This case study using Airfoil Self-Noise dataset.
The NASA data set comprises different size NACA 0012 airfoils at various wind tunnel speeds and angles of attack. The span of the airfoil and the observer position were the same in all of the experiments.
The columns in this dataset are:
- A = Frequency
- B = Angle of attack
- C = Chord length
- D = Free-stream velocity
- E = Suction side displacement thickness
- F = Scaled sound pressure level
Sample Airfoil Self-Noise data
Prediction variables (attributes)
- Frequency, in Hertzs.
- Angle of attack, in degrees.
- Chord length, in meters.
- Free-stream velocity, in meters per second.
- Suction side displacement thickness, in meters.
- Scaled sound pressure level, in decibels.
There are 1503 observations in the dataset.
Use Statsmodels to estimate the model coefficients for the Airfoil Self-Noise data with B (angle of attack):
Interpreting Model Coefficients
Interpretation angle of attack coefficient (β1)
- A “unit” increase in angle of attack is associated with a 0.008927 “unit” increase in F (scaled sound pressure level).
Using the Model for Prediction
Let’s say that where the Angle of attack increased was 70. What would we predict for the scaled sound pressure level? (First approach for prediction)
126.309388 + (0.008927 * 70) = 126.934278
Thus, we would predict scaled sound pressure level of 126.934278.
Use Statsmodels to make the prediction: (Second approach for prediction)
Plotting the Least Squares Line
Make predictions for the smallest and largest observed values of x, and then use the predicted values to plot the least squares line: