BARREL RACING TIMES
Abstract
DARION BEVAN, DARCY MINES, BRENT JOHNSON
Conclusion
Results
The purpose of this study was to
investigate and analyze whether running
position in a barrel race, after the dirt has
been groomed, affects the running time
and finishing position in the race. The
target population we are choosing to
analyze are competitive barrel racers from
around the state of Utah competing in
competitions in Utah and Idaho.
Summary Statistics
Upon Collection, data was organized into statcruch where
summary statistics and boxplots were obtained. The boxplots
showed several outliers that will be removed before analysis of
data. The boxplots showed median times, the middle time, that
were similar in each trial. The Inner-Quartile Ranges were
compared and were found to have differences in dispersion
between the trials. The most compact trials, those will smaller
variance, were the first and fifth positions.
The research question we used as our
guide was : “Does your running position
(1-5) in a barrel race, following the
grooming of the dirt, affect the time you
are able complete the race in?”
H0: The mean finishing time for all position 15 in a barrel will be equal.
Hi: At least one position, 1-5, will have a
mean finishing time that is different from
the others
Methodology
Our method for data collection involved
randomly selecting four random competitions
that have occurred since the beginning of the
year 2013. For a given race, we organized the
data into groups of 5 consecutive runners (i.e.
1-5, 6-10, etc.) We randomly selected 45 of
these groupings from the 4 different races. We
used a random number generator in order to
randomly select these 45 groupings. These
groups of 5 were then broken down and
separated into 5 groups based upon running
order (ie. Group 1 is all riders who rode first in
their group, etc.). All outliers will be removed
and hit barrels times will be adjusted
Results and Analysis
We entered our data into StatCrunch software and
performed a one-way ANOVA Test, which is used to
compare the means of multiple data sets. Our
data contained five outliers (indicated in the
boxplot above). We ran an ANOVA with and without
these outliers and the data can be seen in the
tables to the right. Since the consequences for
making a type 1 error are not very significant, we
chose a level of significance of .05 to compare our
P-values to. The P-Value (.1638) for the data with
the outliers signified that there is no difference in
finishing times for positions 1-5. However the PValue (.0483) for the data set with outliers removed
showed that at least one mean was different from
the others
We constructed a means plot (bottom right),
which shows the confidence intervals for each of
the sample means, of our data with the outliers
removed to see which of the data set(s) were
different. The means plot indicates that Position 5
is faster than position 4. We can’t infer any other
differences from the plot as all other positions
contain overlap with one another.
Math 2040 Statistics for Applied Science
Using the results from the one-way
ANOVA Test with outliers removed, we
reject the null hypothesis (H0) since the
P-Value (.0483) is less than the level of
significance (.05) for at least one mean
Using a Tukey Test and means plot
we find that µ1=µ2=µ3=µ4≠µ5. We can
also see from the means plot that
position 5 has faster finishing times
that position 4.
Based upon these findings, if you
ever find yourself in a barrel race and
can chose between positions 4 or 5,
choosing position 5 will give you the
best opportunity to win
Sources
Data was collected from the official
Utah Barrel Racers Association’s
website
www.utbra.com
Data was analyzed using StatCrunch
Software
www.statcrunch.com