A01=Kosuke Imai
Accuracy and precision
Addition
Alternative hypothesis
Author_Kosuke Imai
Average treatment effect
Bayesian
Bernoulli distribution
Betweenness
Bias of an estimator
Binomial distribution
Birthday problem
Box plot
Calculation
Cartesian coordinate system
Category=JHBC
Causal inference
Central limit theorem
Coefficient
Coefficient of determination
Combination
Conditional probability
Confidence interval
Confounding
Correlation and dependence
Cross-validation (statistics)
Data set
Empirical distribution function
eq_bestseller
eq_isMigrated=0
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Equation
Error
Error term
Estimation
Estimator
Expected value
Exploratory data analysis
Fair coin
False discovery rate
Fisher's exact test
Histogram
Inference
Internal validity
Interquartile range
Joint probability distribution
K-means clustering
Law of large numbers
Law of total variance
Least squares
Linear regression
Margin of error
Measurement
Minimum wage
Monte Carlo method
Multiple comparisons problem
Mutual exclusivity
Normal distribution
Null hypothesis
Observational study
One-Tailed Test
P-value
PageRank
Parameter (computer programming)
Percentage
Percentage point
Permutation
Point estimation
Population proportion
Prediction
Probability
Probability distribution
Proportionality (mathematics)
Quantile
Quartile
Random variable
Randomization
Randomized experiment
Regression discontinuity design
Regression toward the mean
Result
Sample space
Sampling (statistics)
Standard deviation
Standard error
Standard score
Statistic
Statistical hypothesis testing
Statistical significance
Student's t-distribution
Student's t-test
Summation
Test statistic
Type I and type II errors
Uncertainty
Variable (computer science)
Variable (mathematics)
Variance
Weighted arithmetic mean
Z-test
Product details
- ISBN 9780691167039
- Weight: 1066g
- Dimensions: 178 x 254mm
- Publication Date: 09 Feb 2018
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Hardback
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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
Kosuke Imai is professor of politics and founding director of the Program in Statistics and Machine Learning at Princeton University.
