Guide to Using Excel For Basic
Statistical Applications
To Accompany
Business Statistics: A Decision Making
Approach, 5th Ed.
Chapter 15:
Analyzing and Forecasting Time Series
Data
By
Groebner, Shannon, Fry, & Smith
Prentice-Hall Publishing Company
Copyright, 2005
Chapter 15 Excel
Examples

Trend Based Forecasting
Taft Ice Cream Company
 Nonlinear Trend
Harrison Equipment Company
 Seasonal Adjustment
Big Mountain Ski Resort
 Single Exponential Smoothing
Humboldt Electronics Company
More Examples
Chapter 15 Excel
Examples
 Double
Exponential Smoothing
Billingsley Insurance Company
Trend Based Forecasting Taft Ice Cream Company
Issue:
The owners of Taft Ice Cream Company
considering expanding their manufacturing
facilities. The bank requires a forecast of future
sales.
Objective:
Use Excel to build a forecasting model
based on 10 years of data. Data file is Taft.xls
Trend Based Forecasting – Taft Ice Cream Company
Open File Taft.xls
Trend Based Forecasting – Taft Ice Cream Company
First click on
Chart
Wizard, then
select Line
Chart
Trend Based Forecasting – Taft Ice Cream Company
Click on
Series tab and
define range
for Data
Variables.
Click on Next.
Trend Based Forecasting – Taft Ice Cream Company
Remove
unneeded
data sets
and identify
the range
for the X
Variable.
Trend Based Forecasting – Taft Ice Cream Company
Label the axes
and graph
Trend Based Forecasting – Taft Ice Cream Company
Size and format the graph as
needed.
Trend Based Forecasting – Taft Ice Cream Company
To develop the linear model,
start with the original data.
Trend Based Forecasting – Taft Ice Cream Company
Click on
Tools, then
Data Analysis
and finally
Regression
Trend Based Forecasting – Taft Ice Cream Company
Define the
data range for
the X and Y
Variables.
Use the t
column for
time.
Trend Based Forecasting – Taft Ice Cream Company
The
regression
output
determines
the slope
and
intercept of
the linear
model.
Trend Based Forecasting – Taft Ice Cream Company
To visually
compare the
data with a
linear model,
return to the
graph
constructed
using the chart
wizard.
Trend Based Forecasting – Taft Ice Cream Company
Left click on
any data
point, then
right click
and select
Add
Trendline
Trend Based Forecasting – Taft Ice Cream Company
Select Linear
model then click
on Options Tab.
Trend Based Forecasting – Taft Ice Cream Company
Select Display equation on chart.
Trend Based Forecasting – Taft Ice Cream Company
Format chart as
desired.
Trend Based Forecasting – Taft Ice Cream Company
To determine
both MAD
and MSE
values, start
by selecting
the Residuals
option in
Regression
analysis.
Trend Based Forecasting – Taft Ice Cream Company
The
Predicted
values and
Residuals
become
part of the
regression
output.
Trend Based Forecasting – Taft Ice Cream Company
Write and copy
a formula to
determine the
Squared
Residual values.
Trend Based Forecasting – Taft Ice Cream Company
Also write and copy a formula
to find the absolute values of
the residuals.
Trend Based Forecasting – Taft Ice Cream Company
Sum the
squared and
absolute values
of the residuals.
Trend Based Forecasting – Taft Ice Cream Company
Divide both
summed
values to find
the MSE and
MAD values.
Nonlinear Trend Harrison Equipment Company
Issue:
Harrison Equipment is interested in
forecasting future repair costs for a crawler tractor it
leases to contractors.
Objective:
Use Excel to develop a nonlinear forecasting
model. Data file is Harrison .xls
Nonlinear Trend – Harrison Equipment Company
Open File Harrison.xls
This tutorial will start by
finding the trend line. It will
also show how to find
residuals.
Nonlinear Trend – Harrison Equipment Company
First click on
Chart
Wizard, then
select Line
Chart
Nonlinear Trend – Harrison Equipment Company
Click on
Series tab and
define range
for both Y
and X
Variables.
Nonlinear Trend – Harrison Equipment Company
Format,
size and
label chart
as desired.
Nonlinear Trend – Harrison Equipment Company
To add
trendline, left
click on any
data point,
then right
click and
select Add
Trendline
Nonlinear Trend – Harrison Equipment Company
Choose Linear
Nonlinear Trend – Harrison Equipment Company
Trendline appears.
Nonlinear Trend – Harrison Equipment Company
To build
linear model,
click on Tools,
then Data
Analysis and
finally
Regression
Nonlinear Trend – Harrison Equipment Company
Define the
data range for
the X and Y
Variables.
Use the t
column for
time. Also
ask for
residuals.
Nonlinear Trend – Harrison Equipment Company
The
regression
output
determines
the slope
and
intercept of
the linear
model.
Nonlinear Trend – Harrison Equipment Company
Calculate bye
MAD value by
first finding the
absolute value
of the residuals
using the ABS
function.
Nonlinear Trend – Harrison Equipment Company
Sum the
absolute value
of the
Residuals and
divide by the
count
(number) of
residuals to
find the
MAD.
Nonlinear Trend – Harrison Equipment Company
To develop
nonLinear
model define a
new variable
found by
squaring the
time values.
Nonlinear Trend – Harrison Equipment Company
Develop a new
regression model with
t2 value as the
independent
variables. The model
becomes y = a + bt2.
The output gives the
new regression
coefficients.
Nonlinear Trend – Harrison Equipment Company
Using the same
SUM and
COUNT
formula find the
MAD for the
nonlinear
model.
Nonlinear Trend – Harrison Equipment Company
To plot the
nonlinear
model, define a
new column of
values
determined by
plugging the
values of t2 into
the regression
model.
Nonlinear Trend – Harrison Equipment Company
Use the Chart
Wizard, Line
options to
develop a graph
comparing the
observed values
with the
nonlinear
model. You will
identify two
Series.
Nonlinear Trend – Harrison Equipment Company
Format and
place chart as
needed.
Seasonal Adjustment Big Mountain Ski Resort
Issue:
The resort wants to build a forecasting model
from data that has a definite seasonal component.
Objective:
Use Excel to develop a forecasting model
adjusting for seasonal data. Data file is Big
Mountain.xls
Seasonal Adjustment – Big Mountain Ski Resort
Open File Big Mountain.xls
Seasonal Adjustment – Big Mountain Ski Resort
To develop the
graph first
click on Chart
Wizard button
then select
Line.
Seasonal Adjustment – Big Mountain Ski Resort
Define the range
for the Y and X
variable values.
Seasonal Adjustment – Big Mountain Ski Resort
Size and format the graph
as desired.
Seasonal Adjustment – Big Mountain Ski Resort
To find the
moving
average
values use the
AVERAGE
function.
Seasonal Adjustment – Big Mountain Ski Resort
Use the
AVERAGE
function again
to find the
centered
moving
average.
Seasonal Adjustment – Big Mountain Ski Resort
Write a
simple
formula to
find the
Ratio to
Moving
Average
values.
Seasonal Adjustment – Big Mountain Ski Resort
To find the season index values
click on PHStat, then Data
Preparation and then Unstack.
Seasonal Adjustment – Big Mountain Ski Resort
To find the
seasonal index
values start by
adding (SUM)
the three ratio to
moving average
values for each
season.
Seasonal Adjustment – Big Mountain Ski Resort
Divide the
total values
to find the
seasonal
index
numbers.
Seasonal Adjustment – Big Mountain Ski Resort
Write a formula to select
the correct seasonal value to
use to deseasonalize the
data. Copy the formula into
all cells.
Seasonal Adjustment – Big Mountain Ski Resort
Use the Select
Chart Wizard
to graph the
deseasonalized
data.
Seasonal Adjustment – Big Mountain Ski Resort
Format and
place the
chart as
desired.
Seasonal Adjustment – Big Mountain Ski Resort
Use the Tools,
Data Analysis,
Regression
option to
develop a
regression
model of the
deseasonalized
data.
Single Exponential Smoothing
Humboldt Electronics
Issue:
The company needs to develop a forecasting
model to help make inventory decisions, and wants the
model to give more weight to recent values than to
regression model do.
Objective:
Use Excel to develop a single exponential
smoothing forecasting model. Data file is
Humboldt.xls
Single Exponential Smoothing – Humboldt Electronics
Open File Humboldt.xls
Single Exponential Smoothing – Humboldt Electronics
Click on
the Chart
Wizard
button
then
select
Line.
Single Exponential Smoothing – Humboldt Electronics
Click on the
Series tab,
then identify
ranges for the
Y and X
variables.
Single Exponential Smoothing – Humboldt Electronics
Label the axes.
Single Exponential Smoothing – Humboldt Electronics
Format
and place
graph as
desired.
Single Exponential Smoothing – Humboldt Electronics
To develop the
exponential
smoothing
model, return
to the original
data. Set initial
forecast to 400
and write
formula for
following
forecasts.
Single Exponential Smoothing – Humboldt Electronics
Write
similar
formula to
determine
Forecast for
Period 11.
Single Exponential Smoothing – Humboldt Electronics
To determine
MAD start by
writing formula
to find the
forecast error.
Single Exponential Smoothing – Humboldt Electronics
Find absolute
value of forecast
error. Then write
a formula to find
MAD.
Single Exponential Smoothing – Humboldt Electronics
Construct a
graph of
actual and
forecast sales
by returning
to Chart
Wizard, Line
option.
Identify the
two Series to
graph.
Single Exponential Smoothing – Humboldt Electronics
Format and
place graph
as desired.
Double Exponential Smoothing
Billingsley Insurance
Issue:
The claims manager has data for 12 months
and wants to forecast claims for month 13. But the
time series contains a strong upward trend
Objective:
Use Excel to develop a double exponential
smoothing model. Data file is Billingsley.xls
Double Exponential Smoothing – Billingsley Insurance
Open file Billingsley.xls
Double Exponential Smoothing – Billingsley Insurance
Click on
the Chart
Wizard
button
then
select
Line.
Double Exponential Smoothing – Billingsley Insurance
Click on the
Series tab,
then identify
ranges for the
Y and X
variables.
Double Exponential Smoothing – Billingsley Insurance
Label the axes.
Double Exponential Smoothing – Billingsley Insurance
Format the graph as desired.
Double Exponential Smoothing – Billingsley Insurance
To develop the double
exponential
smoothing equations,
return to the data
sheet and determine
the initial values for
C and T.
Double Exponential Smoothing – Billingsley Insurance
Use Equations
13-18 and 13-19
to determine the
values for the
Constant and
Trend.
Double Exponential Smoothing – Billingsley Insurance
Use Equation 13-20
to determine the
Forecast values and
the Forecast for
Period 13.
Double Exponential Smoothing – Billingsley Insurance
Write equations to find both the
Forecast Error and Absolute
Forecast Error.
Double Exponential Smoothing – Billingsley Insurance
Write
equations
to find both
the Total
Absolute
Error and
the MAD.