What term describes a data point in a scatter chart that deviates significantly from other points?

What term describes a data point in a scatter chart that deviates significantly from other points?

• A. Axis
• B. Legend
• C. Cluster
• D. Outlier

Answer: (D)

The term that describes a data point in a scatter chart that deviates significantly from other points is "outlier." An outlier is a value that lies outside the general distribution of the data. It can indicate variability in your measurements, experimental errors, or a novel phenomenon that may warrant further investigation. In a scatter plot, an outlier can skew the interpretation of data trends, potentially leading to misleading conclusions if not identified and understood correctly. Outliers can affect the results of statistical analyses, regression models, and forecasting, making it essential for analysts and data scientists to recognize their presence and decide how to handle them. The presence of outliers can be caused by various factors, such as data entry errors, incorrect measurements, or actual variability in the data set. Identifying outliers allows analysts to ensure that they are working with the most accurate representation of their data, enabling more robust decision-making based on data analysis.

The term that describes a data point in a scatter chart that deviates significantly from other points is "outlier." An outlier is a value that lies outside the general distribution of the data. It can indicate variability in your measurements, experimental errors, or a novel phenomenon that may warrant further investigation.

In a scatter plot, an outlier can skew the interpretation of data trends, potentially leading to misleading conclusions if not identified and understood correctly. Outliers can affect the results of statistical analyses, regression models, and forecasting, making it essential for analysts and data scientists to recognize their presence and decide how to handle them.

The presence of outliers can be caused by various factors, such as data entry errors, incorrect measurements, or actual variability in the data set. Identifying outliers allows analysts to ensure that they are working with the most accurate representation of their data, enabling more robust decision-making based on data analysis.