We visualized the distribution of Quantity Sold from the dataset using a histogram. The histogram, complemented by a box plot as a marginal plot, provides insights into the spread and central tendency of the Quantity Sold data. The visualization was created using Plotly with a dark theme.

The linear regression model, predicting Quantity Sold based on Price and Advertising, exhibits a strong relationship with an R-squared value of 0.77, indicating that 77% of the variance in Quantity Sold can be explained by the model. The MSE indicates the average squared difference between the estimated values and the actual value.

The ANOVA test, with Price as a categorical independent variable and Quantity Sold as the dependent variable, resulted in an F-Value of 2.53 and a P-Value of 0.23. The P-Value being greater than 0.05 suggests that there is not a statistically significant difference in the mean Quantity Sold across the different Price categories.

The correlation coefficient between Price and Quantity Sold is -0.81, indicating a strong negative correlation. This suggests that as the Price increases, the Quantity Sold tends to decrease, and vice versa.