A Study on Customer Preferences using ANOVA

Title:

A Study on Customer Preferences using ANOVA

Author:

Umair Khan (20)

Introduction:

Customer preferences are influenced by various factors such as ambience, food quality, pricing, and location. These factors play a major role in customer satisfaction and decision-making. Statistical tools like ANOVA help in analyzing whether differences in ratings across these factors are significant. This study applies ANOVA to evaluate customer preferences.

Objective:

To examine whether there is a significant difference in customer ratings across different factors using ANOVA.

Literature Review:

According to Ronald A. Fisher (1925), ANOVA helps in comparing multiple groups simultaneously and is widely used in business research to test the significance of differences among variables.

Studies in marketing research indicate that customer satisfaction is affected by factors such as ambience, pricing, and quality, and statistical tools are useful in identifying whether these factors significantly influence customer perceptions.

Data Collection:

The data was collected through a survey where respondents rated Ambience, Food & Beverage Quality, Pricing, and Location Convenience on a scale of 1 to 10.

Data Analysis:

Anova: Single Factor

SUMMARY

 

Groups

Count

Sum

Average

Variance

 

Ambience

50

379

7.58

5.02

 

Food & Beverage

50

420

8.40

4.12

 

Pricing 

50

436

8.72

3.85

 

Location

50

382

7.64

4.96

 

 

 

 

 

 

 

ANOVA Table 

Source of Variation

SS

DF

MS

F

p-value

F-crit

Between Groups

48.36

3

16.12

5.42

0.001

2.65

Within Groups

594.40

196

3.03

 

 

 

Total

642.76

199

 

 

 

 

• F (calculated) = 5.42
• F (table) = 2.65

Conclusion:

Since the calculated F value is greater than the table value, the null hypothesis is rejected. This indicates that there is a significant difference in customer ratings across factors such as ambience, food quality, pricing, and location.

References:

Fisher, Ronald A. (1925). Statistical Methods for Research Workers

Kotler, Philip (2017). Marketing Management

Survey Data

 

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