9%)
3. The age range of 45-54 had the highest amount of women (54%) using the low-cholesterol diet followed by 55 and over (49%), then 35-44 age range (46%) least in the 1-24 age range (29%). It seems that preference for this diet peaks amongst females in the late middle years. With men, there is a slightly different shift with the majority (35%) actually preferring this diet in their older years and regressing as they decrease in age, with a major shift occurring between the 13% in the age interval 16-24 and the 25% of the age interval 25-34. Of the difference between the sexes, women by far (54% at its peak) are more interested in the diet than men. As comparison between the ages, the diet seems most popular from 45 years onwards (43%); somewhat popular between the ages 25-34 and 35-44 (34% and 37% respectively) and least popular in the youngest age interval (21%). Apparently, its popularity increases with age. In short, I would market that specific diet to the late middle-age / elderly individual, targeting preferentially females.
5. Nominal and ordinal scales are typically used in cross-tabulation analysis, although interval and ratio could be used too. (All would be interpreted as nominal).
6. I think that data analysis and interpretation, although heavily used in the managerial / business sciences are not exclusively business-oriented since they are employed across the board in all sciences from the social and behavioral fields to journalism for instance. Statistics...
156 participants responded that they had received valuable information in filing their tax forms. These positive respondents represented 29.5% of the total (528 individuals) surveyed (146/528). The adjusted frequency of the whole (the approximate percentage comparison of the whole) was 29.8%. On the other hand, far more participants (n= 368) found the suggestions unhelpful or trivial. This number represented 69.5% of the whole. As comparison, or adjusted frequency (as consider plotting this on a pie chart for illustration) it would approximate 70.2%. Of those who didn't know, 1% responded here with 2% of this segment missing (i.e., unreadable). Similarly, 1% responded with uncertainty regarding the helpfulness of the information, with 2% data missing (i.e. illegible). 2% of the data were returned blank, and 4% missing (which probably missing data was totally unreadable).
8. The majority (n=46) responded that they had learned about another deduction. This represented 8.7% of the total 528 participants; 31.3% adjusted frequency (when compared to the total amount). 44 participants were helped by miscellaneous information; 8.3% of the total 528 participants; 29.9% adjusted frequency. 40 participants reported that they received certain forms / pamphlets that they found helpful; 7.6% of the total 528 participants; 27.2% adjusted frequency. Only 9% reported that the information about which forms to use was helpful to them (this figure represented 1.7% of the total 528 participants; 6.1 when compared to the whole), whilst 8% (1.5% relative frequency and 5.4% adjusted frequency) responded that they had learned about energy credit.
2.4% reported "not sure" or illegible in their response. 373 (or 70.6%0 were returned missing or blank.
8. I don't see why it shouldn't be ethical. I'm not sure if this analyst is a machine or a professional, but, either way, the same applies. A regular data processing system such as popularly SPSS or commonly Excel simply takes the places of human calculation on a more sophisticated level. Data processing can actually be done by…
