2023年6月24日发(作者：)

Tips for the Statistics with List

Editor Application for the TI-89

©

2013 April 17, version 2.6, Wm J. Larson, @, Switzerland. Corrections welcome.

This is a guide to some of the more commonly

used functions needed for the AP Statistics

curriculum in the Statistics with List Editor

Application for the TI-89/TI-92 Plus,

hereinafter referred to as Stats/List Editor. As

far as I can tell the statistics package in the

Stats/List Editor is in every way superior to the

statistics package that comes in the TI-89. So

the statistics package that comes in the TI-89

need never be studied.

The TI-83 Plus was designed (several years ago)

with a very extensive statistics package and is

currently the calculator of choice for most AP

Statistics classes. Stats/List Editor installs this

extensive statistics package in a TI-89. Because

the user interface on the TI-89 is superior to

that of the TI-83 Plus and because the TI-89 is

more powerful and enjoyable to use than the

TI-83 Plus and because the TI-89 does things

that the TI-83 does not do (such as, multiple

regression, one and two-way analysis of

variance), I strongly recommend statistics

students buy a TI-89, not a TI-83 Plus.

Stats/List Editor is available as a free upgrade

from TI at /calc/flash/.

You can also download the free 200-page

manual. To install this upgrade you need to first

install the Advanced Mathematics Software

Operating System Version 2.05 (AMS 2.05) [or

whatever is the current version] available as a

free upgrade from TI at

/calc/flash/. Both

of the above can be installed only if you have

the TI GraphLink software (available free at

/calc/docs/) on your

computer and a gray or black TI GraphLink

cable (about $30 - See the dealer list at the

GraphLink site.) to transfer it to your TI-89.

Page numbers listed below refer to the page in

the Statistics with List Editor Application

manual  1999 Texas Instruments. There is

little contained herein that is not said better in

the official manual, but this guide, hopefully, is

be a bit more focussed and less imposing than

the 200 page guide.

Running & Quitting

Stats/List Editor

To use the Statistics with List Editor

Application, key  APPS Stats/List Editor

ENTER. The first time you use the Stats/List

Editor, you will be required to Select Current

Folder. Select Main.

To toggle between

ndStats/List Editor and the

Home screen key 2 APPS.

The statistical functions listed below are most

easily used from the Stats/List Editor screen,

but they can also be accessed from the Home

screen (p. 3) by keying CATALOG, F3 Flash

Apps. To move to the desired functions key the

first letter of its name (without keying ALPHA

first). The function's syntax is displayed in the

status line. All further mention of the functions

assumes they are being used from the Stats/List

Editor screen.

Managing Lists

Using the List Editor p. 18

To move to the bottom of a list key  ▼.

To move to the top of a list key  ▲.

To delete a list element key  DEL.

To delete an entire list highlight the list name

at the top of the list, key ENTER (which

highlights the list elements), then  DEL. The

list name will not be deleted. To delete the list

including the list name highlight the list name Tips for “Statistics with the List Editor Application for the TI-89”, page 2

key  DEL. But the list is still retained in

memory and can be recovered by keying its

name back in or by highlighting its name in 2nd

VAR-LINK and keying ENTER. To

completely delete the list key 2nd VAR-LINK,

use F4 to highlight the lists to delete, key F1

Manage 1: Delete. You will be prompted to

confirm the names of the variables to delete. If

the names are correct, key ENTER.

To edit a list highlight the list name at the top

of the list and key ENTER. Now the entire list

can be edited in the entry line at the bottom of

the screen. Or highlight a particular list element

and key ENTER. Now that element can be

edited.

To create a new list either move the cursor to

the top of the first unnamed column and press

ENTER or if you want to insert a list to the left

of a list move the cursor to the top of the list

where you want to insert a list and key 2nd INS.

Key in a valid name. Names must begin with a

letter and cannot be a pre-assigned name such

as abs.

Random Number

Generators p. 103

A random number generator produces

numbers in such a way that every number in

the range has an equal possibility of being

produced. F4 Calc 4: Probability has seven

different kinds of random number generators.

Note that in EXACT mode none of the random

number functions work right. For instance,

randint(0,10,5) gave me {-1.,-1.,-1.,-1.,-1.)

and rand83(3) gave me {0, 0, }. So use

AUTO mode.

rand83( F4 Calc 4: Probability 1: rand83(n)

generates a list of n random real numbers, x,

such that 0 < x < 1. Move the cursor to the

name of a list that you want to fill with

random numbers and key rand83(n). E.g.

rand83(3) put .73381, .04399 and .33936 in a

list. Actually each number had 14 digits, but

the format was set to display only five digits.

randInt( F4 Calc 4: Probability 5:

randInt(lower, upper [, n]) generates a list of

n random integers, x, where lower  x 

upper. The parameter n is optional. If it is

omitted, one random integer is generated.

Move the cursor to the name of a list that you

want to fill with random numbers and key

randInt(lower, upper [, n]). E.g. randInt(10,

20, 5) put 20, 12, 18, 20 & 12 in a list.

.randNorm( F4 Calc 4: Probability

6: .randNorm(, , n) generates a list of n

normally distributed random real numbers

with mean, , and standard deviation, . E.g.

randNorm(100, 10, 5) put 97.978, 101.95,

94.582, 103.30 & 108.72 in a list. Actually

each number had 14 digits, but the format was

set to display only five digits.

Thus .randNorm can be used to display a

graph of typical normally distributed data. E.g.

to show that if n is small, a histogram of

normally distributed data does not look

symmetric.

randBin( F4 Calc 4: Probability 7: randBin(n,

p, ntrials) generates a list of integers with a

binomial distribution (n, p), where p is the

probability of a success, n is the number of

trials and ntrials is the number of such

numbers generated. E.g. to simulate tossing a

fair (p=.5) coin five times key randBin(5, .5,

1), which will generate one number between

0 and 5, representing the number of heads. To

simulate repeating this experiment 100 times,

key randBin(5, .5, 100) which will generate

100 numbers between 0 and 5 with a binomial

distribution (5, .5).

randSamp( F4 Calc 4: Probability 8:

randSamp(list1, choose [, norep] makes a

random sample from an already existing

list, where list1 is the name of the list, choose

is the sample size and norep = 0 means

without replacement & norep = 1 means with

replacement. The default is with replacement.

You could, for example, run rand83,

randInt, .randNorm or randBin to generate a

large list of random numbers and then use

randSamp to then see how many of these

numbers you needed before randInt looked

flat or before randBin looked normal, etc. See,

for example, figures 4.1 to 4.4 in Moore.

rand( F4 Calc 4: Probability 9: rand([INT]) is

used with a list element (not a list name)

highlighted, i.e. it creates a list element, not a

list name. If INT is an integer, one integer, x, Tips for “Statistics with the List Editor Application for the TI-89”, page 3

where 1  x  INT, is generated. E.g. rand(8)

might generate 5. If INT is left blank, one real

number, x, where 0  x  1 is generated. E.g.

rand() might generate .7456. It seems that

rand( is not a very useful function.

randSeed( F4 Calc 4: Probability A:

randSeed(integer seed) generates two new

random number seeds (called seed1 and

seed2) for the above random number

generators. Random number generators do

not, in fact, produce truly random numbers.

For example if you set seed1 & seed2 to 1 (by

keying 1 STO seed1, etc.), then key

randInt(0,5,4) you will get {5 5 3 1} every

time! (Running randInt produces a new seed.

So to see this effect, you would have to enter

1 STO seed1, etc. again.) By running

randSeed you assure that a new seed is in use

and that thus a new list is produced. For our

purposes use of randSeed is probably not

necessary.

To find the mean of a sample of random

numbers use mean( To get mean( key F3

List 3: Math 3: mean(. See p. 59. Move the

cursor to the list element where you want the

mean. For example mean(.randNorm(69, 2.5,

4)) generated 4 numbers from an N(69, 2.5)

population and calculated their mean as

68.101.

To create a list of sample means use seq(. To

get seq( key F3 List 2: Ops 5: seq(. See p. 49.

seq(EXPR, VAR, LOW, HIGH [, STEP])

increments VAR from LOW through HIGH

in increments of STEP, evaluates EXPR for

each value of VAR and returns the result as a

list. Move the cursor to the name of a list that

you want to fill with a list of sample means.

For example seq(mean(randBin(5, .1, 2)), x, 1,

100, 1) creates a list of 100 sample means

from a population binomially distributed with

n = 5 p = .1 each sample with 2 trials. To

check normality of this data a histogram

could be made of this list or it could be

sorted and the 68-95-99.7 rule could be

checked. Or this list could be compared with

seq(mean(randBin(5, .1, 30)), x, 1, 100, 1),

which is a similar list, but where each

element is the average of 30 rather than 2

numbers from a population binomially

distributed with n = 5 p = .1. It should be

more normal.

Drawing Distributions

Shade Normal

Drawing the normal distribution p. 117

Shade Normal draws the Normal Distribution

function with the specified lower and upper

values and calculates the probability.

Key F5 Distr 1: Shade 1: Shade Normal. Enter

the lower value, the upper value,  (the default

is 0) and  (the default is 1) For a sample of

size n, enter /n for . To automatically scale

the drawing to fit the screen set Auto-scale to

YES. Press ENTER. The shaded normal curve,

the lower and upper values and the Area (the

probability that z is inside the specified range)

are displayed. Since Normal Cdf only

calculates the probability, Shade Normal is

more useful than Normal Cdf.

Shade t

Drawing the t distribution p. 118

Shade t draws the t Distribution function with

the specified lower and upper values and

calculates the probability.

Key F5 Distr 1: Shade 2: Shade t. For an upper

p-value (i.e. if t is positive) enter the t value

[e.g (-)/(s/n)] as the Lower Value and 

as the Upper Value and Deg of Freedom, df. To

automatically scale the drawing to fit the screen

set Auto-scale to YES. Press ENTER. The

shaded t curve, the lower and upper values and

the Area (the probability that t is inside the

specified range) are displayed. Since t Cdf

only calculates the probability, Shade t is

more useful than t Cdf.

Probability

Distributions Tips for “Statistics with the List Editor Application for the TI-89”, page 4

Normal Cdf *

Normal (z) cumulative probability

distribution function p. 128

Normal Cdf calculates the z-distribution

probabilities, i.e. the probability of finding z in

some interval, E.g.: P(z > a), P(z < a), or P(a <

z < b)

Key F5 Distr, 4: Normal Cdf. To find the

probability of finding x between two values,

enter the lower value, the upper value,  and .

Press ENTER, ENTER.

Example

to calculate P(x > 27| = 23,  = 2), enter

lower value = 27

upper value = 

 = 23

 = 2

The result is P(x > 27| = 23,  = 2) = Cdf =

0.02275

Example

to calculate P(21 < x < 25| = 23,  = 2), enter

lower value = 21

upper value = 25

 = 23

 = 2

The result is P(21 < x < 25| = 23,  = 2) = Cdf

= 0.68269, which agrees with the 68-95-99.7

rule.

For a sample mean key in the value of s/n for

.

t Cdf *

Student-t cumulative probability

distribution function p. 131

t Cdf calculates the t distribution probability, i.e.

the probability of finding t in some interval, e.g.

P[t > (-)/(s/n)].

Key F5 Distr 6: t Cdf.

For an upper p-value (i.e. if t is positive) enter

the t value [i.e. (-)/(s/n)] as the Lower

Value and  as the Upper Value. Enter the

degrees of freedom = df. Press ENTER. The P-value is displayed as Cdf.

For an lower p-value (i.e. if t is negative) enter

the t value as the Upper Value and -  as the

Lower Value. Enter the degrees of freedom = df.

Press ENTER. The P-value is displayed as Cdf.

Binomial Pdf *

Binomial probability distribution function p.

136

Binomial Pdf calculates the probability of a

given number of successes for a given number

of trials and a given probability of one success.

Input the Num of trials, n, Prob of Success, p

and X Value. Press ENTER. Pdf [i.e. the P(X =

X Value | n = n, p = p)], X Value, n and p are

displayed.

Binomial Cdf *

Binomial cumulative probability distribution

function p. 137

Binomial Cdf calculates the cumulative

probability distribution between a lower

number of successes and an upper number of

successes for a given number of trials and a

given probability of one success.

Input the Num of trials, n, Prob of Success, p,

Lower Value (of successes) and Upper Value

(of successes). Press ENTER. Cdf [i.e. the

P(Lower Value  X  Upper Value | n = n, p =

p)], X Value, n and p are displayed.

* For continuous distributions, such as the t & z

distribution, Pdf stands for Probability

distribution function or Probability density

function. Cdf stands for Cumulative probability

distribution function or Cumulative probability

density function. A Cdf is the integral of a Pdf.

A z Pdf is the value of the normal curve itself,

< usually not of interest. A Cdf is the area

under the curve, i.e. the required probability. Tips for “Statistics with the List Editor Application for the TI-89”, page 5

For discrete distributions, such as the binomial

distribution, Pdf stands for Probability

distribution function (only). Cdf stands for

Cumulative probability distribution function

(only). A Pdf is the probability of a given

number of successes, e.g. P(X = 5). A Cdf is

the sum of one or more Pdfs, e.g. P(2  X  5).

Both are of interest.