Category Archives: TI-84 Plus Pocket SE

LU decomposition in TI-84

As an extension to a previous entry on doing LU decomposition in Nspire and R, the TI-84 is covered here. There is no built-in function like in the Nspire for this, but there are many programs available online, with most of them employing a simple Doolittle algorithm without pivoting.

The non-pivoting program described here for the TI-84 series is with a twist. No separate L and U matrix variables are used and the calculations are done in place with the original input matrix A. The end result of both L and U are also stored in this input matrix. This is made possible by the property of the L and U matrices in this decomposition are triangular. Therefore, at the little price of some mental interpretation of the program output, this program will take up less memory and run a little faster than most simple LU decomposition programs online for the same class of calculator. From a simple benchmark with a 5 x 5 matrix, this program took 2 seconds while another standard program took 2.7 seconds.

The input matrix A.
lu84-1

Results are stored in the same matrix.
lu84-2

L, U, and verification.
lu84-3

Optimizing optimization code in TI-84 program

As shown in a previous installment on running the Nelder-Mead optimization of the Rosenbrock function in TI-84, its hardware is not up to the challenge for problems of this kind. It is however an interesting case to learn from this program that took a considerable amount of time (in minutes), when most TI-84 programs complete in seconds, to grasp the technique in writing optimized code.

The problem used in the setting is the Rosenbrock function:
nelder-mead-84-code-optimization3

Two techniques were used to try to optimize the code. The first one is to in-line the function (in this case the Rosenbrock function). In the original version this function is stored in a string value to be evaluated whenever necessary by using the TI-84 expr() function. The idea from common in-lining is borrowed to see if it works in TI-84. As shown in line 44 below, the Rosenbrock function replaced the original expr() function call.

nelder-mead-84-code-optimization2b

Another technique is to remove unnecessary comments. For most modern languages this is never considered a problem but for simple calculators it is speculated valuable processing time may be spent on processing comments that contribute nothing to the algorithm.
nelder-mead-84-code-optimization1

Both programs returned the same results. To compare the gain in speed, the same set of initial parameters and tolerance were used in both the original and code-optimized program. Surprisingly, the inline method does not yield any gain while removing all those “:” that were used for indentation does improve the speed. The average of the original program is 183 seconds while the program with cleaned code is only 174 seconds, which is around 95% of the run time of the original program.
nelder-mead-84-code-optimization4

 

 

 

Coefficient of determination for Multiple linear regression in TI-84 Plus

After determining the parameters of multiple linear regression in TI-84 (which do not have any direct built-in function support of this calculation), the coefficient of determination can also be easily calculated using the rich set of list functions supported by TI-84. Following the previous example, the dependent variable is in Sales list, the other two independent variables are Size and Dist lists.

The Yhat list is to be prepared first. This lists store the predicted values using the regression parameters determined in the previous installment.
mreg84rsq3amreg84rsq3

Next, the mean of Y and Yhat are calculated and stored to a handy list S.

mreg84rsq2

Furthermore, three lists SYY, SYhYh, SYYh are calculated respectively.

mreg84rsq4mreg84rsq3e

mreg84rsq6mreg84rsq5

mreg84rsq8mreg84rsq7

The result is obtained by the formula below.
mreg84rsq9

Graphical visualization of data distribution in TI-84 and R

For visualizing data distribution, the TI-84 Stat plot can provide some insights. Using the same data set as in the previous installment on Shapiro-Wilk test, TI-84 Stat plot is a quick and convenient tool.

shapiro84-graphplot2 shapiro84-graphplot1

In R, the command qqnorm() will show the following plot for the same data.

shapiro84-graphplot3

P value of Shapiro-Wilk test on TI-84

In previous installment, the Shapiro-Wilk test is performed step by step on the TI-84.

To calculate the p-value from this W statistic obtained, the following steps can also be done on the TI-84 using some standard statistics function. Note that the approximation below are for the case 4 ≤ n ≤ 11.

The mean and standard deviation are derived using the below equations.
shapiro84-pvalue1

The W statistic is assigned from the correlation calculation results, and another variable V is calculated for the transformed W.
shapiro84-pvalue2

Finally, the standardized Z statistic and p-value is calculated using the mean, standard deviation, and the transformed W value.
shapiro84-pvalue3 shapiro84-pvalue4

Performing Shapiro-Wilk test on TI-84

On TI-84, although the Shapiro-Wilk test is not available as built-in function, it is possible to calculate the W statistics by using the calculator’s rich set of list and statistics function. It is not trivial, but doable.

First set up a list of sample values. Using the example from the original Shapiro-Wilk paper, the samples are input into a list called W, while the N variable denotes the dimension and U stores inverse of its square root. The data sample in W must be sorted in ascending order.

shapiro84-2 shapiro84-1

Next is to generate the M list based on W. This list is to calculate the inverse normal distribution based on the index value and the dimension of W list. Doing this in TI-84 is easy with the help of the seq() function. The complete expression is:
seq(invNorm( (I-0.375) / (N+0.25), 0, 1), I, 1, N, 1)

shapiro84-3 shapiro84-4

Now that the List M is ready, it is time to derive the sum of square of it and we store it into variable m (not the list M).
shapiro84-5

Since the approximation algorithm is used instead of look up table as proposed in the original paper, the next step is to prepare List A. This list is arranged in such a way that the first two and last two elements have different calculation than all the others. The last two values (N-1, N) of List A are prepared in the Y5 and Y6 equations. These are approximating equations. The first two elements are the negation of these two in a particular order.
shapiro84-6 shapiro84-7

One more variable, ε, has to be calculated at this point before we generate List A.
(m - 2*M(N)² - 2*M(N-1)²) / (1 - 2*Y6² - 2*Y5²)
shapiro84-8

Next is to generate the List A. As explained, four elements namely the first, second, N-1, and N elements of this list is calculated unlike all other elements. The screen below is for all except that four special elements.
shapiro84-9

The remaining steps for the four elements of this List A are then carried out as below.
shapiro84-10

Finally, all three lists are ready for calculation.
shapiro84-11

To calculate the W statistics, use the Linear Regression function in the TI-84 as below for W and A. The correlation coefficient r² is the W statistics as shown in R. See the next installment for the Z statistic and P value calculations from this W statistic, also on the TI-84.

shapiro84-14 shapiro84-18