Professional sports, like many other professions, require a variety of skills for success.
That makes it difficult to evaluate and predict success. Fortunately, sports provide examples we can use to learn about modeling success because of the vast amount of data which are available. Here’s an example.
What makes a golfer successful? The game of golf requires many skills. Putting well or hitting long drives will not, by themselves, lead to success. Success in golf requires a combination of skills. That makes multiple regression a good candidate for modeling golf achievement.
A number of Internet sites post statistics for the current PGA players. We have data for 190 top players of 2017 in the file Golfers 2017.
All of these players earned money on the tour, but the distribution of earnings is quite skewed. So it’s a good idea to take log of Earnings as the response variable.
The variables in the data file include:

Investigate these data. Find a regression model to predict golfers’ success (measured in log earnings). Write a report presenting your model including an assessment of its limitations. Note: Although you may consider several intermediate models, a good report is about the model you think best, not necessarily about all the models you tried along the way while searching for it.