*National Association of Medicaid Directors, Medicaid Forward: Behavioral Health.Michigan Mental Health Diversion Council, Promising Practices for Jail Diversion Across the Sequential Intercept Model.National Institute of Corrections, Veteran Intercepts in the Criminal Justice System.Major County Sheriffs of America (MCSA), Sheriffs Addressing the Mental Health Crisis in the Community and in the Jails.American Bar Association, Criminal Justice Standards on Mental Health.Council of State Governments Justice Center, Embedding Clinicians in the Criminal Justice System*.These across-the-intercept resources touch on aspects of best practices that span the SIM, so they are listed separately from the specific categories and intercepts. 7.5.1 A closer look at the \(\mathbf\) and we find that \(L2\) equals +2.As the Sequential Intercept Model (SIM) gains acceptance and usage, researchers and resource providers are increasingly using the SIM as a cohesive framework for their work.7.5 Connection between contrast and coding schemes.6.12 Relationship between \(F\)- and \(t\)-distributions.6.7.3 Interpreting the regression table.6.7.2 Let R create dummy variables automatically.6.7.1 Creating your own dummy variables.6.7 Analysing categorical predictor variables in R.6.6 Dummy coding for more than two groups.6.5 Two independent variables: one dummy and one numeric variable.6.4 Regression analysis using a dummy variable in R.6.3 Making inferences about differences in group means.6.2 Using regression to describe group means.5.14 Relationship between \(p\)-values and confidence intervals.5.13 Criticism on null-hypothesis testing and \(p\)-values.5.10 Type I and Type II errors in decision making.5.6 Null-hypothesis testing with linear models.5.5 Residual degrees of freedom in linear models.5.3 \(t\)-distribution for the model coefficients.5.2.2 From sample slope to population slope.5.2 Random sampling and the standard error.4.12 Explained and unexplained variance.4.11 Correlation, covariance and slopes in R.4.10 Numerical example of covariance, correlation and least square slope.4.7 Finding the OLS intercept and slope using R.4.1 Dependent and independent variables.3.4 Null-hypothesis concerning a proportion.3.1 Sampling distribution of the sample proportion.2.15 One-tailed testing applied to LH levels.2.14 One-sided versus two-sided testing.2.12 Null-hypothesis testing with \(t\)-values.2.10 Obtaining a confidence interval for a population mean in R.2.8 \(t\)-distributions and degrees of freedom.2.2 Sampling distribution of mean and variance.1.26.3 Numeric by numeric: scatter plot.1.26.2 Categorical by numerical: box plot.1.26.1 Categorical by categorical: cross-table.1.25 Visualising categorical and ordinal variables in R.1.22 Visualising numeric variables: the box plot.1.21 Obtaining quantiles of the normal distribution using R.1.17 Variance, standard deviation, and standardisation in R.1.14 Relationship between measures of tendency and measurement level.1.11 Quartiles, quantiles and percentiles. 1.9 Frequencies, proportions and cumulative frequencies and proportions.1.8 Frequency tables, frequency plots and histograms.1.6.4 Treatment of variables in data analysis.1.4 Multiple observations: wide format and long format data matrices.1.2 Units, variables, and the data matrix.1.1 A collapsible section with markdown.1 Variables, variation and co-variation.
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