INTERPRETING DATA.docx
- 1. INTERPRETING DATA Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions. The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.
- 2. QUICK ANALYSIS The Quick Analysis tool helps you quickly format your data into a chart, table, or sparkline. Toggle navigation. The Quick Analysis function helps you quickly format your data into a chart, table, summary formula, sparkline, or highlighted figures with just a few simple steps.
- 3. DATA & DATUM Data is a collection of discrete values that convey information, describing quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted. A datum is an individual value in a collection of data.
- 4. RANGE OF DATA In statistics, the range is the spread of your data from the lowest to the highest value in the distribution. It is a commonly used measure of variability. Along with measures of central tendency, measures of variability give you descriptive statistics for summarizing your data set.
- 5. TRENDS In statistics, the range is the spread of your data from the lowest to the highest value in the distribution. It is a commonly used measure of variability. Along with measures of central tendency, measures of variability give you descriptive statistics for summarizing your data set.
- 6. CLUSTERS Cluster analysis is the grouping of objects such that objects in the same cluster are more similar to each other than they are to objects in another cluster. The classification into clusters is done using criteria such as smallest distances, density of data points, graphs, or various statistical distributions.
- 7. ANOMALIES/BIAS Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting training of deep anomaly detection models with additional labeled anomaly samples. However, the labeled data often does not align with the target distribution and introduces harmful bias to the trained model.
- 8. INDEPENDENT VARIABLE The independent variable is the cause. Its value is independent of other variables in your study. An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent”
- 9. because it’s not influenced by any other variables in the study. DEPENDENT VARIABLE The dependent variable is the effect. Its value depends on changes in the independent variable. A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the
- 10. outcome you’re interested in measuring, and it “depends” on your independent variable. TEXTUAL The textual presentation of data is used when the data is not large and can be easily comprehended by the reader just when he reads the paragraph. This data format is useful when some qualitative statement is to
- 11. be supplemented with data. The reader does not want to read volumes of data to be represented in the tabular format. TABULAR Tables or Tabular presentation of data is known to be the arrangement of certain values recorded in tables such that they are easy to manage and read. It is mostly done for a reader
- 12. to gain the idea about the data without making it too complicated. The data presentation can be used for proper matter which is informative and creative at the same time. GRAPHICAL Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and
- 13. representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate.