Inferential statistics, by contrast, are less about your data as such, and more about your data as representative of a larger population; they are used to test the properties of your data as a basis for determining whether those properties can be appropriately considered as property of the population from which the data were drawn.
see attachment 3
The data themselves are from a set prepared for teaching purposes by SPSS Inc. As they say:
Distinguish between the interval-level, ordinal level, and categorical level variables in these data. For the interval-level variables, calculate appropriate descriptive statistics, as described in the readings, and in a few sentences, interpret what these results tell you. Since ordinal variables can be treated as interval-level for purposes of many analyses, calculate appropriate descriptive statistics on these as well, and interpret them also.
For the categorical variables, prepare appropriate frequency tables for the various categories, and in a sentence per variable, interpret the results; what do they tell you about the population of respondents?
The given data consists of the following coded variables:
Gender, age, agecat, regular, reason1, reason2, dept, purchase, payment, distance, dist_cat, followup, store, contact, price, numitems, org, service, quality, overall, and index.
The variables were distinguished as follows:
This solution categorizes a given variable as Nominal or categorical, Ordinal, Interval, or Ratio. Appropriate statistical tools were applied.