Introduction

Each graph below provides visual representations of the data set called autoclaims.

Throughout the data set there are zero pieces of missing data, 9,134 rows of claim information, and 26 columns describing each claim. Each row of data represents a customer’s auto claim along with a variety of claim related information. Please note that each claim does not constitute a collision. The reason for claims includes hail damage, scratch/dents, collision, and a broad category called other. The graphs developed for this report depict information related to the number of claims, claim cost, vehicle size and class, the gender of the claimant, area (i.e., rural, suburban, or urban), and the state where the incident occurred.

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## [1] 9134   26

Graph 1

According to Graph 1, we can conclude that the state of Missouri has the highest number of reported auto-related claims compared to all other states recorded in the data set. There were 3,150 claims reported in Missouri, which is approximately 17.4% higher than the next leading state (IA).

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##   State.Code    n
## 3         MO 3150
## 1         IA 2601
## 4         NE 1703
## 5         OK  882
## 2         KS  798

Graph 2

As depicted by Graph 2, suburban areas produce the highest number of claims and the highest total claim cost compared to rural and urban areas.

Claims that occurred in suburban areas resulted in $3.9 million, which accounts for 61% of the total claim cost. An interesting observation is that urban areas have the lowest total claim cost and cost of any one claim. When considering collision related claims, one possible explanation for the lower cost is the driver’s shorten reaction time while driving at slower speeds in an urban area. A shortened reaction time would reduce the likelihood of a collision and result in less physical damage when a crash occurs.

## # A tibble: 8,041 x 3
##    Location.Code Claim.Amount     n
##    <fct>                <dbl> <int>
##  1 Rural                 212.     1
##  2 Rural                 213.     1
##  3 Rural                 222.     1
##  4 Rural                 224.     1
##  5 Rural                 231.     1
##  6 Rural                 231.     1
##  7 Rural                 234.     1
##  8 Rural                 236.     1
##  9 Rural                 237.     1
## 10 Rural                 238.     1
## # … with 8,031 more rows

Graph 3

Based on Graph 3, four-door vehicles were involved in the highest number of auto-related claims compared to all other vehicle classes. Within each class, the majority of incidents involved medium-sized vehicles. Therefore, we can surmise that medium-sized, four-door vehicles are more likely to be involved in an auto claim compared to other vehicle sizes and classes. This presumption is further supported by the fact that medium-sized vehicles account for 70% of the sizes reported.

## # A tibble: 8,043 x 4
##    Vehicle.Class Vehicle.Size Claim.Amount     n
##    <fct>         <fct>               <dbl> <int>
##  1 Luxury Car    Small               8333.     1
##  2 Luxury Car    Medsize             7423.     1
##  3 Luxury SUV    Medsize             7323.     1
##  4 Sports Car    Medsize             6791.     1
##  5 Luxury SUV    Medsize             6603.     1
##  6 Luxury Car    Small               6462.     1
##  7 Luxury SUV    Medsize             6185.     1
##  8 Luxury Car    Medsize             6113.     1
##  9 Luxury SUV    Large               6056.     1
## 10 SUV           Medsize             5875.     1
## # … with 8,033 more rows

Graph 4

According to Graph 4, female drivers reported 2% more auto claims than males. Keep in mind that these are non-zero dollar reported claims and does not account for the total number of auto incidents that occurred. Furthermore, the reason for each the claim varies; therefore, we should not presume that females are more likely to be involved in an auto crash. There is no correlation between gender and auto claim rates based on this analysis.

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Conclusion

In summary, the autoclaims data set indicates that: