The Influences on Gas Prices and Analyzing the Impact of the Prices from Pre Covid to Post Covid

Introduction to the project

As a result of the COVID-19 pandemic, there have been changes in the gas prices.The changes in the price have definitely made it very difficult for low income families who need to get to their everyday jobs. Global businesses have their own standards about getting much of the gasoline. They were caused by several different things which included a sudden drop in demand because of lockdowns and travel limits, as well as a drop in demand for fuel, which caused oil prices to drop. The U.S. Bureau of Labor Statistics says that between April 2019 and April 2020, the selling price of oil fell by 53.0% for producers and 16.5% for consumers. We can better understand what caused the big changes in gas prices by looking at statistics from the pandemic. This can also help businesses learn how to make better choices when events like COVID-19 happen that have never happened before.

Problem statement

Due to all the lockdowns, travel limits, and less industrial activity, the COVID-19 pandemic has had a big impact on the world economy. As a result, there has been a sharp drop in the demand for oil and gas. Because of this drop in demand, market volatility, and problems in the supply chain, oil prices have been all over the place. Consumers, companies, and governments all over the world have been affected by these changes, which have led to financial stress and economic uncertainty.

Solutions

- Economic Impact: Fluctuating oil prices directly affect household spending, business activity, and inflation, so it is important to find solutions to stabilize prices and minimize economic risks.

- Supply Chain Disruption: The pandemic has exposed vulnerabilities in global supply chains. Analyzing oil prices can provide insights to improve the resilience of energy supply chains in future crises.

- Energy Policy: Governments need data-driven strategies to manage energy resources, establish policies that ensure price stability, support economic recovery, and promote long-term sustainability.

Objective

We aim to learn how COVID-19 affected gas prices in everybody’s daily lives. Our goal is to use analytical techniques in the data for the years 2020 & 2021. With the data, our team will develop a hypothesis test model to compare the gas prices within the above-mentioned time frame. Analyzing the trends during the pandemic will provide valuable lessons for businesses to strengthen and adapt their global supply chains.

Method (s)

- Our team will be performing multiple rounds of hypothesis testing using the data provided;

- We will use the data provided through the process of researching;

- Start the testing process by inputting much of the data through the R program;

- Our research will include descriptive statistics that include standard deviation, mean, box plots, and t-test.

Execution, Interpretation and Results

Load the required libraries

library(readxl)
library(tidyr)
library(car)
## Warning: package 'car' was built under R version 4.4.2
## Loading required package: carData
## Warning: package 'carData' was built under R version 4.4.2
library(corrplot)
## Warning: package 'corrplot' was built under R version 4.4.2
## corrplot 0.95 loaded

Import the data

data <- read_excel("10326_retail_fuel_prices_05-16-24.xlsx")
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
data
## # A tibble: 101 × 9
##    Average Retail Fuel Prices …¹ ...2  ...3  ...4  ...5  ...6  ...7  ...8  ...9 
##    <chr>                         <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
##  1 Report Date                   Gaso… E85   CNG   LNG   Prop… Dies… B20   B99/…
##  2 <NA>                          <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
##  3 <NA>                          <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
##  4 <NA>                          <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
##  5 <NA>                          <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
##  6 <NA>                          <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
##  7 <NA>                          <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
##  8 <NA>                          <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
##  9 <NA>                          <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
## 10 <NA>                          <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
## # ℹ 91 more rows
## # ℹ abbreviated name:
## #   ¹​`Average Retail Fuel Prices in the United States (in Gasoline Gallon Equivalents, GGEs)`

Select columns 2 to 9

df <- data[, 2:9]
df
## # A tibble: 101 × 8
##    ...2     ...3  ...4  ...5  ...6     ...7   ...8  ...9    
##    <chr>    <chr> <chr> <chr> <chr>    <chr>  <chr> <chr>   
##  1 Gasoline E85   CNG   LNG   Propane* Diesel B20   B99/B100
##  2 <NA>     <NA>  <NA>  <NA>  <NA>     <NA>   <NA>  <NA>    
##  3 <NA>     <NA>  <NA>  <NA>  <NA>     <NA>   <NA>  <NA>    
##  4 <NA>     <NA>  <NA>  <NA>  <NA>     <NA>   <NA>  <NA>    
##  5 <NA>     <NA>  <NA>  <NA>  <NA>     <NA>   <NA>  <NA>    
##  6 <NA>     <NA>  <NA>  <NA>  <NA>     <NA>   <NA>  <NA>    
##  7 <NA>     <NA>  <NA>  <NA>  <NA>     <NA>   <NA>  <NA>    
##  8 <NA>     <NA>  <NA>  <NA>  <NA>     <NA>   <NA>  <NA>    
##  9 <NA>     <NA>  <NA>  <NA>  <NA>     <NA>   <NA>  <NA>    
## 10 <NA>     <NA>  <NA>  <NA>  <NA>     <NA>   <NA>  <NA>    
## # ℹ 91 more rows

Convert from character to numeric

df <- as.data.frame(lapply(df, function(x) as.numeric(x)))
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
df
##     ...2 ...3 ...4 ...5 ...6 ...7 ...8 ...9
## 1     NA   NA   NA   NA   NA   NA   NA   NA
## 2     NA   NA   NA   NA   NA   NA   NA   NA
## 3     NA   NA   NA   NA   NA   NA   NA   NA
## 4     NA   NA   NA   NA   NA   NA   NA   NA
## 5     NA   NA   NA   NA   NA   NA   NA   NA
## 6     NA   NA   NA   NA   NA   NA   NA   NA
## 7     NA   NA   NA   NA   NA   NA   NA   NA
## 8     NA   NA   NA   NA   NA   NA   NA   NA
## 9     NA   NA   NA   NA   NA   NA   NA   NA
## 10    NA   NA   NA   NA   NA   NA   NA   NA
## 11    NA   NA   NA   NA   NA   NA   NA   NA
## 12    NA   NA   NA   NA   NA   NA   NA   NA
## 13    NA   NA   NA   NA   NA   NA   NA   NA
## 14    NA   NA   NA   NA   NA   NA   NA   NA
## 15    NA   NA   NA   NA   NA   NA   NA   NA
## 16    NA   NA   NA   NA   NA   NA   NA   NA
## 17    NA   NA   NA   NA   NA   NA   NA   NA
## 18    NA   NA   NA   NA   NA   NA   NA   NA
## 19    NA   NA   NA   NA   NA   NA   NA   NA
## 20    NA   NA   NA   NA   NA   NA   NA   NA
## 21    NA   NA   NA   NA   NA   NA   NA   NA
## 22    NA   NA   NA   NA   NA   NA   NA   NA
## 23    NA   NA   NA   NA   NA   NA   NA   NA
## 24    NA   NA   NA   NA   NA   NA   NA   NA
## 25    NA   NA   NA   NA   NA   NA   NA   NA
## 26    NA   NA   NA   NA   NA   NA   NA   NA
## 27    NA   NA   NA   NA   NA   NA   NA   NA
## 28    NA   NA   NA   NA   NA   NA   NA   NA
## 29    NA   NA   NA   NA   NA   NA   NA   NA
## 30    NA   NA   NA   NA   NA   NA   NA   NA
## 31    NA   NA   NA   NA   NA   NA   NA   NA
## 32    NA   NA   NA   NA   NA   NA   NA   NA
## 33    NA   NA   NA   NA   NA   NA   NA   NA
## 34    NA   NA   NA   NA   NA   NA   NA   NA
## 35    NA   NA   NA   NA   NA   NA   NA   NA
## 36    NA   NA   NA   NA   NA   NA   NA   NA
## 37    NA   NA   NA   NA   NA   NA   NA   NA
## 38    NA   NA   NA   NA   NA   NA   NA   NA
## 39    NA   NA   NA   NA   NA   NA   NA   NA
## 40    NA   NA   NA   NA   NA   NA   NA   NA
## 41    NA   NA   NA   NA   NA   NA   NA   NA
## 42    NA   NA   NA   NA   NA   NA   NA   NA
## 43    NA   NA   NA   NA   NA   NA   NA   NA
## 44    NA   NA   NA   NA   NA   NA   NA   NA
## 45    NA   NA   NA   NA   NA   NA   NA   NA
## 46    NA   NA   NA   NA   NA   NA   NA   NA
## 47    NA   NA   NA   NA   NA   NA   NA   NA
## 48    NA   NA   NA   NA   NA   NA   NA   NA
## 49    NA   NA   NA   NA   NA   NA   NA   NA
## 50    NA   NA   NA   NA   NA   NA   NA   NA
## 51    NA   NA   NA   NA   NA   NA   NA   NA
## 52    NA   NA   NA   NA   NA   NA   NA   NA
## 53    NA   NA   NA   NA   NA   NA   NA   NA
## 54    NA   NA   NA   NA   NA   NA   NA   NA
## 55    NA   NA   NA   NA   NA   NA   NA   NA
## 56    NA   NA   NA   NA   NA   NA   NA   NA
## 57    NA   NA   NA   NA   NA   NA   NA   NA
## 58    NA   NA   NA   NA   NA   NA   NA   NA
## 59    NA   NA   NA   NA   NA   NA   NA   NA
## 60    NA   NA   NA   NA   NA   NA   NA   NA
## 61    NA   NA   NA   NA   NA   NA   NA   NA
## 62    NA   NA   NA   NA   NA   NA   NA   NA
## 63  2.50 2.68 2.17 2.66 3.88 2.63 2.55 3.41
## 64  2.67 2.87 2.18 2.57 3.87 2.70 2.59 3.39
## 65  2.88 3.05 2.22 2.60 3.85 2.89 2.75 3.48
## 66  2.91 3.10 2.19 2.64 3.93 2.99 2.78 3.57
## 67  2.27 2.59 2.19 2.71 3.99 2.65 2.52 3.50
## 68  2.76 3.00 2.22 2.38 3.97 2.75 2.59 3.44
## 69  2.76 3.06 2.21 2.46 3.87 2.71 2.58 3.55
## 70  2.68 2.97 2.20 2.40 3.79 2.74 2.58 3.65
## 71  2.59 2.96 2.18 2.47 3.82 2.71 2.60 3.65
## 72  1.91 2.28 2.19 2.43 3.74 2.33 2.13 3.44
## 73  2.22 2.58 2.15 2.40 3.75 2.20 2.11 3.08
## 74  2.18 2.54 2.18 2.42 3.74 2.13 2.06 3.26
## 75  2.32 2.65 2.19 2.45 3.90 2.35 2.18 3.11
## 76  2.82 3.12 2.19 2.39 4.01 2.77 2.53 3.49
## 77  3.09 3.40 2.22 2.54 4.08 2.90 2.74 3.56
## 78  3.25 3.55 2.33 2.45 4.34 3.10 2.96 3.73
## 79  3.28 3.87 2.49 2.69 4.69 3.22 3.08 3.88
## 80  4.13 4.60 2.59 2.82 4.83 4.50 4.16 4.96
## 81  4.70 5.10 2.76 3.15 5.19 5.02 4.80 5.48
## 82  4.05 4.13 2.88 3.23 4.86 4.60 4.40 5.15
## 83  3.31 3.60 3.25 4.23 5.02 4.08 4.01 5.11
## 84  3.69 3.88 2.99 4.02 4.98 3.78 3.62 4.85
## 85  3.59 3.84 2.86 3.38 4.46 3.45 3.40 4.44
## 86  3.72 3.96 2.85 3.49 4.51 4.02 3.98 4.76
## 87  3.06 3.32 2.95 3.34 4.78 3.51 3.45 4.60
## 88  3.65 3.85 2.90 3.43 4.72 3.62 3.55 4.48
## 89    NA   NA   NA   NA   NA   NA   NA   NA
## 90    NA   NA   NA   NA   NA   NA   NA   NA
## 91    NA   NA   NA   NA   NA   NA   NA   NA
## 92    NA   NA   NA   NA   NA   NA   NA   NA
## 93    NA   NA   NA   NA   NA   NA   NA   NA
## 94    NA   NA   NA   NA   NA   NA   NA   NA
## 95    NA   NA   NA   NA   NA   NA   NA   NA
## 96    NA   NA   NA   NA   NA   NA   NA   NA
## 97    NA   NA   NA   NA   NA   NA   NA   NA
## 98    NA   NA   NA   NA   NA   NA   NA   NA
## 99    NA   NA   NA   NA   NA   NA   NA   NA
## 100   NA   NA   NA   NA   NA   NA   NA   NA
## 101   NA   NA   NA   NA   NA   NA   NA   NA

Clean the data

df_clean <- na.omit(df)
df_clean
##    ...2 ...3 ...4 ...5 ...6 ...7 ...8 ...9
## 63 2.50 2.68 2.17 2.66 3.88 2.63 2.55 3.41
## 64 2.67 2.87 2.18 2.57 3.87 2.70 2.59 3.39
## 65 2.88 3.05 2.22 2.60 3.85 2.89 2.75 3.48
## 66 2.91 3.10 2.19 2.64 3.93 2.99 2.78 3.57
## 67 2.27 2.59 2.19 2.71 3.99 2.65 2.52 3.50
## 68 2.76 3.00 2.22 2.38 3.97 2.75 2.59 3.44
## 69 2.76 3.06 2.21 2.46 3.87 2.71 2.58 3.55
## 70 2.68 2.97 2.20 2.40 3.79 2.74 2.58 3.65
## 71 2.59 2.96 2.18 2.47 3.82 2.71 2.60 3.65
## 72 1.91 2.28 2.19 2.43 3.74 2.33 2.13 3.44
## 73 2.22 2.58 2.15 2.40 3.75 2.20 2.11 3.08
## 74 2.18 2.54 2.18 2.42 3.74 2.13 2.06 3.26
## 75 2.32 2.65 2.19 2.45 3.90 2.35 2.18 3.11
## 76 2.82 3.12 2.19 2.39 4.01 2.77 2.53 3.49
## 77 3.09 3.40 2.22 2.54 4.08 2.90 2.74 3.56
## 78 3.25 3.55 2.33 2.45 4.34 3.10 2.96 3.73
## 79 3.28 3.87 2.49 2.69 4.69 3.22 3.08 3.88
## 80 4.13 4.60 2.59 2.82 4.83 4.50 4.16 4.96
## 81 4.70 5.10 2.76 3.15 5.19 5.02 4.80 5.48
## 82 4.05 4.13 2.88 3.23 4.86 4.60 4.40 5.15
## 83 3.31 3.60 3.25 4.23 5.02 4.08 4.01 5.11
## 84 3.69 3.88 2.99 4.02 4.98 3.78 3.62 4.85
## 85 3.59 3.84 2.86 3.38 4.46 3.45 3.40 4.44
## 86 3.72 3.96 2.85 3.49 4.51 4.02 3.98 4.76
## 87 3.06 3.32 2.95 3.34 4.78 3.51 3.45 4.60
## 88 3.65 3.85 2.90 3.43 4.72 3.62 3.55 4.48

Rename the columns

colnames(df_clean) <- c("GASOLINE", "E85", "CNG", "LNG", "PROPANE","DIESEL", "B20", "B99/B100")
df_clean
##    GASOLINE  E85  CNG  LNG PROPANE DIESEL  B20 B99/B100
## 63     2.50 2.68 2.17 2.66    3.88   2.63 2.55     3.41
## 64     2.67 2.87 2.18 2.57    3.87   2.70 2.59     3.39
## 65     2.88 3.05 2.22 2.60    3.85   2.89 2.75     3.48
## 66     2.91 3.10 2.19 2.64    3.93   2.99 2.78     3.57
## 67     2.27 2.59 2.19 2.71    3.99   2.65 2.52     3.50
## 68     2.76 3.00 2.22 2.38    3.97   2.75 2.59     3.44
## 69     2.76 3.06 2.21 2.46    3.87   2.71 2.58     3.55
## 70     2.68 2.97 2.20 2.40    3.79   2.74 2.58     3.65
## 71     2.59 2.96 2.18 2.47    3.82   2.71 2.60     3.65
## 72     1.91 2.28 2.19 2.43    3.74   2.33 2.13     3.44
## 73     2.22 2.58 2.15 2.40    3.75   2.20 2.11     3.08
## 74     2.18 2.54 2.18 2.42    3.74   2.13 2.06     3.26
## 75     2.32 2.65 2.19 2.45    3.90   2.35 2.18     3.11
## 76     2.82 3.12 2.19 2.39    4.01   2.77 2.53     3.49
## 77     3.09 3.40 2.22 2.54    4.08   2.90 2.74     3.56
## 78     3.25 3.55 2.33 2.45    4.34   3.10 2.96     3.73
## 79     3.28 3.87 2.49 2.69    4.69   3.22 3.08     3.88
## 80     4.13 4.60 2.59 2.82    4.83   4.50 4.16     4.96
## 81     4.70 5.10 2.76 3.15    5.19   5.02 4.80     5.48
## 82     4.05 4.13 2.88 3.23    4.86   4.60 4.40     5.15
## 83     3.31 3.60 3.25 4.23    5.02   4.08 4.01     5.11
## 84     3.69 3.88 2.99 4.02    4.98   3.78 3.62     4.85
## 85     3.59 3.84 2.86 3.38    4.46   3.45 3.40     4.44
## 86     3.72 3.96 2.85 3.49    4.51   4.02 3.98     4.76
## 87     3.06 3.32 2.95 3.34    4.78   3.51 3.45     4.60
## 88     3.65 3.85 2.90 3.43    4.72   3.62 3.55     4.48

Summarize the data

summary(df_clean)
##     GASOLINE          E85             CNG             LNG       
##  Min.   :1.910   Min.   :2.280   Min.   :2.150   Min.   :2.380  
##  1st Qu.:2.610   1st Qu.:2.893   1st Qu.:2.190   1st Qu.:2.450  
##  Median :2.895   Median :3.110   Median :2.220   Median :2.620  
##  Mean   :3.038   Mean   :3.329   Mean   :2.451   Mean   :2.837  
##  3rd Qu.:3.520   3rd Qu.:3.848   3rd Qu.:2.828   3rd Qu.:3.210  
##  Max.   :4.700   Max.   :5.100   Max.   :3.250   Max.   :4.230  
##     PROPANE          DIESEL           B20           B99/B100    
##  Min.   :3.740   Min.   :2.130   Min.   :2.060   Min.   :3.080  
##  1st Qu.:3.870   1st Qu.:2.703   1st Qu.:2.558   1st Qu.:3.450  
##  Median :4.000   Median :2.895   Median :2.745   Median :3.610  
##  Mean   :4.253   Mean   :3.167   Mean   :3.027   Mean   :3.962  
##  3rd Qu.:4.713   3rd Qu.:3.592   3rd Qu.:3.525   3rd Qu.:4.570  
##  Max.   :5.190   Max.   :5.020   Max.   :4.800   Max.   :5.480

Data visualization: boxplot

boxplot(df_clean,
        main = "Fuel types",
        ylab = "price (USD)",
        col = "orange",
        las = 2)

Reshape the data from wide to long format

pl_data <- df_clean %>%
  pivot_longer(cols = everything(), names_to = "Fuel_types", values_to = "Price_in_USD")

Compute ANOVA test statistic

anova <- aov(Price_in_USD ~ Fuel_types, data = pl_data)
summary(anova)
##              Df Sum Sq Mean Sq F value Pr(>F)    
## Fuel_types    7  63.16   9.022   22.28 <2e-16 ***
## Residuals   200  80.99   0.405                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Interpretation: p_value (0.00) < alpha(0.05), we reject H0 and conclude that there is a statistically significant difference in the mean prices of the different fuel types.

Pairwise comparison:

pairwise.t.test(pl_data$Price_in_USD, pl_data$Fuel_types, p.adj = "bonferroni")
## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  pl_data$Price_in_USD and pl_data$Fuel_types 
## 
##          B20     B99/B100 CNG     DIESEL  E85     GASOLINE LNG    
## B99/B100 8.5e-06 -        -       -       -       -        -      
## CNG      0.03639 8.2e-14  -       -       -       -        -      
## DIESEL   1.00000 0.00032  0.00199 -       -       -        -      
## E85      1.00000 0.01168  4.0e-05 1.00000 -       -        -      
## GASOLINE 1.00000 1.2e-05  0.02940 1.00000 1.00000 -        -      
## LNG      1.00000 3.4e-08  0.84443 1.00000 0.16216 1.00000  -      
## PROPANE  1.5e-09 1.00000  < 2e-16 1.2e-07 1.2e-05 2.1e-09  2.4e-12
## 
## P value adjustment method: bonferroni
Interpretation:
- B20 vs B99/B100: p-value (0.00), which is less than alpha (0.05);
- B20 vs CNG: p-value (0.04), which is less than alpha (0.05);
- B20 vs DIESEL: p-value (1.00), which is greater than alpha (0.05);
- B99/B100 vs CNG: p-value ( 0.00), which is less than alpha (0.05);
- DIESEL vs E85: p-value (1.00), which is greater than alpha (0.05);

There are significant differences in the prices of certain fuel types. Specifically, there is a statistically significant difference between B20 and B99/B100, between B20 and CNG, and between B99/B100 vs CNG. However, no significant difference was found between B20 and DIESEL or between DIESEL and E85.

Perform Levene’s test for homogeneity of variance

levene_result <- leveneTest(Price_in_USD ~ Fuel_types, data = pl_data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
levene_result
## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   7  1.6466 0.1243
##       200
# Interpretation: p_value(0.12) > alpha(0.05), we fail to reject H0, which indicates that variances are equal across fuel types.
Interpretation: p_value(0.12) > alpha(0.05), we fail to reject H0, which indicates that variances are equal across fuel types.

Compute the correlation matrix

cor_matrix <- cor(df_clean)
cor_matrix
##           GASOLINE       E85       CNG       LNG   PROPANE    DIESEL       B20
## GASOLINE 1.0000000 0.9881192 0.7286583 0.6140873 0.8753738 0.9526024 0.9480207
## E85      0.9881192 1.0000000 0.6997831 0.5694837 0.8786178 0.9384997 0.9302512
## CNG      0.7286583 0.6997831 1.0000000 0.9494823 0.9026420 0.8043824 0.8378527
## LNG      0.6140873 0.5694837 0.9494823 1.0000000 0.8088951 0.7041987 0.7426770
## PROPANE  0.8753738 0.8786178 0.9026420 0.8088951 1.0000000 0.9153619 0.9235126
## DIESEL   0.9526024 0.9384997 0.8043824 0.7041987 0.9153619 1.0000000 0.9961120
## B20      0.9480207 0.9302512 0.8378527 0.7426770 0.9235126 0.9961120 1.0000000
## B99/B100 0.8916978 0.8762823 0.9035662 0.8205770 0.9373394 0.9668667 0.9751682
##           B99/B100
## GASOLINE 0.8916978
## E85      0.8762823
## CNG      0.9035662
## LNG      0.8205770
## PROPANE  0.9373394
## DIESEL   0.9668667
## B20      0.9751682
## B99/B100 1.0000000
Correlation analysis: The correlation values are relatively high across fuel types, indicating that prices for different types of fuel are not fully independent. Which suggests that fuel prices are influenced by similar external factors, leading to correlated price changes across types of fuel.

Conclusions

Our analysis demonstrated that there are significant differences in the average gas prices between certain fuel types across pre, during and post COVID periods.

The ANOVA test results confirm that gas prices have shifted significantly highlighting the influence of unprecedented global events on fuel markets.

STATISTICALLY SIGNIFICANT DIFFERENCES:
- Price disparities are notable between: B20 vs. B99/B100, B99/B100 vs. CNG and B20 vs. CNG. 

- B20, B99/B100, and CNG, showed greater volatility in response to market changes. This suggests these fuels are more sensitive to external factors like supply chain issues, demand fluctuations, and economic policies.

NON-SIGNIFICANT DIFFERENCES:
- No significant price difference was found between B20 and Diesel or between Diesel and E85.

- Fuel types such as Diesel and E85 displayed more stable prices with fewer significant differences across time periods. This could indicate these conventional fuels are less influenced by the same market conditions, possibly due to established infrastructure or higher demand stability.

Recommendation

CONTINUOUS MONITORING AND FACTOR ANALYSIS:
Set up ongoing data tracking for fuel prices across all types to capture shifts that may be driven by external economic changes. Focus on the impact on global supply chain disruptions, geopolitical events, and regulatory changes on each specific fuel type to gain targeted insights.

POLICY INTERVENTIONS:
Advocate for regulatory measures to stabilize prices, especially for more volatile fuel types (e.g., B20, CNG). Possible interventions could include subsidies, incentives for alternative fuels, or strategic reserves.

CROSS-INDUSTRY COLLABORATION:
Engage with industry stakeholders to share findings and support industry-wide strategies aimed at mitigating price volatility, particularly for transportation and logistics sectors heavily reliant on specific fuels.