Forecasting Model
Presented by
Masoud Ahmed Al Ahbabi
#111300609
Hamad Raga Al Ahbabi
#111300449
Choice of a Particular Forecast
Model
Degree of Accuracy Required
Cost of Producing Forecasts
Forecast Horizon
Degree of Complexity Required
Available Data
Cl

Basic EOQ Model Total Minimum Cost
Q
CD
TC
= o + Cc opt
min Qopt
2
Qopt =
2Co D
Cc
Non-Instantaneous Receipt Model
p = daily rate at which the order is received over time
d = daily rate at which inventory is demanded
Maximum inventory level = Q1 d
p
Aver

Real Estate Investment Example
Decision
Apartment building
Office building
Warehouse
Maximax decision =
Maximin decision =
States of Nature
Good Economic
Poor Economic
Conditions
Conditions
50000
30000
100000
-40000
30000
10000
100000
30000
Maximum
50000

Queuing Analysis Formulas
= the arrival rate
= the service rate
1 Server, Poisson arrival rate, Exponential service times
Probability that no customers are in the queuing system:
P0 = 1
Probability that n customers are in the system:
n
n
Pn = P0 = 1
A

Queuing Analysis Formulas
1 Server, Poisson arrival rate, Exponential service times
P0 = 1
n
n
P = 1
Pn = 0
W = 1 =L
Wq =
Undefined Service Times
P0 =1
L = Lq +
Wq =
U =
Lq
L=
U =
Lq =
2
I =1U =1
2 2 + /
Lq =
21 /
2
1
W =Wq +
Constant S

Case 5
Sample Size Selection for Estimating
Reaction to New Sandwich
Controlling Confidence Interval Length
1
To find the sample size of customers required
to achieve a sufficiently narrow confidence for
the mean rating of the new sandwich.
2
Background I

Case 4
Analyzing Variability in Diameters of
Machine Parts
Confidence Interval for a
Standard Deviation
1
To use StatToolss One-Sample Confidence
Interval procedure to find a confidence
interval for the standard deviation of part
diameters, and to see how

Case 1.3
Estimating the Response to a New
Sandwich
Confidence Interval for a Proportion
1
To illustrate the procedure for finding a
confidence interval for the proportion of
customers who rate the new sandwich at least
6 on a 10-point scale.
2
Background

Case 1.2
Estimating Total Tax Refunds
Confidence Interval for a Total
1
To use StatToolss One-Sample Confidence
Level procedure, with an appropriate
modification, to find a 95% confidence interval
for the total (net) amount the IRS must pay
out to these 1

Case 1.1
Customer Response to a New
Sandwich
Confidence Interval for a Mean
1
To use StatToolss one-sample procedure to
obtain a 95% confidence for the mean
satisfaction rating of a new sandwich.
2
Sandwich1.xls
This file contains the results of a survey

Chapter 6
Confidence Interval Estimation
1
Chapter Goals
After completing this chapter, you should be able to:
Distinguish between a point estimate and a confidence interval
estimate
Construct and interpret a confidence interval estimate for a single
popu

Case 5
Explaining Overhead Costs at Bendrix
Multiple Regression
1
To use StatToolss Regression procedure to
estimate the equation for overhead costs at
Bendrix as a function of machine hours and
production runs.
2
Background Information
In case 3 we creat

Case 4
Explaining Overhead Costs at Bendrix
Simple Linear Regression
1
To use StatToolss Regression procedure to
regress overhead expenses at Bendrix
against machine hours and then against
production runs.
2
Background Information
In previous case we crea

Case 3
Explaining Overhead Costs at Bendrix
Scatterplots: Graphing Relationships
1
To use scatterplots to examine the
relationships between overhead, machine
hours, and productions runs at Bendrix.
2
Background Information
The Bendrix Company manufactures

Case 2
Sales Versus Promotions at Pharmex
Simple Linear Regression
1
To use StatToolss regression procedure to
find the least squares line for sales as a
function of promotional expenses at Pharmex.
2
Background Information
Find the least squares line for

Case 4.1
Sales Versus Promotions At Pharmex
Scatterplots: Graphing Relationships
1
To use a scatterplot to examine the
relationship between promotional expenses
and sales at Pharmex.
2
Background Information
Pharmex is a chain of drugstores that operates

Chapter 3
Multiple Regression
Chapter Goals
After completing this chapter, you should be able to:
apply multiple regression analysis to business decision-
making situations
analyze and interpret the computer output for a multiple
regression model
perform

1
An Example of a Maximization Problem
LawnGrow Manufacturing Company must determine the unit
mix of its commercial riding mower products to be
produced next year. The company produces two product
lines, the Max and the Multimax. The average profit is $40

Target Cell (Max)
Cell
$B$12
Name
Profit =
Original Value
1360
Final Value
1360
Adjustable Cells
Cell
$B$10
$B$11
Name
Bowls =
Mugs =
Original Value
24
8
Final Value
24
8
Name
labor (hr/unit) Usage
clay (lb/unit) Usage
Cell Value
40
120
Constraints
Cell
$

Microsoft Excel 11.0 Answer Report
Worksheet: [Example.xls]Sheet1
Report Created: 28-Apr-12 11:12:15 AM
Target Cell (Max)
Cell
Name
$B$13
Profit =
Original Value Final Value
13722.49070632 13722.49071
Adjustable Cells
Cell
Name
$B$11 Part 1 =
$B$12 Part 2

bow77477_appx.qxd
10/14/2005
824
07:07 PM
Page 824
Appendix A
T A B L E A.3
Statistical Tables
A Table of Areas under the Standard Normal Curve
0
z
z
.00
.01
.02
.03
.04
.05
.06
.07
.08
.09
0.0
0.1
0.2
0.3
0.4
0.5
.0000
.0398
.0793
.1179
.1554
.1915
.0040