Code:

```
Row │ hryear4 prmjind1 prcnt_minority median_wage wage_10 median_age
│ Int64 Int64 Float64 Float64 Float64 Float64
─────┼─────────────────────────────────────────────────────────────────────
1 │ 2010 1 0.549525 10.0 8.0 36.0
2 │ 2010 2 0.285844 18.0 11.0242 40.0
3 │ 2010 3 0.389158 16.0 10.0 37.0
4 │ 2010 4 0.372013 14.5 9.0 43.0
5 │ 2010 5 0.33117 10.0 7.5 34.0
```

Code:

```
Continuous Response Model
Number of observations: 143
Null Loglikelihood: -366.63
Loglikelihood: -325.47
R-squared: 0.4378
LR Test: 82.32 ∼ χ²(2) ⟹ Pr > χ² = 0.0000
Formula: median_wage ~ 1 + median_age + prcnt_minority
Variance Covariance Estimator: OIM
───────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
───────────────────────────────────────────────────────────────────────────────
(Intercept) 6.0373 2.52437 2.3916 0.0181 1.04648 11.0281
median_age 0.380779 0.0465285 8.18378 <1e-12 0.28879 0.472769
prcnt_minority -13.9391 3.37746 -4.12709 <1e-04 -20.6165 -7.26168
───────────────────────────────────────────────────────────────────────────────
```

Code:

```
Continuous Response Model
Number of observations: 143
Null Loglikelihood: -366.63
Loglikelihood: -178.47
R-squared: 0.9285
Wald: 85.49 ∼ F(2, 128) ⟹ Pr > F = 0.0000
Formula: median_wage ~ 1 + median_age + prcnt_minority + absorb(prmjind1)
Variance Covariance Estimator: OIM
─────────────────────────────────────────────────────────────────────────────────
PE SE t-value Pr > |t| 2.50% 97.50%
─────────────────────────────────────────────────────────────────────────────────
(Intercept) -7.10788 3.64651 -1.94923 0.0535 -14.3231 0.107359
median_age 0.175965 0.0849071 2.07244 0.0402 0.00796144 0.343968
prcnt_minority 37.7827 2.8899 13.0741 <1e-24 32.0645 43.5008
─────────────────────────────────────────────────────────────────────────────────
```

I would really appreciate any guidance and/or suggestions for reading materials to help me get a better handle on panel data modelling techniques.