In a consumer study 103 consumers scored their preference of 12 danish carrot types on a scale from 1 to 7. Moreover the consumers scored the degree of sweetness, bitterness and crispiness in the products.
Usage
data(carrots)Format
- Consumer
factor with 103 levels: numbering identifying consumers.
- Frequency
factor with 5 levels; "How often do you eat carrots?" 1: once a week or more, 2: once every two weeks, 3: once every three weeks, 4: at least once month, 5: less than once a month.
- Gender
factor with 2 levels. 1: male, 2:female.
- Age
factor with 4 levels. 1: less than 25 years, 2: 26-40 years, 3: 41-60 years, 4 more than 61 years.
- Homesize
factor with two levels. Number of persons in the household. 1: 1 or 2 persons, 2: 3 or more persons.
- Work
factor with 7 levels. different types of employment. 1: unskilled worker(no education), 2: skilled worker(with education), 3: office worker, 4: housewife (or man), 5: independent businessman/ self-employment, 6: student, 7: retired
- Income
factor with 4 levels. 1: <150000, 2: 150000-300000, 3: 300000-500000, 4: >500000
- Preference
consumer score on a seven-point scale.
- Sweetness
consumer score on a seven-point scale.
- Bitterness
consumer score on a seven-point scale.
- Crispness
consumer score on a seven-point scale.
- sens1
first sensory variable derived from a PCA.
- sens2
second sensory variable derived from a PCA.
- Product
factor on 12 levels.
Details
The carrots were harvested in autumn 1996 and tested in march 1997. In
addition to the consumer survey, the carrot products were evaluated by
a trained panel of tasters, the sensory panel, with respect to a
number of sensory (taste, odour and texture) properties. Since usually
a high number of (correlated) properties (variables) are used, in this
case 14, it is a common procedure to use a few, often 2, combined
variables that contain as much of the information in the sensory
variables as possible. This is achieved by extracting the first two
principal components in a principal components analysis (PCA) on the
product-by-property panel average data matrix. In this data set the
variables for the first two principal components are named
(sens1 and sens2).
Examples
fm <- lmer(Preference ~ sens2 + Homesize + (1 + sens2 | Consumer), data=carrots)
anova(fm)
#> Type III Analysis of Variance Table with Satterthwaite's method
#> Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
#> sens2 58.685 58.685 1 102.02 54.8236 3.888e-11 ***
#> Homesize 5.979 5.979 1 100.95 5.5852 0.02003 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1