Casio ALGEBRA FX 2.0 Manuale utente
Examples
2
examples
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k1-Sample ZTest
Set Up
1. On the icon menu, select STAT2.
Execution
2. When using list data (List is selected as the Data parameter), be sure to input data into
the list first.
3. 3(TEST)b(Z)b(1-Smpl) ... 1-Sample ZTest
4. Set calculation parameters.
5. Align the cursor with [Execute]
1(CALC) ... Performs calculation.
6(DRAW) ... Draws graph.
3
examples
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Example Five new members of a football team are timed for the 100-
meter dash, yielding the following times.
A: 12.5 B: 11.6 C: 10.8 D: 12.8 E: 11.4
The average time of current team members is 11.4 seconds,
with a standard deviation of 1.30. Test the null hypothesis that
the times of the five new members are at the same level as
current team members at the 0.05 level of significance.
Procedure
1mSTAT2
2 bc.fwbb.gwba.iw
bc.iwbb.ew
33(TEST) b(Z)b(1-Smpl)
41(LIST) c
1(≠) c
bb.ew
b.dw
1(LIST) bwc
1(1)c
1(None)c
51(CALC)
6(DRAW)
Result Screen
Since P = 0.47003508 > 0.05 (level of significance), we can't reject the null hypothesis and
can conclude the times of the five new numbers is at the same level as current team
members.
4
examples
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k2-Sample ZTest
Set Up
1. On the icon menu, select STAT2.
Execution
2. When using list data (List is selected as the Data parameter) , be sure to input data into
the list first.
3. 3(TEST)b(Z)c(2-Smpl) ... 2-Sample ZTest
4. Set calculation parameters.
5. Align the cursor with [Execute]
1(CALC) ... Performs calculation.
6(DRAW) ... Draws graph.
5
examples
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Example A consumer group is testing camp stoves. To test the heating capacity
of a stove they measure the time required to bring 2 qt of water from
50qq
qq
qF to boiling (at sea level). Two competing models are under
consideration. 36 stoves of each model were tested and the following
results were obtained.
Model 1: mean time oo
oo
o1= 11.5min; standard deviation σ1= 2.4min
Model 2: mean time oo
oo
o2= 10 min; standard deviation σ2= 3 min
Is there any difference between the performances of these two
models? (Use a 5% level of significance.)
Procedure
1mSTAT2
23(TEST) b(Z)c(2-Smpl)
32(VAR) c
1(≠) c
c.ew
dw
bb.fw
dgw
baw
dgw
1(None) c
41(CALC)
6(DRAW)
Result Screen
Since P = 0.01914957 < 0.05 (level of significance), we reject the null hypothesis µ1-µ2 =0
and conclude the performances of the two models are different.
6
examples
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k1-Prop ZTest
Set Up
1. On the icon menu, select STAT2.
Execution
2. 3(TEST)b(Z)d(1-Prop) ... 1-Prop ZTest
3. Set calculation parameters.
4. Align the cursor with [Execute]
1(CALC) ... Performs calculation.
6(DRAW) ... Draws graph.
7
examples
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Example A team of eye surgeons has developed a new technique for a risky eye
operation to restore the sight of people blinded from a certain disease.
Under the old method it is known that only 30% of the patients who
undergo this operation recover their eyesight. Suppose that surgeons
in various hospitals have performed a total of 250 operations using
the new method and that 93 have been successful (the patients fully
recovered their sight). Can we justify the claim that the new method is
better than the old one?(Use a 1% level of significance.)
Procedure
1mSTAT2
23(TEST) b(Z)d(1-Prop)
33(>) c
.dw
jdw
cfaw
1(None) c
41(CALC)
6(DRAW)
Result Screen
Since P = 6.4915e-3 < 0.01 (level of significance), we can reject the null hypothesis that the
successful probability of new operation is 0.3 and conclude that it can be said the new
method obtains good results more than existing method. That is to say it is more effective by
new methods.
kk
kk
k2-Prop ZTest
Set Up
1. On the icon menu, select STAT2.
Execution
2. 3(TEST)b(Z)e(2-Prop) ... 2-Prop ZTest
3. Set calculation parameters.
4. Align the cursor with [Execute]
1(CALC) ... Performs calculation.
6(DRAW) ... Draws graph.
8
examples
9
examples
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Example The County Clerk wishes to improve voter registration. One method
under consideration is to send reminders in the mail to all citizens in
the county who are eligible to register. As a pilot study to determine if
this method will actually improve voter registration, a random sample
of 1224 potential voters was taken. Then this sample was randomly
divided into two groups.
Group A : There were 612 people in this group. No reminders to
register were sent to them. The number of potential
voters from this group who registered was 280.
Group B : This group also contained 612 people. Reminders were
sent in the mail to each member in the group, and the
number who registered to vote was 335.
The County Clerk claims that the proportion of people to register was
significantly greater in group B. On the basis of this claim the clerk
recommends that the project be funded for the entire population of
Macek County. Use 5% level of significance to test the claim that the
proportion of potential voters who registered was greater in group B,
the group that received reminders.
Procedure
1mSTAT2
23(TEST) b(Z)e(2-Prop)
32(<) c
ciaw
gbcw
ddfw
gbcw
1(None) c
41(CALC)
6(DRAW)
Result Screen
Since P = 8.3277e-4 < 0.05 (level of significance), we can reject the null hypothesis (P1>P2)
and conclude that the County Clerk‘s claim is valid at the 0.05 level of significance.
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k1-Sample tTest
Set Up
1. On the icon menu, select STAT2.
Execution
2. When using list data (List is selected as the Data parameter), be sure to input data into
the list first.
3. 3(TEST)c(T)b(1-Smpl) ... 1-Sample tTest
4. Set calculation parameters.
5. Align the cursor with [Execute]
1(CALC) ... Performs calculation.
6(DRAW) ... Draws graph.
10
examples
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