Table of Contents
A6.8.1 What Is the Correlation Between Student’s Height and Parent’s Height? Also Produce a
Scatterplot. Interpret the Results, Including Statistical Significance, Direction, and Effect Size.. .3
A6.8.2 Write a Question That Can Be Answered via Correlational Analysis with Two
Approximately Normal or Scale Variables. Run the Appropriate Statistics to Answer the
Question. Interpret the Results........................................................................................................5
A6.8.3 Make a Correlation Matrix Using At least Four Appropriate Variables. Identify, Using
the Variable Names, The Two Strongest and Two Weakest Correlations. What Were the R and P
Values for Each Correlation?...........................................................................................................6
A6.8.4 Is There a Combination of Gender at Birth and Same-Sex Parent’s Height that
Significantly Predicts Student’s Height?.........................................................................................7
References......................................................................................................................................10
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BUSI 820 – A6 QUANTITATIVE ANALYSIS
A6.8.1 What Is the Correlation Between Student’s Height and Parent’s Height? Also
Produce a Scatterplot. Interpret the Results, Including Statistical Significance, Direction,
and Effect Size.
Table 1 shows a total of 50 cases in analysis. According to Table 2, the correlation
between student’s height in inches and same sex parent’s height is 0.842.
To investigate if there was a statistically significant association between student’s height
and parent’s height, the Pearson Correlation was computed. r(48) = 0.84, the significant level, or
p, is less than 0.001. The direction of the correlation was positive, which means the student
whose same sex parent is tall, tend to be tall and vice versa. Using Cohen’s guidelines, the size
effect is much larger than typical.
Table 1
Descriptive Statistics
Table 2
Correlations
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BUSI 820 – A6 QUANTITATIVE ANALYSIS
A6.8.2 Write a Question That Can Be Answered via Correlational Analysis with Two
Approximately Normal or Scale Variables. Run the Appropriate Statistics to Answer the
Question. Interpret the Results.
Question: Is there a statistically significant association between hours per week spent
working and student current gpa?
To investigate if there was a statistically significant association between hours per week
spent working and student current gpa, the Pearson Correlation was computed. r(47) = 0.303, the
significant level, or p, is less than 0.034. The direction of the correlation was positive, which
means students who work more per week, tend to have higher GPAs. Using Cohen’s guidelines,
the size effect is medium or typical.
Table 3
Descriptive Statistics
Table 4
Correlations
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BUSI 820 – A6 QUANTITATIVE ANALYSIS
A6.8.3 Make a Correlation Matrix Using At least Four Appropriate Variables. Identify,
Using the Variable Names, The Two Strongest and Two Weakest Correlations. What Were
the R and P Values for Each Correlation?
A correlation matrix was created with 4 variables: student’s current gpa, hours per week
spent working, hours of study per week, and amount of tv watched per week.
The strongest correlation is between amount of tv watched per week and hours per week
spent working. The Peason Correlation r(47) = -0.541. The significant level, or p, is less than
0.001.
The second strongest correlation is hours of study per week and amount of tv watched per
week. The Peason Correlation r(47) = -0.358. The significant level, or p, is 0.011.
The weakest correlation is between hours of study per week and student’s current gpa.
The Peason Correlation r(47) = 0.114. The significant level, or p, is 0.436.
The second weakest correlation is between hours of study per week and hours per week
spent working. The Peason Correlation r(47) = 0.219. The significant level, or p, is 0.131.
Table 5
Descriptive Statistics
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BUSI 820 – A6 QUANTITATIVE ANALYSIS
Table 6
Correlations
A6.8.4 Is There a Combination of Gender at Birth and Same-Sex Parent’s Height that
Significantly Predicts Student’s Height?
Simultaneous multiple regression was conducted to investigate the best prediction of
student’s height. Multiple linear regression remains a mainstay analysis in organizational
research, social science, economics and finance (Nimon & Oswald, 2013). The means, standard
deviations and intercorrelations can be found in Table 7 and Table 8. The combination of
variables to predict student’s height from sex at birth and same-sex parent’s height was
statistically significant, F(2,47)=69.725, p < 0.001. The beta coefficients are presented in Table
12. Note that sex at birth and same sex parent’s height significantly predict student’s height
when both variables are included. The adjusted R2 value was 0.737. This indicates that 73.7% of
the variance in student’s height was explained by the model. According to Cohen’s guidelines,
the size effect is much larger than typical.
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BUSI 820 – A6 QUANTITATIVE ANALYSIS
References
Morgan, G. A.; Barrett, K. C.; Leech, N. L.; Gloeckner, G. W. (2020) IBM SPSS for introductory
statistics: Use and interpretation, sixth edition. Taylor and Francis. Kindle Edition.
Nimon, K. F., & Oswald, F. L. (2013). Understanding the results of multiple linear regression:
Beyond standardized regression coefficients..Organizational Research Methods,.16(4),
650-674.Khttps://doi.org/10.1177/1094428113493929
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