The experiment must fulfill two goals: (1) to produce a professional report of your experiment, and (2) to show your understanding of the topics related to to the lowest degree squargons regression as described in Moore & transnational international ampereere; McCabe, Chapter 2. In this experiment, I will determine whether or not at that place is a relationship amongst average sit oodles of incoming freshmen versus the acceptance prise of appli faecests at top universities in the country. The cases being used be 12 of the very topper universities in the country harmonize to US News & World Report. The average SAT scores of incoming freshmen are the explanatory changeables. The response variable is the acceptance rate of the universities. Â Â Â Â Â Â Â Â I used September 16, 1996 cater of US News & World Report as my source. I started out by choosing the top fourteen Best discipline Universities. Next, I graphed the fourteen schools utilise a scatterplo t and decided to stripe it down to 12 universities by throwing out odd data. A scatterplot of the 12 universities data is on the pursuit page (page 2) The elongate 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 cut that the power regression had the best correlation of the non-linear fractures.
A scatterplot of the transformation can be seen on page 4. The big businessman Regression equating is ACCEPTANCE RATE=(2.475x10^23)(SAT SCORE)^-7.002 R= -.683 R^2=.466 The power regre ssion seems to be the better position for t! he experiment that I have chosen. there is a higher(prenominal) correlation in the power transformation than there is in the linear regression model. The R for the linear model is -.632 and the R in the power transformation is -.683. base on R^2 which measures the fraction of the variation in the... If you want to conk out a full essay, order it on our website:
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