Consider the following regression results:

Call:

lm(formula = y ~ x2 + x3 + x4)

Residuals:

Min 1Q Median 3Q Max

-11.0404 -3.4221 0.2717 3.3710 7.9343

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -13.3247 1.4238 -9.358 1.02e-14 *

x2 -0.4115 0.2118 -1.943 0.055368 .

x3 -1.9581 0.4874 -4.017 0.000127 *

x4 1.6512 0.5516 2.993 0.003612 **

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Signif. codes: 0 * 0.001 * 0.01 0.05 . 0.1 1

Residual standard error: 4.378 on 85 degrees of freedom

Multiple R-squared: 0.2267, Adjusted R-squared: 0.1994

F-statistic: 8.304 on 3 and 85 DF, p-value: 6.607e-05

studentized Breusch-Pagan test

data: fit

BP = 1.7513, df = 3, p-value = 0.6256

Breusch-Godfrey test for serial correlation of order up to 6

data: fit

LM test = 27.302, df = 6, p-value = 0.0001271

RESET test

data: y ~ x2 + x3 + x4

RESET = 0.66698, df1 = 3, df2 = 82, p-value = 0.5747

Comment on the results, pointing out the important items in the output, and conducting any tests you think relevant. Is this model satisfactory as a representation of the data?