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Conjoint Analysis

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Conjoint Analysis
1. Explain the interpretation of the following tables ->
(a) Utilities table (highest and lowest utility for each attribute)

The preference of Package B* is highest (1.867 Utility value) among all other options for the Package and it is lowest in the case of A* (-2.233 utility value). The preference for K2 brand is highest (0.367 Utility value) and the Glory brand has the lowest value (-0.350 Utility value). The preference for the Price level of $1.19 is the highest (-1.108 Utility value) among all other price options and the price level 1.59 has the lowest value (-3.325 utility value). The preference of Seal to yes is more (4.0 utility value) and the Preference for money as yes is more (2.5 utility value).
(b) Importance Values

It gives us the relative importance of the factors in percentage. Like for Package the importance value is 35.6%, and is the most important factor. Similarly, Price has the importance of 29.5%, Brand has the importance of 15%, Seal has the importance of 11.7% and money is the least important factor with importance of 8.8% only. (Values are the average importance factor)

(c) Coefficients

The Linear Regression B Coefficient estimate for Price comes out to be -1.108, Seal comes out to be 2.000 and money comes out to be 1.250, when there coefficients are multiplied with value associated with different options of factors this gives us the utility for the same.
When factor value for seal as yes (2) is multiplied with coefficient 2.000 this gives us the utility value of 4.000.

(d) Correlations

As the Pearson’s R value is 0.982, which is very high, this indicates that the correlation is very high and shows that the correlation between preference model and holdout profiles exists.
(e) No. of reversals

Number of Reversals | Factor | Price | 3 | | Money | 2 | | Seal | 2 | | Brand | 0 | | Package | 0 | Subject | 1 | Subject 1 | 1 | | 2 | Subject 2 | 2 | | 3 | Subject 3 | 0 | | 4 | Subject 4 | 0 | | 5 | Subject 5 | 0 | | 6 | Subject 6 | 1 | | 7 | Subject 7 | 0 | | 8 | Subject 8 | 0 | | 9 | Subject 9 | 1 | | 10 | Subject 10 | 2 |
Syntax –
/FACTORS = PACKAGE BRAND (DISCRETE) PRICE (LINEAR LESS) SEAL (LINEAR MORE) MONEY (LINEAR MORE)

The above table shows that the number of profiles that changed from less to more for Price was 3 and for Money and seal it was 2. The reversal table indicates that how many numbers of profiles were changed from pre-defined coding. It also shows that the subject 1, 6 and 9 made 1 reversals and the subject 2 and 10 made 2 reversals.

2. Also, calculate the utility of best profile and the worst profile

Best profile: 1.867 (B*, Package) + 0.367(K2R, Brand) + (-1.108) ($1.19, Price) + 4.000 (Seal, Yes) + 2.500 (Money, Yes) + 7.383 (constant) = 15.009 (Total Utility)

Worst profile:
-2.233 (A*, Package) + (-0.350) (Glory, Brand) + (-3.325) ($1.59, Price) + 2.000 (Seal, No) + 1.250 (Money, No) +7.383 (constant) = 4.725 (Total Utility)

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