natoli2001

[natoli2001]

I know you don't relate... can't relate to the darkest parts of me. It just means you still have light in you, and I'm grateful for that.

[natoli2001]

هل أفكر بتخلص من حياتي أن أذهب بعيداً للبعد الاخر

لشي مختلف جديد ..

هل أفكر في الاموات الذين انتهت حياتهم واصبحوا الان في سبات آخر أرواحهم تخلصت من هم الدنيا ..

يارب القوة والصبر ..

[natoli2001]

صعب جداً علي تقبل الحياة التي لم أخترها حياة بكل مافيها معقده جداً تفتك فيني لا أعرف لماذا يحدث كل هذا لي ولكل من أهلي ..

الضغوط الصعبة التي أواجهها في حياتي هي المسيطر على علاقاتي خسرت الكثير وفقدت الكثير ممن كانوا معي ..

والتعب والمرض شي آخر وكأن لاشي ينقصني سواه وكأن لاشي ينقص جسدي المتهالك وعقلي المزدحم بالمصائب الدنيويه التي تلاحقني من كل حدب ..

[natoli2001]

There are very difficult times now l am live with it ..

[natoli2001]

In addition, equipment failure occurred in manufacturing for nearly half of the batches (48%; N=329) and trainees were involved in the production of 51% (N=351) of the batches

[natoli2001]

[natoli2001]

Descriptive Statistics Table 1 shows descriptive statistics for manufacturing lead time and the quantitative predictors. The average manufacturing lead time was 21.45 hours (sd=3.86), with a minimum lead time of 9.40 hours and a maximum of 33.37 hours.

[natoli2001]

The sample included N=435 injection drug production batches manufactured at the Chicago plant from Jan 1, 2015 to December 31, 2015. All batches were high yield batches, meaning that each batch produced between 500,000 and 1 million 0.5 mg drug units. Measures The manufacturing lead time response variable was measured for each drug batch by calculating the number of hours between release of the batch manufacturing order and completion of product packaging. Predictors included 1) an average of the number of units of each drug ingredient on the bill of materials that was in stock at the time of release of the batch manufacturing order, 2) any equipment failure during production (yes/no) based on Engineering reports, and 3) the number of production steps that were required to complete the manufacturing process.

[natoli2001]

An Algorithm for Predicting Manufacturing Lead Time from Production Related Factors

[natoli2001]

[natoli2001]

The means on the clustering variables showed that, compared to the other clusters, adolescents in cluster 1 had moderate levels on the clustering variables. They had a relatively low likelihood of using alcohol or marijuana, but moderate levels of depression and self-esteem. They also appeared to have fairly low levels of school connectedness parental presence, parental involvement in activities and family connectedness. With the exception of having a high likelihood of having used alcohol or marijuana, cluster 2 had higher levels on the clustering variables compared to cluster 1, but moderate compared to clusters 3 and 4. On the other hand, cluster 3 clearly included the most troubled adolescents. Adolescents in cluster three had the highest likelihood of having used alcohol, a very high likelihood of having used marijuana, more alcohol problems, and more engagement in deviant and violent behaviors compared to the other clusters. They also had higher levels of depression, lower self-esteem, and the lowest levels of school connectedness, parental presence, involvement of parents in activities, and family connectedness. Cluster 4 appeared to include the least troubled adolescents. Compared to adolescents in the other clusters, they were least likely to have used alcohol and marijuana, and had the lowest number of alcohol problems, and deviant and violent behavior. They also had the lowest levels of depression, and higher self-esteem, school connectedness, parental presence, parental involvement in activities and family connectedness.

In order to externally validate the clusters, an Analysis of Variance (ANOVA) was conducting to test for significant differences between the clusters on grade point average (GPA). A tukey test was used for post hoc comparisons between the clusters. Results indicated significant differences between the clusters on GPA (F(3, 3197)=82.28, p<.0001). The tukey post hoc comparisons showed significant differences between clusters on GPA, with the exception that clusters 1 and 2 were not significantly different from each other.  Adolescents in cluster 4 had the highest GPA (mean=2.99, sd=0.71), and cluster 3 had the lowest GPA (mean=2.36, sd=0.78)

[natoli2001]

The elbow curve was inconclusive, suggesting that the 2, 4 and 8-cluster solutions might be interpreted. The results below are for an interpretation of the 4-cluster solution.  

Canonical discriminant analyses was used to reduce the 11 clustering variable down a few variables that accounted for most of the variance in the clustering variables. A scatterplot of the first two canonical variables by cluster (Figure 2 shown below) indicated that the observations in clusters 1 and 4 were densely packed with relatively low within cluster variance, and did not overlap very much with the other clusters. Cluster 2 was generally distinct, but the observations had greater spread suggesting higher within cluster variance. Observations in cluster 3 were spread out more than the other clusters, showing high within cluster variance. The results of this plot suggest that the best cluster solution may have fewer than 4 clusters, so it will be especially important to also evaluate the cluster solutions with fewer than 4 clusters.  

Figure 2. Plot of the first two canonical variables for the clustering variables by cluster.

[natoli2001]

[natoli2001]

A k-means cluster analysis was conducted to identify underlying subgroups of adolescents based on their similarity of responses on 11 variables that represent characteristics that could have an impact on school achievement. Clustering variables included two binary variables measuring whether or not the adolescent had ever used alcohol or marijuana, as well as quantitative variables measuring alcohol problems, a scale measuring engaging in deviant behaviors (such as vandalism, other property damage, lying, stealing, running away, driving without permission, selling drugs, and skipping school), and scales measuring violence, depression, self-esteem, parental presence, parental activities, family connectedness, and school connectedness. All clustering variables were standardized to have a mean of 0 and a standard deviation of 1.

Data were randomly split into a training set that included 70% of the observations (N=3201) and a test set that included 30% of the observations (N=1701). A series of k-means cluster analyses were conducted on the training data specifying k=1-9 clusters, using Euclidean distance. The variance in the clustering variables that was accounted for by the clusters (r-square) was plotted for each of the nine cluster solutions in an elbow curve to provide guidance for choosing the number of clusters to interpret.  

Figure 1. Elbow curve of r-square values for the nine cluster solutions

[natoli2001]

[natoli2001]

A lasso regression analysis was conducted to identify a subset of variables from a pool of 23 categorical and quantitative predictor variables that best predicted a quantitative response variable measuring school connectedness in adolescents. Categorical predictors included gender and a series of 5 binary categorical variables for race and ethnicity (Hispanic, White, Black, Native American and Asian) to improve interpretability of the selected model with fewer predictors. Binary substance use variables were measured with individual questions about whether the adolescent had ever used alcohol, marijuana, cocaine or inhalants. Additional categorical variables included the availability of cigarettes in the home, whether or not either parent was on public assistance and any experience with being expelled from school. Quantitative predictor variables include age, alcohol problems, and a measure of deviance that included such behaviors as vandalism, other property damage, lying, stealing, running away, driving without permission, selling drugs, and skipping school. Another scale for violence, one for depression, and others measuring self-esteem, parental presence, parental activities, family connectedness and grade point average were also included. All predictor variables were standardized to have a mean of zero and a standard deviation of one.

Data were randomly split into a training set that included 70% of the observations (N=3201) and a test set that included 30% of the observations (N=1701). The least angle regression algorithm with k=10 fold cross validation was used to estimate the lasso regression model in the training set, and the model was validated using the test set. The change in the cross validation average (mean) squared error at each step was used to identify the best subset of predictor variables.

Figure 1. Change in the validation mean square error at each step

[natoli2001]

Random forest analysis was performed to evaluate the importance of a series of explanatory variables in predicting a binary, categorical response variable. The following explanatory variables were included as possible contributors to a random forest evaluating regular smoking (my response variable), age, gender, (race/ethnicity) Hispanic, White, Black, Native American and Asian. Alcohol use, marijuana use, cocaine use, inhalant use, availability of cigarettes in the home, whether or not either parent was on public assistance, any experience with being expelled from school, alcohol problems, deviance, violence, depression, self-esteem, parental presence, parental activities, family connectedness, school connectedness and grade point average.

The explanatory variables with the highest relative importance scores were  marijuana use, White ethnicity, deviance and grade point average. The accuracy of the random forest was 78%, with the subsequent growing of multiple trees rather than a single tree, adding little to the overall accuracy of the model, and suggesting that interpretation of a single decision tree may be appropriate