A-8a 2003 Summary of Food Stamp Errors - Active and Negative Cases, April 2003 - September 2003

Summary of food stamp errors.

A8a Summary of Food Stamp Errors-Active and Negative cases, July 2004

A-8A summary of food stamp errors active and negative cases

A-8a 2003 Summary of Food Stamp Errors - Active and Negative Cases, April 2001 - September 2001

Summary of food stamp errors.

Biosciences Path for Development: Economic and Core Competency Analyses, March 2004

Report produced by Iowa Department of Economic Development

A-8a Summary of Food Stamp Errors Active and Negative Cases, April-September 2004

A-8a Summary of Food Stamp Errors Active and Negative Cases, Apr. 2004 - Sept. 2004

A-8a Summary of Food Stamp Errors Active and Negative Cases, October 2004-March 2005

A-8a Summary of Food Stamp Errors Active and Negative Cases, Oct. 2004 - Mar. 2005

A-8a Summary of Food Stamp Errors - Active and Negative Cases, April 2003-September 2003

Summary of food stamp errors.

A-8a Summary of Food Stamp Errors - Active and Negative Cases, October 2001-March 2002

Summary of food stamp errors.

A-8a Summary of Food Stamp Errors - Active and Negative Cases, Aprril 2002-September 2002

Summary of food stamp errors.

From Empirical Bayes to Full Bayes: Methods for Analysing Traffice Safety Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this slight, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

From Empirical Bayes to Full Bayes: Methods for Analyzubg Traffic Safey Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this light, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

Estimating Design-Flood Discharges for Streams in Iowa Using Drainage-Basin and Channel-Geometry Characteristics, HR-322, 1993

Drainage-basin and channel-geometry multiple-regression equations are presented for estimating design-flood discharges having recurrence intervals of 2, 5, 10, 25, 50, and 100 years at stream sites on rural, unregulated streams in Iowa. Design-flood discharge estimates determined by Pearson Type-III analyses using data collected through the 1990 water year are reported for the 188 streamflow-gaging stations used in either the drainage-basin or channel-geometry regression analyses. Ordinary least-squares multiple-regression techniques were used to identify selected drainage-basin and channel-geometry regions. Weighted least-squares multiple-regression techniques, which account for differences in the variance of flows at different gaging stations and for variable lengths in station records, were used to estimate the regression parameters. Statewide drainage-basin equations were developed from analyses of 164 streamflow-gaging stations. Drainage-basin characteristics were quantified using a geographic-information-system (GIS) procedure to process topographic maps and digital cartographic data. The significant characteristics identified for the drainage-basin equations included contributing drainage area, relative relief, drainage frequency, and 2-year, 24-hour precipitation intensity. The average standard errors of prediction for the drainage-basin equations ranged from 38.6% to 50.2%. The GIS procedure expanded the capability to quantitatively relate drainage-basin characteristics to the magnitude and frequency of floods for stream sites in Iowa and provides a flood-estimation method that is independent of hydrologic regionalization. Statewide and regional channel-geometry regression equations were developed from analyses of 157 streamflow-gaging stations. Channel-geometry characteristics were measured on site and on topographic maps. Statewide and regional channel-geometry regression equations that are dependent on whether a stream has been channelized were developed on the basis of bankfull and active-channel characteristics. The significant channel-geometry characteristics identified for the statewide and regional regression equations included bankfull width and bankfull depth for natural channels unaffected by channelization, and active-channel width for stabilized channels affected by channelization. The average standard errors of prediction ranged from 41.0% to 68.4% for the statewide channel-geometry equations and from 30.3% to 70.0% for the regional channel-geometry equations. Procedures provided for applying the drainage-basin and channel-geometry regression equations depend on whether the design-flood discharge estimate is for a site on an ungaged stream, an ungaged site on a gaged stream, or a gaged site. When both a drainage-basin and a channel-geometry regression-equation estimate are available for a stream site, a procedure is presented for determining a weighted average of the two flood estimates.

Prevalence and Distribution of Young Driver Distraction Errors in Naturalistic Driving, TPF-5(207), 2014

Naturalistic driving studies are the latest resource for gathering data associated with driver behavior. The University of Iowa has been studying teen driving using naturalistic methods since 2006. By instrumenting teen drivers’ vehicles with event-triggered video recorders (ETVR), we are able to record a 12-second video clip every time a vehicle exceeds a pre-set g-force threshold. Each of these video clips contains valuable data regarding the frequency and types of distractions present in vehicles driven by today’s young drivers. The 16-year old drivers who participated in the study had a distraction present in nearly half of the events that were captured. While a lot of attention has been given to the distractions associated with technology in the vehicle (cell phones, navigation devices, entertainment systems, etc.), the most frequent type of distraction coded was the presence of teen passengers engaging in conversation (45%). Cognitive distractions, such as singing along with the radio, were the second most common distraction. Cell phone use was the third most common distraction, detected in only 10% of the events containing distraction.

Addendum to Perception and Interpretation of Advance Warning Signs on County Roads, HR-256, 1985

This contract extension was granted to analyze data obtained in the original contract period at a level of detail not called for in the original contract nor permitted by the time constraints of the original contract schedule. These further analyses focused on two primary questions: I. What sources of variation can be isolated within the overall pattern of driver recognition errors reported previously for the 16 signs tested in Project HR-256? 2. Were there systematic relations among data on the placement of signs in a simulated signing exercise and data on the respondents' ability to detect the presence of a sign in a visual field or their ability to recognize quickly and correctly a sign shown them or the speed with which these same persons can respond to a sign for a driver decision?

Techniques for Estimating Flood-Frequency Discharges for Streams in Iowa, HR-395A, 2001

A statewide study was conducted to develop regression equations for estimating flood-frequency discharges for ungaged stream sites in Iowa. Thirty-eight selected basin characteristics were quantified and flood-frequency analyses were computed for 291 streamflow-gaging stations in Iowa and adjacent States. A generalized-skew-coefficient analysis was conducted to determine whether generalized skew coefficients could be improved for Iowa. Station skew coefficients were computed for 239 gaging stations in Iowa and adjacent States, and an isoline map of generalized-skew-coefficient values was developed for Iowa using variogram modeling and kriging methods. The skew map provided the lowest mean square error for the generalized-skew- coefficient analysis and was used to revise generalized skew coefficients for flood-frequency analyses for gaging stations in Iowa. Regional regression analysis, using generalized least-squares regression and data from 241 gaging stations, was used to develop equations for three hydrologic regions defined for the State. The regression equations can be used to estimate flood discharges that have recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years for ungaged stream sites in Iowa. One-variable equations were developed for each of the three regions and multi-variable equations were developed for two of the regions. Two sets of equations are presented for two of the regions because one-variable equations are considered easy for users to apply and the predictive accuracies of multi-variable equations are greater. Standard error of prediction for the one-variable equations ranges from about 34 to 45 percent and for the multi-variable equations range from about 31 to 42 percent. A region-of-influence regression method was also investigated for estimating flood-frequency discharges for ungaged stream sites in Iowa. A comparison of regional and region-of-influence regression methods, based on ease of application and root mean square errors, determined the regional regression method to be the better estimation method for Iowa. Techniques for estimating flood-frequency discharges for streams in Iowa are presented for determining ( 1) regional regression estimates for ungaged sites on ungaged streams; (2) weighted estimates for gaged sites; and (3) weighted estimates for ungaged sites on gaged streams. The technique for determining regional regression estimates for ungaged sites on ungaged streams requires determining which of four possible examples applies to the location of the stream site and its basin. Illustrations for determining which example applies to an ungaged stream site and for applying both the one-variable and multi-variable regression equations are provided for the estimation techniques.