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.

Target variables

- 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:

Really interesting take on airfoil self noise data… I may try to play around it myself. However, it would really have been great to see your Python code and not just the output and visualisations. I came here for some code.

Thank you. Soon we will release the code.