可以参考一下eviews的用户手册的解释
Hausman Test for Correlated Random Effects
A central assumption in random effects estimation is the assumption that the random effects
are uncorrelated with the explanatory variables. One common method for testing this
assumption is to employ a Hausman (1978) test to compare the fixed and random effects
estimates of coefficients (for discussion see, for example Wooldridge (2002, p. 288), and
Baltagi (2005, p. 66)).
To perform the Hausman test, you must first estimate a model with your random effects
specification. Next, select View/Fixed/Random Effects Testing/Correlated Random
Effects - Hausman Test. EViews will automatically estimate the corresponding fixed effects
specifications, compute the test statistics, and display the results and auxiliary equations.
Redundant Fixed Effects Tests
Equation: Untitled
Test cross-section and period fixed effects
Effects Test Statistic d.f. Prob.
Cross-section F 113.351303 (17,303) 0.0000
Cross-section Chi-square 682.635958 17 0.0000
Period F 6.233849 (18,303) 0.0000
Period Chi-square 107.747064 18 0.0000
Cross-Section/Period F 55.955615 (35,303) 0.0000
Cross-Section/Period Chi-square 687.429282 35 0.0000
Panel Equation Testing—675
For example, Baltagi (2005) considers an example of Hausman testing (Example 1, p. 70), in
which the results for a Swamy-Arora random effects estimator for the Grunfeld data
(“Grunfeld_baltagi_panel.WF1”) are compared with those obtained from the corresponding
fixed effects estimator. To perform this test we must first estimate a random effects estimator,
obtaining the results:

原假设是选取随机效应模型，因为P值小于0.05，所以拒绝原假设，选取固定效应模型

英文的伤不起啊

我表示也是新手啊、、、