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St t t , th science of ll t ng, analyzing, presenting, nd nt r r t ng data. G v rnm nt l n d for census data w ll nf rm t n b ut a variety f economic t v t provided mu h f th rl impetus for th f ld f statistics. Curr ntl th n d t turn the l rg amounts f data v l bl n many l d f ld into useful nf rm t n h t mul t d b th th r t l nd practical d v l m nt n t t t . Data r th f t and f gur th t r ll t d, n l z d, nd summarized for r nt t n nd nt r r t t n. Data m b l f d either u nt t t v or qualitative. Qu nt t t v d t m ur either h w much r h w m n f something, nd u l t t v data provide l b l , or names, for categories f l k items. For x m l , u th t a rt ul r study interested n characteristics u h as age, g nd r, m r t l t tu , nd nnu l n m f r a m l of 100 nd v du l . Th h r t r t would be called th variables f th tud , nd d t v lu for each of th v r bl w uld b t d w th each individual. Thu , the d t v lu f 28, m l , single, nd $30,000 would b recorded for a 28-year-old ngl m l w th n nnu l income of $30,000. W th 100 nd v du l nd 4 v r bl , the data t w uld h v 100 x 4 = 400 t m . In th example, g nd annual income are u nt t t v v r bl ; th rr nd ng d t v lu indicate how many years nd h w mu h m n for h nd v du l. Gender nd m r t l status are qualitative v r bl . Th l b l m l nd female provide th u l t t v d t f r g nd r, nd th labels single, m rr d, divorced, nd w d w d nd t m r t l t tu .

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