Final answer:
In the descriptive statistics phase, analysts calculate measures of central tendency (mean, median, mode), variability (standard deviation, quartiles), and analyze distributions (normal, skewed) to summarize and understand datasets. They also detect patterns and correlations between variables.
Step-by-step explanation:
When an analyst deals with concepts such as central tendency, variability, and distributions, they are in the phase of conducting descriptive statistics. Descriptive statistics help to summarize and describe the key features of a dataset. Measures of central tendency involve the mean, median, and mode.
The mean is the arithmetic average of a dataset, and it is used as the best estimate of the central value when there are no extreme outliers. The median represents the middle value of an ordered dataset and is preferable when outliers are present. The mode is the most frequently occurring value in a dataset.
For determining the spread or variability of data, analysts use measures like the standard deviation, which indicates how far data points are from the mean, and quartiles, which divide the dataset into four equal parts. Distributions, such as normal, skewed, positive (right-skewed), or negative (left-skewed), describe the shape of the dataset and can impact the measures of central tendency and spread. Pattern discovery includes identifying basic correlations between variables and recognizing trends within the data.
An analyst typically graphs the data to better understand these concepts, using various visualizations like histograms or box plots. In symmetric distributions, the mean, median, and mode will be the same, whereas in skewed distributions, they will differ and provide insight into the shape of the data.