MATH SPOKEN HERE! TI83 STAT and DISTRIBUTION Menus

$Class Table$

TI83-84 STATISTICS and DISTRIBUTION Menus

Table of Contents for CALCULATORing
Edit Calculate Tests
Distribution Diagnostic On Distribution Draw

EDIT
1:Edit...
2:SortA(
3:SortD(
4:ClrList
5:SetUpEditor

CALCULATE
1:1-Var Stats
2:2-Var Stats
3:Med-Med
4:LinReg(ax+b)
5:QuadReg
6:CubicReg
7:QuadReg
8:LinReg(a+bx)
9:LnReg
A:PwrReg
B:Logistic
C:SinReg
D:Manual-Fit

Click on image to enlarge.

TESTS On 1 page of notes.

For using data in calculator.
Here for a 2-tail test.

For using data not in calculator.
Here for a 1-tail test.

For the test on the right, the following results are returned for [CALCULATE] and [DRAW].

The user must know the statistics to complete the problem. The calculator will just do the computation.

See problem 2, A normally distributed standardized math test is known to have a mean of 70% and a standard deviation of 12%. The test is given to 36 freshmen and the class average is 75%. With 99% confidence, complete a hypothesis test to see if the population average is greater than 70%.

1:Z-Test...
significance test for a mean, sigma known
2:T-Test...
significance test for a mean, sigma unknown
3:2-SampleZ-Test...
4:2-SampleT-Test...
5:1-PropZ-Test...
significance test for a proportion
6:2-PropZ-Test...
significance test for the difference of two proportions
7:Z-Interval...
confidence interval for a mean,known
8:TInterval...
confidence interval for a mean,unknown
9:2-SampZInt...
significance test for the difference of two means, sigma1 and sigma2 known
0:2-SampTInt...
significance test for the difference of two means, sigma1 and sigma2 unknown
A:1-ProfZInt...
confidence interval for a proportion
B:2-ProfZInt...
confidence interval for the difference of two proportions
C:

²-Test...

² test of homogeneity or independence (not goodness-of-fit)
D:

²GOF-Test...

²-Test
E:2-SampFTest...
F:LinRegTTest...
significance test for a slope
G:LinRegTInt...
H:ANOVA(

DISTRIBUTION
1:normalpdf(x[,mean,standard deviation])
normal probability density function
2:normalcdf(lower bound,upper bound[,mean, standard deviation])
normal cumulative distribution function (area under the curve)
3:invNorm(area[,mean,standard deviation])
normal distribution’s x-value or z-score corresponding to a known area
4:invT(area, degrees of freedom inverse cumulative student-t probability
5:tpdf(x, degrees of freedom) probability density function
t probability density function
6:tcdf(lower bound, upperbound, degrees of freedom)
t cumulative distribution function (area under the curve)
7:

²pdf(x,degrees of freedom)

² probability density function
8:

²cdf(lower bound,upperbound,degrees of freedom)

²cumulative distribution function (area under the curve)
9:Fpdf(x,numerator df,denominator degrees of f)
0:Fcdf(lowerbound,upperbound,numerator df,denominator df)
F distribution probability between upper and lower bound
A:binompdf(number of trials,probability[,successes])
binomial probability
B:binomcdf(number of trials,probability[,successes])
binomial cumulative probability
C:poissonpdf(mean,x) probability at x for the Poisson distribution
D:poissoncdf(mean,x) cumulative Poisson probability at x
E:geometpdf(probability,trial of first success)
geometric probability
F:geometcdf(probability,trial of first success) geometric cumulative probability

DISTRIBUTION DRAW
1:ShadeNorm(lowerbound,upperbound[,m,s])
shades normal curve between lowerbound and upperbound.
2:Shade_t(lowerbound,upperbound,df)
shades Student-t distribution
3:Shade

²(lowerbound,upperbound,df)
shades

² between lowerbound and upperbound.
4:ShadeF(lowerbound,upperbound,numer df,denom df)
shades F curve between lowerbound and upperbound.

DIAGNOSTIC ON
In CATALOG toggle DiagnosticOn or DiagnosticOff
to display
r (correlation coefficient) and
r^2 (coefficient of determination)
R^2 (coefficient of determination).



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