Trends in statistical data are interpreted using scatter diagrams. A scatter diagram presents each data point in two coordinates. The first point of data representation is done in correlation to the x-axis while the second coordinate corresponds to the y axis. The intersection of the two coordinates represents the point of observation ( Mindrila & Balentyne 1 ). The independent variable is represented on the horizontal axis while the dependent variable is placed on the vertical axis. The horizontal and the vertical axes cross a point referred to as the point of origin. The coordinates at the point of origin are represented as (0,0).

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Discussion
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The data presented in the question is obtained from the weekly sales of the case study of American Toys Inc. The data presents the sales clerk hours against the number of sales. A scatter diagram of the data is represented on the attached excel file.

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From the data presented, the work hours represent the independent variable and hence is placed on the horizontal axis ( Nguyen et al. 2 ). The number of sales is the dependent variable and is placed on the vertical axis. The horizontal axis is the x-axis, while the vertical axis is also referred to as the y-axis ( Smart & Szafir 3). The data presented in the excel scatter diagram indicates an uphill pattern as one moves from left to right hence a positive correlation between the work hours and the number of sales is deduced ( Stockemer et al. 4 ). It is accurate to conclude that an increase in x-values results in a significant increase in y-values.

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Conclusion
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The positive correlation between the x-values and the y-values indicates that more work hours result in more sales. There is no linear correlation between the x-values and the y-values. It is vital to note that the relationships deduced from the scatter diagram do not necessarily indicate a cause-and-effect relationship.

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Sources
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Mindrila, D., & Balentyne, P. (2017). Scatterplots and correlation.
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Retrieved from
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.

Nguyen, Q. V., Miller, N., Arness, D., Huang, W., Huang, M. L., & Simoff, S. (2020). Evaluation on interactive visualization data with scatterplots.
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Visual Informatics
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,
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4
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(4), 1-10.

Smart, S., & Szafir, D. A. (2019, May). Measuring the separability of shape, size, and color in scatterplots. In
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Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
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(pp. 1-14).

Stockemer, D., Stockemer, & Glaeser. (2019).
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Quantitative Methods for the social sciences
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(Vol. 50, p. 185). Springer International Publishing.