Journal Article•

# The life, letters and labours of Francis Galton. Vol. I.; 1822-1853

TL;DR: The Life, Letters and Labours of Francis Galton as discussed by the authors is the most complete account of Galton's life and work, with a detailed account of all the more important, not too technical, contributions made to science.

Abstract: THESE two stately volumes, which will be followed by a third, form a worthy memorial of a great man. It has been a labour of love to Prof. Karl Pearson to write them, a piety which must have cost him much, especially in the case of the second volume when the outer eye began to fail. He has earned the deep gratitude of all students of science, for besides giving us a living portrait, he has brought together a readable account of all the more important, not too technical, contributions that Galton made to science. The value of this is inestimable, for Galton scattered his papers widely, and many are not readily accessible.The Life, Letters and Labours of Francis Galton.By Prof. Karl Pearson. Vol. 1: Birth 1822 to Marriage 1853. Pp. xxiv + 246 + 66 plates. Vol. 2: Researches of Middle Life. Pp. xii + 425 + 54 plates. (Cambridge: At the University Press, 1914–1924.) Vol. 1, 30s. net; Vol. 2, 45s. net.

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01 Jan 1989TL;DR: The "model" represents the bridge between theory and data, by translating the "semantic" components of a theory into "syntactic" definitions so that the rules of logical and mathematical inference can be used to deduce new predictions about the results of empirical studies.

Abstract: ideas about causation are crystalized in a model for the statistics that can be derived from data on various sets of relatives. The "model" represents the bridge between theory and data, by translating the "semantic" components of a theory (Torgersen, 1958) into "syntactic" definitions so that the rules of logical and mathematical inference can be used to deduce new predictions about the results of empirical studies. Without such a quantitative model, it is impossible to know, for example, what parent-offspring correlation to expect from a knowledge of the correlations between twins. The model forces us to look more closely at the data by making us expect particular quantitative patterns. If these patterns occur then we may conclude that our model receives some support; if they clearly do not then our model is obviously wrong and some better alternative must be found. Figure 4 .1 summarizes the place of the model in diagramatic form. It is convenient to distinguish two important parts of the modeling process: model building and model fitting. The stage of model building consists in deciding how the causes of variation can be expressed in a mathematical form. The stage of model fitting consists of estimating the parameters of a model and deciding whether it fits the actual data. Each is considered separately. Elaboration/Decision Model f i t t ing Model building Experimental design Figure 4.1 The place of the model in the analysis of individual differences. 4. Introduction to Model Fitting 47 4 . 2 M O D E L BUILDING The way in which models are developed will become clearer by looking at the examples in the following chapters. However, we do not begin building models for personality differences in a vacuum. W e are guided by the cumulative experience of quantitative and behavioral genetics over the last eighty years. M a n y of the most informative studies have been conducted in plants and animals, which are far more amenable to genetic and environmental manipulation than man. This body of research suggests some of the broad features that models for human differences may encounter. 4 . 2 . 1 Genotype and phenotype The first basic distinction that we need to make is that made originally by Johannsen (1909) between genotype and phenotype. He observed that certain kinds of differences were transmissible between generations and could be modified b y selection within a population derived by crossing and recrossing pure breeding-strains. O n the other hand, even though pure breeding-strains were not uniform for many characteristics, such withinstrain differences could not be transmitted to subsequent generations or modified by selection. The discrimination between transmissible differences that were available to artificial selection and differences that were neither transmissible nor selectable led to the distinction between those characteristics of the organism that were expressed and measurable (the "phenotype") and those that influenced the phenotype but were capable of alteration b y selective breeding (the "genotype"). This basic idea can be represented by a (linear) model in which the phenotype of the zth individual (P,), expressed as a deviation from the average value of the population, is the sum of a "genotypic effect" G, and an environmental effect Thus Pi = G , +£,• (4 .1) Since we can only measure the phenotype of an individual directly, there is an infinite number of genetic and environmental effects that can satisfy the equation for each individual, so that neither the genetic nor the environmental effects can be identified statistically for any individual and there would be as many equations like (4 .1) as there are individuals in a sample. However, if genetic and environmental effects are independent (see Section 4 .2 .3 below) the phenotypic variance VP is the sum of the variances of the genetic effects G and the environmental effects E:

664 citations

### Additional excerpts

...At about the time Mendel had published his Experiments in Plant Hybridization, Francis Galton was turning his attention to the laws of heredity in Man (for a more detailed discussion of Gal ton's contribution see e.g. Burt, 1962; Forrest, 1974; Pearson, 1973) ....

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01 Jan 2008

TL;DR: The graphic portrayal of quantitative information has deep roots, which reach into histories of thematic cartography, statistical graphics, and data visualization, which are intertwined with each other.

Abstract: The graphic portrayal of quantitative information has deep roots These roots reach into histories of thematic cartography, statistical graphics, and data visualization, which are intertwined with each other They also connect with the rise of statistical thinking up through the 19th century, and developments in technology into the 20th century From above ground, we can see the current fruit; we must look below to see its pedigree and germination There certainly have been many new things in the world of visualization; but unless you know its history, everything might seem novel

325 citations

••

York University

^{1}TL;DR: The graphic representation of quantitative information has deep roots that reach into the histories of the earliestmap making and visual depiction, and later into thematic cartography, statistics and statistical graphics, medicine and other fields.

Abstract: It is common to think of statistical graphics and data visualization as relatively modern developments in statistics. In fact, the graphic representation of quantitative information has deep roots. These roots reach into the histories of the earliestmap making and visual depiction, and later into thematic cartography, statistics and statistical graphics, medicine and other fields. Along the way, developments in technologies (printing, reproduction), mathematical theory and practice, and empirical observation and recording enabled the wider use of graphics and new advances in form and content.

303 citations

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04 Nov 2010

TL;DR: In this article, the authors introduce persistence models and Bootstrap Confidence Intervals for univariate and bivariate time series analysis, and present a future direction for future directions. But, they do not discuss the use of spectral analysis.

Abstract: Part I: Fundamental Concepts.- 1 Introduction.- 2 Persistence Models.- 3 Bootstrap Confidence Intervals.- Part II: Univariate Time Series.- 4 Regression I.- 5 Spectral Analysis.- 6. Extreme Value Time Series.- Part III: Bivariate Time Series.- 7 Correlation.- 8 Regression II.- Part IV: Outlook.- 9 Future Directions.

261 citations

##### References

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01 Jan 1989TL;DR: The "model" represents the bridge between theory and data, by translating the "semantic" components of a theory into "syntactic" definitions so that the rules of logical and mathematical inference can be used to deduce new predictions about the results of empirical studies.

Abstract: ideas about causation are crystalized in a model for the statistics that can be derived from data on various sets of relatives. The "model" represents the bridge between theory and data, by translating the "semantic" components of a theory (Torgersen, 1958) into "syntactic" definitions so that the rules of logical and mathematical inference can be used to deduce new predictions about the results of empirical studies. Without such a quantitative model, it is impossible to know, for example, what parent-offspring correlation to expect from a knowledge of the correlations between twins. The model forces us to look more closely at the data by making us expect particular quantitative patterns. If these patterns occur then we may conclude that our model receives some support; if they clearly do not then our model is obviously wrong and some better alternative must be found. Figure 4 .1 summarizes the place of the model in diagramatic form. It is convenient to distinguish two important parts of the modeling process: model building and model fitting. The stage of model building consists in deciding how the causes of variation can be expressed in a mathematical form. The stage of model fitting consists of estimating the parameters of a model and deciding whether it fits the actual data. Each is considered separately. Elaboration/Decision Model f i t t ing Model building Experimental design Figure 4.1 The place of the model in the analysis of individual differences. 4. Introduction to Model Fitting 47 4 . 2 M O D E L BUILDING The way in which models are developed will become clearer by looking at the examples in the following chapters. However, we do not begin building models for personality differences in a vacuum. W e are guided by the cumulative experience of quantitative and behavioral genetics over the last eighty years. M a n y of the most informative studies have been conducted in plants and animals, which are far more amenable to genetic and environmental manipulation than man. This body of research suggests some of the broad features that models for human differences may encounter. 4 . 2 . 1 Genotype and phenotype The first basic distinction that we need to make is that made originally by Johannsen (1909) between genotype and phenotype. He observed that certain kinds of differences were transmissible between generations and could be modified b y selection within a population derived by crossing and recrossing pure breeding-strains. O n the other hand, even though pure breeding-strains were not uniform for many characteristics, such withinstrain differences could not be transmitted to subsequent generations or modified by selection. The discrimination between transmissible differences that were available to artificial selection and differences that were neither transmissible nor selectable led to the distinction between those characteristics of the organism that were expressed and measurable (the "phenotype") and those that influenced the phenotype but were capable of alteration b y selective breeding (the "genotype"). This basic idea can be represented by a (linear) model in which the phenotype of the zth individual (P,), expressed as a deviation from the average value of the population, is the sum of a "genotypic effect" G, and an environmental effect Thus Pi = G , +£,• (4 .1) Since we can only measure the phenotype of an individual directly, there is an infinite number of genetic and environmental effects that can satisfy the equation for each individual, so that neither the genetic nor the environmental effects can be identified statistically for any individual and there would be as many equations like (4 .1) as there are individuals in a sample. However, if genetic and environmental effects are independent (see Section 4 .2 .3 below) the phenotypic variance VP is the sum of the variances of the genetic effects G and the environmental effects E:

664 citations

01 Jan 2008

TL;DR: The graphic portrayal of quantitative information has deep roots, which reach into histories of thematic cartography, statistical graphics, and data visualization, which are intertwined with each other.

Abstract: The graphic portrayal of quantitative information has deep roots These roots reach into histories of thematic cartography, statistical graphics, and data visualization, which are intertwined with each other They also connect with the rise of statistical thinking up through the 19th century, and developments in technology into the 20th century From above ground, we can see the current fruit; we must look below to see its pedigree and germination There certainly have been many new things in the world of visualization; but unless you know its history, everything might seem novel

325 citations

••

York University

^{1}TL;DR: The graphic representation of quantitative information has deep roots that reach into the histories of the earliestmap making and visual depiction, and later into thematic cartography, statistics and statistical graphics, medicine and other fields.

Abstract: It is common to think of statistical graphics and data visualization as relatively modern developments in statistics. In fact, the graphic representation of quantitative information has deep roots. These roots reach into the histories of the earliestmap making and visual depiction, and later into thematic cartography, statistics and statistical graphics, medicine and other fields. Along the way, developments in technologies (printing, reproduction), mathematical theory and practice, and empirical observation and recording enabled the wider use of graphics and new advances in form and content.

303 citations

•

26 Mar 2010

TL;DR: 1. Testing and assessment in context 2. Standardised testing 3. Classroom assessment 4. Deciding what to test 5. Designing test specifications 6. evaluating, prototyping and piloting

Abstract: 1. Testing and assessment in context 2. Standardised testing 3. Classroom assessment 4. Deciding what to test 5. Designing test specifications 6. Evaluating, prototyping and piloting 7. Scoring language tests 8. Aligning tests to standards 9. Test administration 10. Testing and teaching Epilogue Glossary

241 citations