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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.

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**Model**: Linear Regression**Dependent Variable**: Quantity Sold**Independent Variables**: Price, Advertising**Mean Squared Error (MSE)**: 819634.37**R-squared**: 0.77

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.

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**Dependent Variable**: Quantity Sold**Independent Variable (Categorical)**: Price**F-Value**: 2.53**P-Value**: 0.23

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.

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**Variables**: Price and Quantity Sold**Correlation Coefficient**: -0.81

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.

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- The residuals plot displays the difference between the observed and predicted values of Quantity Sold.
- A residuals plot is useful for identifying patterns in the prediction errors.
- The plot shows that residuals are scattered around the horizontal axis, indicating a generally good fit of the model to the data, although some outliers are present.
- This visualization was created using Plotly with a dark theme.

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