Jeremy Arkes

Describes the condition and performance of California's K-12 public schools in terms of student population, resources, and outcomes, analyzing trends and comparing California to other states and the nation as a whole. This report describes California's K-12 public schools - specifically, their student population, the resources they are provided (finances, teachers, facilities), and their outcomes. It describes both student academic achievement and outcomes that schools may influence and that are inadequately captured in test scores. The report analyzes trends within the state and compares California to other states and to the nation as a whole.

This thoroughly practical and engaging textbook conveys the skills needed to responsibly develop, conduct, scrutinize, and interpret statistical analyses, without requiring any high-level math. Regression Analysis details the most common sources of statistical biases, including some not covered in other textbooks. Rather than focusing on complicated equations, the book describes these biases visually and with examples of situations in which they could arise. As the author argues, just learning how to conduct regressions without learning how to properly assess and interpret regressions can do more harm than good. Other unique features include an innovative approach to describing the elusive concept of "holding other factors constant" and proper interpretations of the strength of evidence in light of the Bayesian critique of hypothesis testing. This third edition enhances the emphasis on ethical and responsible research practices and creates more examples demonstrating how the biases and their corrections could affect the regression results.This is the textbook the author wishes he had learned from, as it would have helped him avoid many research mistakes he made in his career at research organizations and in academia. It is ideal for undergraduate and postgraduate students learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand regressions.

This thoroughly practical and engaging textbook conveys the skills needed to responsibly develop, conduct, scrutinize, and interpret statistical analyses, without requiring any high-level math. Regression Analysis details the most common sources of statistical biases, including some not covered in other textbooks. Rather than focusing on complicated equations, the book describes these biases visually and with examples of situations in which they could arise. As the author argues, just learning how to conduct regressions without learning how to properly assess and interpret regressions can do more harm than good. Other unique features include an innovative approach to describing the elusive concept of "holding other factors constant" and proper interpretations of the strength of evidence in light of the Bayesian critique of hypothesis testing. This third edition enhances the emphasis on ethical and responsible research practices and creates more examples demonstrating how the biases and their corrections could affect the regression results.This is the textbook the author wishes he had learned from, as it would have helped him avoid many research mistakes he made in his career at research organizations and in academia. It is ideal for undergraduate and postgraduate students learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand regressions.

This thoroughly practical and engaging textbook is designed to equip students with the skills needed to undertake sound regression analysis without requiring high-level math.Regression Analysis covers the concepts needed to design optimal regression models and to properly interpret regressions. It details the most common pitfalls, including three sources of bias not covered in other textbooks. Rather than focusing on equations and proofs, the book develops an understanding of these biases visually and with examples of situations in which such biases could arise. In addition, it describes how ‘holding other factors constant’ actually works and when it does not work. This second edition features a new chapter on integrity and ethics, and has been updated throughout to include more international examples. Each chapter offers examples, exercises, and clear summaries, all of which are designed to support student learning to help towards producing responsible research.This is the textbook the author wishes he had learned from, as it would have helped him avoid many research mistakes he made in his career. It is ideal for anyone learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand regressions. Additional digital supplements are available at: www.youtube.com/channel/UCenm3BWqQyXA2JRKB_QXGyw.

This thoroughly practical and engaging textbook is designed to equip students with the skills needed to undertake sound regression analysis without requiring high-level math.Regression Analysis covers the concepts needed to design optimal regression models and to properly interpret regressions. It details the most common pitfalls, including three sources of bias not covered in other textbooks. Rather than focusing on equations and proofs, the book develops an understanding of these biases visually and with examples of situations in which such biases could arise. In addition, it describes how ‘holding other factors constant’ actually works and when it does not work. This second edition features a new chapter on integrity and ethics, and has been updated throughout to include more international examples. Each chapter offers examples, exercises, and clear summaries, all of which are designed to support student learning to help towards producing responsible research.This is the textbook the author wishes he had learned from, as it would have helped him avoid many research mistakes he made in his career. It is ideal for anyone learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand regressions. Additional digital supplements are available at: www.youtube.com/channel/UCenm3BWqQyXA2JRKB_QXGyw.

The authors review Air Force purchases of 3low-demand2 parts, analyzing how much the Air Force spends on such parts and the types of parts that have a low demand. They then identify and synthesize best commercial purchasing and supply chain management practices used for developing supply strategies for such items, concluding with recommendations for the Air Force to improve its supply strategies for such items.

One tool the Air Force is using to improve its logistical support to the warfighter is purchasing and supply-chain management (PSCM). Is PSCM doing what it was intended to do? If so, how well? Are other factors influencing its intended outcomes? The authors have developed an econometric model that helps examine all these things. While data availability and quality can be a challenge, the model may also be useful in other contexts.

The U.S. Air Force (USAF) needs accurate models to develop retention policies that ensure the force has a sufficient number of experienced officers to meet requirements. Unlike the annualized cost of leaving (ACOL) model, the dynamic retention model (DRM) can be used to take into account the retention effect of the availability of multi-year contracts such as the Aviator Continuation Pay (ACP) program to certain classes of USAF officers.

Given that reserve components are increasingly being called upon to provide support across the entire spectrum of military operations, the authors examine reserve recruting and the likely challenges facing the reserve components. The policy variables the authors included in the models had sizeable and significant effects on both active duty and reserve recruiting. The authors also modeled the effects of economic and demographic variables.