Sale prices, assessed values, and physical characteristics of 88 homes sold in the Boston, Massachusetts area in 1990. The data are widely used to illustrate regression and heteroskedasticity-consistent inference.
Format
A tibble with 88 rows and 10 variables:
- price
House price, in thousands of U.S. dollars.
- assess
Assessed value, in thousands of U.S. dollars.
- bdrms
Number of bedrooms.
- lotsize
Size of the lot, in square feet.
- sqrft
Size of the house, in square feet.
- colonial
Indicator equal to 1 if the home is of colonial style.
- lprice
Natural logarithm of
price.- lassess
Natural logarithm of
assess.- llotsize
Natural logarithm of
lotsize.- lsqrft
Natural logarithm of
sqrft.
Source
Wooldridge, J. M. (2020). Introductory Econometrics: A Modern Approach,
7th ed. Cengage Learning, Boston, MA. The hprice1 data are distributed with
the wooldridge R package and were originally collected from the real estate
pages of the Boston Globe.
Examples
data(Hprice)
head(Hprice)
#> # A tibble: 6 × 10
#> price assess bdrms lotsize sqrft colonial lprice lassess llotsize lsqrft
#> <dbl> <dbl> <int> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 300 349. 4 6126 2438 1 5.70 5.86 8.72 7.80
#> 2 370 352. 3 9903 2076 1 5.91 5.86 9.20 7.64
#> 3 191 218. 3 5200 1374 0 5.25 5.38 8.56 7.23
#> 4 195 232. 3 4600 1448 1 5.27 5.45 8.43 7.28
#> 5 373 319. 4 6095 2514 1 5.92 5.77 8.72 7.83
#> 6 466. 414. 5 8566 2754 1 6.14 6.03 9.06 7.92
fit <- lm(price ~ lotsize + sqrft + bdrms, data = Hprice)
hcinfer(fit, type = "hcbeta")
#>
#> ── 🔎 HCbeta robust inference ──────────────────────────────────────────────────
#> 📐 Model: `price ~ lotsize + sqrft + bdrms`
#> Observations: 88 | Parameters: 4
#> 🥪 Robust covariance: HCbeta
#> Confidence level: 95.0% | Normal critical value: 1.9600
#> 💡 Use `summary()` for p-values, test results, confidence intervals, and
#> diagnostics.