Construct A Scatter Plot For Amazon's Net Income And Sales

Construct a scatter plot for Amazon's net income/loss and sales figures for the period 1995–2015

Construct a scatter plot for Amazon's net income/loss and sales figures for the period 1995–2015.

Determine a polynomial model, including its order (or degree), for Amazon's net income/loss and sales figures. Use Excel to complete the following:

Explain your process of determining the polynomial model.

Submit your work in a Word document and attach your Excel file.

Paper For Above instruction

Construct a scatter plot for Amazon s net income loss and sales figures for the period 19952015

Introduction

Understanding the relationship between Amazon's sales and its net income or loss over time provides valuable insights into its business performance and growth trajectory. This analysis involves two primary steps: creating a visual representation through a scatter plot and developing a polynomial model to capture the underlying trend. Both steps utilize Microsoft Excel for data visualization and analysis, followed by a process explanation and documentation in a Word document.

Constructing the Scatter Plot

The initial step involves plotting Amazon's annual sales against its net income or loss from 1995 to 2015. This period covers significant growth phases, including the company's expansion into various markets and diversification of revenue streams. To create the scatter plot:

  1. Gather the data: Obtain Amazon's annual sales figures and net income or loss data for each year between 1995 and 2015. This data can often be found in the company's annual reports, SEC filings, or reliable financial databases.
  2. Input data into Excel: Arrange the data in two columns, with years in one column and corresponding sales and net income/loss in the adjacent columns.
  3. Create the scatter plot: Select the sales and net income/loss data, then insert a scatter plot via the 'Insert' tab. Choose the appropriate scatter chart style to visualize the relationship clearly.
  4. Customize the chart: Add axis labels, a descriptive title, and data labels if necessary to enhance clarity.

The resulting scatter plot reveals patterns, potential correlations, or trends, such as the relationship between increased sales and profitability or losses during early years.

Determining the Polynomial Model

Next, developing a polynomial model involves fitting a curve to the scatter plot data to describe the relationship between sales and net income or loss mathematically. The process includes:

  1. Choosing the polynomial degree: Start with a linear model (degree 1), then evaluate higher degrees like quadratic (degree 2), cubic (degree 3), etc., based on the pattern of data points and how well the model fits.
  2. Using Excel's trendline feature: Select the data points in the scatter plot, then add a trendline. In the trendline options, select 'Polynomial' and specify the degree. Record the equation provided by Excel, which includes coefficients for the polynomial terms.
  3. Assessing model fit: Check the R-squared value to determine how well the polynomial fits the data. Higher R-squared values indicate a better fit. Residual analysis can further validate the model's appropriateness.
  4. Deciding the degree: Typically, the simplest model that sufficiently captures the trend without overfitting is preferred. In this context, a quadratic or cubic model often balances complexity and accuracy.

The chosen polynomial model encapsulates the relationship and enables predictions or interpretations about sales and net income over the given period.

Explanation of the Polynomial Model Determination Process

The process begins by visual assessment of the scatter plot to identify the pattern of data points. A linear model is tested initially; however, if the data exhibits curvature—such as rapid growth or decline—a polynomial model of higher degree may better describe the trend. Using Excel's trendline feature simplifies this process, allowing intuitive fitting of polynomial curves and immediate evaluation of their statistical significance via R-squared values. Comparing models of different degrees helps select the most appropriate one. The iterative approach involves:

  • Adding trendlines of increasing degree
  • Comparing R-squared values
  • Checking residual plots for randomness
  • Balancing model complexity with interpretability

This systematic method ensures that the selected polynomial model robustly captures the relationship between Amazon's sales and net income/loss over 1995–2015.

Conclusion

By constructing a scatter plot and fitting a polynomial model, analysts can visualize and quantify Amazon's financial performance relationship over two decades. This approach provides a foundation for deeper financial analysis, forecasting, and strategic decision-making tailored to the company's growth trends.

References

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  • Microsoft Support. (2020). Create a trendline in an Excel chart. Microsoft.
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