Download Summary of calculator commands

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TI 84 Calculator Commands
AP Statistics
Command & Parameters
Stat/Calc
2nd Distr.
2nd Distr
Stat/Calc #4
1-Var Stats L1
Normalcdf(left,right,Mean,SD)
InvNorm(Area below z-score)
LinReg (ax+b) Xlist, Ylist
Stat Plot/Plot 1
Scatterplot(xlist,Resid)
Math/Prb
Stat/Calc
2nd Distr
RandInt(left,right,# of rand. Digits)
1-Var Stats L1,L2
L1 is values of Variables (outcomes)
L2 is Probability of each outcome
Geompdf(p,x)
p is probability
x is # of the first success
Geomcdf(p,x)
p is probability
x is # of the first success
Binompdf(n, p, x)
n is # of trials
p is probability of success
x is desired # of successes
Binomcdf(n, p, x)
n is # of trials
p is probability of success
x is desired # of successes
1-Prop Z-Interval
Enter successes, sample size, & confidence
level
1-Prop Z-Test
P0 is population proportion
X is observed # of successes in sample
n is sample size
2-Prop Z-Test
X1 is observed successes in sample 1 (Whole #)
n1 is Sample 1 size
x2 is Observed successes in Sample 2 (Whole #)
n2 is sample 2 size
2-Prop Z-Interval
X1 is observed successes in sample 1 (Whole #)
n1 is Sample 1 size
x2 is Observed successes in Sample 2 (Whole #)
n2 is sample 2 size
Confidence Level
tcdf(left,right,df)
2nd Distr
Invt(Area below t-score,df)
Stat/Test
T-interval
Data L1
Stats x is sample mean
Sx is Sample Standard Deviation
N is sample size
2nd Distr
2nd Distr
2nd Distr
2nd Distr
Stat/Test
Stat/Test
Stat/Test
Stat/Test
Use
Gives Summary stats for Mean/SD, Median/IQR, Max, Min, etc…
Area under curve between 2 bounds
Gives the z-score based on the area to the
Gives Regression equation, r & r2 between 2 lists. (Diagnostic
must be turned on in Catalog)
Scatterplot of the Residuals to check appropriateness of Linear
Regression Model
Generates Random Numbers
Calculates Mean and Standard Deviation of a Random Variable
Probability that a specific outcome is the first success
(Probability that x is the first success)
Probability that the first success happens on or before the xth
trial
Probability of getting exactly x successes in n trials
Probability of getting x or fewer successes in n trials
Will calculate the confidence interval
Will run the 1 proportion z-test and give you z-score, P-value and
other statistics
Test for 2 proportions will give you summary statistics
between the 2 samples
Test for 2 proportions will give you the confidence interval for the
difference between the 2 samples
Gives the area under a t-model curve between the specific
bounds for the degrees of freedom
Gives the critical t-score when entering the area below the tscore and the degrees of freedom
Finds the T-interval for data(list) or statistics when given 1 sample
Stat/Test
Stat/Test
Stat/Test
Stat/Test
Stat/Test
Stat/Test
Stat/Test
Stat/Test
T-Test
U0 is population mean
2-Samp T Int
Data: Put the samples in L1 & L2
Stats: xx & x2 is sample means
Sx1 & Sx2 is Sample Standard Deviations
n1 & n2 is sample size
Hypothesis Test for 1 sample means. Gives p-value, t-score, etc…
2-Samp T Test
Data: Put the samples in L1 & L2
Stats: xx & x2 is sample means
Sx1 & Sx2 is Sample Standard Deviations
n1 & n2 is sample size
T-interval
Data L1
Stats x is sample mean
Sx is Sample Standard Deviation
N is sample size
T-Test
U0 is population mean
Test the difference between two independent samples for the
mean and provides test statistics. Gives p-value, t-score, etc…
DO NOT POOL THE SAMPLES
X2 GOF Test
L1 is observed counts (Whole numbers)
L2 is expected counts (May be decimals)
X2 Test
[A]: Matrix A is Observed Counts
Run the test and look at [B] to see expected
counts
LinReg T-Test or LinReg T-interval
L1 is X list
L2 is Y list
Run the 2 tail test with ≠ 0 or
the interval with the confidence level
Find the confidence interval for the 2 difference between two
independent samples for the mean.
DO NOT POOL THE SAMPLES
Use a one sample interval for matched pairs (2 samples that are
not independent). You find the difference between the 2 samples
and this difference is your one list to test.
Use a one sample interval for matched pairs (2 samples that are
not independent). You find the difference between the 2 samples
and this difference is your one list to test. Gives p-value, t-score,
etc…
Goodnes of fit test for categorical variables. To check if a
distribution is the same as what you would expect (Is a sample
the same as the population?)
Categorical Data for homogeneitity (are 2 samples the same?) or
independence (are 2 variables associated?)
T-Test to find out if 2 lists are associated through linear
regression
Find the Confidence Interval for a level of confidence and degrees
of freedom