F-Ratio Is Designed in Such a Way

¶ … F-ratio is designed in such a way that there is no individual difference with reference to contribution between denominator and numerator. The numerator of F-ratio measures the means difference that exists between one treatment to the other and the F-ratio is designed in such a way that both denominator and numerator measure exactly the same variance and when the null hypothesis is true, and there will be no systematic treatment effect.

When there is no treatment effect, the F-ratio balances the numerator and denominator because both are measured exactly in the same variance, making F-ratio to have the value equal to 1.00. When a research finding concludes that F-ratio is equal to 1.00, the research will conclude that there will be no treatment effect, thus, the research will fail to reject the null hypothesis, and the null hypothesis is true. However, when the treatment effect exists, this contributes to the numerator only, which produces large value for the F-ratio.

Thus, the large value indicates that there is a real treatment effect and the research should reject the null hypothesis. The numerator in the F-ratio does not include individual differences and the individual difference needs to be eliminated from the denominator to achieve the balance between numerator and denominator. (Gravetter, 2011). 2. The T-test is appropriate to assess whether the means of two groups are statistically different, and t-test is appropriate to compare means of two groups.

On the other hand, ANOVA assesses whether the means of three or more group are statistically different. ANOVA assists in comparing the means of three or more group. Thus, ANOVA should be used to carry out a research to determine the statistical differences of the means of the three or more independent (unrelated) groups. However, the t-test will not be appropriate to carry out this type of test because the t-test is only appropriate to test the different of the means of two groups.

Platt, (1998) argues that the t-test is a commonly use statistical procedure to compare the means of two different populations. The difference between the two means divided by standard deviation is known as t-distribution. Platt, (1998) further argues that t-test is appropriate to compare the means of two different group of population, however, ANOVA is an appropriate statistical tool to compare the means of three or more groups.

While the t-test is appropriate to carry out the regression of only two values, the ANOVA could be used to carry out the regression of multiple covariant where the t-test is not appropriate for this kind of tasks. Second Part Among the top social problem in the United States is domestic violence, drug abuse and gang membership. Domestic violence is a risk factor for children because children exposed to domestic violence suffer a severe long effect from the violence.

Typically, majority of offenders in juvenile and correctional institutions come from families with histories of domestic violence. Prevalence of gang membership is associated with children growing up in abusive and violent and homes. Contrary to domestic violence, people of all ages could be exposed to drug abuse and the causes include pressure from friends, environmental factors, and peer group. Typically, 10% of people who experiment with drug later become addicted with drug. Drug abuse is associated with the misuse of illegal drugs such as marijuana, cocaine and heroine.

Drug abuse is also associated with misuse of medical prescription. Among the top effect of drug abuse are: Changes in body image Blood shot eyes Sore throat Increased blood pressure and heart rate. A gang is a group of individuals with identifiable internal organization and leadership with aim of claiming control over territory, and often engages individually or collectively in illegal behavior and other forms of violent activities. In the United States, youth gangs are on the increase and they are in virtually every major and small city.

Typically, gang membership is involved in violent and non-violent criminal offenses. Some offenses committed by gangs are murder, robbery, hit and run, threat, burglary, motor-vehicle theft and selling of illegal drugs. Olate, et al. (2012) argues that low empathy, educational difficulty, low future orientation and low social support are among the predictor growth of gang membership. The authors collect 174 sample and the results reveal that low empathy is the major predictor of gang membership scoring 58%. Rodney.

& Radall, (2004) in their own case carry out regression analysis on predictor of domestic violence and the authors reveal that race and ethnicity score high for the predictor of support for official attention for people suffering from domestic violence. White victim scores 47.2%, followed by the African-American race that scores 29.6%. Judith, Tao, & David, (2007) provide correlation between father who use drugs and prediction of adolescent aggression. The authors sample fathers of 296 Hispanic adolescent whose their father are on drug abuse.

The results of the regression analysis reveal that "the latent construct of paternal drug abuse had the third greatest total effect on adolescent aggression at T2 (standardized total effects 1/4-0.18, t-value 1/4-4.51, p < 0.001)." (Judith, Tao, & David, 2007, P 413). The adolescent vulnerable personality rank the first greatest predictor of adolescent aggression, while the adverse environmental rank the second greatest predictor of adolescent aggression.

Third Part Taxman, & Kitsantasb, (2009) perform regression analysis to investigate "the structural and organizational factors that contribute to the availability and increased capacity for substance abuse treatment programs in correctional setting" (p S43). The authors use the "classification and regression tree statistical procedures to identify how multi-level data can explain the variability in availability and capacity of substance abuse treatment programs in jails and probation/parole offices." (Taxman, & Kitsantasb, 2009 p S43). The authors collect data from the 2000 census and NCJTP (National Criminal Justice Treatment Practices) survey with 295-sample size.

The independent variables include attributes of the correctional administrators, jurisdictional-level structural variables, and "attributes of the correctional administrators, and program and service delivery characteristics of the correctional agency." (Taxman, & Kitsantasb, 2009 p S43). The results of regression analysis reveal that two.