Preview & Tips

The initial analysis of the dataset provides valuable insights into the key determinants of the Score variable. Here's a detailed breakdown of the analysis:

Descriptive Statistics

The dataset contains 3,171 records with the following key variables:

• Score: Ranges from 61 to 82, with a mean of approximately 69.63.
• DrivingDistance: Ranges from 0 to 357.8 yards, with a mean of approximately 297.32 yards.
• DrivingAccuracy: Averages around 68.55%, with a standard deviation of 14.36%.
• PuttingAverage: Averages around 1.75, with a standard deviation of 0.15.

Correlation Analysis

The correlation matrix indicates how different variables relate to the Score. Key correlations with Score include:

• PuttingAverage (PA): High negative correlation (-0.64).
• SGP (Strokes Gained Putting): Moderate negative correlation (-0.58).
• Par4 (Average Par 4 Score): Moderate positive correlation (0.45).
• GIR (Greens in Regulation): Moderate negative correlation (-0.40).

Key Determinants Analysis

To determine the top factors influencing the Score, we need to consider:

• Interactions between top determinants
• Non-linear relationships using polynomials
• Categorical intervals for continuous variables
• Log transformations to handle skewed distributions

Visual Analysis

Let's visualize these relationships to better understand the trends and interactions.

Scatter Plots and Polynomial Fits

We will create scatter plots with polynomial fits for key variables.

Pair Plot for Key Variables

We will create a pair plot for key variables to visualize interactions.

Binning Continuous Variables

We will bin variables such as DrivingDistance and PuttingAverage into categorical intervals.

Log Transformations

We will apply log transformations to skewed variables and visualize their distributions.

Visualizations

Let's create these visualizations. ​