Quantitative Social Science

- An Introduction

  • Format
  • Bog, hardback
  • Engelsk

Beskrivelse

An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it--or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results--it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior.Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. * Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science* Provides hands-on instruction using R programming, not paper-and-pencil statistics* Includes more than forty data sets from actual research for students to test their skills on* Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools* Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises* Offers a solid foundation for further study* Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides

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  • Vægt1049 g
  • coffee cup img
    10 cm
    book img
    17,8 cm
    25,4 cm

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    Equation Least squares. Percentage Statistical hypothesis testing Sampling (Statistics) Inference Central limit theorem Minimum wage Monte Carlo method Law of large numbers Uncertainty Measurement Error Addition Binomial distribution permutation Probability Confidence interval Statistical Significance Linear regression Exploratory data analysis Margin of error Bayesian Conditional probability Confounding Observational study K-means Clustering Betweenness Probability distribution Prediction Coefficient of determination Random variable Weighted arithmetic mean Normal distribution Point estimation Bernoulli distribution Pagerank Data set Calculation Variance Accuracy and precision Standard deviation Combination Randomization Estimation Randomized experiment Causal inference Statistic Histogram Quantile Estimator Expected value Null hypothesis Standard error Coefficient P-value Result Cartesian coordinate system Alternative hypothesis Birthday Problem Bias of an estimator Average treatment effect Cross-validation (statistics) Box Plot Error term Empirical distribution function Correlation and dependence Joint probability distribution Law of total variance Internal Validity Fair coin False Discovery Rate Mutual exclusivity Parameter (computer programming) Proportionality (mathematics) Percentage point Quartile Interquartile Range Regression Discontinuity Design Regression toward the mean Student's t-test summation Sample Space Test Statistic Variable (mathematics) Standard score z-test Type I and type II errors Variable (computer science) Multiple comparisons problem Population Proportion One-Tailed Test

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