In your experience, how do you determine which statistical model is the best fit for a given dataset?

During my final year project in college, I worked on predicting student performance based on various factors like attendance, assignment grades, and participation in discussions. I started by exploring the dataset to understand its structure and the relationships between variables. I tested several models, including linear regression and decision trees. I chose decision trees because they provided clear insights into how different factors influenced performance and were easy to interpret for my audience. This approach not only helped me present my findings effectively but also received positive feedback from my professors for its clarity and accuracy.

While volunteering for a local nonprofit, I analyzed community feedback from a survey on service satisfaction. I initially considered using a simple average to summarize feedback but realized that this wouldn't capture the nuances in the data. Instead, I decided to use a Likert scale analysis to quantify responses. I applied logistic regression to understand the factors that influenced satisfaction levels. By presenting the results in a straightforward manner, I was able to help the nonprofit make informed decisions about service improvements, demonstrating the importance of selecting an appropriate model for the context.

In my first job as an analyst, I was tasked with analyzing sales data to identify trends and forecast future sales. I experimented with various models, including ARIMA for time series forecasting and simple linear regression for trend analysis. After testing, I found that ARIMA provided the best fit for our seasonal data patterns. I validated this model using historical sales data and presented my findings to the team, which helped us make strategic decisions for the upcoming quarter based on accurate forecasts.