Data Mining

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Global Econ

Final 75-85Question. 7:15, Tuesday 19th
10% begining, 20% previous exam, 70% new materials

Chapter 15
EXPORTING

Major Concern: tax, currency, control, risk,
Proper procedure to manage risk of exporting

PO-Purchase Order
letter of credit <-- request letter of credit, can take money from their bank if they do not pay
draft: Instruction document that dictate payment terms, zero risk with US dollars.
"" : who's going to pay, how, when? wire transfer
Bill of lading: receipt, contract, document of title

manage risk associate with currency by using Dollars
letter of credit, make sure getting paid
P.0 dictate all the terms.

P.O. -> Ship -> Invoice

Cash flow

Global economic

Entry Modes, 6 diff methods firm may choose to enter market.

1. exporting
+Adv: Low cost, Central Planing (pyramid), Economy of scale "benefits of making alot"
- Disadvantage: location economy, high transportation cost, Tariff, give up value chain adv ex. marketing, core competency.

2. turn key project: Technology transfer, all of the start up company are hand over to a contractor
+Adv: Access to know how (buyer), Risk reduction (seller),
-Disadvantage: Make competitor(seller)

3. Licensing: Property Rights for Royalty Fees, on a specific time basis
+Adv: low development cost, Avoid tariff,
-Dis: No control, lose value chain, lost strategic coherence, lost of technical know how

4. Franchise: strict control over function "clarity over total offer"
+ Adv of franchisor (inc.), lower cost
- Dis: no control over business, Franchisor (less control),

5. Joint Venture: 2 or more company come together
+Adv: lower cost alternative, Less gov error(1), Access to technology, know how(2)
: localize knowlege (1)
-Dis: loss of technical control, lose strategic coherence, conflict between parent co.

6. Wholly own, green field (build it), subsidy, own new operation or buy it
+Adv: control tech & operation, scale of economy, strategic coherence, global web
-Dis: Risk & high cost,

Data mining

Detecting outliers:
Method 1
|z| >= 3

ex. 60
use Pivot table in excel
count IP address
how many time the address show up, see the standard dev, calculate the z score

z= x - mu / sigma
if Z is > than 3 its an outlier

mu = mean or average = 11.6
Standard deviation = 141.1525

2nd Popular method.

IQR 3rd Quater - 1st Quarter
Median 2nd quarter
1.5 x IQR = outlier

outliers 1st 2nd 3rd outliers
|---------|====|====|-----------|
1.5xIQR 3rd-1st
25% 50% 25%


q1<-quantile(data[,3],.25,na.rm=true)
q3<-quantile(data[,3],.75,na.rm=true)

Q1= 150
Q3=175
interquartile range = (175-150)/2 = 26
iqr<-q3-q1
iqr = 26

26*1.5 = 39

q1 = 150 - 39 = 111 (any score below 111 is an outlier)
q3 = 176 +39 = 215 (any score above 215 is an outlier)


Proximity-Based Outlier Detection.
K-nearest neighbor

ex.62
Lm - regression
a "linear model"
x_2 = Beta_o + B_1, X_1 + Ephsilon

model<-lm(data[,3]~data[,2]
predict data 3 using data 2

x_2 = -36+1.1x_1