"Bullying," according to noted expert Dan Olweus, "poisons the educational environment and affects the learning of every child." Bullying and victimization are evident as early as preschool, with the problem peaking in middle school. Suppose you are interested in the emotional well-being of not only the victims but also bystanders, bullies, and those who bully but who are also victims (bully-victims). You decide to measure depression in a group of victims and a group of bully-victims using a 26-item, 3-point depression scale. Assume scores on the depression scale are normally distributed and that the variances of the depression scores are the same among victims and bully-victims. The group of 25 victims scored an average of 25.3 with a sample standard deviation of 9 on the depression scale. The group of 23 bully-victims scored an average of 20.5 with a sample standard deviation of 8 on the same scale. You do not have any presupposed assumptions about whether victims or bully-victims will be more depressed, so you formulate the null and alternative hypotheses as:
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
"Bullying," according to noted expert Dan Olweus, "poisons the educational environment and affects the learning of every child." Bullying and victimization are evident as early as preschool, with the problem peaking in middle school. Suppose you are interested in the emotional well-being of not only the victims but also bystanders, bullies, and those who bully but who are also victims (bully-victims). You decide to measure depression in a group of victims and a group of bully-victims using a 26-item, 3-point depression scale. Assume scores on the depression scale are
The group of 25 victims scored an average of 25.3 with a sample standard deviation of 9 on the depression scale. The group of 23 bully-victims scored an average of 20.5 with a sample standard deviation of 8 on the same scale. You do not have any presupposed assumptions about whether victims or bully-victims will be more depressed, so you formulate the null and alternative hypotheses as:
Ho µvictims - µbully-victims = 0
H1: µvictims - µbully-victims ≠ 0
You conduct an independent-measures t test. Given your null and alternative hypotheses, this is a [ Select ] ["two-tailed", "one-tailed"] test. The degree of freedom is [ Select ] ["46", "48", "24", "22"] and for an alpha of .01, the critical t is +/- [ Select ] ["2.704", "2.021", "1.684"] .
In order to calculate the t statistic, you first need to calculate the standard error under the assumption that the null hypothesis is true. In order to calculate the standard error, you first need to calculate the pooled variance. The pooled variance is [ Select ] ["72.8696", "145", "0.3756", "71.3201"] . The standard error is [ Select ] ["2.4664", "2.5572", "1.9543", "72.8712"] .
The t-statistic is [ Select ] ["1.95", "2.55", "-1.70", "-2.25"]
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