Are there leaks in your product pipeline?

Jump Starting Technologies, Patent Issues, and Translational Medicine Poster SessionSuccessful businesses move new products through the development pipeline swiftly and efficiently. An integral part of this process is the research design and execution. The field of statistics can provide knowledge and guidance that aides a successful flow through the developmental pipeline. The involvement of a professional statistician as a team member can help plug potential leaks and increase your probability of success. It pays to consider all sources of measurement variation in the design of an experiment and to account for them in the data analysis. Randomly assigning subjects to treatments reduces bias and controls for important, but unknown, factors. Various randomization strategies differ in their time and cost. More powerful analyses are possible when subjects are matched so that when different treatments are compared, other sources of variation are controlled. More powerful experiments are more sensitive at the same cost as less powerful experiments. When baseline measurements are incorporated into data analysis, treatment effects beyond baseline can be identified. Sample sizes should be large enough to detect real differences, yet small enough to be manageable and cost effective. New technology allows the measurement of many variables at many time points. The skills of a statistician can be useful in collaboration with the scientist to find the best way to transform large amounts of data into useful information. Finally, the presentation of study results needs to include the relevant statistical methods. Potential investors want to see data and be confident it has been subjected to the appropriate analysis. Meeting the requirements of regulatory agencies (FDA and EPA) will proceed more quickly if the analysis has been conducted by a professional statistician. The University of Missouri System has statisticians on several campuses. There are graduate programs at the University of Missouri, UMKC and Missouri University of Science and Technology. Graduate students are available for internships and/or summer employment. Graduate student support often leads to long term collaborations with statistics faculty

An introduction to queuing theory concepts

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Evaluation of "Straw Man" Model 1, the Simple Linear Model, for Soybean Yields in Iowa, Illinois and Indiana

Straw man model 1 is one of the simplest regression models which can be used to predict crop yields. A one-line regression of yield over time, it represents the increases in yield which have occurred through the adoption of improved varieties, and the increased use of fertilizer and other cultural practices. The performance of this model in predicting soybean yields in Iowa, Illinois, and Indiana is evaluated. Eight model characteristics are discussed. Indicators of yield reliability obtained from bootstrap testing show that the bias is generally small for this model. However, the model is unable to predict the low and high yields accurately. The model is objective, adequate, timely, simple, and not costly. However it does not consider known scientific relationships and does not provide a good current measure of modeled yield reliability

One, Two, and Three Line Segment "Straw Man" Models: Soybean Yields in Iowa, Illinois, and Indiana

All of the straw man models attempt to explain differences in yields over time by fitting trend lines to the yield data. The one line segment straw man model, simple linear regression, describes a uniform increase in yields over time. The two and three line segment straw man models allow the rate of change in yields to vary over the time period. The performance of the three models in predicting soybean yields in Iowa, Illinois, and Indiana is compared based on eight model characteristics. There is little difference between the three models in relation to seven of the characteristics: objectivity, consistency with scientific knowledge, adequacy, timeliness, minimum costs, simplicity, and accurate current measures of modeled yield reliability. The one line model performs somewhat better than the other straw man models on the remaining characteristics, yield indication reliability