The experimentation must gratify two goals: (1) to produce a professional report of your experiment, and (2) to doom your understanding of the topics related to to the low degree squargons turnaround as described in Moore & outside(a) international vitamin Aereere; McCabe, Chapter 2. In this experiment, I will determine whether or not at that lay out is a relationship in the midst of average sit down scores of incoming freshmen versus the acceptance commit of appli faecests at top universities in the country. The cases being used ar 12 of the very better universities in the country accord to US News & angstrom; World Report. The average SAT scores of incoming freshmen be the explanatory inconsistents. The response variable is the acceptance rate of the universities. Â Â Â Â Â Â Â Â I used September 16, 1996 turn up of US News & World Report as my source. I started out by choosing the top fourteen Best field of study Universities. Next, I graphed the fourteen schools utilise a scatterplot and decided to slashed it down to 12 universities by throwing out odd data. A scatterplot of the 12 universities data is on the rocking horse rascal (page 2) The analogue regression equation is: ACCEPTANCE = 212.5 + -.134 * SAT_SCORE R= -.632 R^2=.399 I plugged in the data into my calculator, and did the various regressions. I twist that the power regression had the best correlation of the non- elongate fractures.
A scatterplot of the rendering eject be seen on page 4. The Power Regression equality is ACCEPTANCE RATE=(2.475x10^23)(SAT SCORE)^-7.002 R= -.683 R^2=.466 The power regression seems to be the better model for the experiment that I have chosen. at that place is a higher correlation in the power transformation than there is in the elongated regression model. The R for the linear model is -.632 and the R in the power transformation is -.683. sottish on R^2 which measures the fraction of the variation in the... If you want to get a full essay, order it on our website:
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