In my data analysis project, I will evaluate the effect of gender, age, and distance on customer spending at a supermarket using ANOVA. Additionally, I will investigate the potential correlation between purchases of basic and premium goods and sales transactions using regression. The variables and their ranges are as follows:

• Response Variable: Sales per visit (Range: 9.20-93.65)
• Factors: Gender (male, female), Distance (3.70-12.60), Age (12-98)
• Other Variables: Basic sales (Range: 0-49.07), Premium Sales (Range: 0-50.66)

Challenges:

1. One of the main challenges in this project is categorizing age and distance. As these variables are continuous, I will need to create categories or levels for them to run ANOVA. The number of the levels may be a challenge and will require careful consideration. I don't know how to do it properly.
2. Another and probable most important challenge is the high variance in the data, which may violate the two basic assumptions of ANOVA: normality of residuals and homoscedasticity. To address this, I will explore techniques such as data transformation or reduction to determine if these challenges can be resolved. But they have not been resolved yet.
3. Similar challenges, which violated basic assumptions of the model, are also present in the regression analysis.

I welcome any feedback or suggestions to improve my project and overcome these challenges. Please feel free to ask for more information if needed.