In the study of association of attributes, two attributes A and B are considered independent if (AB) = (A)(B)/n. If (AB) > (A)(B)/n, the observed frequency of the joint occurrence is greater than what would be expected under independence, indicating a positive association between the two attributes.

A coefficient of association of +1 indicates a perfect positive linear relationship. This means that as one variable increases, the other variable increases proportionally. It represents the maximum possible value for a positive correlation, signifying that all data points lie exactly on a straight line with a positive slope.

A scatter diagram, or scatter plot, is a graphical tool used to display the relationship between two quantitative variables. By plotting individual data points on a Cartesian coordinate system where the x-axis represents one variable and the y-axis represents the other, researchers can visually identify patterns, trends, or correlations between the two variables. It is a fundamental exploratory data analysis tool rather than a formal statistical test.

Correlation can be assessed both through visual inspection using scatter diagrams and through various graphical methods that represent the relationship between two variables. Both approaches are standard tools in exploratory data analysis to identify patterns, trends, and the strength of associations between datasets.

Outliers are data points that differ significantly from other observations. In this dataset, 5 and 150 are located at the extreme ends of the distribution, far from the central cluster of values, and are thus statistically identified as outliers.

Outliers are data points that differ significantly from other observations in a dataset. They may indicate variability in the measurement, experimental errors, or a heavy-tailed distribution. Detecting outliers is a crucial step in exploratory data analysis to ensure that statistical models are not unduly influenced by extreme values.

A frequency polygon is constructed by plotting the frequency of each class against its midpoint and connecting these points with straight line segments. This graphical representation is useful for comparing multiple distributions on the same axes, as it provides a clearer view of the shape of the distribution than a histogram alone.

Histograms and their related graphical representations, such as bar charts used for frequency distribution, can be categorized into various types including deviation, paired, and grouped formats depending on the nature of the data being visualized.

In statistical diagrammatic representation, one-dimensional diagrams are those where only one dimension, typically the length or height of the bars, is used to represent the magnitude of the data. While pie charts are technically two-dimensional, they are often grouped in basic statistics texts under simple visual representations where the arc length or angle is the primary metric of comparison.

A symmetrical distribution, particularly the normal distribution, is characterized by a bell-shaped curve. In this distribution, the mean, median, and mode coincide at the center, and the tails on either side are mirror images of each other, indicating balanced data dispersion.