Page 341 - The Handbook for Quality Management a Complete Guide to Operational Excellence

328 C o n t i n u o u s I m p r o v e m e n t A n a l y z e S t a g e 329

The mathe matical model for multiple linear regression has additional terms
for the addi tional independent variables, for example:

y = b + b x + b x + e
0
1 1
2 2
where y is the dependent variable, x and x are independent variables, b 0
1
2
is the intercept, b is the coefficient for x ,b is the coefficient for x , and e is
2
1 2
1
the error. More variables can be added to the model as needed.
Example. A restaurant conducted surveys of 42 customers, obtaining
customer rat ings on staff service, food quality, and overall satisfaction
with their visit to the restaurant. Figure 15.9 shows the regression anal-
ysis output from a spread sheet regression function.
The data consist of two independent variables, staff and food quality,
and a single dependent variable, overall satisfaction. The basic premise is
that the qual ity of staff service and the food are causes and the overall sat-
isfaction score is an effect.

Interpretation of Computer Output for Regression Analysis
The regression output is interpreted as follows:

Multiple R. The multiple correlation coefficient. It is the correlation
between y (actual satisfaction) and y’ (satisfaction estimated from
the model). For the example, multiple R = 0.847, which indicates that
y and y’ are highly correlated, which implies that there is an association
between overall satisfaction and the quality of the food and service.

Figure 15.9 Microsoft Excel regression analysis output.

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