Change Me Into Zeuss Daughter essays

Change Me Into Zeus's Daughter essays Barbara Moss wrote "Change Me into Zeus's Daughter" which tells about her life growing up with an abusive and alcoholic father. If something were not well in her father's life he would take it out on Barbara or her mother. Her father would come home drunk on various nights at three o'clock P.M. and would wake up everyone in the house. He would make everyone clean house and was cars. Sometimes the eight kids would only get a few hours of sleep before they were awaken to do house hold chores which would sometimes last until morning when they would have to go to school. One time Barbara's father shot the family dog and also a pony, which they had only had for a very short time. Her book, "Change Me Into Zeus's Daughter", starts out with her father leaving to go to Pennsylvania in hope of getting a new job, which he had recently lost. His brothers lived in Pennsylvania and he was hoping that they could help him get a job. Apparently Barbara's father did not get a job and never sent home any money for the family of eight kids. They soon ran out of food and Barbara and her family had to move in with their Aunt Janet for a year. Barbara as a child did not have a beautiful face. She had many painful operations to get her face to look as it does today. As a child she wanted a beautiful face and a beautiful life, which she did not have in her childhood. Growing up she had no medical or dental care. Barbara went to art school and is a visual artist. Many of her paintings have been in well-known magazines and have been bought from her. Even though Barbara grew up in her family with all the bad memories of her father, she has forgiven him. Her mother was very forgiving also and she would forgive him the day after he would get drunk. She was a very loving person and would shave her husband's face every morning before he went off to work. She did this every day that they were married. Barbara feels that if her mother could forgive him than ...

An Introduction to Akaikes Information Criterion (AIC)

An Introduction to Akaike's Information Criterion (AIC) The Akaike Information Criterion (commonly referred to simply as AIC) is a criterion for selecting among nested statistical or  econometric models. The AIC is essentially an estimated measure of the quality of each of the available econometric models as they relate to one another for a certain set of data, making it an ideal method for model selection. Using AIC for Statistical and Econometric Model Selection The Akaike Information Criterion (AIC) was developed with a foundation in information theory. Information theory is a branch of applied mathematics concerning the quantification (the process of counting and measuring) of information. In using AIC to  attempt to measure the relative quality of econometric models for a given data set, AIC provides the researcher with an estimate of the information that would be lost if a particular model were to be employed to display the process that produced the data. As such, the AIC works to balance the trade-offs between the complexity of a given model and its goodness of fit, which is the statistical term to describe how well the model fits the data or set of observations. What AIC Will Not Do Because of what the Akaike Information Criterion (AIC) can do with a set of statistical and econometric models and a given set of data, it is a useful tool in model selection. But even as a model selection tool, AIC has its limitations. For instance, AIC can only provide a relative test of model quality. That is to say that AIC does not and cannot provide a test of a model that results in information about the quality of the model in an absolute sense. So if each of the tested statistical models are equally unsatisfactory or ill-fit for the data, AIC would not provide any indication from the onset. AIC in Econometrics Terms The AIC is a number associated with each model: AICln (sm2) 2m/T Where m is the number of parameters in the model, and sm2  (in an AR(m) example) is the estimated residual variance: sm2 (sum of squared residuals for model m)/T. That is the average squared residual for model m. The criterion may be minimized over choices of m to form a trade-off between the fit of the model (which lowers the sum of squared residuals) and the models complexity, which is measured by m. Thus an AR(m) model versus an AR(m1) can be compared by this criterion for a given batch of data. An equivalent formulation is this one: AICT ln(RSS) 2K where K is the number of regressors, T the number of observations, and RSS the residual sum of squares; minimize over K to pick K. As such, provided a set of econometrics models, the preferred model in terms of relative quality will be the model with the minimum AIC value.