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Unformatted text preview: d the explanation for each step.
Again, we use the data in 2_3.m.
>la 23
>od _;
>[,sml]=picm(';
>U
ape
rnopX)
>sml =sml(,:)
>ape
ape:12; We carry out Principal Component Analysis (PCA) to reduce the dimensionality from 64 to 2.
>y=zrs401;
>
eo(0,)
>y2140 =1
>(0:0)
; We let y represent the set of labels coded as 0 and 1.
>x[apeoe(,0);
>=sml;ns140] Construct x by adding a row of vector 1 to data.
>bivxx)xy
>=n(*'**; Calculate b, which represents β in the linear regression model.
>x='
>1x;
>fri140
>o
=:0
i x(,)b05
f 1i:*>.
po(1i1,1i2,.)
ltx(,)x(,)''
hl o
od n
esi x(,)b<05
lef 1i:*
.
po(1i1,1i2,r'
ltx(,)x(,)'.)
ed
n
ed
n Plot the fitted y values. wikicour senote.com/w/index.php?title= Stat841&pr intable= yes 26/74 10/09/2013 Stat841  Wiki Cour se Notes the figure shows that the classification of the data points in 2_ 3.m by the linear regression model Comme nts about Line ar re gre s s ion mode l
Linear regression model is almost the easiest and most popular way to analyze the relationship of different data sets. However, it has some disadvantages as well as its
advantages. We should be clear about them before we apply the model.
Adv ant ages: Linear least squares regression has earned its place as the primary tool for process modeling because of its effectiveness and completeness. Though there are
types of data that are better described by functions that are nonlinear in the parameters, many processes in science and engineering are well described by linear models. This
is because either the processes are inherently linear or because, over short ranges, any process can be well approximated by a linear model. The estimates of the unknown
parameters obtained from linear least squares regression are the optimal estimates from a broad class of possible parameter estimates under the usual assumptions used for
process modeling. Practically speaking, linear least squares regression makes very efficient use of the data. Good resu...
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 Winter '13

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