Download Chapter 9 Minitab Recipe Cards

Chapter 9
Minitab Recipe Cards
Contingency tests
• Enter the data from
Example 9.1 in C1,
C2 and C3.
• Select Tables from
the Stat menu and
choose Chi-Square
Test (Two-Way table
in Worksheet) from
the sub-menu.
• Type C2 and C3 as
the Columns
containing the table
and click OK.
• The output includes
the test statistic, ChiSq, the degrees of
freedom, DF and the
probability of getting
the test statistic if
there were no
association, the PValue.
Hypothesis tests for regression
model coefficients
• Enter the data from
Example 9.6 into two
worksheet columns.
• Choose Regression
from the Stat menu
and Regression from
the sub-menu.
• Type C2 in the
Response box and
C1 in the Predictor
box.
• Click OK.
• The analysis in the
session window an
analysis begins with the
regression equation.
• In the table below it the
Predictor column lists
the two components of
the model; the Constant
(intercept) and the
temperature variable.
• The Coef (coefficient)
column contains the
sample intercept and the
sample slope.
• The SECoeff column contains
the estimated standard errors
of the sample intercept and
slope.
• The T column contains the test
statistics based on the sample
intercept (the upper figure) and
the sample slope (the lower
figure). The adjacent P column
contains the p-values for the
sample intercept and the
sample slope.
•
•
•
The figures in the P column
assess the hypotheses that the
population intercept and slope are
zero. The first is the probability
that the sample intercept is 0.738
or more if the population intercept
is zero. The P value of 0.917
suggests that it is zero.
The second P value, 0.001, is the
probability that the sample slope is
2.3788 or more if the population
slope is zero. The figure is below
the level of significance, 0.05 the
null hypothesis should be rejected.
S is the standard deviation of the
residuals.
Residual plots
• Enter the data from
Example 9.6 into two
worksheet columns.
• Select Regression
from Stat menu and
Regression from the
sub-menu.
• Type C2 and C1as
the Response and
Predictor variables
respectively then click
the Graphs button.
• Click next to
Residuals versus
fits then click OK and
OK on the
Regression
command window.
• The graph that appears is
the plot of the residuals
against the fits (the
values of the Y variable
that should, according to
the line, have occurred).
This shows whether there
is some systematic
variation not explained by
the model.