Learning problems in kindergarten student with extremely preterm birth by H Gerry Taylor, PhD, Nancy Klein, PhD, Marcia G Anselmo, MEd, Nori Minish, BS, Kimberly A Espy, PhD, and Maureen Hack, MB ChB
Physical activity but not energy expenditure is reduced in obese adolescents: a case-control study 1-3 by Ulf Ekelund, Jan Aman, Agneta Yngve, Cecilia Renman, Klaas Westerterp, and Michael Sjostrom
Fruit and vegetable intake and risk of cardiovascular disease in US adults: the first national health and nutrition examinations survey epidemiologic follow-up study 1-3
By Lydia A Bazzano, Jiang He, Lorraine G Ogden, Catherine M Loria, Suma Vupputuri, Leann Myers, and Paul K Whelton

The first study; Learning Problems in Kindergarten Students with Extremely Preterm, objective by the research objective was to implement a cohort study to assess early learning problems in extremely preterm(EP) children in kindergarten compared with term-born normal birth weight (NBY) controls; to identify risk factors of educational outcomes in the extremely preterm group with neonatal factor, early childhood neurodevelopmental impairment, and socioeconomic status (SES).
Variables included the age, sex and ethnicity and children born January 2001-December 2003 (n=148), and term-born normal birth weight classmate controls (n=111). Main outcome measures are achievement testing, teacher ratings of learning progress, and individual educational assistance. Analysis of covariance (ANCOVA) was used to compare the extremely preterm (EP) and term-born normal birth weight (NBW) groups on tests achievement tests and total of teacher ratings of progress in written language and mathematics learning progress. Logistic regression was also used in this study to examine differences in learning problems as defined by scores on the achievement tests, special education, teacher ratings of learning and other related classroom services. This type of statistics is used when there are two nominal variables and two measurement variables and to compare two or more linear regression lines to see if the slopes are significantly different or not. This study concluded that there were significant learning problems in preterm children in kindergarten so the ANCOVA is used and the common slope is the weighted average. Second study, Physical Activity but Not Energy Expenditure is Reduced in Obese Adolescents: A Case-Control Study, was conducted to test the influence of physical activity on body weigh in adolescents. Variables included age, weight, height, fat-free mass, fat mass, body fat, and sex. The control groups included sex and group (obese compared with control subjects boys (n=8) and girls (n=10)). This study used two types of methods Analysis of variance (ANOVA) and analysis of covariance (ANCOVA), and double labeled water (DLW). ANCOVA was used to measure the resting metabolic rate (RMR), total energy expenditure (TEE), and activity-related energy expenditure (AEE) between the groups as dependent variables with fat-free mass (FFM) as the covariate. ANOVA was used in this study to show whether or not group and sex affected RMR and TEE. In this case, there was a significant group and sex effect on RMR and TEE, and AEE was higher for boys than girls. The second method DLW was used to measure free-living energy expenditure to get an average measure of TEE over a 1-3 week period. By combining two methods, researchers were better able to assess the physical activity simultaneously with the direct measurement of physical activity. The study resulted in no significant differences in adjusted TEE, RMR or AEE between groups with obese children in physical activity related energy expenditure between groups. The third and final study, Fruit and Vegetable Intake and Risk of Cardiovascular Disease in US Adults: the First National Health and Nutrition Examination Survey Epidemiologic Follow-up Study 1-3, compared the relationship between fruit and vegetable intake and the risk of cardiovascular disease in US adults. This ongoing cohort study used participants aged 25-74 between 1971-1975, during the participants baseline examination. Methods included participation of a National Health and Nutrition survey follow-up study and other baseline questionnaire including on medical history of selected health conditions and medications. Additional interviewer-administered questionnaires included 3 month food frequency questionnaire and consumption of food groups in 13 major categories being the control group, and a 24 hour dietary-recall questionnaire data. Variables included age, sex, race, and diabetic status with birth cohort being the control variable for calendar period. This study utilized the Spearman rank-order correlation analysis for the 3-month food frequency questionnaire to measure the strength of association between two ranked variables. In this case, the 3-month data was compared with data from the 24-hour dietary recall. This method tests the correlation relationship between two sets of ranked data whether ordinal data or interval and ration data that have been changed to ranks (ordinal data). The significance of differences was analyzed using analysis of variance (continuous) and the chi-square test (categorical variables). Kaplan-Meier method was used to calculate cumulative incidence of and cardiovascular disease mortality by category of fruit and vegetable intake. Cox proportional hazards regression models explored the relationship between the two. Multivariate models were used to adjust physical activity, education level, alcohol consumption, cigarette smoking, vitamin supplement use, energy intake, sex, race, and diabetic status. The study concluded inverse relationship between fruit and vegetable intake and the risk of cardiovascular disease and all-cause mortality in the US. Statistical methods and analyses are often used to communicate research findings and to support hypotheses and give credibility to research methodology and conclusions. When implementing a study or research, depending on the types of data you are measuring, good judgment and knowing ways to apply statistics helps to inform, find solutions to real-life problems or discover important information to help advance the theory and practice of statistics. Understanding and having the ability to interpret in order to communicate the results gives people a better understanding of the data, its effect, and the ability to understand the big picture and make appropriate decisions.