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We can first use descriptive statistics by examining historical data on customer flow
Examine the number of customers based on different days of the week, month (perhaps even year).
Also examine the number of customers at different times of the day.
By summarizing the common traits of busy times/days, we can develop a strategy on when to open
The most important data would be the number of customers per time period (hour, or 15-minutes,
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up more cash registers.
epending on how flexible the work force scheduling is).
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Arrival Day of The Week (Month/Year)
Length of Stay
Use of Mini Bar
Cash or Credit Customer
Use of Extra Hotel Services (such as Wi-Fi, Room Service, On Demand Movies etc.)
Using these data measurements, the hotel can decide which customers are likely to spend more mo
For example, if a certain group of customers are likely to spend a lot of money on room service, tho
Or, using the arrival day and length of stay, one can identify whether a customer is a business travell
These business travellers might spend more money if their company is paying for the trip and identi
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ney within the hotel.
e customers may be offered discounts at nightly stay prices.
ler or not (we can assume that they tend to arrive weekdays and don't stay the weekend)
ying them may prove to be lucrative.
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Customer Arrival Date
Customer Arrival Time
Customer Service Time
Purchase Type
Revenue Generated
Just using these basic types of data, a fast food restaurant will be able to identify rush hours in a giv
Using this information, they can decide how many registers to open at different times of the day.
Also looking at the purchase patterns, they can decide on how to stock up on different food items o
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n day.
different days of the week.
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Cust ID Region Payment Transaction C Source Amount Product
10001 East Paypal 93816545 Web $20.19 DVD
Ordinal Categorical Categorical Ordinal Categorical Ratio Categorical
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Time Of Day
22:19
Interval
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Homeowner Credit Score rs of Credit His volving Balan volving Utilizati Decision
Y 725 20 11,320$ 25% Approve
Categorical Interval Interval Ratio Ratio Categorical
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Gender Age Ethnicity
Length of
Residency Satisfaction
Quality of
Schools
Categorical Interval Categorical Interval Ordinal Ordinal
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MODEL:
BALANCE = -17,732 + 367 x AGE + 1300 x YEARS EDUCATION + 0.116 x HOUSEHOLD WEALTH
a. 367 The average account balance increases by approximatel
1300 The average account balance increases by approximatel
0.116 The average account balance increases by approximatel
b. AGE 36 years old
EDUCATION 16 years
WEALTH 175,000.00$
PREDICTED BALANCE 36,580.00$
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ly $367 for each year increase in AGE
ly $1300 for each year increase in EDUCATION
ly $0.116 for each $1 increase in WEALTH
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MODEL:
D = k - pP + aA + tT + qQ
a. P: As Price increases, Demand goes down.
A: As Advertising increases, Demand goes up.
T: As Transportation increases, Demand goes up.
Q: As Product Quality increases, Demand goes up.
b. The variables do not influence each other.
c. The relationship of D to P is overly simplistic. If P is too high, the model predicts negative
The variables might influence each other as well. For example, high production quality m
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D, in fact D will be at least ZERO.
ay cost more and hence may have a higher price tag.
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Variable Cost 9.00$ /unit Variable Cost 12.00$ /unit
Fixed Cost 4,000.00$ Fixed Cost -$
a. VOLUME 1000 units
Cost of Manufacturing 13,000.00$
Cost of Outsourcing 12,000.00$
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E: Earnings
T: Turnover
S: Sales
C: Cost of Sales
TI: Total Investment
CA: Current AssetsFA: Fixed Assets T = S / TI
MC: Mill Cost of Sales
SC: Sales Expense
FC: Freight and Delivery
AC: Admin Costs
TI = CA + FA
Turn
Total Investment
Current Assets Fixed Assets
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ROI = T * E / S
E = S - C
ROI
over Earnings
SALES Cost of Sales
Mill Cost ofSales
SellingExpense
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C = MC + SC + FC + AC
Freight &Delivery
AdminCosts
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a 10
x -0.25 0 0.5 1 1.5
0.25 14.14 10.00 5.00 2.50 1.25
0.50 11.89 10.00 7.07 5.00 3.54
0.75 10.75 10.00 8.66 7.50 6.50
1.00 10.00 10.00 10.00 10.00 10.001.25 9.46 10.00 11.18 12.50 13.98
1.50 9.04 10.00 12.25 15.00 18.37
1.75 8.69 10.00 13.23 17.50 23.15
2.00 8.41 10.00 14.14 20.00 28.28
2.25 8.16 10.00 15.00 22.50 33.75
2.50 7.95 10.00 15.81 25.00 39.53
When b
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-
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
- 0.50 1.00 1.50 2.00 2.50 3.00
SAMPLE SKETCHES
b
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MODEL:
G = (m x d ) / vf
m 24 miles
d 20 days
480 total miles per month
vf 30 mpg
G 16 gallons used per month
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DEMAND MODEL
D = 2000 - 3P
COST MODEL
C = 5000 + 4D = 5000 + 4 x ( 2000 - 3P) = 13000 - 12P
TOTAL REVENUE
TR = D x P = ( 2000 - 3P) x P = 2000P - 3 P^2
TOTAL COST
TC = 13000 - 12P
TOTAL PROFIT
TP = TR - TC
= 2000P - 3 P^2 - (13000 - 12P)
= -13,000 + 2012 P - 3 P^2
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P D
600.00$ 500
300.00$ 1200
Revenue = P x D= P x (-2.333 P + 1900 )
= -2.333 P^2 + 1900 P
P Revenue
1.00$ 1,898$
10.00$ 18,767$
100.00$ 166,670$
1,000.00$ (433,000)$
500.00$ 366,750$
750.00$ 112,688$
250.00$ 329,188$
375.00$ 384,422$
425.00$ 386,102$
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P + 1900
0.00 $400.00 $500.00 $600.00 $700.00
vs Demand