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\"This book introduces the basic techniques for shaping data models in Excel and Power BI. It's meant for readers who are new to data modeling as well as for experienced data modelers looking for tips from the experts. If you want to use Power BI or Excel to analyze data, the many real-world examples in this book will help you look at your reports in a different way--like experienced data modelers do.\"--Provided by publisher.

This book was written for those who want basic understanding of how to use analytics in the business world without all the details of statistical theory. The focus is on providing clear word descriptions and step-by-step Excel instructions, rather than including lots of x's and y's everywhere. We have specifically avoided including a lot of technical information. This book was written for the managers and students who may need a quick refresher on how to complete Excel analysis without having to read pages of technical explanations. We have aimed at concisely defining the key concepts and providing simple descriptions and explanations on how to use them.

The security of the U.S.commercial aviation system has been a growing concern since the 1970's when the hijacking of aircraft became a serious problem.Over that period, federal aviation officials have been searching for more effective ways for non-invasive screening of passengers, luggage, and cargo to detect concealed explosives and weapons.

The Covid-19 pandemic had an unequal impact across businesses and communities and rapidly accelerated digital trends in the economy. What role, then, did website use play in community resilience and small business outcomes? This article examines a new source of population data on domain name hosts to provide a unique measure of digital economic activity within communities. Seventy-five percent are commercial, including online-only, brick-and-mortar, small, and microbusinesses. With geolocated data on 20 million US domain name hosts, we investigate how their density (per 100 people) affected economic outcomes in the nation’s largest metros during the pandemic. Using monthly time series data for the 50 largest metropolitan areas, the domain host data is merged with the US Census Small Business Pulse Surveys and Chetty et al.’s Opportunity Insights data. Results indicate metros with higher concentrations of businesses with an online presence experienced more positive economic perceptions and outcomes from April to December 2020. This high-frequency, granular data on digital economic activity suggests that digitally enabled small and microbusinesses played an important role in local economic resilience and demonstrates how commercial data can be used to generate new insights in a fast-changing environment.Plain English SummaryNew data show websites were a resource for small business and community resilience in Covid-19. While some studies have shown how digital technologies helped businesses during the pandemic, little research has examined how website use during this time affected communities and their small businesses. Data on the number of domain name hosts (per 100 people) provides a measure of the prevalence of website use in a community. Seventy-five percent of these domain name sites are commercial, primarily small, and microbusinesses. We examine economic outcomes for the 50 largest metros from April to December 2020, including credit and debit card spending, small business revenues and openings, and the perceptions of small business owners. With monthly data and across multiple measures, we find that this digital economic activity positively affected the resilience of communities and small businesses. These findings suggest that policies for an inclusive and effective recovery should consider support for digital skills and effective website use for small and microbusinesses.

In the financial sector, alternatives to traditional datasets, such as financial statements and Securities and Exchange Commission filings, can provide additional ways to describe the running status of businesses. Nontraditional data sources include individual behaviors, business processes, and various sensors. In recent years, alternative data have been leveraged by businesses and investors to adjust credit scores, mitigate financial fraud, and optimize investment portfolios because they can be used to conduct more in-depth, comprehensive, and timely evaluations of enterprises. Adopting alternative data in developing models for finance and business scenarios has become increasingly popular in academia. In this article, we first identify the advantages of alternative data compared with traditional data, such as having multiple sources, heterogeneity, flexibility, objectivity, and constant evolution. We then provide an overall investigation of emerging studies to outline the various types, emerging applications, and effects of alternative data in finance and business by reviewing over 100 papers published from 2015 to 2023. The investigation is implemented according to application scenarios, including business return prediction, business risk management, credit evaluation, investment risk prediction, and stock prediction. We discuss the roles of alternative data from the perspective of finance theory to argue that alternative data have the potential to serve as a bridge toward achieving high efficiency in financial markets. The challenges and future trends of alternative data in finance and business are also discussed.

Commercial data and intellectual property (IP) rights cover similar objects, sharing a consistent theoretical foundation and compatible institutional goals. This suggests the IP system is potentially adaptable for protecting commercial data. However, unlike the type-based objects of IP, commercial data demonstrates distinct characteristics in terms of property form, interest appeals, and value connotations. These differences pose difficulties in applying the current IP system to the protection of commercial data, necessitating a new institutional approach. Nevertheless, to improve the legal protection for commercial data, principles from established IP doctrines and regulatory designs can be adapted, including incentivizing property rights, facilitating market circulation, unlocking data’s value as a production factor, and balancing competing interests.

The objective of this paper was to investigate how the predicted level of body energy mobilized and the stage of lactation affects performance and energy partitioning in lactating sows kept under commercial conditions. Seventy-seven lactating sows from three consecutive batches were weaned at 28 d and all measures were taken over the first 20 d. Total feed consumption was measured and sows’ live weight was registered when entering the lactation facilities and at 21 d of lactation. Blood samples were collected at farrowing and once a week thereafter. Net energy (NE) mobilization or loss was calculated by difference using the general NRC equation for ME partitioning. Compared to low mobilizers (low NE loss values), high mobilizing sows had lower feed intake and higher loss of live weight, body fat and body protein. High mobilizers also weaned more piglets and had heavier litters than low mobilizers. Energy mobilization (NE loss) was higher from day 1 to 10 of lactation compared to day 11 to 20, and the difference in mobilized energy between high and low mobilizing sows was also higher in the first than in the second half of lactation. Body weight and back fat thickness losses were significantly correlated with NE loss. A more accurate prediction of the changes in live weight or back fat thickness over lactation should help better predict total amount of energy mobilized, and more research is needed to assess the relative contribution of lean and fat to mobilized tissue.

Commercially available business (CAB) datasets for food environments have been investigated for error in large urban contexts and some rural areas, but there is a relative dearth of literature that reports error across regions of variable rurality. The objective of the current study was to assess the validity of a CAB dataset using a government dataset at the provincial scale. A ground-truthed dataset provided by the government of Newfoundland and Labrador (NL) was used to assess a popular commercial dataset. Concordance, sensitivity, positive-predictive value (PPV) and geocoding errors were calculated. Measures were stratified by store types and rurality to investigate any association between these variables and database accuracy. NL, Canada. The current analysis used store-level (ecological) data. Of 1125 stores, there were 380 stores that existed in both datasets and were considered true-positive stores. The mean positional error between a ground-truthed and test point was 17·72 km. When compared with the provincial dataset of businesses, grocery stores had the greatest agreement, sensitivity = 0·64, PPV = 0·60 and concordance = 0·45. Gas stations had the least agreement, sensitivity = 0·26, PPV = 0·32 and concordance = 0·17. Only 4 % of commercial data points in rural areas matched every criterion examined. The commercial dataset exhibits a low level of agreement with the ground-truthed provincial data. Particularly retailers in rural areas or belonging to the gas station category suffered from misclassification and/or geocoding errors. Taken together, the commercial dataset is differentially representative of the ground-truthed reality based on store-type and rurality/urbanity.

Abstract Numerous methods have been suggested to incorporate crossbred (CB) phenotypes and genotypes into swine selection programs, yet little research has focused on the implicit trade-off decisions between generating data at the nucleus or commercial level. The aim of this study was to investigate the impact of altering the proportion of purebred (PB) and CB phenotypes and genotypes in genetic evaluations on the response to selection of CB performance. Assuming CB and PB performance with moderate heritabilities (h2=0.4), a three-breed swine crossbreeding scheme was simulated and selection was practiced for six generations, where the goal was to increase CB performance. Phenotypes, genotypes, and pedigrees for three PB breeds (25 and 175 mating males and females for each breed, respectively), F1 crosses (400 mating females), and terminal cross progeny (2,500) were simulated. The genome consisted of 18 chromosomes with 1,800 quantitative trait loci and 72k single nucleotide polymorphism (SNP) markers. Selection was performed in PB breeds using estimated breeding value for each phenotyping/genotyping strategy. Strategies investigated were: 1) increasing the proportion of CB with genotypes, phenotypes, and sire pedigree relationships, 2) decreasing the proportion of PB phenotypes and genotypes, and 3) altering the genetic correlation between PB and CB performance (rpc). Each unique rpc scenario and data collection strategy was replicated 10 times. Results showed that including CB data improved the CB performance regardless of  rpc or data collection strategy compared with when no CB data were included. Compared with using only PB information, including 10% of CB progeny per generation with sire pedigrees and phenotypes increased the response in CB phenotype by 134%, 55%, 33%, 23%, and 21% when rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively. When the same 10% of CB progeny were also genotyped, CB performance increased by 243%, 54%, 38%, 23%, and 20% when the rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively, compared with when no CB data were utilized. Minimal change was observed in the average CB phenotype when PB phenotypes were included or proportionally removed when CB were genotyped. Removal of both PB phenotypes and genotypes when CB were genotyped greatly reduced the response in CB performance. In practice, the optimal inclusion rate of CB and PB data depends upon the genetic correlation between CB and PB animals and the expense of additional CB data collection compared with the economic benefit associated with increased CB performance.

Free-range egg production plays a key role in the global food system, and current market trends suggest that consumer demand for free-range eggs will continue to rise. Free-range egg production is susceptible to a wide range of factors, including climatic conditions, management practices, and disease presence. These factors can cause variability in the laying rate of a flock over time, leading to fluctuations in egg production. The main purpose of this study was to investigate the risk of short-term free-range egg production losses using data derived from a combination of sensing technologies and management activities. Production and environmental data were collected from a commercial farm comprising seven flocks of laying hens. The variables studied included laying rate, feed intake, water intake, solar radiation, humidity, precipitation, and indoor/outdoor temperature. These were processed into a set of aggregate features calculated across a 14-day moving window. Generalized estimating equations were used to analyze the association between the derived production and environmental features and the probability of a short-term drop in egg production, expressed through deviations in the laying rate on the day immediately following the data window. Odds ratios were used to express the relative risk of a production drop by comparing the features for window periods where production drops occur to the window periods where production drops did not occur. The results demonstrated that a range of data features based on the laying rate, feed intake, water intake, and indoor/outdoor temperatures all had significant associations with the odds of a production drop. Key findings from the study show that an increase in feed intake and laying rate measured across the 14-day data window were correlated with a lower risk of a sudden drop in egg production. Conversely, a low mean indoor temperature (x < 16.1 °C group), measured through environmental sensing data, was correlated with a higher risk of a sudden drop in egg production. This study quantifies the link between data features derived from production and environmental monitoring and egg production issues, thereby providing useful insights on the most important data items captured through day-to-day monitoring, which can be used for proactive management. Further research should be carried out to investigate how technologies such as machine learning and analytics platforms can be applied for the task of forecasting production interruptions using the data features explored in this study.