Dataset:  forklift_acoustic.csv

Source: E. Zhang, Q. Zhang, J. Xiao, L. Hou, T. Guo (2018). "Acoustic 
Comfort Evaluation Modeling and Improvement Test of a Forklift Based on
Rank Score Comparison and Multiple Linear Regression," Applied Acoustics,
Vol. 135, pp. 29-36.


Description: Multiple Regression of 50 noise samples of a forklift.
Response was the mean annoyance score for the sample. Predictors:
Linear sound pressure level, A-weighted Sound Pressure Level, loudness,
sharpness, roughness, fluctuation, totality, articulation index, and
impulsiveness.

Variable Names:
caseNum
LSPL
ASPL
loud
sharp
rough
fluc
tonal
AI
impulsive
annoyance