# Stats without Tears

TI-83/84 Cheat Sheet

Updated 17 Nov 2020
(What’s New?)

Copyright © 2015–2022 by Stan Brown, BrownMath.com

TI-83/84 Cheat Sheet

Updated 17 Nov 2020
(What’s New?)

Copyright © 2015–2022 by Stan Brown, BrownMath.com

In this course, you used a lot of calculator procedures. This cheat sheet brings them all together. You’ll find just the key points here, but each section links back to the original full discussion, complete with screen shots.

(You can use any lists, not just L1 and L2.)

Ask your instructor whether you can use this sheet during tests.

Pick a number haphazardly and type it into the calculator,
then [`STO`

] and [`MATH`

] »
`PROB`

» `rand`

.

Details: Seeding the Random-Number Generator in Chapter 1.

`randInt(1,`

*SizeOfPopulation*```
), then
press [
```

`ENTER`

] until you have as many distinct numbers as you
need for your sample, ignoring duplicates.

Details: Selecting Members of the Sample in Chapter 1.

Pick a number *k*, where you’ll be taking data
from every *k*th individual. Then
`randInt(1,`

*k*`)`

using your chosen
number *k*.

Details: Taking a Systematic Sample in Chapter 1.

For all of these, remember to
**use σ or s based on whether you have the whole population or just a sample**.

Enter numbers in L1, then
`1-VarStats L1`

. Check *n* before you look at
anything else.

Details: from a List of Numbers in Chapter 3.

Enter the values in L1 and the frequencies in L2,
then `1-VarStats L1,L2`

. Check *n* before you look
at anything else.

Details: from an Ungrouped Distribution in Chapter 3.

Enter the values in L1 and the weights in L2,
then `1-VarStats L1,L2`

. Check *n* before you look
at anything else — it should equal the total of the
weights.

Details: Weighted Average in Chapter 3.

Find class midpoints and enter them in L1;
enter frequencies in L2.
`1-VarStats L1,L2`

. Before looking at anything else,
verify that *n* is total sample size, not number of classes.

Details: from a Grouped Distribution in Chapter 3.

Caution: If classes are 100–199, 200–299, …, then class midpoints are 150, 250, … (not 149.5, 249.5, …).

Use the procedure for stats of a plain list of numbers or an ungrouped distribution of Numbers with Frequencies, above. Scroll down to the second screen.

Caution: The five-number summary is not meaningful with a grouped distribution.

Enter the numbers in one list. If you have frequencies, enter them in a second list. Use MATH200A Program part 2. If you don’t have the program, see Box-Whisker Plots on TI-83/84.

Details: Box-Whisker Diagrams in Chapter 3.

Caution: The boxplot is not meaningful with a grouped distribution.

*x*’s in L1, *y*’s in L2. Turn off all plots on the [`Y=`

] screen, then
[`2nd`

`Y=`

*makes* `STAT PLOT`

]
[`1`

] [`ENTER`

] [`▼`

] [`ENTER`

]. Specify lists and the mark for
plotting, then [`ZOOM`

] [`9`

].

Details: Step 1. Make the Scatterplot in Chapter 4.

Have *x*’s in L1 and *y*’s in L2. `LinReg(ax+b) L1,L2,Y1`

.

Details: Step 2. Perform the Regression in Chapter 4.

Note: The first time only, you must set up the calculator
before doing `LinReg(ax+b)`

. Here’s how:
[`2nd`

`0`

*makes* `CATALOG`

]
[`x`

], scroll down to ^{-1}`DiagnosticOn`

,
and press [`ENTER`

] twice.

Details: Step 0. Setup in Chapter 4.

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Press [`GRAPH`

].

After doing the
regression, press [`TRACE`

] [`▼`

], enter
the *x* number, and press [`ENTER`

].

Details: Method 1: Trace on the Regression Line Graph in Chapter 4.

Details: *Optional*: Display the Residuals in Chapter 4.

Values in L1, probabilities in L2.
`1-VarStats L1,L2`

. Verify that *n* is exactly 1
and `Sx`

is blank.

Details: Mean and Standard Deviation of a DPD in Chapter 6.

Caution: For geometric and binomial models you can’t use
`1-VarStats`

but must use the formulas.

Probability that the first success comes
**on trial number x** is

`geometpdf(`

`,`

`)`

.
Probability that the first success comes
**within the first x trials** is

`geometcdf(`

`,`

`)`

.Details: Computing Probabilities in Chapter 6.

Use MATH200A Program part 3. If you don’t have the program, then:

- Probability of
**exactly**in*x*successes*n*trials is`binompdf(`

*n*`,`

*p*`,`

*x*`)`

. - Probability of
**0 to**in*b*successes*n*trials is`binomcdf(`

*n*`,`

*p*`,`

*b*`)`

. - Probability of
in*a*to*b*successes*n*trials is`binomcdf(`

*n*`,`

*p*`,`

*b*`)`

`−`

`binomcdf(`

*n*`,`

*p*`,`

*a*`−1`

`)`

.

Details: Computing Probabilities in Chapter 6.

- Have left boundary only?
`normalcdf(`

*LeftBoundary*`,`

`10^99`

`,`

*Mean*`,`

*SD*`)`

- Have right boundary only?
`normalcdf(`

`-10^99`

`,`

*RightBoundary*`,`

*Mean*`,`

*SD*`)`

- Have both boundaries?
`normalcdf(`

*LeftBoundary*`,`

*RightBoundary*`,`

*Mean*`,`

*SD*`)`

For standard ND, either specify *Mean*=0 and
*SD*=1 or just omit them.

Details: From Boundaries, Find Probability in Chapter 7.

- Have area of left tail?
`invNorm(`

*AreaToLeft*`,`

*Mean*`,`

*SD*`)`

- Have area of right tail?
`invNorm(`

`1-`

*AreaToRight*`,`

*Mean*`,`

*SD*`)`

- Have area of middle? Subtract from 1 to get area of two tails,
divide by 2 to get area of one tail. Left boundary is
`invNorm(`

*AreaOfOneTail*`,`

*Mean*`,`

*SD*`)`

and right boundary is`invNorm(`

`1-`

*AreaOfOneTail*`,`

*Mean*`,`

*SD*`)`

For standard ND, either specify *Mean*=0 and
*SD*=1 or just omit them.

Details: From Probability, Find Boundaries in Chapter 7.

Enter the points in a list and use MATH200A Program part 4. If you don’t have the program, see Normality Check on TI-83/84.

Details: Checking Data Sets in Chapter 7.

Probability of getting a sample mean between *LeftBoundary* and
*RightBoundary* is
`normalcdf(`

*LeftBoundary*`,`

*RightBoundary*`,`

*Mean*`,`

*SD*/√*SampleSize*`)`

.

Details: Example 1: Bank Deposits in Chapter 8.

Compute standard error as
`√(`

*p*`*(1-`

*p*`)/`

*n*`)`

[`STO→`

] [`x,T,θ,n`

]. Then probability of getting a sample
proportion between *LeftBoundary* and *RightBoundary* is
`normalcdf(`

*LeftBoundary*`,`

*RightBoundary*`,`

*p*`,`

[`x,T,θ,n`

]`)`

.

Details: Example 5: Swain v. Alabama in Chapter 8.

See Inferential Statistics: Basic Cases.

**17 Nov 2020**: Converted from HTML 4.01 to HTM5, and italicized variable names.- (intervening changes suppressed)
**3 Jan 2015**: New article.

Because this textbook helps you,

please click to donate!Because this textbook helps you,

please donate at

BrownMath.com/donate.

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please donate at

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Updates and new info: https://BrownMath.com/swt/