CompTIA Data+ DA0-001 Syllabus Exam Questions Answers

The measure of dispersion is used to describe the spread of data around a central value. In the context of a data set containing actual and projected sales, the measure of dispersion will help to understand the variability or consistency of sales figures. The variance is the most appropriate statistical method for finding the measure of dispersion because it calculates the average of the squared differences from the Mean, providing a clear picture of data spread. It is especially useful in comparing the spread between different data sets and understanding the distribution of data points.

• Mean is a measure of central tendency, not dispersion.
• Correlation measures the relationship between two variables, not the spread of a single variable.
• Confidence intervals are used to estimate the range within which a population parameter will fall, but they do not measure dispersion within the data set itself.

References:

• Measures of Dispersion in Statistics1
• Measures of Dispersion - Definition, Formulas, Examples2
• Statistical dispersion - Wikipedia3

The correct answer is C. Indexing documents.

Indexing documents is a query optimization technique that involves creating a data structure that allows faster access to the data in the documents. Indexing documents can reduce the amount of data that needs to be scanned for a particular query, thus improving the performance and efficiency of the query. Indexing documents can also help with searching, sorting, filtering, and aggregating the data in the documents12

  A Pie chart is the most suitable for representing the average spending of active customers versus inactive customers. Pie charts are effective for comparing parts of a whole, which makes them ideal for visually displaying the proportion of spend between two distinct groups. They are widely used to depict percentage distributions and are straightforward, allowing immediate analysis of the active vs. inactive customer spending distribution at a glance.

This is because exploratory data analysis is a type of process that performs initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization, such as box plots, histograms, scatter plots, etc. Exploratory data analysis can be used to understand and summarize the data, as well as to generate hypotheses or questions for further analysis or research. For example, exploratory data analysis can be used to identify and visualize the characteristics, features, or behaviors of the data, as well as to measure their distribution, frequency, or correlation. The other options are not types of processes that perform initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization. Here is what they mean:

• A t-test is a type of statistical method that tests whether there is a significant difference between the means of two groups or samples, such as whether there is a difference between the average exam scores of two classes in this case. A t-test can be used to test or verify a claim or an assumption about the data, as well as to measure the confidence or the error of the estimation.
• A performance analysis is a type of process that measures whether the data meets certain goals or objectives, such as targets, benchmarks, or standards. A performance analysis can be used to identify and visualize the gaps, deviations, or variations in the data, as well as to measure the efficiency, effectiveness, or quality of the outcomes. For example, a performance analysis can be used to determine if there is a gap between a student’s test score and their expected score based on their previous performance.
• A link analysis is a type of process that determines whether the data is connected to other datapoints, such as entities, events, or relationships. A link analysis can be used to identify and visualize the patterns, networks, or associations among the datapoints, as well as to measure the strength, direction, or frequency of the connections. For example, a link analysis can be used to determine if there is a connection between a customer’s purchase history and their loyalty program status.