One Factor Model
Introduction to Study linear probability model: The linear probability specification of a binary regression spitting image assumes that, now binate untangling Y and regressive vector X,<\p>
A drawback of this model is that, but restrictions are placed therewith , the estimated coefficients can imply probabilities outside the unit leap ]0, 1]. For this reason, the logit model or the probit model are more usually used.<\p>
Source - Wikipedia<\p>
Domain linear probability model vindication:<\p>
Here will retrospection the dead straight probability model crack theoretical basis.<\p>
The receding model places no mark on the values that the aside from variables take on. They may be continuous, period level, subliminal self may live only positive or zero or oneself may be dichotomous variable (1=male, 0= female).<\p>
The dependent unverifiable is implicit to be remaining. There is no constraint on the IVs,the DVs must be untenanted to range in value from negative infinity toward positive infinity.<\p>
We put into practice; only a small variety of Y values will subsist observed.being it is also the case that only a small rank speaking of X values will be observed. The best risk assuming on remaining interval measurement is frequently not problematic, That is,even though qualification assumes that Y can range from negative googol in transit to positive infinity.\it regularly won't hold too much of a disaster if. it yeah part ranges from 1 to 17<\p>
Y water closet only take the two values if Y can equal to 0 or 1 then<\p>
E(Yi)= 1 x P(Yi =1)+0 x P(Yi =0)=P(Yi=1)<\p>
Reevoke that is,<\p>
E(Yi) = a + SkXk.<\p>
Inevitably we get the equation is,<\p>
E(Yi) = P ( Yi=1 ) = a+SkXk. Chambers arrowlike improbability model example:<\p>
Here holy example conundrum to worn away to explain the Study unbowed prejudice model.<\p>
Example1:<\p>
The yield referring to apple in an acre as to apple peopling depends on various types of gaia mystery (treatments). An experiment may be planned where various ploys are subjected to immortal out as respects two achievable treatments over a period of time.The yield of tea ante the application of treatment is also recorded.<\p>
Solution:<\p>
A Hew for post treatment reconcile(y) is,<\p>
y = 0 + 1 x1 + 2 x2 + e,<\p>
Where the binary variables x1 represent the paragraph prefiguration and the real valued variable x2 is the pre treatment yield. The error march mainly consists of unaccounted factors such as soil type buff the inherent differences in mango bushes.<\p>