{"product_id":"probability-and-statistics-for-economics-and-business-isbn-9780262553360","title":"Probability and Statistics for Economics and Business","description":"\u003cb\u003eA modern introduction to probability and statistics for economics and business undergraduates, using the R programming language.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eDesigned for an introductory course in probability and statistics for economics and business undergraduates, this comprehensive textbook introduces students to the\u003ci\u003e R \u003c\/i\u003estatistical programming language. While covering the standard topics found in traditional textbooks, Jason Abrevaya takes a modern approach that directly integrates \u003ci\u003eR\u003c\/i\u003e, highlights the use of simulation methods, and provides a general treatment of statistical inference for asymptotically normal estimators. Coverage emphasizes concepts that are useful to economists and data analysts, including general statistical-inference results that apply well beyond averages and variances. The book offers a higher level of mathematical rigor than traditional business statistics textbooks to prepare students for future coursework and for a professional climate where employers increasingly emphasize competence in data science and statistics.\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eIntroduces students to the \u003ci\u003eR \u003c\/i\u003estatistical programming language\u003c\/li\u003e\n\u003cli\u003eUses real-world examples and datasets related to economics and business\u003c\/li\u003e\n\u003cli\u003eProvides extensive coverage of simulation methods\u003c\/li\u003e\n\u003cli\u003eFocuses on large-sample (asymptotic) results\u003c\/li\u003e\n\u003cli\u003eIs classroom-tested at Emory University, the University of Texas at Austin, Princeton University, and elsewhere\u003c\/li\u003e\n\u003cli\u003eSuits undergraduate and graduate students in business, economics, data science, and statistics with knowledge of calculus\u003c\/li\u003e\n\u003cli\u003eOffers companion website and extensive instructor resources\u003c\/li\u003e\n\u003c\/ul\u003ePreface ix\u003cbr\u003eAcknowledgments xv\u003cbr\u003e1 The basics of R 1\u003cbr\u003e1.1 Installing R 1\u003cbr\u003e1.2 Arithmetic operations and mathematical functions 2\u003cbr\u003e1.3 Variables and data types 4\u003cbr\u003e1.4 Vectors 9\u003cbr\u003e1.5 Output 18\u003cbr\u003e1.6 Programming 18\u003cbr\u003e1.7 Writing functions 22\u003cbr\u003e1.8 Data frames and file input 24\u003cbr\u003e1.9 Missing values 29\u003cbr\u003e1.10 R packages 30\u003cbr\u003eExercises 32\u003cbr\u003e2 Introduction to probability theory 37\u003cbr\u003e2.1 Experiments and sample spaces 39\u003cbr\u003e2.2 Events 42\u003cbr\u003e2.3 What is a probability? 45\u003cbr\u003e2.4 Properties of probabilities 51\u003cbr\u003eExercises 55\u003cbr\u003e3 Conditional probabilities and independence 59\u003cbr\u003e3.1 Definition and properties of conditional probabilities 59\u003cbr\u003e3.2 Multiplication rule and Bayes’ Theorem 60\u003cbr\u003e3.3 Probability tables 63\u003cbr\u003e3.4 Independence 66\u003cbr\u003e3.5 Examples with an infinite number of outcomes 70\u003cbr\u003eExercises 72\u003cbr\u003e4 Combinatorics (counting methods) 77\u003cbr\u003e4.1 Product rule and sum rule 77\u003cbr\u003e4.2 Permutations and combinations 78\u003cbr\u003e4.3 Probabilities for equally likely choices 81\u003cbr\u003eExercises 83\u003cbr\u003e5 Economic data and sampling 89\u003cbr\u003e5.1 Types of data 89\u003cbr\u003e5.2 Types of variables 91\u003cbr\u003e5.3 The population and sampling 93\u003cbr\u003eExercises 96\u003cbr\u003e6 Descriptive statistics and visuals: univariate data 99\u003cbr\u003e6.1 Dataset examples 99\u003cbr\u003e6.2 Categorical data: sample proportions and bar charts 102\u003cbr\u003e6.3 Numerical data: histograms 104\u003cbr\u003e6.4 Numerical data: measures of location 110\u003cbr\u003e6.5 Numerical data: measures of dispersion 116\u003cbr\u003e6.6 Modal outcomes 126\u003cbr\u003e6.7 Linear transformations of univariate data 128\u003cbr\u003e6.8 Time-series plots 133\u003cbr\u003eExercises 136\u003cbr\u003e7 Descriptive statistics and visuals: bivariate data 143\u003cbr\u003e7.1 Categorical variables 143\u003cbr\u003e7.2 Numerical data: scatter plots, sample covariance and correlation 151\u003cbr\u003e7.3 Correlation is not causation 172\u003cbr\u003eExercises 173\u003cbr\u003e8 Discrete random variables 179\u003cbr\u003e8.1 Using sample proportions to calculate descriptive statistics 179\u003cbr\u003e8.2 Random variables and discrete random variables 180\u003cbr\u003e8.3 Population descriptive statistics 188\u003cbr\u003e8.4 Multiple discrete random variables 191\u003cbr\u003e8.5 Linear transformations 202\u003cbr\u003e8.6 Linear combination of multiple random variables 204\u003cbr\u003e8.7 Expected values of functions of discrete random variables 207\u003cbr\u003eExercises 208\u003cbr\u003e9 Models of discrete random variables 215\u003cbr\u003e9.1 Bernoulli random variable 215\u003cbr\u003e9.2 Binomial random variable 216\u003cbr\u003e9.3 Geometric random variable 222\u003cbr\u003e9.4 Negative binomial random variable 224\u003cbr\u003e9.5 Poisson random variable 227\u003cbr\u003eExercises 230\u003cbr\u003e10 Continuous random variables 237\u003cbr\u003e10.1 Continuous random variables vs. discrete random variables 237\u003cbr\u003e10.2 Probability density function 238\u003cbr\u003e10.3 Cumulative distribution function 243\u003cbr\u003e10.4 Population descriptive statistics 249\u003cbr\u003e10.5 Linear transformations of one random variable 256\u003cbr\u003e10.6 Multiple continuous random variables 258\u003cbr\u003e10.7 Linear transformations and combinations of multiple random variables 268\u003cbr\u003e10.8 Expected values of functions of continuous random variables 273\u003cbr\u003e10.9 Strictly increasing transformations of random variables 275\u003cbr\u003e10.10 Random variables with discrete and continuous outcomes 277\u003cbr\u003eExercises 278\u003cbr\u003e11 Models of continuous random variables 285\u003cbr\u003e11.1 Normal random variable 285\u003cbr\u003e11.2 Log-normal random variable 297\u003cbr\u003e11.3 Chi-square random variable 301\u003cbr\u003e11.4 Exponential random variable 303\u003cbr\u003e11.5 Mixture of normal random variables 307\u003cbr\u003eExercises 309\u003cbr\u003e12 Sampling distributions: exact 315\u003cbr\u003e12.1 Sampling distribution of the sample mean 317\u003cbr\u003e12.2 Sampling distribution of the sample variance 322\u003cbr\u003e12.3 Sampling distribution of other statistics 328\u003cbr\u003eExercises 332\u003cbr\u003e13 Sampling distributions: asymptotic 337\u003cbr\u003e13.1 Asymptotic distribution of the sample mean 337\u003cbr\u003e13.2 Asymptotic distribution of the sample variance 345\u003cbr\u003e13.3 Asymptotic distribution of other statistics 348\u003cbr\u003eExercises 354\u003cbr\u003e14 Estimation and confidence intervals 359\u003cbr\u003e14.1 Estimation and properties of estimators 359\u003cbr\u003e14.2 Finite-sample confidence intervals: population mean of i.i.d. normal random variables 363\u003cbr\u003e14.3 Asymptotic confidence intervals: population mean of i.i.d. random variables 372\u003cbr\u003e14.4 Asymptotic confidence intervals: parameters with asymptotically normal estimators 377\u003cbr\u003e14.5 Functions of consistent estimators 392\u003cbr\u003e14.6 Asymptotic predictive intervals for continuous random variables 393\u003cbr\u003eExercises 394\u003cbr\u003e15 The bootstrap 401\u003cbr\u003e15.1 Bootstrap sampling 402\u003cbr\u003e15.2 Bootstrap sampling distribution 405\u003cbr\u003e15.3 Bootstrap standard errors and bootstrap confidence intervals 406\u003cbr\u003eExercises 414\u003cbr\u003e16 Hypothesis testing 417\u003cbr\u003e16.1 Finite-sample hypothesis testing: population mean of i.i.d. normal random variables 418\u003cbr\u003e16.2 Asymptotic hypothesis testing: parameters with asymptotically normal estimators 429\u003cbr\u003e16.3 Statistical significance versus practical significance 437\u003cbr\u003e16.4 Hypothesis testing for multiple hypotheses: the Wald test 438\u003cbr\u003eAppendix: Details for the Wald test 444\u003cbr\u003eExercises 450\u003cbr\u003e17 Simple linear regression 455\u003cbr\u003e17.1 The simple linear regression model 455\u003cbr\u003e17.2 The least-squares estimator 460\u003cbr\u003e17.3 Fitted values, estimated residuals, and regression fit 468\u003cbr\u003e17.4 Asymptotic normality and statistical inference 477\u003cbr\u003e17.5 Causality and prediction 487\u003cbr\u003eExercises 490\u003cbr\u003e18 Multiple linear regression 497\u003cbr\u003e18.1 The multiple linear regression model 497\u003cbr\u003e18.2 The least-squares estimator 499\u003cbr\u003e18.3 Standard errors and confidence intervals 509\u003cbr\u003e18.4 Inference for linear combinations of regression parameters 513\u003cbr\u003e18.5 Hypothesis testing 515\u003cbr\u003e18.6 Modeling approaches and explanatory variables 518\u003cbr\u003e18.7 Log-transformed outcome variable 528\u003cbr\u003e18.8 Asymptotic predictive intervals 530\u003cbr\u003e18.9 Linear probability model 535\u003cbr\u003eExercises 539\u003cbr\u003eReferences 544“While there are many books covering introductory probability and statistics for economics\/business students, the new text by Jason Abrevaya stands out in three ways. First, the text is more rigorous than most of its competitors. Second, it emphasizes large-sample and simulation methods for statistical inference and thus is compatible with modern practice. And third, it teaches and effectively uses R to give students insight into important (and sometimes confusing) concepts and provides them with practical experience for later empirical work. This is destined to become a leading text in the field.” \u003cbr\u003e\u003cb\u003e—Mark W. Watson, Howard Harrison and Gabrielle Snyder Beck Professor of Economics and Public Affairs, Princeton University\u003c\/b\u003eJason Abrevaya is Professor of Economics at the University of Texas at Austin and is the holder of the Murray S. Johnson Chair in Economics. He has served on editorial boards for several leading econometrics journals, including the \u003ci\u003eJournal of Econometrics\u003c\/i\u003e, the \u003ci\u003eJournal of Applied Econometrics\u003c\/i\u003e, and the \u003ci\u003eJournal of Business and Economic Statistics,\u003c\/i\u003e and was a founding coeditor of the \u003ci\u003eJournal of Econometric Methods.\u003c\/i\u003e","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":48233492152549,"sku":"NP9780262553360","price":130.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262553360.jpg?v=1767735143","url":"https:\/\/k12savings.com\/es\/products\/probability-and-statistics-for-economics-and-business-isbn-9780262553360","provider":"K12savings","version":"1.0","type":"link"}