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Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.

Statistics. --- Quantitative research. --- Statistics—Computer programs. --- Statistical Theory and Methods. --- Data Analysis and Big Data. --- Applied Statistics. --- Statistical Software. --- Estadística --- Econometria --- Macroeconomia --- R (Llenguatge de programació)

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Programming --- Mathematical statistics --- Statistics --- Statistique --- Statistique mathématique --- Computer programs --- Logiciels --- 681.3*G3 --- 519.22 --- -Statistics --- -#TCPW V1.0 --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Statistical theory. Statistical models. Mathematical statistics in general --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Statistique mathématique --- #TCPW V1.0 --- Statistics - Computer programs --- Mathematical statistics - Computer programs

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Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.

Statistics. --- Quantitative research. --- Statistics—Computer programs. --- Statistical Theory and Methods. --- Data Analysis and Big Data. --- Applied Statistics. --- Statistical Software. --- Data analysis (Quantitative research) --- Exploratory data analysis (Quantitative research) --- Quantitative analysis (Research) --- Quantitative methods (Research) --- Research --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics

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This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

Time-series analysis. --- Statistics—Computer programs. --- Econometrics. --- Python (Computer program language). --- Machine learning. --- Statistics. --- Time Series Analysis. --- Statistical Software. --- Python. --- Machine Learning. --- Statistics in Business, Management, Economics, Finance, Insurance. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Learning, Machine --- Artificial intelligence --- Machine theory --- Scripting languages (Computer science) --- Economics, Mathematical --- Statistics --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Probabilities --- Computer programs. --- Anàlisi de sèries temporals --- Python (Llenguatge de programació) --- Python (Computer program language)

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“Getting Started with a SIMPLIS Approach” is particularly appropriate for those users who are not experts in statistics, but have a basic understanding of multivariate analysis that would allow them to use this handbook as a good first foray into LISREL. Part I introduces the topic, presents the study that serves as the background for the explanation of matters, and provides the basis for Parts II and III, which, in turn, explain the process of estimation of the measurement model and the structural model, respectively. In each section, we also suggest essential literature to support the utilization of the handbook. After having read the book, readers will have acquired a basic knowledge of structural equation modeling, namely using the LISREL program, and will be  prepared to continue with the learning process.

Social sciences -- Statistical methods -- Data processing. --- Statistics -- Computer programs -- Handbooks, manuals, etc. --- Statistics -- Data processing. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- SIMPLIS (Computer program language) --- Structural equation modeling. --- Social sciences --- Statistical methods. --- LISREL (Computer file) --- SEM (Structural equation modeling) --- LISREL --- Statistics. --- Computer simulation. --- Econometrics. --- Statistical Theory and Methods. --- Simulation and Modeling. --- Multivariate analysis --- Factor analysis --- Regression analysis --- Path analysis (Statistics) --- Command languages (Computer science) --- Mathematical statistics. --- Economics, Mathematical --- Statistics --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics