How to Test Goodness of Fit on TI83/84
Copyright © 2012–2024 by Stan Brown, BrownMath.com
Copyright © 2012–2024 by Stan Brown, BrownMath.com
Summary: You can use your TI83/84 to calculate a goodnessoffit test, also known as a multinomial experiment.
Alternative: MATH200A Program part 6 does the calculations and graphs the χ² curve automatically for you. This is significantly easier than using native TI83/84 commands, so I recommend you get the program if possible.
See also: How to Test Goodness of Fit on TI89
Model ratio  Observed  

Greeneyed winged  9  120 
Greeneyed wingless  3  49 
Redeyed winged  3  36 
Redeyed wingless  1  12 
Total  16  217 
An example in Dabes & Janik [full citation at https://BrownMath.com/swt/sources.htm#so_Dabes1999] had to do with the offspring of hybrid fruit flies; see figures at right. The null hypothesis H_{0} is that the 9:3:3:1 model is good, and the alternative H_{1} is that the model is bad. Use α=0.05.
The test statistic χ² is a standardized measure of how far the observations differ from the model. You’ll compute that first, by using some list operations, and then you’ll use χ²cdf to compute the pvalue.
The model goes in L1. It can be percentages, ratios, or whole numbers. Enter the model numbers for each category, but don’t enter the total even if you have it. 
Press [STAT ] [ENTER ]. Cursor to L1, the actual column head
and not the first number under L1, and press
[CLEAR ] [ENTER ]. Enter the numbers. 
The observed counts go in L2. Even if the model is in percentages, the observed numbers must be the actual counts. Don’t enter the total.  Cursor to L2, the actual column head and not the first number
under L2, and press [CLEAR ] [ENTER ]. Enter the
numbers. 
Next, fill L3 with the expected counts. Each
expected count equals the corresponding percent in the model,
times the sample size. Symbolically,
L3 = L1/sum(L1)*sum(L2) (There’s no need to clear L3 before entering the formula.) 
Cursor to the L3 column head and press [2nd 1 makes L1 ]
[÷ ].
Press [ 2nd STAT makes LIST ] [◄ ] [5 ] to paste
sum(. Continue with [2nd 1 makes L1 ] [) ] [* ].
Again press [ 2nd STAT makes LIST ] [◄ ] [5 ] to paste
sum(. Finish with [2nd 2 makes L2 ] [) ] [ENTER ].

L3 now contains the expected counts (expected for this sample size if H_{0} is true and the model is correct). Before you continue, verify that the requirements are met for a GoF hypothesis test:
The requirements are met. If you have a TI84 Plus or Silver, skip down to Computing Goodness of Fit (TI84s).
Next, fill L4 with the χ² contributions. These are
(observed−expected) squared, the divided by expected,
(OE)²/E. Symbolically, L4 = (L2−L3)²/L3 (There’s no need to clear L4 before entering the formula.) 
Cursor to the L4 column head and press [( ]
[2nd 2 makes L2 ] [− ] [2nd 3 makes L3 ]
[) ] [x² ] [÷ ] [2nd 3 makes L3 ]
[ENTER ]. 
After you press [ENTER ], the screen will look like
this. 
L4 now contains the χ² contributions.
Get back to the home screen for the remaining calculations.  Press [2nd MODE makes QUIT ]. 
Sum up the χ² contributions that you computed in L4. This is your χ² test statistic. 
Press [2nd STAT makes LIST ] [◄ ] [5 ] to paste
sum(. Finish with [2nd 4 makes L4 ]
[) ] [ENTER ]. 
The pvalue is the probability of getting this χ² statistic or greater. You have to specify degrees of freedom, which is (number of categories) minus 1. 
Press [2nd VARS makes DISTR ]. Scroll down to χ²cdf
(not χ²pdf) and press [ENTER ] then
[2nd () makes ANS ], which will use the previous answer. (This is
faster and more accurate than retyping the number yourself.)
Continue with [ , ] [1 ] [0 ] [^ ] [9 ] [9 ] [, ] and the number of degrees of
freedom, then finish with [) ] [ENTER ]. 
The χ² test statistic is 2.45 and the pvalue is 0.4838. p>α; fail to reject H_{0}.
TI84s can compute the χ² contributions and pvalue for you, although you still have to compute expected counts yourself.
Select the χ² GoodnessofFit Test.  Press [STAT ] [◄ ] and scroll up to
χ²GOFTest . 
Enter L2 for Observed and L3 for Expected. For degrees of freedom df enter number of categories minus 1. In this problem, that’s 4−1 = 3.  
Select Calculate and read off the results: the
χ² test statistic is 2.45 and the pvalue is
0.4838.
p > α; fail to reject H_{0}. 
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