder rate per 100,000 population.
- hs_grad
Percentage of the population that graduated from high school or higher.
- poverty
Percentage of the population living below the poverty line.
- single
Percentage of households headed by a single parent.
- white
Percentage of the population that is white.
- urban
Percentage of the population living in urban areas.
Source
French, J. P. (2023). api2lm: Functions and Data Sets for the Book 'A
Progressive Introduction to Linear Models'. R package version 0.2.
doi:10.32614/CRAN.package.api2lm
. The same data are distributed as the
statecrime dataset in the Python statsmodels package (Seabold and
Perktold, 2010, https://www.statsmodels.org/); the underlying figures come
from the Statistical Abstract of the United States (2009) and are in the
public domain.
Examples
data(Crime2009)
Crime2009[Crime2009$state == "Alabama", ]
#> # A tibble: 1 × 8
#> state violent murder hs_grad poverty single white urban
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Alabama 460. 7.1 82.1 17.5 29 70 48.6
fit <- lm(violent ~ poverty + single, data = Crime2009)
hcinfer(fit, type = "hcbeta")
#>
#> ── 🔎 HCbeta robust inference ──────────────────────────────────────────────────
#> 📐 Model: `violent ~ poverty + single`
#> Observations: 51 | Parameters: 3
#> 🥪 Robust covariance: HCbeta
#> Confidence level: 95.0% | Normal critical value: 1.9600
#> 💡 Use `summary()` for p-values, test results, confidence intervals, and
#> diagnostics.