Introduction
Customer ratings play a crucial role in evaluating two-wheelers in today’s competitive automobile market. Bikers often rate bikes based on performance, comfort, design, and overall riding experience. These ratings help manufacturers and potential buyers compare different models.
This study applies Analysis of Variance (ANOVA) to examine whether there is a significant difference in bikers’ average ratings for selected bikes rated on a scale of 1 to 10.
Objectives of the Study
· To analyze bikers’ ratings for selected bike models.
· To compare the mean ratings of different bikes using ANOVA.
· To determine whether bikers’ ratings differ significantly among the bikes.
· To draw conclusions based on statistical evidence.
Literature Review
Previous studies suggest that customer ratings significantly influence automobile purchase decisions. According to Kotler & Keller (2016), consumer perception and satisfaction are key determinants of product success.
Research by Malhotra (2019) highlights the usefulness of ANOVA in comparing customer opinions across multiple products, making it a reliable tool for market and consumer research.
Data Collection
· Type of Data: Primary data
· Method: Structured questionnaire
· Scale Used: Rating scale from 1 (Very Poor) to 10 (Excellent)
· Sample Size: 39 respondents
· Bike Models Considered:
o Classic 350
o GT 650
o RTR 310
o Triumph Speed 400
o Hunter 350
Data analysis
|
Source of Variation |
Sum of Squares (SS) |
Degrees of Freedom (df) |
Mean Square (MS) |
F-value |
p-value |
|
Between Groups |
38.67 |
4 |
9.67 |
2.22 |
0.069 |
|
Within Groups |
830.26 |
190 |
4.37 |
— |
— |
|
Total |
868.93 |
194 |
— |
— |
— |
GT 650 received the highest average rating, while RTR 310 had the lowest.
Hypothesis Testing
Null Hypothesis (H₀):
There is no significant difference in mean ratings of the selected bikes.
Alternative Hypothesis (H₁):
There is a significant difference in mean ratings of the selected bikes.
Decision Rule
Level of significance (α) = 0.05
If p-value ≤ 0.05, reject H₀
If p-value > 0.05, fail to reject H₀
Result (ANOVA Test)
- F-statistic: 2.216
- p-value: 0.069
Interpretation
The p-value (0.069) is greater than the significance level of 0.05. This indicates that the observed differences in mean ratings among the bikes are not statistically significant.
Inference
Although the mean ratings differ numerically, these differences may be due to random variation and not strong enough to conclude a real difference in bikers’ preferences.
Conclusion
The ANOVA results reveal that there is no significant difference in bikers’ ratings for the selected bike models at the 5% level of significance. Therefore, all bikes are perceived similarly by riders in terms of overall rating.
References
· Kotler, P., & Keller, K. L. (2016). Marketing Management. Pearson Education.
· Malhotra, N. K. (2019). Marketing Research: An Applied Orientation. Pearson.
– Sanika Pandit