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Nominal level is used for categorizing data without any order or ranking. Each category is distinct and does not have a meaningful order. Examples include name, color, or label. We identify the measurements that fit this description.

Ordinal level categorizes data with a meaningful order, but the difference between the categories is not uniform or measurable. Terms such as rankings or performance levels fit this category. We need to identify which variables have an inherent order but unequal intervals.

Interval level involves ordered categories with equal spacing between them, but there is no true zero point. This means differences are meaningful, but ratios are not. We identify variables that can be categorized as interval data.

Ratio level possesses all the characteristics of interval level with the addition of a true zero point, allowing for meaningful ratios. It is used when comparisons of absolute magnitudes are required. Identification of such data points is needed.

Using the explanations from Steps 1 to 4, categorize each measurement: (a) Salesperson's performance: This is ordinal because the categories 'below average', 'average', and 'above average' have a natural order. (b) Price of company's stock: This is ratio because the stock price can be measured with a true zero and meaningful ratios. (c) Names of new products: This is nominal as it only serves as labels without any intrinsic order. (d) Temperature 6 6{F} 6{F}6{F}: This is interval because temperature has equal intervals but no true zero (06 6{F} 6{F}6{F} doesn't mean no temperature). (e) Gross income for each of the past 5 years: This is ratio because it has a true zero and allows for ratio comparisons. (f) Color of product packaging: This is nominal since colors are labels with no ordering.