This page will examine and step through multiple solutions for two sample confidence itnervals using the TI83/84 calculators.
We have a number of different instances to examine:
size  mean  stnd. dev.  
sample 1  42  57.90  3.60 
sample 2  47  63.60  3.40 

We are going to create a confidence interval given by `barx_1  barx_2 + t_(alpha/2)*sqrt((s_1^2)/(n_1) + (s_2^2)/(n_2))` using the smaller of `(n_11)` and `(n_21)` as the degrees of freedom. We use this as the "simple method" for determining the number of degrees of freedom. It is certainly the case that the more complex method, shown later, will give a "higher" degrees of freedom. However, higher degrees of freedom translate into smaller tvalues which in turn yield smaller margins of error which result in narrower confidence intervals. By using the "simple method" our final result will be a broader confidence interval, one that will most certainly cover the more compact one found later. Our first step is to determine the tvalue to use for a 99.5% confidence interval. On a TI84 (with the newer operating system) we have the invT( option in the DIST menu ( ). 

The command that we want is invT(.9975,41) where the .9975 comes from
the fact that we want 99.5% inside the confidence interval, leaving
0.5% outside, and that has to be split, half above and have below. Thus,
there will only be 0.25% to the right. The invT( command returns the
tvalue having the specified area (for us, .9975) for
all values to the left of the point,
and for the given degrees of freedom. The calculator produces
the tvalue: 2.96696131.
For those having a calculator without the invT( option, we do have the INVT program. This will give approximately the same answer. In Figure 2 we start the program, giving the required values. 

Figure 3 shows the rest of the program output. The value produced is 2.966928428. Not quite as good as the better and faster builtin routine, but good enough for our purposes. Note that the program also give us the fact that the value it produced has .9974997809 of the area to its left, not the asked for .9975. 

The variance of the difference of the two means is the sum of the variances of the two means. Since we do not know the variance of the populations we use the variance of the samples (which is why we used the tvalues rather than use zscores above). The desired variance is thus `s_1^2/n_1 + s_2^2/n_2`. This, of course, makes the standard deviation of the difference of the means be given by `sqrt(s_1^2/n_1 + s_2^2/n_2)`. For our problem this becomes `sqrt(3.6^2/42+3.4^2/47`, an expression given in Figure 5. Thus, for this problem the standard deviation of the sample means is .744666956. 

Figure 5 shows the start of running the program CALCVSDF, providing exactly the data from the problem we are doing. 

Figure 6 completes the data entry. 

Figure 7 provides the computed results. Note that the display gives both the variance and the standard deviation, the latter being exactly what we found back in Figure 4. 

The margin of error is the product of the tvalue and the standard deviation of the difference of the means. For this problem we actually type in the two values that we found first in Figure 2 and in Figure 4. [We might note that by reading through the program we can see that the standard deviation value is stored in the variable S. Therefore, we could have written the our expression as 2.966928*S.] 

In Figure 9 we first store the margin of error in X. Then we compute the difference in the means, `x_1x_2`, and store it in A. That means that we can get the lower end of the confidence interval via AX. 

Then, it is easy to get the upper end of the confidence interval from A+X. Combining these we have the confidence interval as (7.909, 3.4906). 

The work done so far has been based, as noted in Figure 1,
on the "simple method" for determining the number of degrees of freedom
that we will use. There is complex formula to derive a larger number of degrees of freedom
which will result in a smaller t=value, thus closing the confidence interval a bit.
This other formula is `(( (s_1^2)/(n_1) + (s_2^2)/(n_2) )^2) / ((((s_1^2)/(n_1))^2)/(n_11)+(((s_2^2)/(n_2))^2)/(n_21))`.
We would not want to type that into the calculator each time
we solve a problem. However, it is not too much to include it in a program.
That is exactly what was done in the program given above.
Lines 18 and 19 compute the numerator and the denominator of the
expression. Lines 20 and 20 display the result. Thus, back in Figure 7,
we were given the additional information that the
degrees of freedom can be computed to be 84.5376476.
In Figure 11 we have used this strange value with the invT( command to get a new tvalue, namely, 2.883070818, As expected this is less than the 2.96696131 we found using 41 degrees of freedom in Figure 2. Note that we not only generate the answer, we have also stored it in the variable T. The computed "degrees of freedom" is "strange" in that it is not a whole number. The TI calculator does not have a problem with this but we sure are not going to find and table in a text that has tvalues for 2.882575285 degrees of freedom. We take a minute here to see just how much the tvalue changes with slight changes in the degrees of freedom. For example, if we round down to 84 degrees of freedom we find a value of 2.883070818. This is not much different from 2.883070818. 

In Figure 12 we look at the same computation with 85 degrees of freedom,
generating 2.8821542185, also not much different from 2.883070818.
Then, returning to use the value we stored into the varaible T in Figure 11, we walk through the same steps that we used in Figures 9 and 10 to find the margin of error, and store it in X. This new margin of error is 2.146558563, again smaller than the value we found using the simple method above. We go on to find the lower end of the confidence interval. 

And we use A+X to get the upper end. The new confidence interval, (7.84656, 3.5534) is indeed narrower than the interval we found in Figure 10. 

To this point we have done this problem by following the
formula for finding the confidence interval for the difference of two means.
Now we will do the same problem but we will let the calculator
do all the work.
In the STAT menu, under the TESTS submenu, we find the 2SampTInt... command. Selecting this command takes us to the input screen shown in Figure 15. 

Figure 15 shows the screen as it was found on a particular calculator. In this case, the input screen is set to input locations of actual Data. We, however, have a problem that gives us the statistics , not the data of the experiment. We use the key to move the blinking highlight over to the Stat option and then press to move to Figure 16. 

Now we have the input screen changed to accept statistic.
We continue by entering the required values:
`barx_1=57.9`, `S_(x_1)=3.6`, `n_1=42`,
`barx_2=63.6`, `S_(x_2)=3.4`, and `n_2=47`.
Then use to move down for the remaining settings. 

Make sure that we specify the given confidence level as
the full 99.5%, entered as .995. Leave the "Pooled" option on No. Highlight the calculate option and press . 

The result, shown in Figure 18, gives the confidence interval (the same as we found in Figures 12 and 13) and the computed degrees of freedom that we found (shown in Figure 7 but discussed in Figure 11). 

One of the values not shown in the output of the 2SampTInt command is the margin of error. However, since the margin of error is just half the width of the confidence interval we could calculate it. Such a calculation is shown in Figure 18a. This should be compared to the 2.146558563 found in Figure 12. The difference is, of course, due to the use of the rounded values for the confidence interval in Figure 18. 

We could retrieve the more complete values for the lower
and upper ends of the confidence interval by moving
to the VARS menu, selecting the Statistics option,
and then moving to the bottom of the TEST submenu.
To move to Figure 18c we press to paste the identifier upper onto the main screen. 

Here we see the longer version of the upper end of the confidence interval
to be 3.553441437.
Figure 18c goes on to form the expression (upperlower)/2 to generate the same value that we found back in Figure 12. Typing the expression (upperlower)/2 takes between 20 and 45 keystrokes, depending on how you maneuver around the calculator screens. Even at 20 this is an excessive process. I(f we need to do this many times then this would be a perfect case for creating a tiny program to just do and display the computation for us. We will do that. 

gets us to Figure 18d. From there we use to move to Figure 18e. 

Here we give the program a name; our name will be MOE. The calculator starts Figure 18e in alphabetic mode, so press to generate MOE Press to move to Figure 18f. 

Here we are ready to write the lines of the program. The only line we want to construct is Disp (upperlower)/2. We use to move to Figure 18g where we can find the Disp command. 

Highlight the Disp command and press . 

We have pasted the Disp command into the program. 

Then we go through the 20 to 45 keystrokes to generate the rest of the
command, ending with a .
The program is complete. Press to get out of the program editor and return to the main screen. 

On the main screen we have recalled the program and run it. The output
is what we should expect.
One small warning is appropriate here. The program that we just wrote uses values that have been stored into upper and lower. You should use this program immediately after you have constructed a confidence interval. The program will simply use the values that it finds in upper and lower. The program will not recall some much earlier computation of these values. 
Based upon our samples we want to construct 99.0% confidence intervals for the difference of the two means.

In this case we are given the actual data.
One approach, and the first one used here, is to just
get the statistics that we need and then follow the same path
that we took in the first example.
To that end, we generate the data. Note that the generation program, GNRND4, always generates this data in L_{1}. Therefore, in order to keep the calculator looking like the problem that we have been given, we will actually generate the second set of data first. Then, we will store that data into L_{2} before we generate the first set of data. As a result, when we finally get to Figure 33 we will have the first set of data in L_{1} and the second set in L_{2}. 

Once the data has been generated, we can do a 1Var Stats on it. 

The three values we need are given here, the mean, the standard deviation, and
the sample size. Remember that these are for the second set of data. Therefore we have
`x_2=56.56818182`, `S_(x_2)=5.263952566` and `n_2=44`.
Note that at the bottom of Figure 21 we have copied L_{1} to L_{2}. 

Now generate the first data set. [This will overwrite the data that was in L_{1}.] 

Get the statistics for the first data set. 

Therefore we have `x_1=55.4170218`, `S_(x_2)=5.050928419` and `n_2=47`. 

Recalling that we can compute the combined variance and standard deviation, not to mention the complex number of degrees of freedom, by using the program shown above (between Figures 4 and 5), we run that program. Figure 25 shows the start of the program and the first values that we have entered. 

Figure 26 shows the rest of the data input for the program. 

Figrue 27 gives us the combined standard deviation, namely, 1.082848272 and the complex number of degrees of freedom, namely, 87.97533872. 

Again, following our original path in example 1 above,
we find the required tvalue by using invT,
giving it the .995 value (because we split the 1% between the top and the bottom)
and the complex number of degrees of freedom.
That tvalue is computed to be 2.632874324.
We then use that answer times the combined standard deviation to get the margin of error which we store in X as 2.851003412. Finally, in Figure 28, we compute the difference of the means and store that in A. 

Having done all that, we can get the lower and upper ends of the confidence interval via AX and A+X, respectively. 

Alternatively, as we did for the first example, we could just move to the STAT menu, TEST submenu, and choose the 2SampTInt... option. This takes us back to our input screen, shown in Figure 30 after we have entered the values for this problem. 

Figure 31 shows the rest of the data input,
including setting the confidence level at .99.
Once the values have been entered we highlight the Calculate option and press . 

The results are given in Figure 32.
At this point we have simply followed the path we laid out in the first example above. However, the TI calculators give us another option. 

We return to the 2SampTInt screen and change the option back to DATA.
Now the calculator is looking for the raw data. We tell the calculator that the first list of data is in L_{1} and the second list is in L_{2}. These lists contain the data values. There are no associated frequency lists. Therefore, we leave the Freq1: and Freq2: settings as 1. Set the confidence level at .99. 

Move to the Calculate option. Press to have the calculator do all the work. 

The results are shown in Figure 35. 
We are given the following statistics for two independent random samples of a large population:
Sample Size 
Number of desired Items  
Sample 1  75  43 
Sample 2  94  62 

The formula that we want is that the confidence interval is given by `hatp_1  hatp_2 + (z_(alpha/2))* sqrt(( hatp_1*(1hatp_1) )/(n_1) + ( hatp_2*(1hatp_2) )/(n_2) )` Therefore, we compute `hatp_1` as 43/75 and store it in A. Then we compute `hatp_2` as 62/94 and store it in B. At that point it is a small taks to compute the variance of the difference of the proportions as A*(1A)/75+B*(1B)/94 and store that in C. 

Then, taking the square root of C we get the standard error of the difference of the proportions and store that in D. 

We remember that we are using the normal distribution for the difference of the proportions. Therefore we need to get our zscore. From the DIST menu we select invNorm(. 

We complete the command by asking for the .975 zscore.
The result is 1.959963986.
Multiply that answer by the standard error which we stored in D and we get the margin of error, .1473276438, which we store in X. Find the difference of the proportions and store that in F. Form the command FX to find the lower end of the confidence interval. 

The command F+X will give us the upper end of the confidence interval. 

Now that we have walked through the computation,
we return to let the calculator do all the work.
In the STAT menu, the TESTS submenu, we find the 2PropZInt... command. We select that command and press . 

This brings up the data input screen. We give that screen the values from the problem, including the confidence level. Then highlight the Calculate option and press . 

The calculator does all the work and displays both the confidence interval and the individual proportions. 

The first thing to do is to generate the data. Here we generate Data Set 3. 

Notice that the program reports that it will generate 88 values.
Our particular concern in this problem is the proportion of 3's in those 88 values. Once the data is generated we need a way to find the number of 3's in the list. 

One of the most straight forward methods is to just create a histogram of the values. In Figure 47 we have moved to the Plot1 screen and made sure that the plot is ON, that we will be doing a histogram, and that the data for the histogram will come from L_{1}. 

We then move to the ZOOM menu and use the ZoomStat option. 

Once the histogram is displayed we move to use the TRACE feature and then move the highlight onto what we know is the bar for the 3's. From Figure 48, at the bottom, we see that there are 30 such values in our data. 

An alternative approach is to use the COLLATE2
program. Please note that the version shown here is version 2.1.
This version includes a prompt to let the program complete without displaying all of the
computed statistics.
We tell the program that the data is in L_{1}. 

The program then tells us that there are 88 items that were found. This version then asks if we want all of the statistics. Our response is 0 to indicate "no". 

After the program has completed we move to the Stat Editor.
Here we find that ITEM 3 has been found 30 times.
Either way approach the problem we now know that the first list has 30 items that are 3 out of a total of 88 items. 

Now we can generate the other set of data. 

Again, we need to find the number of 3's in this list. Figure 53 uses the histogram method to do this. We can see that there are 23 instances of 3 in this data set. 

Of course, we could go back through all the steps that we took in Figures
36 through 40 to compute the confidence interval.
However, using the 2PropZInt... command will take less time.
Here we have used that command to open the input form. Then we go ahead and supply all of the values, including the confidence level. When all the values are in place we highlight the Calculate option and press . 

The result is the confidence interval given in Figure 55.
Some note should be taken that by using this method we do not get to see any of the intermediary values such as the standard deviation of the proportion difference and the margin of error. We can take care of the latter by running the program, MOE, that we saw in Figures 18d through 18j. 

This gives us the margin of error based upon the values of lower
and higher that we just computed.
As for the other values, those computations can easily be placed into a program. Let us look at the listing of the program CALCPSD given below. 

Figure 56 gives the start of a run of the program. Note that we supply the sample size as 88 and the proportion (which know in decimal format from Figure 55) we merely enter as 30/88, letting the calculator recompute that value. 

Figure 57 completes the data entry for the program. 

Figure 58 shows the output. 

Figure 59 merely demonstrates that we could have done the calculations by hand. In this case we use the confidence interval endpoints to recompute the margin of error. 
We want to construct a 95.0% confidence interval for the difference between the paired measures.

As usual, we can start by generating the two lists of paired values. Note that for paired values the GNRND4 program produces lists in L_{1} and L_{2}. 

The whole idea of the paired values is to derive the difference within each pair.
We do this and store the result in L_{3}.
Then we can look at the 1Var Stats for L_{3}. 

This particular problem then resolves to finding the 95% confidence interval for the mean of the difference. That will be `barx_d + t_(alpha/2)(s_d)/sqrt(n)`. We use the values given here to do those computations. 

To find `s_d/sqrt(n)`, rather than retype the value for `s_d` we move to the VARS, Statistics menu and select `S_x`. 

Using that we can compute `S_x/sqrt(25)` and store it in S.
Then find the required Tvalue and store it in T. Next, find the product S*T, the margin of error, and store it in X. 

We return to the VARS, Statistics menu to retrieve
`barx`. Finally, we form `barx  X` and `barx + X` to get the limits on the confidence interval. 

Of course, since this is just getting the confidence interval for the difference of the paired values, we could have let the calculator do all the work. We just need to move to the STAT menu, the TESTS submenu, and move down to the TInterval option. 

This brings up the data input screen. We actually have the data stored in L_{3}. Making sure that we tell the calculator that the data is in L_{3} and that we want a .95 confidence level, we highlight the Calculate option and press . 

The calcualtor does all the work and produces the confidence interval. 
©Roger M. Palay
Saline, MI 48176
November, 2012