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Table 4: Econometric Estimates of Categorical Amounts of Drinking For Persons Consuming Some Alcohol During the Last Month

in Does Drinking Really Decrease in Bad Times
by Christopher J. Ruhm, William E. Black, Christopher J. Ruhm, William E. Black, Christopher J. Ruhm, William E. Black 2001
"... In PAGE 16: ... Binge drinking could occur without heavy alcohol use over the course of a month, although this is uncommon in our data.36 The econometric results, displayed in Table4 , demonstrate that alcohol use falls in bad economic times because of reductions in heavy drinking. A one percentage point increase in unemployment decreases the expected probability of consuming 60 (100) or more drinks by 0.... ..."

Table 6 Comparison of the Fixed Effect Model in SAS, STATA, LIMDEP*

in unknown title
by unknown authors
"... In PAGE 33: ...ttp://www.indiana.edu/~statmath 4.8 Summary Table6 summarizes the estimation of panel data models in SAS, STATA, and LIMDEP. The SAS REG and TSCSREG procedures are generally preferred to STATA and LIMDEP commands.... ..."

Table 6.1: Experimental Results B4CZ BPBDBHB5.

in Realization of Multiple-Output Functions by Reconfigurable Cascades
by Yukihiro Iguchi , Tsutomu Sasao, Munehiro Matsuura 2001
Cited by 6

Table 1 Categorization of product design and production relationships.

in A New Design For Production (DFP) Methodology with Two Case Studies Abstract
by Lee Ming, Wong G. Gary, Wang Doug Strong
"... In PAGE 10: ... T hus indirect relationships are usually hard to describe via a parametric equation; instead knowledge - based, case - based reasoning or other similar methods may be used. For instance in Table1 , the relationship between product tolerance with the machine fee d rate is complex and dependent on other factors such as product material, complexity of shape, the machine properties, and so on. Thus such a relationship is classified as indirect.... In PAGE 12: ...12 Table1 forms a general qualitative guideline on developing the relationship of product design and production. It l ists the relationships between four types of design variables, namely dimension, material, tolerance, and geometric shape, with the four main cost elements as stipulated by the OBC method.... In PAGE 12: ... Ho wever, the relations between product design and production issues are complex and vary with certain products and manufacturers. It is acknowledged assumptions made above might be violated and the categorization and content of Table1 might change. Thus T able 1 should be reexamined and modified for a specific design task.... In PAGE 12: ... For the purpose of optimization , one needs to quantify the relationships between design variables and cost elements, so that whenever a design is changed, its corresponding product costs can be estimated and thus the optimum searched. For direct relationships in Table1 , usually a para metric equation can be built. While ... ..."

Table 1. Categorization of product design and production relationships.

in A New Design for Production (DFP) Methodology with Two Case Studies
by Lee Ming Wong, G. Gary Wang, Doug Strong, Lee Ming Wong, G. Gary Wang, Doug Strong
"... In PAGE 5: ... Instead of parametric equations, knowledge-based, case-based reasoning or other similar methods may be used. For instance in Table1 , the relationship between product tolerance with the machine feed rate is complex and dependent on other factors such as product material, complexity of shape, the machine properties, and so on. Thus such a relationship is classified as indirect.... In PAGE 5: ... Such negligible relationships define the design-independent issues and thus help us concen- trate only on the direct and indirect relationships, or design-dependent production issues. Table1 forms a general qualitative guideline on developing the relationship of product design and production. It lists the relationships between four types of design variables, namely dimension, material, tolerance, and geometric shape, with the four main cost elements as stipulated by the OBC method.... In PAGE 5: ... However, the relations between product design and production issues are complex and vary with certain products and manufacturers. It is acknowledged that the assumptions made above might be violated and the categorization and content of Table1 might change. Thus, Table 1 should be reexamined and modified for a specific design task.... In PAGE 5: ... It is acknowledged that the assumptions made above might be violated and the categorization and content of Table 1 might change. Thus, Table1 should be reexamined and modified for a specific design task. It is hoped that the essence of the table is useful, i.... ..."

Table 1: Categorization of years of education variable.

in Bayesian Methods for Cumulative, Sequential and Two-step Ordinal Data Regression Models
by Jim Albert, Siddhartha Chib 1998
"... In PAGE 19: ... Thus a new covariate, parent education, was created by averaging each parent apos;s years of schooling. The response variable is created by categorizing the number of years of education as shown in Table1 . Note from the table that each category of the response is related to the completion of high school or college.... In PAGE 27: ...005 (.148) log m(y) = ?678:09 Table1 0: Posterior moments of the regression coe cients and transformed cut-points and marginal likelihood from the sequential model using a training sample prior. The sequential model was t again to the educational dataset using the prior based on the training sample.... In PAGE 27: ... In each case, a proper prior was obtained using the training sample of Table 3. The log marginal likelihood of the sequential model with the training sample prior (from Table1 0) is given by log m(y) = ?678:91 | the log Bayes factor in support of the basic ordinal model is log BF = log(exp(?670:82 + 678:09) = 3:15. It appears that there is no evidence for considering the more sophisticated sequential model for this particular dataset.... In PAGE 29: ... In other words, the ve covariates are not helpful in explaining the variation in large numbers of visits. Number of visits 0 1 2-3 4-6 7-10 11-15 16 and over Category 1 2 3 4 5 6 7 Subset 1 2 3 3 4 4 4 Table1 1: Categorization of response variable and suggested partition for physician visits dataset. Table 14 provides posterior moments for the covariates in the preferred model (Model 3).... In PAGE 30: ...333 (.098) Table1 2: Posterior moments of the regression coe cients and transformed cut-points based on a at prior and the training sample. Model Description log m(y) Comparison log Bayes factor 1 Training ?1690:6 2 Training + 1 = 0 + 2 = 0 ?1715:3 2 vs.... In PAGE 30: ... 1 ?10:7 3 Training + 2 = 0 ?1687:3 3 vs. 1 1:4 Table1 3: Marginal likelihoods for three two-stage models for physician visits dataset. covariate at this level seems to be CHRONIC | individuals with chronic health problems are more likely to have 4-5 visits than individuals without these problems.... In PAGE 31: ...134 (.168) Table1 4: Posterior mean estimates of regression parameters for model 3 in physician visits dataset. Posterior standard deviations are in parentheses.... ..."
Cited by 1

Table 7. Pooled Regressions Categorized By Third Variables

in unknown title
by unknown authors 2002
"... In PAGE 19: ...Are Some Firms More Inclined To Rebalance? Insert Table7 Here: Pooled Regressions Categorized By Third Variables A reasonable question is whether firms tend not to return to their previous (pre- sumably then optimal) capital structure because they do not need to: maybe they do not pay attention, because they are too large too fail, or they are too profitable to pay attention, or their tax rate is not high enough to reduce taxes, or their bankruptcy risk is too low to be meaningfully influenced by value changes. Table 7 shows that there is some mild evidence that smaller and more unprof- itable firms are less inert than larger and medium profitable firms.... In PAGE 19: ...Are Some Firms More Inclined To Rebalance? Insert Table 7 Here: Pooled Regressions Categorized By Third Variables A reasonable question is whether firms tend not to return to their previous (pre- sumably then optimal) capital structure because they do not need to: maybe they do not pay attention, because they are too large too fail, or they are too profitable to pay attention, or their tax rate is not high enough to reduce taxes, or their bankruptcy risk is too low to be meaningfully influenced by value changes. Table7 shows that there is some mild evidence that smaller and more unprof- itable firms are less inert than larger and medium profitable firms. However, neither a high tax rate, nor the credit rating (bankruptcy risk), the two primary variables used in the theoretical literature, show much influence.... In PAGE 39: ...Table7 continued) Panel E: By BVE0/MVE0: Value vs Growth BVE0/MVE0 con. IDR0;1 ADR0 c IDR ADR R2 N growth 1 1.... ..."

TABLE 1 FORECASTING PERFORMANCE LITERATURE CATEGORIZED BY ERROR MEASURE AND TYPE OF ECONOMETRIC MODEL

in unknown title
by unknown authors

Table 4.1: Results of Anova calculations using STATA on values of Qcorrect.

in Title Localisation of Surround Sound Signals
by R. J. H. Groeneveld 1992

Table 3 Mean (S.D.) values, percentages, and significance values for associations between acculturation status (time since immigration) and BMI, body weight dissatisfaction, dieting, binge eating, and compensatory behaviours Acculturation status

in Body Weight, Body Image, and Eating Behaviours:
by Relationships With Ethnicity, Kylie Ball A, Justin Kenardy B 2002
"... In PAGE 6: ...ther groups reporting that they did diet ( P lt; .01). The use of binge eating and compensatory behaviours did not differ significantly across groups defined by country of birth. The means and percentages for BMI, weight dissatisfaction, dieting, and disordered eating behaviours by acculturation status (time in Australia) are shown in Table3 . With the exception of compensatory behaviours, significant associations were found between accul- turation status and all of the weight and eating variables.... ..."
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