A quintessential guide to the fundamentals of ministry, now updated and expanded. Since Aubrey Malphurs first wrote Ministry Nuts and Bolts in 1997 he has gathered even more insights and strategies thanks to his work consulting for churches, his further research, and the classes he teaches at Dallas Theological Seminary. He has applied this expanded knowledge to the second edition of Ministry Nuts and Bolts, a helpful guide designed to teach pastors what they need to know about the day-to-day, nitty-gritty of leading a pastoratethings that arent taught in seminary
Do you want change-driven values or values-driven change? As criteria for making decisions and setting priorities, values should be considered before taking action. In response to immediate pressures, however, many organizations allow change to drive values rather than the other way around. This practice leads to shortsighted decisions that jeopardize long-term success. Ken Hultman, critically acclaimed author and one of the world's leading authorities on organizational values, draws from his knowledge and expertise of over 30 years to bring forth an important compilation of theoretical concepts, instruments, and exercises designed to help today's leaders, managers, and OD practitioners develop and maintain values-driven organizations. Using a systems approach, and packed with practical strategies, tools, and seven proven instruments, this book gives you the resources necessary to make values the focus for: Team change, Personal change, Organizational change, and Multi-level change (personal, team, and organizational)
Business Research Methods provides students with the knowledge, understanding and necessary skills to complete a business research. The reader is taken step-by-step through a range of contemporary research methods, while numerous worked examples an
In Making Things Happen, James Woodward develops a new and ambitious comprehensive theory of causation and explanation that draws on literature from a variety of disciplines and which applies to a wide variety of claims in science and everyday life. His theory is a manipulationist account, proposing that causal and explanatory relationships are relationships that are potentially exploitable for purposes of manipulation and control. This account has its roots in the commonsense idea that causes are means for bringing about effects; but it also draws on a long tradition of work in experimental design, econometrics, and statistics. Woodward shows how these ideas may be generalized to other areas of science from the social scientific and biomedical contexts for which they were originally designed. He also provides philosophical foundations for the manipulationist approach, drawing out its implications, comparing it with alternative approaches, and defending it from common criticisms. In doing so, he shows how the manipulationist account both illuminates important features of successful causal explanation in the natural and social sciences, and avoids the counterexamples and difficulties that infect alternative approaches, from the deductive-nomological model onwards. Making Things Happen will interest philosophers working in the philosophy of science, the philosophy of social science, and metaphysics, and as well as anyone interested in causation, explanation, and scientific methodology.
A selection of articles presented at the Eighth Lukacs Symposium held at the Bowling Green State University, Ohio. They discuss consistency and accuracy of the sequential bootstrap, hypothesis testing, geometry in multivariate analysis, the classical extreme value model, the analysis of cross-classified data, diffusion models for neural activity, e
Google, officially known as Alphabet Inc., is an American multinational technology company. It was founded in September 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University. Initially, it started as a research project to develop a search engine, but it rapidly grew into one of the largest and most influential technology companies in the world. Google is primarily known for its internet-related services and products, with its search engine being its most well-known offering. It revolutionized the way people access information by providing a fast and efficient search engine that delivers highly relevant results. Over the years, Google expanded its portfolio to include a wide range of products and services, including Google Maps, Google Drive, Gmail, Google Docs, Google Photos, Google Chrome, YouTube, and many more. In addition to its internet services, Google ventured into hardware with products like the Google Pixel smartphones, Google Home smart speakers, and Google Nest smart home devices. It also developed its own operating system called Android, which has become the most widely used mobile operating system globally. Google's success can be attributed to its ability to monetize its services through online advertising. The company introduced Google AdWords, a highly successful online advertising program that enables businesses to display ads on Google's search engine and other websites through its AdSense program. Advertising contributes significantly to Google's revenue, along with other sources such as cloud services, app sales, and licensing fees. The dataset used in this project starts from 19-Aug-2004 and is updated till 11-Oct-2021. It contains 4317 rows and 7 columns. The columns in the dataset are Date, Open, High, Low, Close, Adj Close, and Volume. You can download the dataset from https://viviansiahaan.blogspot.com/2023/06/google-stock-price-time-series-analysis.html. In this project, you will involve technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. In this book, you will learn how to perform forecasting based on regression on Adj Close price of Google stock price, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, Lasso regression, and Ridge regression. The machine learning models used to predict Google daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.
Experience learning made easy - and quickly teach yourself how to manage the complete project life cycle with Project 2013. With Step by Step, you set the pace - building and practicing the skills you need, just when you need them! Work with Project 2013 on your PC or touch-enabled device Build and fine-tune your project plan Schedule tasks and milestones, and assign resources Track progress and costs, and manage variances Troubleshoot delays and budget overruns Customize Gantt chart views, tables, and calendars Learn project-management best practices
Macroeconomics tries to describe and explain the economywide movement of prices, output, and unemployment. The field has been sharply divided among various schools, including Keynesian, monetarist, new classical, and others. It has also been split between theorists and empiricists. Ray Fair is a resolute empiricist, developing and refining methods for testing theories and models. The field cannot advance without the discipline of testing how well the models approximate the data. Using a multicountry econometric model, he examines several important questions, including what causes inflation, how monetary authorities behave and what are their stabilization limits, how large is the wealth effect on aggregate consumption, whether European monetary policy has been too restrictive, and how large are the stabilization costs to Europe of adopting the euro. He finds, among other things, little evidence for the rational expectations hypothesis and for the so-called non-accelerating inflation rate of unemployment (NAIRU) hypothesis. He also shows that the U.S. economy in the last half of the 1990s was not a new age economy.
