NAME: Fan Data
TYPE: Designed experiment
SIZE: Data for four experiments (4 variables with 8 observations, 
      4 variables with 16 observations, 4 variables with 32 
      observations, 4 variables with 64 observations)

DESCRIPTIVE ABSTRACT: 
This dataset contains the results of a experiment designed to 
determine the best of two levels for each of three factors used 
in manufacturing industrial fans:  

(1) the shape of the hole in the fan blade (spyder); 
(2) the type of assembly method; and 
(3) the shape of the hub or barrel.  

Destructive testing was used to break the spyder loose from the 
barrel for each of the fans.  The breaking torque was measured 
in foot-pounds.

SOURCES: 
The data represent the results of an actual industrial 
experiment.  Company representatives have asked that the name 
of the company not be revealed.

VARIABLE DESCRIPTIONS:
Data columns 1, 5, 9, and 13 are values for the two types of 
  hole tested (coded -1 and 1)

Data columns 2, 6, 10, and 14 are values for the two types of 
  assembly method used (coded -1 and 1)

Data columns 3, 7, 11, and 15 are values for the two barrel 
  shapes used (coded -1 and 1)

Data columns 4, 8, 12, and 16 are the breaking torques in 
  foot-pounds for the various factor combinations

Data columns 1 - 4 give the results of the experiment with 
  one observation for each factor combination

Data columns 5 - 8 give the results of the experiment with 
  two replications for each factor combination

Data columns 9 - 12 give the results for the experiment with 
  four replications for each factor combination

Data columns 13 - 16 give the results for the experiment with 
  eight replications for each factor combination

SPECIAL NOTES: Columns are delimited with spaces.

STORY BEHIND THE DATA:
A mid-western company that manufactures large industrial fans 
recently acquired a smaller competitor.  The companies used 
different manufacturing methods to attach the fan blades 
(spyders) to the fan hubs.  In an attempt to determine the 
best manufacturing process, an experiment was designed to 
investigate:

(1) two types of hole in the spyder, 
(2) two hub (or barrel) shapes, and 
(3) two assembly methods.  

Destructive testing was used to break the spyder from the 
hub and the torque required to break the part was measured in 
foot-pounds.  The full factorial design resulted in eight 
factor combinations. 

Since the fans had to be destroyed in order to test the 
effectiveness of the manufacturing methods, the company's 
engineers wanted to use as few fans as possible in the 
experiment.  

The minimum number of fans necessary to estimate the main 
effects and the two factor interactions was eight.  The 
authors encouraged the engineers to use a larger sample size 
than one observation per factor combination.

This dataset can be used to show how smaller sample sizes 
may lead to incorrect conclusions about the process under 
investigation.  If the engineers had conducted only the 
original experiment, they would have missed an opportunity 
to significantly strengthen the fans manufactured by the 
company.

PEDAGOGICAL NOTES:
Analysis of Variance (ANOVA) can be used to determine which 
factors are important in producing a stronger fan.  Students 
analyzing the first four columns of data would conclude that 
only the barrel shape was an important factor.  The average 
breaking torque of the best barrel shape was 176 foot-pounds.

Conversely, an analysis of columns 5 through 8 (two 
replications of each factor combination) would suggest that 
all three factors are important in producing a strong fan.  
Students will observe that, by increasing the sample size 
to 32 and finally 64 observations, all main effects and all 
interactions become significant.  In fact, the average 
breaking torque for the best combination of the factor 
levels is 196 foot-pounds.  By identifying the best 
combination of factor levels and using those settings in 
the manufacturing process, the company can produce a 
stronger fan than otherwise possible.

SUBMITTED BY:
LeRoy A. Franklin
Rose-Hulman Institute of Technology
550 Wabash Avenue
Terre Haute, IN
USA
LeRoy.A.Franklin@Rose-Hulman.edu

Belva J. Cooley
University of Montana
School of Business Administration
Missoula, MT 59812
belva.cooley@business.umt.edu