A Comparative Study on Electric Vehicles Review & Analysis Using Anova: single Factor

Author:- Amansingh Barthwal

A Comparative Study on Electric Vehicle Review Using ANOVA

Introduction

The electric vehicle (EV) market in India has grown rapidly due to rising fuel costs, environmental concerns, and government incentives. With several EV models available, understanding customer perceptions through ratings is essential for manufacturers, consumers, and policymakers. Statistical tools like Analysis of Variance (ANOVA) help determine whether differences in customer ratings among various EV models are statistically significant.

This study applies Single Factor ANOVA to compare customer review ratings of selected electric vehicles.

Objectives of the Study

1. To compare customer ratings of selected electric vehicle models.

2. To examine whether there is a statistically significant difference in average ratings among EVs.

3. To support objective decision-making using ANOVA.

4. To identify which EV models perform better based on customer reviews.

Literature Review

Previous studies indicate that customer satisfaction in electric vehicles depends on factors such as performance, battery range, design, charging infrastructure, and cost efficiency.

• Kumar & Singh (2021) found that consumer ratings significantly influence EV adoption.

• Sharma et al. (2022) emphasized the use of statistical analysis like ANOVA to evaluate differences in customer perceptions across automobile brands.

These studies highlight the importance of quantitative methods in comparing product performance based on consumer feedback.

 

Data Collection

• Source of Data: Primary data collected through an Electric Vehicle Review Form.

• Type of Data: Quantitative customer ratings.

• Sample Size:

  • 41 responses for each EV model

  • Total observations = 205

• Electric Vehicles Studied:

  • TATA Nexon EV

  • MG Comet EV

  • Hyundai Creta EV

  • Mahindra BE-6 EV

  • BYD Seal

Data Analysis

Descriptive Statistics (Summary)

 

Mahindra BE-6 EV shows the highest average rating, while MG Comet EV has the lowest.

Hypothesis Testing

Null Hypothesis (H₀):

There is no significant difference in mean customer ratings among the selected electric vehicles.

Alternative Hypothesis (H₁):

There is a significant difference in mean customer ratings among the selected electric vehicles.

ANOVA Results

Source	SS	df	MS	F	P-value
Between Groups	467.34	4	116.83	52.56	3.29 × 10⁻³⁰
Within Groups	444.59	200	2.22
			Total 911.92 204

Decision Rule

 

• Level of Significance (α) = 0.05

• F Critical = 2.4168

• Decision Criterion:

  • Reject H₀ if F calculated > F critical

Result

Since:

• F calculated (52.56) > F critical (2.4168)

• P-value (3.29 × 10⁻³⁰) < 0.05

 

👉 The null hypothesis is rejected.

Interpretation

 

The ANOVA results clearly indicate that customer ratings differ significantly among the selected electric vehicles. This implies that customers perceive these EV models differently in terms of satisfaction, features, or overall performance.

Inference

 

• Mahindra BE-6 EV received the highest customer rating.

• MG Comet EV received the lowest average rating.

• The variation in ratings is not due to random chance but reflects genuine differences in customer perception.

Conclusion

 

The study concludes that there is a statistically significant difference in customer ratings of electric vehicles. Manufacturers can use these insights to improve product design and service quality, while consumers can rely on ratings for informed decision-making. ANOVA proves to be an effective tool in comparing multiple EV models objectively.

References

 

1. Kumar, R., & Singh, A. (2021). Consumer perception and adoption of electric vehicles in India. Journal of Sustainable Transportation.

2. Sharma, P., Mehta, R., & Joshi, K. (2022). Statistical analysis of customer satisfaction in the automobile sector. International Journal of Business Analytics.