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220 14: Additional Examples
File name : English-M02-1-040308(Print).doc Print data : 2004/3/9
In other words, it tests whether discrepancies between the observed
frequencies (
O
i
) and the expected frequencies (E
i
) are significant, or
whether they might reasonably result from chance. The equation is:
2
2
1
()
n
ii
i
i
OE
E
χ
=
−
=
∑
If there is a close agreement between the observed and expected
frequencies,
χ
2
will be small. If the agreement is poor, χ
2
will be large.
Solver Equations for
χ
2
Calculations:
If the expected value is a constant:
name1
name1
If the expected values vary:
name1
name1
name2
name2
(To enter the Σ character, press .)
CHI2 = the final χ
2
value for your data.
name1 = the name of the SUM list that contains the observed values.
name2 = the name of the SUM list that contains the expected values.
EXP = the expected value when it is a constant.
When you create and name the SUM list(s), make sure the name(s)
match
name1 (and name2, if applicable) in the Solver equation.
To solve the equation, press
once or twice (until you see the
message
).
The following example assumes that you have entered the CHI equation
into the Solver, using OBS for
name1. For instructions on entering Solver
equations, see “Solving Your Own Equations,” on page 30.
Example: Expected Throws of a Die. To determine whether a suspect
die is biased, you toss it 120 times and observe the following results.
(The expected frequency is the same for each number, 120
÷ 6, or 20.)