Hypothesis test for normality on the TI-84?

  • Thread starter moonman239
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In summary, the conversation is about running a hypothesis test to check for normal distribution without looking at a plot. The person has tried to build a program using normal distribution functions, but is encountering errors. They share a resource for help and discuss the code they are using, including a possible issue with the placement of the normalcdf function. They also mention creating another macro for cumulative frequency and encountering a problem with different p-values.
  • #1
moonman239
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I'm trying to run a hypothesis test to see if a data set follows a normal distribution. I don't want to look at a plot. Is there any way? I tried to build a program that uses the built-in normal distribution functions to calculate how far the points on a normality plot would fall from forming a straight line, but I get errors.

Any ideas?
 
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  • #3
Here is the code I am using. Instead of filling List 3 with the normal CDF values, it fills List 3 with a bunch of 0's.

Code:
SortA(L1)
N->dim(L1)
For(I,1,N)
(I/N)->L2(I)
normalcdf(0,L1(I),mean(L1),stdDev(L1))->L3(I)
End
 
  • #4
moonman239 said:
Here is the code I am using. Instead of filling List 3 with the normal CDF values, it fills List 3 with a bunch of 0's.

Code:
SortA(L1)
N->dim(L1)
For(I,1,N)
(I/N)->L2(I)
normalcdf(0,L1(I),mean(L1),stdDev(L1))->L3(I)
End

Shouldnt the normalcdf be outside loop?
 
  • #5
jedishrfu said:
Shouldnt the normalcdf be outside loop?
I don't think normalcdf will take lists for X values. But I will try that as soon as I can.
As it happens, I created another macro that just calculates the cumulative frequency. That one does work. I know how to have my normalcdf macro execute my cumulative frequency macro. For the reference, I just press "PRGM," in the EXEC highlight that macro and press "ENTER," that big button on the bottom-right.
 
  • #6
moonman239 said:
Here is the code I am using. Instead of filling List 3 with the normal CDF values, it fills List 3 with a bunch of 0's.

Code:
SortA(L1)
N->dim(L1)
For(I,1,N)
(I/N)->L2(I)
normalcdf(0,L1(I),mean(L1),stdDev(L1))->L3(I)
End

Update: I changed the code.

Now it's this:

Code:
prgmCUMFREQ
(L1-mean(L1))/stdDev(L1)->L3
For (I,1,dim(L1))
normalcdf(-1e99,L3(I),0,1)->L4(I)
End

Now it won't give me those zeroes.

But now I have another problem. When I fill in data from my statistics textbook, I get a different p-value than what my statistics textbook reads. I am reasonably certain I did not mistype any data.

UPDATE: Here's the code for my cumulative frequency macro:

Code:
PROGRAM:CUMFREQ
SortA(L1)
For(I,1,dim(L1))
I/dim(L1)->L2(I)
End
 

1. How do I conduct a hypothesis test for normality on the TI-84 calculator?

To conduct a hypothesis test for normality on the TI-84 calculator, follow these steps:

  1. Enter your data into a list on the calculator.
  2. Press "2nd" and "STATPLOT" to access the "STATPLOT" menu.
  3. Choose an empty plot and select "Normal Probability Plot" as the plot type.
  4. Press "ZOOM" and then select "9: ZoomStat" to view the normal probability plot.
  5. If the points on the plot roughly follow a straight line, the data is likely normally distributed. If the points do not follow a straight line, the data may not be normally distributed.

2. What is the null hypothesis in a hypothesis test for normality?

The null hypothesis in a hypothesis test for normality is that the data is normally distributed. This means that the data follows a bell-shaped curve and that the mean, median, and mode are all equal.

3. How do I interpret the results of a hypothesis test for normality on the TI-84?

If the p-value is greater than the chosen significance level (usually 0.05), we fail to reject the null hypothesis and conclude that the data is normally distributed. If the p-value is less than the significance level, we reject the null hypothesis and conclude that the data is not normally distributed.

4. Can the TI-84 calculator perform a hypothesis test for normality on a large dataset?

Yes, the TI-84 calculator can perform a hypothesis test for normality on a large dataset. However, it may take longer to input and analyze the data compared to a smaller dataset.

5. What is the purpose of conducting a hypothesis test for normality?

The purpose of conducting a hypothesis test for normality is to determine if a dataset follows a normal distribution. This information is important because many statistical methods and tests assume that the data is normally distributed. If the data is not normally distributed, these methods may not be appropriate to use, and alternative methods may need to be considered.

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