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PSYC-FPX4700 Assessment 5 Data Analysis and Application with
Complete Solution | Latest Update 2026
Data Analysis and Application
Sarah Jeffs
Capella University
, 2
Data Analysis and Application
This analysis seeks to unveil relationships and connections that might exist between
these different variables, shedding light on correlations, patterns, and insights within the sample
dataset grades.sav. The dataset captures a teacher's records of student demographics and their
performance on quizzes and a final exam across three separate sections of the course. With a
total of 105 students represented within the data, the data encompasses a diverse range of 21
variables, which include student identifiers such as ID number, first name, last name, as well as
attributes like gender identity, ethnicity, college year, division, course section, GPA, quiz scores,
final scores, overall class grades, and more. Correlations can help identify relationships and
dependencies between different variables (Privitera, 2017), providing valuable insights into the
factors that may influence student performance.
Data Analysis Plan
The analysis encompasses four variables: Quiz 1, Final, Total, and Grade Point Average
(GPA). "Quiz 1" refers to the initial assessment in the course, and this variable represents the
count of correct answers achieved by a student on this particular quiz. "Final" denotes the
concluding examination of the course, and this variable indicates the total of accurate responses
obtained by a student on this ultimate exam. "Total" signifies the cumulative sum of points
attained by a student throughout the class duration, covering all evaluations and assignments.
"GPA" stands for Grade Point Average, and this variable represents the numerical reflection of a
student's academic performance in prior courses.
By examining these four variables, this analysis explores whether there is a significant
association between a student's Final grade and GPA. This correlation assigns GPA as the
PSYC-FPX4700 Assessment 5 Data Analysis and Application with
Complete Solution | Latest Update 2026
Data Analysis and Application
Sarah Jeffs
Capella University
, 2
Data Analysis and Application
This analysis seeks to unveil relationships and connections that might exist between
these different variables, shedding light on correlations, patterns, and insights within the sample
dataset grades.sav. The dataset captures a teacher's records of student demographics and their
performance on quizzes and a final exam across three separate sections of the course. With a
total of 105 students represented within the data, the data encompasses a diverse range of 21
variables, which include student identifiers such as ID number, first name, last name, as well as
attributes like gender identity, ethnicity, college year, division, course section, GPA, quiz scores,
final scores, overall class grades, and more. Correlations can help identify relationships and
dependencies between different variables (Privitera, 2017), providing valuable insights into the
factors that may influence student performance.
Data Analysis Plan
The analysis encompasses four variables: Quiz 1, Final, Total, and Grade Point Average
(GPA). "Quiz 1" refers to the initial assessment in the course, and this variable represents the
count of correct answers achieved by a student on this particular quiz. "Final" denotes the
concluding examination of the course, and this variable indicates the total of accurate responses
obtained by a student on this ultimate exam. "Total" signifies the cumulative sum of points
attained by a student throughout the class duration, covering all evaluations and assignments.
"GPA" stands for Grade Point Average, and this variable represents the numerical reflection of a
student's academic performance in prior courses.
By examining these four variables, this analysis explores whether there is a significant
association between a student's Final grade and GPA. This correlation assigns GPA as the
