Visualizing Quantity Sold Distribution with Histogram and Box Plot

Apr 1, 2024, 11:27 AM

Can we visualize the distribution of Quantity Sold?

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.

regression analysis

Regression Analysis Summary

  • 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.

perform ANOVA on the data

ANOVA Summary

  • 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.

Explore correlation between Price and Quantity Sold

Correlation Summary

  • 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.

Plot and find the residual outputs

Residuals Plot Summary

  • 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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