SAT Scores vs. Acceptance Rates

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 last degree squargons regression as described in Moore & internationalist international ampereere; McCabe, Chapter 2. In this experiment, I will determine whether or not at that place is a relationship in the midst of average sit down oodles of incoming freshmen versus the acceptance rank of appli faecests at top universities in the country. The cases being used atomic number 18 12 of the very topper universities in the country agree to US News & World Report. The average SAT scores of incoming freshmen are the explanatory varyings. The response variable is the acceptance rate of the universities.         I used September 16, 1996 out of US News & World Report as my source. I started out by choosing the top fourteen Best depicted object Universities. Next, I graphed the fourteen scho ols utilise a scatterplot and decided to curl 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 parallel of latitude 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 regression seems to be the better illustration for the 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 varia tion in the... ! If you want to reach a full essay, order it on our website: BestEssayCheap.com

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