# Buisness Analytics Gas prices project

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Gas Prices Project Report

Student’s Name
Course
Instructor
Date

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Gas Prices Project Report
Phase 1 of the Report
Introduction
This detailed research examines the complex upper Midwest fuel price environment
across 11 urban regions. We concentrate on Shell, BP, and Marathon fuel brands. The main
to understand the complexity of pricing differences across the three big brands and metropolitan
regions (Yu et al., 2022). Consumers, companies, and stakeholders need this exploration to make
educated decisions in a context of volatile gasoline costs. By examining and comparing these
characteristics, we aim to equip upper Midwest decision-makers with data to navigate the
complex gasoline price landscape and improve consumption, procurement, and business
planning.
Calculations and Data Summary
Shell
Mean = ∑ 3.77+3.72+3.87+3.76+3.83+3.85+3.93+3.79+3.78+3.81+3.69 = 41.2
41.2/ 11
3.737
Median = Median=3.805

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BP
Mean = 3.83,3.83,3.85,3.77,3.84,3.84,4.04,3.78,3.84,3.84,3.83
∑ 41.39
Mean = 41.39/11
3.762
Median=3.84

Marathon
Mean = 3.78,3.87,3.89,3.79,3.87,3.87,3.99,3.79,3.79,3.86,3.86
∑ 42.26
42.26/11

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3.860
Median=3.87
Standard Deviation

Interpretation of the Calculated Data
We used mean, median, mode, range, and standard deviation to compare Shell, BP, and
Marathon gasoline prices. These metrics reveal brand averages, standard deviations, and price
variations. Shell estimates petrol prices to be \$3.737 per gallon. A median of \$3.805, the dataset's
midpoint, suggests an equal distribution. Shell's dataset has no mode, indicating no repeated
values, yet the \$0.24 range shows prices from lowest to highest. Shell has a low standard
deviation of 0.0322, indicating pricing consistency around the mean. BP has a median price of
\$3.84 and an average price of \$3.762, lower than Shell. Both BP and Shell's datasets lack a

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mode. However, at \$0.27, the range is wider, indicating a larger price dispersion. The higher
standard deviation of 0.0706 for BP than Shell indicates more unpredictable gasoline pricing.
Marathon has a median price of \$3.87 and a mean of \$3.860. Marathon has no mode like
the others, but at \$0.21, it has the shortest range, implying a minimal price spread. Marathon has
a 0.0782 gasoline price variance, the greatest of the three brands. Despite a higher mean price,
Marathon has more price fluctuation than Shell and BP. After reading this lengthy research,
Shell, BP, and Marathon's pricing movements are better understood statistically. (Achen, 2021)
Marathon has the narrowest spread but bigger price volatility, whereas Shell is stable around its
mean. However, BP has a wider price range. These data help consumers and companies
understand the difficult fuel pricing situation.
Visual Representation of the Data

Data Visualization of Gasoline Project
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Mean

Mode

Median
Shell

BP

Range

Standard
Deviation

Marathon

Analysis of the data and Conclusion
Compare Shell, BP, and Marathon gasoline prices to see if population averages and
standard deviations vary. The calculated metrics show that the brands are considerably diverse.
BP charges \$3.762 per gallon, while Shell charges \$3.737. Marathon charges \$3.860. Means

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vary, therefore, these brands' pricing techniques may change. Standard deviations, which
measure price unpredictability, provide a more complicated picture. Shell has the lowest standard
deviation at 0.0322, indicating the most consistent price pattern. Marathon has severe price
volatility at 0.0782, while BP has the largest standard deviation at 0.0706. These differences in
standard deviations imply that the brands' prices are less consistent or predictable. A similar
research suggests that standard deviations and averages may differ. Businesses and consumers
seeking stability and efficiency must pick locations with low standard deviation and mean. To
make an educated choice, pick the gasoline brand with the lowest mean and standard deviation.
Shell is the most stable and affordable option due to its low mean and standard deviation.
Finding metro regions with the lowest standard deviation and mean reveals low-cost, stable gas
prices.
Conclusion
In conclusion, the research shows significant population means and standard deviations
for both brands and metropolitan regions, but "substantially different" requires a more thorough
statistical examination. These discrepancies require further hypothesis testing and advanced
statistical approaches to prove their relevance. However, mean and standard deviation trends
help consumers and organizations make better decisions.
Phase 2 of the Report
ANOVA data Analysis
To test the hypothesis that there is a significant difference in the mean price for the three brands
at a 5% level of significance
The null and alternative hypothesis
Ho: U1-U2

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Ha : at least one U1 is unequal
The F test for the three brands

From the Excel output, the F-test statistic for brands is 6.58
The p-value for this test is
From the Excel output, the p-value for this test is 0.006.
Decision The conclusion is that the p-value in this context is less than 0.05, which is 0.006, so
the null hypothesis is rejected at a 5% significance level. There is sufficient evidence to indicate
a significant difference in the mean price of gasoline for the three brands. The results are
statistically significant.
Comparison of Phase 1 with Phase 2 of ANOVA analysis
The ANOVA test can determine if the differences are statistically significant by
comparing the means of different groups. An important indication is the F-test statistic, which
was computed as 6.58. The associated p-value comes out to be 0.006. We reject the null
hypothesis since the p-value is less than 0.05. For business inquiries, this ANOVA finding has
far-reaching consequences. Rejecting the null hypothesis leads us to believe that the three brands'
average petrol costs are significantly different (Lakens & Caldwell, 2019). This is consistent with
the results from Section 2 of Phase 1, where descriptive statistics and boxplots suggested that the
brands might vary.
There is clear consistency when looking at the ANOVA findings compared to the prior
study in Section 2 of Phase 1. The ANOVA test adds further depth to the analysis in Section 2,

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which already included useful metrics like mean, median, mode, range, and standard deviation.
To get a more definitive conclusion, it thoroughly investigates if the observed mean variations
are statistically significant. Section 2's findings corroborate the ANOVA results, crediting the
first observations. The two studies agree that the three brands' average petrol costs are
significantly different. The dependability of the findings reached is strengthened by the
uniformity among approaches. The ANOVA results back up the descriptive data, giving a solid
statistical basis for the claim that Shell, BP, and Marathon have significantly different mean
petrol prices across the 11 metro regions. Consistent with the business inquiries presented in the
first phase, this all-encompassing strategy guarantees a strong and complete investigation of the
petrol price fluctuations.

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Reference
Achen, C. H. (2021). The statistical analysis of quasi-experiments. University of California
Press.
Yu, Z., Guindani, M., Grieco, S. F., Chen, L., Holmes, T. C., & Xu, X. (2022). Beyond t-test and
ANOVA: applications of mixed-effects models for more rigorous statistical analysis in
neuroscience research. Neuron, 110(1), 21-35.
Lakens, D., & Caldwell, A. R. (2019). Simulation-based power-analysis for factorial ANOVA
designs.

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