Linear Regression Model. Using only plots with at least one snag, and the comprehensive modeling approach described in Section
Linear Regression Model. Using only plots with at least one hardwood tree, and the comprehensive modeling approach described in Section 3.1
Linear Regression Model. A regression model was constructed as another approach, using the Red and Green band variables as its base. Forty remaining spectral and spatial variables were tested in several permutations and progressively narrowed down to the most significant coefficients. The dependent variable was the ratio of conifer trees to all trees for each plot. Field data was modified by adding a binary coding of 1=conifer 0=deciduous. This was based on the species of the tree. This value was then summed for each plot and divided by the plot’s total tree count.
Linear Regression Model. Using only plots with at least one piece of large woody debris, and the comprehensive modeling approach described in Section 3.1
Linear Regression Model. The model will take the following form: Yi = Xi β + δTi + ei Where: Yi is the outcome: earnings in quarters 5 through 8 after the random assignment quarter Xi is a vector of baseline covariates β is the vector of coefficients indicating the contribution of each covariate to the outcome Ti is a binary variable equal to 1 if the individual is in the treatment group and 0 if the individual is in the control group δ is the effect of treatment ei is the individual-specific error term The following list includes the variables and categories within variables that the Independent Evaluator may consider including in the regression model. Some variables and/or categories may need to be eliminated or combined depending on the composition of Study Population Members. Independent Evaluator will also check for strong collinearity between variables and remove one variable from each collinear pair, if necessary. • Gender (binary variable for female versus male) • Age • Race/Ethnicity (categorical variable with expected categories of Black, Hispanic, White, Asian/Pacific Islander, Other, and Unknown) • Highest Level of Education (categorical variable with expected categories of less than high school, high school diploma or equivalent degree, and any college degree) • Citizen/Resident Alien Status (categorical variable with expected categories of citizen, resident alien-temporary protected status, and resident alien-not temporary protected status) • Number of Years Residing in the United States • Ever Worked in a Job for Pay in the United States (binary variable for yes versus no) • Employment Status at Intake (categorical variable with categories of employed full time at intake, employed part time at intake, not employed but worked in the US within the past 6 months, not employed and last worked more than 6 months ago or never in the US) • Earnings During Quarters 1 through 8 Prior to the Random Assignment Quarter (including zero earnings, based on DUA data) • Parent of Child Under Age 18 (binary variable for yes versus no) • Number of Adults in Household Including Participant • Household Receiving TAFDC at Intake (binary variable for yes versus no) • Household Receiving SNAP at Intake (binary variable for yes versus no) • Household Receiving Unemployment Benefits at Intake (binary variable for yes versus no) • Region (categorical variable with categories for each of the four regions in which EfA will take place) • Month of random assignment (e.g., month 1 through month 36...
Linear Regression Model. As stated above in Section 3.4.1, for each phenotype, the differences among subjects arisen with respect to the included covariates may lead to the unwanted between- subject variation that needs to be controlled. The within-phenotype differences explained by the covariates or cross-phenotype mean variation should be excluded prior to the later analysis (if possible) so that the genetic effects will not be con- founded with these redundant nuisance effects during the statistical analysis. Moreover, the variances of unobservable subject-specific errors for distinct pheno- types may differ substantially, and the use of the phenotypic datasets with different variation may result in confounding and possibly lead to misleading conclusions. This problem is described as variance instability, and the purpose of normalization is to remove the non-negligible variation of the phenotypic variances and resolve the instability of variance. For each phenotype, we attempt to further variance- normalize the derived demeaned data (or residual) by dividing the residual by its sample standard deviation (a scalar quantity) to account for different variances in the residuals obtained from multiple phenotypes. This variance normalization deals with the probable variation in phenotypic variances and allows those residuals to be compared in the same scale. The variance-normalized (or scaled) residual is a quotient, which is written as e(k) = e(k) (4.9) ˜ s.e(k)Σ for phenotype k (k = 1, . . . , J) from all subjects, where the residual using OLS is expressed as . Σ
