Code:
. tab lub lub | Freq. Percent Cum. ------------+----------------------------------- 100.00 | 1 0.65 0.65 200.00 | 3 1.95 2.60 300.00 | 5 3.25 5.84 400.00 | 26 16.88 22.73 450.00 | 1 0.65 23.38 500.00 | 1 0.65 24.03 600.00 | 57 37.01 61.04 800.00 | 16 10.39 71.43 900.00 | 11 7.14 78.57 1000.00 | 7 4.55 83.12 1050.00 | 1 0.65 83.77 1200.00 | 21 13.64 97.40 1400.00 | 1 0.65 98.05 1500.00 | 2 1.30 99.35 2000.00 | 1 0.65 100.00 ------------+----------------------------------- Total | 154 100.00 . reg lub t Source | SS df MS Number of obs = 151 -------------+---------------------------------- F(1, 149) = 10.84 Model | 996788.379 1 996788.379 Prob > F = 0.0012 Residual | 13696754.7 149 91924.528 R-squared = 0.0678 -------------+---------------------------------- Adj R-squared = 0.0616 Total | 14693543 150 97956.9536 Root MSE = 303.19 ------------------------------------------------------------------------------ lub | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- t | 4.627758 1.405351 3.29 0.001 1.850765 7.40475 _cons | 513.9383 64.98879 7.91 0.000 385.5196 642.357 ------------------------------------------------------------------------------
My concern is the fact that lub, although a continuous variable, can only take specific values. Is it legitimate to use OLS in this situation or should I consider other techniques such as npregress.
I would be grateful for any advice.
This is a data subset:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int(lub t) 400 54 600 27 1000 42 800 29 200 30 200 29 400 34 400 48 400 39 400 36 300 43 800 12 100 48 2000 51 1000 46 400 29 600 30 400 15 400 37 400 31 600 19 800 66 800 21 400 29 800 29 400 38 400 27 400 61 400 59 400 46 1400 54 400 29 800 43 800 22 600 29 800 67 600 100 400 66 1200 67 800 . 400 31 800 29 800 32 600 67 800 55 600 59 400 61 1200 60 1000 51 400 18 end
Janet
Stata IC 16.0
0 Response to Appropriate regression model
Post a Comment