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As described below, Galton used this representation of his data to illustrate basic foundations of what statisticians still call regression. Galton's first insights about regression sprang from a two-dimensional diagram plotting the sizes of daughter peas against the sizes of mother peas. This characteristic eliminated, or at least postponed, having to deal with the problem of statistically assessing genetic contributions from multiple sources. He chose the sweet pea because that species could self-fertilize daughter plants express genetic variations from mother plants without contribution from a second parent. Galton initially approached this problem by examining characteristics of the sweet pea plant. The thoughts that prompted the development of the PPMC began with a then vexing problem of heredity – understanding how strongly the characteristics of one generation of living things manifested in the following generation. Galton's fascination with genetics and heredity provided the initial inspiration that led to regression and the PPMC. Historians have also suggested that his cousin's lasting fame unfairly overshadowed the substantial scientific contributions Galton made to biology, psychology and applied statistics (see, for example, FitzPatrick 1960). Galton, a cousin of Charles Darwin and an accomplished 19th century scientist in his own right, has often been criticized in this century for his promotion of “eugenics” (planned breeding of humans see, for example, Paul (1995). Although Pearson did develop a rigorous treatment of the mathematics of the Pearson Product Moment Correlation (PPMC), it was the imagination of Sir Francis Galton that originally conceived modern notions of correlation and regression. The complete name of the correlation coefficient deceives many students into a belief that Karl Pearson developed this statistical measure himself.