Discrete data are data that can only assume specific values that are countable and distinct. For example, the number of books, the number of heads in a coin toss, or the number of patientsin a hospital are discrete data. Discrete data cannot have fractional or decimal values, and there are clear spaces between the possible values12.

Continuous data are data that can assume any value within a range and can be meaningfully divided into smaller parts. For example, the weight, height, length, time, or temperature are continuous data. Continuous data can have fractional or decimal values, and there are infinite numbers of possible values between any two points12.

When creating a dashboard to identify trends in data sets, the most appropriate consideration is the data sources and attributes. This is because the quality, reliability, and structure of the data sources directly influence the dashboard’s ability to accurately reflect trends. Attributes, such as the type of data and the time frame it covers, are crucial for trend analysis. A well-designed dashboard should provide a clear and intuitive representation of the data, allowing for easy identification of trends and patterns.

Frequently asked questions (B) can inform the design of the dashboard but are not a direct consideration for the development process itself. A report from the data source © might be an output of the dashboard but does not guide its development. A comparison of data sets (D) could be a feature of the dashboard, but the underlying data sources and attributes must be considered first to ensure accurate and meaningful comparisons.

References:

Best practices in dashboard design emphasize the importance of understanding and consolidating different data sources and creating a mix of useful metrics, which aligns with the choice of data sources and attributes1.

Fundamental dashboard design principles include the clear and efficient display of information, which is dependent on the proper selection and use of data sources and attributes2.

Effective dashboard communication is achieved by using colors, shapes, sizes, labels, and legends meaningfully, all of which rely on the underlying data sources and attributes3.

 Creating a new report that automatically calculates weekly totals would streamline the process for the recipient. By setting this report to run at the end of business on Friday, it would provide the recipient with the necessary information for the entire week in one consolidated document. This eliminates the need for manual calculations and combines the previous week’s data into one report, making it more efficient and less time-consuming.

References:

Best practices in business analytics suggest automating repetitive tasks and consolidating reports where possible to improve efficiency and reduce the potential for human error.

Comprehensive and Detailed In-Depth Explanation:

To compile a comprehensive annual revenue report from multiple quarterly sales reports, the analyst should integrate the datasets appropriately:

Data Merge: This technique involves combining datasets based on common fields or keys. In this scenario, merging would align data from different quarters based on shared attributes, such as location identifiers, to create a unified dataset.

Data Append: Appending adds datasets sequentially, stacking them on top of each other. While this could combine the reports, it doesn't ensure integration based on common fields, which is necessary for accurate analysis.

Data Blending: Blending combines data from different sources or formats. If the quarterly reports are in varied formats or from different systems, blending would be appropriate. However, if they share the same structure, blending isn't necessary.

Data Imputation: Imputation addresses missing or incomplete data by filling in gaps. This technique isn't relevant to combining complete quarterly reports.

Therefore, merging the data ensures that all quarterly reports are integrated based on common fields, providing a cohesive annual revenue report.

The Chi-squared test is used to determine whether there is a statistical association between two categorical variables. In this case, both "number of doors" and "number of gears" are categorical variables, making the chi-squared test the appropriate method​.

The mean (average) is the most sensitive measure when it comes to outliers. If a dataset contains extreme values (either very high or very low), they disproportionately affect the mean, making it a less robust measure of central tendency​.

Comprehensive and Detailed In-Depth Explanation:

Measures of central tendency are statistical metrics that describe the center point or typical value of a dataset. The primary measures include mean (average), median (middle value), and mode (most frequent value).

Descriptive Statistics: This branch of statistics involves summarizing and organizing data to describe its main features. It includes calculating measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation). Descriptive statistics provide a comprehensive snapshot of the dataset's characteristics.

Forecasting: This involves making predictions about future data points based on historical data. While valuable for planning, it doesn't provide insights into the current dataset's central tendency.

Trend Analysis: This technique examines data over time to identify patterns or trends. It's useful for understanding data direction but doesn't focus on central tendency measures.

Gap Analysis: This method compares actual performance with potential or desired performance, identifying gaps between current and expected outcomes. It doesn't relate to measures of central tendency.

Therefore, to obtain basic measures of central tendency, an analyst should employ descriptive statistics.