### Table 11.2 Comparison between the 2-Shewhart chart with optimal sample size and the 2-CUSUM algorithm.

### Table 12: Estimation results from multinomial pooled logit for regional, intraregional and local moves (t-statistics in italics) Local Intraregional Regional

1999

"... In PAGE 18: ... For an interpretation consider the following example: the value 1.416, the RRR associated with being single in the equation for moving locally, Table12 , states that the relative chances for moving locally rather than not moving house at all is about two fifths higher for single persons relative to couples with children, all other things equal. Estimation Results We limit the discussion to a set of core variables, which we set out to investigate in detail.... In PAGE 18: ...15 It should be noted that here the definition of mover considers all moves, including those within the same local authority district. Although this is a generous definition of residential mobility, Table12 compares the determinants of regional and local moves and is discussed later. The pooled probit does not control for unobserved individual heterogeneity, although the standard errors are corrected for repeated observations on the same persons.... In PAGE 22: ... Local vs Regional Moves The definition of mover used previously considers all moves, including those within the same local authority district. Table12 presents a multinomial logit model for local (within a local authority district), intraregional moves (between local authority districts but within a region) and regional (between the standard regions), highlighting the differences in the determinants of local and regional moves. The figures reported are the relative risk ratios 24 To further investigate the relationship between the error terms in the migration and labour market mobility, we have run the bivariate probit separately for regional, intraregional and local moves.... ..."

Cited by 1

### Table 11.2 Comparison between the #1F 2 -Shewhart chart with optimal sample size and the #1F 2 -CUSUM algorithm.

### Table 11.1 Comparison between the 2-Shewhart chart with nonoptimal sample size (N = 10) and the 2-CUSUM algorithm.

### Table 7: Logit coefficient estimates of shock variables

"... In PAGE 19: ... Unmarried individuals without children do not show identifiable patterns. Estimates of the shock variables are presented in Table7 , those of household structure are in table 8 and demographic, labor 18 Unpaid and firm owners were dropped due to the small sample size in each job type. ... ..."

### Table 11.1 Comparison between the #1F 2 -Shewhart chart with nonoptimal sample size (N =10)andthe#1F 2 -CUSUM

### Table 1. Sampling variables.

2003

"... In PAGE 2: ... Our goal is to apply this theory to: (1) identify a minimal but representative sample from the population for microarchitecture simulation, and (2) estab- lish a confidence level for the error on sample estimates. Table1 summarizes the standard statistical sampling variables and terminology relevant to this paper. Simple random sampling selects a sample of n elements (a.... ..."

Cited by 113

### Table 1. Sampling variables.

2003

"... In PAGE 2: ... Our goal is to apply this theory to: (1) identify a minimal but representative sample from the population for microarchitecture simulation, and (2) estab- lish a confidence level for the error on sample estimates. Table1 summarizes the standard statistical sampling variables and terminology relevant to this paper. Simple random sampling selects a sample of n elements (a.... ..."

Cited by 113

### Table 6: Public Investment, Property Rights, and Growth (robust standard errors in parentheses) Equation 1 2 3

"... In PAGE 26: ...25 Table6 displays the core results. Equation 1 is the base specification and includes no institutional variables or interactions.... ..."

### Table 1. Sampling variables.

"... In PAGE 2: ... Our goal is to apply this theory to: (1) identify a minimal but representative sample from the population for microarchitecture simulation, and (2) estab- lish a confidence level for the error on sample estimates. Table1 summarizes the standard statistical sampling variables and terminology relevant to this paper. Simple random sampling selects a sample of n elements (a.... ..."