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9 results for "logit"
Logistic Regression Inside and Out By Jeffrey Strickland
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If you have a yes or no question, then you can probably answer it with a logistic regression model. Logistic regression is most appropriate when the dependent variable has two possible outcomes. Will... More > customers respond to an offer or unsubscribe, will the enemy fight or flee, will subjects respond to treatment or grow ill, will livestock live or die? Yes or no? I am often asked if logistic regression is a machine learning algorithm. I say that it is not, for I can formulate it mathematically and solve it using matrix equations, for example. Its solution is derived deterministically, and estimation is performed mathematically, through optimization methods. The logit link functionis the mathematical expression—a nonlinear, exponential equation, and we transform it to a linear equation by applying the natural logarithm. Here we find mathematical modeling, probability, and statistics. Here I will take you on a journey into the art and science of predictive modeling using logistic regression, inside-and-out.< Less
The 4PL By Stanley N. Deming
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(Click on "Preview" below the photo at the left to see inside.) This book explains the mathematical form of the four-parameter logistic model that is the basis of many current bioassays.... More > You'll acquire an understanding of how relative potency is estimated by comparing two 4PL models. You’ll learn what probits are, why so called “probit analysis” was once necessary to estimate relative potency, why probit analysis (and "logit analysis") became known as “parallel line analysis.” You’ll also see why some questionable extensions of parallel line analysis are easily manipulated by the bioassayist and should raise the eyebrows of regulatory agencies. You’ll see why analytic dilution assays are so robust and forgiving of certain mistakes and blunders made by the bioassayist. You’ll be introduced to historical figures like Trevan, Bliss, Gaddum, Wood, Finney, Jerne, Hill, and others, and you'll appreciate the hard work and deep thinking they did to bring us to where we are today.< Less
Predictive Modeling and Analytics By Jeffrey Strickland
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This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and... More > machine learning. We define A predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)—this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this as a textbook with problem solving in R (there are no “by-hand” exercises).< Less
Econometrics By Bahrum Lamehdasht
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This book contains revision notes for econometrics at the undergraduate level. This book includes revision notes on: - OLS; - Normality; - Inferential tests (t test and F test); - Modelling and model... More > specification tests; - Multicollinearity, heteroskedasticity and autocorrelation; - The Koyck transformation; - Spurious regression; - Time stationarity and unit root tests; - Cointegration and Cointegration tests; - Error correction models and their estimation; - Panel data and instrumental variables; - Logit and Probit models; - Sample selection bias.< Less
NITC-RR-677 By Kelly Clifton
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Most research on walking behavior has focused on mode choice or walk-trip frequency. In contrast, this study is one of the first to analyze the destination choice behaviors of pedestrians. Using... More > about 4,500 walk trips from a 2011 household travel survey in the Portland, OR, region, we estimated multinomial logit pedestrian destination choice models for six trip purposes. Independent variables included terms for impedance (walk-trip distance); size (employment by type, households); supportive pedestrian environments (parks, a pedestrian index of the environment variable called PIE); barriers to walking (terrain, industrial-type employment); and traveler characteristics. Unique to this study was the use of small-scale destination zone alternatives.< Less
OTREC-RR-510 By Kelly Clifton
eBook (PDF): $0.00
There is growing support for investments to promote pedestrian travel. Metropolitan planning organizations (MPOs) are improving travel demand forecasting models to better address policy-relevant... More > issues like air quality, public health, and the smart allocation of infrastructure. This report describes an innovative method to integrate walking activity into trip-based travel models. Using data for the Portland, OR, metropolitan area, the method applies trip generation at a new micro-scale spatial unit: a pedestrian analysis zone (PAZ). Next, a binary logit walk mode split model estimates the number of walk trips generated. Non-walk trips are aggregated up to transportation analysis zones (TAZs) for destination choice, mode choice, and traffic assignment. This method improves travel models’ sensitivity to walking influences, and it could operate as a standalone tool for rapid scenario analysis. Care must be taken when applying this method with respect to scalability, forecasting, and operational challenges.< Less
OTREC-RR-510 By Kelly Clifton
Paperback: $4.46
Prints in 3-5 business days
There is growing support for investments to promote pedestrian travel. Metropolitan planning organizations (MPOs) are improving travel demand forecasting models to better address policy-relevant... More > issues like air quality, public health, and the smart allocation of infrastructure. This report describes an innovative method to integrate walking activity into trip-based travel models. Using data for the Portland, OR, metropolitan area, the method applies trip generation at a new micro-scale spatial unit: a pedestrian analysis zone (PAZ). Next, a binary logit walk mode split model estimates the number of walk trips generated. Non-walk trips are aggregated up to transportation analysis zones (TAZs) for destination choice, mode choice, and traffic assignment. This method improves travel models’ sensitivity to walking influences, and it could operate as a standalone tool for rapid scenario analysis. Care must be taken when applying this method with respect to scalability, forecasting, and operational challenges.< Less
La fidelizzazione del cliente nelle aziende agrituristiche: un’analisi statistica By Thomas Michael Boscaro
Hardcover: $25.92
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Come determinare in ambito agrituristico una strategia per la customer satisfaction con pochi dati impiegando alcuni modelli statistici e programmi anche opensource. Il modello decisionale può... More > trovare applicazione anche in realtà diverse mantenendo le caratteristiche di economicità e la propria efficacia.< Less
La fidelizzazione del cliente nelle aziende agrituristiche: un’analisi statistica By Thomas Michael Boscaro
eBook (PDF): $9.15
Come determinare in ambito agrituristico una strategia per la customer satisfaction con pochi dati impiegando alcuni modelli statistici e programmi opensource. Il modello decisionale può... More > trovare applicazione anche in realtà diverse mantenendo le caratteristiche di economicità e la propria efficacia. Vengono presentati gli strumenti impiegati per l’analisi: il software Gretl e il modello econometrico Logit; nel capitolo quinto, viene presentato il lavoro svolto, e i risultati ottenuti nella misurazione della customer satisfaction in relazione e con particolare riguardo ai dati socio-demografici dello specifico campione analizzato. Questa sezione costituisce il corpo centrale dell’elaborato, in essa vengono in effetti analizzati i dati raccolti durante l’indagine statistica, con la finalità specifica di mettere in relazione le caratteristiche sociodemografiche della clientela con la percezione di soddisfazione da parte della stessa. In Appendice si sono allegati i dati raccolti in formato Microsoft Excel.< Less

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