Kediska Dib u noqoshada Caadiga ah

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay hantida qiimayntu ay ilaaliso dib-u-celinta caadiga ah?
   • Nidaamkooda iyada oo aan u baahnayn nusqaamo siman
   • Masaafada tirada saxda ah
   • Madaxbanaanidooda oo ka madax bannaan saadaaliyayaal
   • Inta jeer ee qaybta siman
2. Marka la eego tafsiir-xaddidan, sidee loo soo saaray qaybaha qiimaynta?
   • Waxay ku habboon tahay moodooyinka qaybta aan xidhiidhka lahayn waxayna tuuraysaa nidaamka qaybta
   • Heerarka u kala qaybinta u janjeera daahsoon ee safafka la amray
   • Waxa ay u daawaysaa natiijooyinka summada leh sidii cabbirro siman oo kala fogaaday
   • Waxay u qoondaysaa qayb kasta jaan-gooyo tiro madax-bannaan
3. Waa maxay fursadaha saami-qaybsiga sida caadiga ah u qaataan saamaynta saadaalinta?
   • Dhammaan xadka waa isku mid
   • Qayb kastaa waxay leedahay itimaalka siman
   • Isla jiirada ayaa khuseysa dhammaan qaybaha kala-goynta ee isugeynta
   • Predictors have no effect on rankings
4. Why can ordinary regression on codes 1, 2, 3 and 4 be inappropriate for satisfaction levels?
   • It cannot include predictor variables
   • It treats category codes as nominal labels and ignores their order
   • It estimates cumulative probabilities but never uses the category codes as distances
   • It assumes numeric gaps between codes and can predict values outside the category set
5. A predictor shifts latent satisfaction upward. What may happen to a middle-category probability?
   • Every category probability must rise by the same amount
   • A middle-category probability may rise or fall as mass crosses its thresholds
   • Only the lowest and highest categories can change probability
   • The category probability becomes equal to the latent score
6. Why should category labels be encoded in their intended order?
   • To force all predictor coefficients to be positive
   • To make all thresholds equally spaced
   • To ensure software uses the intended semantic category order
   • To guarantee the proportional-odds assumption holds
7. Which model restriction should be assessed in a common ordered-logit specification?
   • Whether cut points are equally spaced
   • Whether categories occur equally often
   • Whether each threshold has a different slope by definition
   • Whether a common predictor effect across cumulative splits is plausible
8. A wrong prediction one level away has a different cost from one three levels away. Which evaluation choice is appropriate?
   • Use an order-aware metric or explicit costs that distinguish near from distant mistakes
   • Use ordinary accuracy because it records how many levels apart each error is
   • Use squared error on category codes without checking whether equal spacing is meaningful
   • Ignore validation because category order already defines model quality