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Abstract: Economics: the study of production and consumption - business and prices in our economy - the role of industry, labor and agriculture - the creation and employment of money -private and national wealth and income - our american economy in a divided world.

Abstract: The issues - the accelerator-residual funds hypothesis - notes on samples and variables - cyclical patterns, 1946 to 1958 - investment decisions, 1951 - 1954 : a preliminary cross-section analysis - investment decisions, 1951-1954: a cross-section evaluation of alternative models - investment behavior, 1948-1958: a time-series analysis - multicollinearity and constrained regression estimation - forecasting investment : concepts and empirical implications - method, thoery and policy : s ...; [Read more...] The issues - the accelerator-residual funds hypothesis - notes on samples and variables - cyclical patterns, 1946 to 1958 - investment decisions, 1951 - 1954 : a preliminary cross-section analysis - investment decisions, 1951-1954: a cross-section evaluation of alternative models - investment behavior, 1948-1958: a time-series analysis - multicollinearity and constrained regression estimation - forecasting investment : concepts and empirical implications - method, thoery and policy : some implications.

Abstract: Capital power - the technostructure -the entrepreneur and the technostructure - a digression of socialism - the approved contradiction - the general theory of motivation - motivation in perspective - the principle of consistency the goals of industrial system.

Abstract: The role of money in national economic policy - The federal reserve's modus operandi - Tactis and targets of monetary policy - A neo-keynesian view of monetary policy - Some rules for the conduct of monetary policy.

Abstract: The depression as malfunction of the self-regulation economy - the depression as the end of american economic growth - modern interpretations.

Abstract: What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-- but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and ...; [Read more...] What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-- but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and value of your data and applications. You'll also explore pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data"--. Part 1. The basics. Mind the semantic gap -- Semantic modeling elements -- Semantic and linguistic phenomena -- Semantic model quality -- Semantic model development -- Part 2. The pitfalls. Bad descriptions -- Bad semantics -- Bad model specification and knowledge acquisition -- Bad quality management -- Bad application -- Bad strategy and organization -- Part 3. The dilemmas. Representation dilemmas -- Expressiveness and content dilemmas -- Evolution and governance dilemmas -- Looking ahead.

Abstract: Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you s ...; [Read more...] Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.

Abstract: The four fundamental algebraic operations - Linear equations in one unknow - Fractions and fractional equations - Percentage - Exponents an radicals - Logarithms - Functions and graphs - Progressions, the binomial theorem - statistics - Simple Interest - Compound Interest - Simple annuities - Amortization and sinking funds - Depreciation and capitalized cost - Bonds - General annuities - Life annuities and life insurance.

Abstract: IL VOLUME SI DIVIDE IN DUE PARTI. LA PRIMA ANALIZZA LA TEORIA PURA DEL COMMERCIO INTERNAZIONALE MENTRE NELLA SECONDA SI INTRODUCONO ELEMENTI DI DINAMICA MONETARIA E SI SVILUPPA LA TEORIA DELL'AGGIUSTAMENTO DELLA BILANCIA DEI PAGAMENTI