High School Students and Standardized College Placement Test (SAT)
Kimmy Givins
NorthCentral University
Dr. Boysen-Rotelli
09/28/2014

Research Study
Most university process for accepting new students depends on their scores on the standardized college placement test. More than 2000 colleges and universities uses high school students SAT test scores to determine eligibility of acceptance. Because most college and universities depends on SAT test scores, students with high scores are more likely to get accepted than student with lower test scores. Majority of colleges and university exempt students who meet grade-point averages or class ranking criteria while others require their SAT scores for placement purposes or to conduct a …show more content…
• The test statistic is t=d¯−d0sd/n√=27.14−036.38/7√=1.974.
• With n−1=6 degrees of freedom, the p-value is p=tcdf(1.974,∞,6)=0.0479.
• Since p<α, we reject H0.

Data Set Analyzes
The evidence indicates that test scores after the course are higher than before the course. A test preparation course measures student scores on sample tests before and after the course.
Descriptive data Analyze
For the following sample of students, test the claim that the mean score after the course is higher than before the course. Al Bob Carrie Don Ellen Faith Gina
Before: 550 450 580 500 480 520 510
After: 560 480 660 540 460 510 570
We begin by computing the differences. Al Bob Carrie Don Ellen Faith Gina
Differences: 10 30 80 40 -20 -10 60
The population standard deviation is unknown, and the sample size is small. A normal probability plot provides evidence that the original population of differences could have come from a normal population. Therefore, we can use the t-distribution. The sample statistics are d¯=27.14, sd=36.38, and n=7. This example is based on 27 college students. The independent variable (x) is SAT score and the dependent variable (y) is GPA. We are interested in understanding if a student’s GPA can be predicted using their SAT …show more content…
• Since 0 is not contained in our interval, we have reason to believe there is a linear relationship between GPA and SAT scores at the .05 level of significance
Bennett, Briggs, and Triola (2009) Evaluating the Model Using Hypothesis Testing. We hope to answer the question, “Does a linear relationship exist between x and y?” If there is no linear relationship between these variables, =0. If there is a linear relationship, then ≠ 0. Determine if there is overwhelming evidence at the α=.05 level of a linear relationship between GPA and SAT scores. We’ll use a t-test with n-2 degrees of freedom. Our critical values are 2.06 and -2.06. Our sample test statistic is given in the “t Stat” column and the “SAT SCORE” row, t-test statistic = 2.46. Since 2.46 > 2.06 we reject in favor of the alternative hypothesis H0. Therefore, there is overwhelming evidence at the α=.05 level that 1 β ≠ 0. So we find it reasonable to believe that there is a linear relationship between GPA and SAT score. The strength of this relationship can be analyzed using our correlation coefficient and coefficient of determination. The simplest method to test any hypothesis is the P-value method. The P-value is the probability of observing a test statistic more extreme than what we observed assuming that the null hypothesis is true. The P-value is given in the “P-value” column and