Description Usage Format Source References Examples

The `table.b4`

data frame has 24 observations on property
valuation.

1 |

This data frame contains the following columns:

- y
sale price of the house (in thousands of dollars)

- x1
taxes (in thousands of dollars)

- x2
number of baths

- x3
lot size (in thousands of square feet)

- x4
living space (in thousands of square feet)

- x5
number of garage stalls

- x6
number of rooms

- x7
number of bedrooms

- x8
age of the home (in years)

- x9
number of fireplaces

Montgomery, D.C., Peck, E.A., and Vining, C.G. (2001) Introduction to Linear Regression Analysis. 3rd Edition, John Wiley and Sons.

Narula, S.C. and Wellington (1980) Prediction, Linear Regression and Minimum Sum of Relative Errors. Technometrics, 19, 1977.

1 2 3 4 5 |

```
Attaching package: 'MPV'
The following object is masked from 'package:datasets':
stackloss
Call:
lm(formula = y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9)
Residuals:
Min 1Q Median 3Q Max
-3.720 -1.956 -0.045 1.627 4.253
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.92765 5.91285 2.525 0.0243 *
x1 1.92472 1.02990 1.869 0.0827 .
x2 7.00053 4.30037 1.628 0.1258
x3 0.14918 0.49039 0.304 0.7654
x4 2.72281 4.35955 0.625 0.5423
x5 2.00668 1.37351 1.461 0.1661
x6 -0.41012 2.37854 -0.172 0.8656
x7 -1.40324 3.39554 -0.413 0.6857
x8 -0.03715 0.06672 -0.557 0.5865
x9 1.55945 1.93750 0.805 0.4343
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.949 on 14 degrees of freedom
Multiple R-squared: 0.8531, Adjusted R-squared: 0.7587
F-statistic: 9.037 on 9 and 14 DF, p-value: 0.000185
```

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