Market Research Assignment
Author – Deepak Chhabra – 021330424013, Shubham Dholakia, Mahak Aggarwal
FORM – ITM BUSNIESS SCHOOL
Introduction-
Maruti Suzuki is the largest automobile manufacturer in India, specializing in small cars. In this assignment we talking about Maruti Suzuki car Features and safety for example- air bag, boot space, fuel efficiency etc.
Objective-
To find out leverage the insights gained from the Product results effectively.
Data collection- The data for Maruti Suzuki Car was created google from and taking survey from Hostel, College and Outskirts. Its based on ranking from 1 to 10 . In this data we will give rank to theme on the basis of their performance.
KMO and Bartlett’s Test-
- Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy–
- Value: 0.487
- Interpretation: The KMO statistic ranges from 0 to 1, with values closer to 1 indicating that the data is suitable for factor analysis. A value below 0.5 suggests that the sampling may not be adequate. In this case, a KMO of 0.487 indicates that the sample size may not be sufficient for PCA.
- Bartlett’s Test of Sphericity–
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- Approx. Chi-Square: 43.677
- Degrees of Freedom (df): 45
- Significance (Sig.): 0.528
- Interpretation: This test checks whether the correlation matrix is an identity matrix (i.e., variables are uncorrelated). A significant result (p < 0.05) indicates that factor analysis may be appropriate. Here, the p-value of 0.528 suggests that the correlations among the variables are not significant enough, further questioning the suitability of the data for PCA.
Component Matrix-
- Variables: Each feature has a loading on the components, which indicates how much of the variance in the variable is explained by the component.
- Interpretation:
- Higher loadings (closer to 1 or -1) indicate a stronger association with the component.
- For example, “Extra boot space” and “Features” load strongly on Component 1 (0.703 and 0.691, respectively), suggesting that these attributes are closely related.
- “Comfort” has a negative loading (-0.632) on Component 1, indicating an inverse relationship.
Total Variance Explained Interpretation-
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- Component 1 explains 15.585% of the variance, Component 2 explains 13.214%, and so on. The first five components together explain over 62% of the variance, which is a substantial amount, but ideally, you want to see a higher percentage for more robust results.
Rotated Component Matrix Interpretation-
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- After rotation, “Extra boot space” and “Features” still load highly on Component 1, confirming their importance.
- “Air bags” and “Easily modify” load strongly on Component 2, suggesting they are related.
- “High technology” and “Affordable price” load on Component 3, indicating these features are grouped together.
- “Reliability” and “Fuel efficiency” have lower loadings but are still relevant to Components 4 and 5, respectively.
- “Bright lighting” has a very high loading (0.921) on Component 5, indicating it is a strong factor in that component.
Conclusion-
The analysis suggests that the dataset may not be ideal for PCA due to the KMO value and Bartlett’s test results. However, the PCA did extract five components that explain a substantial portion of the variance in the dataset. The rotated component matrix provides a clearer picture of how the various features group together, which can be useful for further analysis or decision-making.
ANALYSIS–
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KMO and Bartlett’s Test |
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Kaiser-Meyer-Olkin Measure of Sampling Adequacy. |
.487 |
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Bartlett’s Test of Sphericity |
Approx. Chi-Square |
43.677 |
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df |
45 |
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Sig. |
.528 |
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Component Matrix |
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Component |
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1 |
2 |
3 |
4 |
5 |
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Features |
.691 |
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Extra boot space |
.690 |
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Comfort |
-.632 |
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Air bags |
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.664 |
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Fuel efficiency |
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.532 |
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Affordable price |
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.619 |
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Easily modify |
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.510 |
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-.553 |
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High technology |
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-.524 |
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Reliability |
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Bright lighting |
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.708 |
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Extraction Method: Principal Component Analysis. |
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a. 5 components extracted. |
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Total Variance Explained |
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Component |
Extraction Sums of Squared Loadings |
Rotation Sums of Squared Loadings |
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Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
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1 |
1.558 |
15.585 |
15.585 |
1.536 |
15.356 |
15.356 |
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2 |
1.321 |
13.214 |
28.799 |
1.246 |
12.461 |
27.816 |
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3 |
1.229 |
12.292 |
41.091 |
1.202 |
12.019 |
39.836 |
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4 |
1.084 |
10.840 |
51.931 |
1.149 |
11.490 |
51.326 |
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5 |
1.029 |
10.286 |
62.217 |
1.089 |
10.891 |
62.217 |
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Extraction Method: Principal Component Analysis.
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Rotated Component Matrix |
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Component |
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1 |
2 |
3 |
4 |
5 |
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Extra boot space |
.703 |
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Features |
.667 |
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Comfort |
-.659 |
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Air bags |
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.787 |
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Easily modify |
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.640 |
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High technology |
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.744 |
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Affordable price |
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.650 |
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Reliability |
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|
.764 |
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Fuel efficiency |
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.522 |
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Bright lighting |
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.921 |
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Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. |
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a. Rotation converged in 10 iterations.
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