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    To see the regression graph:

  1. Access the equation menu. The regression equation will be put into Y1.

    Y= button

  2. Access the vars menu and navigate to <5: Statistics>

    vars button , five button

  3. Navigate to <EQ> .
  4. <1: RegEQ> contains the regression equation which will be entered in Y1.

    enter button

  5. Press graph button . The regression line will be superimposed over scatter plot.

    To see the residuals and use them to calculate the critical point for an outlier:

  1. Access the list. RESID will be an item on the menu. Navigate to it.

    2nd button , [LIST] , <RESID>

  2. Confirm twice to view the list of residuals. Use the arrows to select them.

    enter button , enter button

  3. The critical point for an outlier is: 1.9 V SSE n 2 where:
    • n = number of pairs of data
    • SSE = sum of the squared errors
    • residual 2
  4. Store the residuals in [L3] .

    store button , 2nd button , [L3] , enter button

  5. Calculate the (residual) 2 n 2 . Note that n 2 8

    second button , [L3] , x-squared button , divide button , eight button

  6. Store this value in [L4] .

    store button , 2nd button , [L4] , enter button

  7. Calculate the critical value using the equation above.

    one button , decimal button , nine button , multiply button , 2nd button , [V] , 2nd button , [LIST] right button , right button , five button , 2nd button , [L4] , right parenthesis button , right parenthesis button , enter button

  8. Verify that the calculator displays: 7.642669563. This is the critical value.
  9. Compare the absolute value of each residual value in [L3] to 7.64 . If the absolute value is greater than 7.64, then the (x, y) corresponding point is an outlier. In this case, none of the points is an outlier.

To obtain estimates of y for various x-values:

There are various ways to determine estimates for "y". One way is to substitute values for "x" in the equation. Another way is to use the trace button on the graph of the regression line.

Ti-83, 83+, 84 instructions for distributions and tests


Access DISTR (for "Distributions").

For technical assistance, visit the Texas Instruments website at (External Link) and enter your calculator model into the "search" box.

    Binomial distribution

  • binompdf(n,p,x) corresponds to P(X = x)
  • binomcdf(n,p,x) corresponds to P(X ≤ x)
  • To see a list of all probabilities for x: 0, 1, . . . , n, leave off the " x " parameter.

    Poisson distribution

  • poissonpdf(λ,x) corresponds to P(X = x)
  • poissoncdf(λ,x) corresponds to P(X ≤ x)

    Continuous distributions (general)

  • uses the value -1EE99 for left bound
  • uses the value 1EE99 for right bound

    Normal distribution

  • normalpdf(x,μ,σ) yields a probability density function value (only useful to plot the normal curve, in which case " x " is the variable)
  • normalcdf(left bound, right bound, μ,σ) corresponds to P(left bound<X<right bound)
  • normalcdf(left bound, right bound) corresponds to P(left bound<Z<right bound) - standard normal
  • invNorm(p,μ,σ) yields the critical value, k: P(X<k) = p
  • invNorm(p) yields the critical value, k: P(Z<k) = p for the standard normal

    Student-t distribution

  • tpdf(x,df) yields the probability density function value (only useful to plot the student-t curve, in which case " x " is the variable)
  • tcdf(left bound, right bound, df) corresponds to P(left bound<t<right bound)

    Chi-square distribution

  • Χ 2 pdf(x,df) yields the probability density function value (only useful to plot the chi 2 curve, in which case " x " is the variable)
  • Χ 2 cdf(left bound, right bound, df) corresponds to P(left bound<Χ 2 <right bound)

    F distribution

  • Fpdf(x,dfnum,dfdenom) yields the probability density function value (only useful to plot the F curve, in which case " x " is the variable)
  • Fcdf(left bound,right bound,dfnum,dfdenom) corresponds to P(left bound<F<right bound)

Tests and confidence intervals

Access STAT and TESTS .

For the Confidence Intervals and Hypothesis Tests, you may enter the data into the appropriate lists and press DATA to have the calculator find the sample means and standard deviations. Or, you may enter the sample means and sample standard deviations directly by pressing STAT once in the appropriate tests.

    Confidence intervals

  • ZInterval is the confidence interval for mean when σ is known
  • TInterval is the confidence interval for mean when σ is unknown; s estimates σ.
  • 1-PropZInt is the confidence interval for proportion
The confidence levels should be given as percents (ex. enter " 95 " or " .95 " for a 95% confidence level).

    Hypothesis tests

  • Z-Test is the hypothesis test for single mean when σ is known
  • T-Test is the hypothesis test for single mean when σ is unknown; s estimates σ.
  • 2-SampZTest is the hypothesis test for 2 independent means when both σ's are known
  • 2-SampTTest is the hypothesis test for 2 independent means when both σ's are unknown
  • 1-PropZTest is the hypothesis test for single proportion.
  • 2-PropZTest is the hypothesis test for 2 proportions.
  • Χ 2 -Test is the hypothesis test for independence.
  • Χ 2 GOF-Test is the hypothesis test for goodness-of-fit (TI-84+ only).
  • LinRegTTEST is the hypothesis test for Linear Regression (TI-84+ only).
Input the null hypothesis value in the row below " Inpt ." For a test of a single mean, " μ∅ " represents the null hypothesis. For a test of a single proportion, " p∅ " represents the null hypothesis. Enter the alternate hypothesis on the bottom row.

Questions & Answers

Application of nanotechnology in medicine
what is variations in raman spectra for nanomaterials
Jyoti Reply
I only see partial conversation and what's the question here!
Crow Reply
what about nanotechnology for water purification
RAW Reply
please someone correct me if I'm wrong but I think one can use nanoparticles, specially silver nanoparticles for water treatment.
yes that's correct
I think
what is the stm
Brian Reply
is there industrial application of fullrenes. What is the method to prepare fullrene on large scale.?
industrial application...? mmm I think on the medical side as drug carrier, but you should go deeper on your research, I may be wrong
How we are making nano material?
what is a peer
What is meant by 'nano scale'?
What is STMs full form?
scanning tunneling microscope
how nano science is used for hydrophobicity
Do u think that Graphene and Fullrene fiber can be used to make Air Plane body structure the lightest and strongest. Rafiq
what is differents between GO and RGO?
what is simplest way to understand the applications of nano robots used to detect the cancer affected cell of human body.? How this robot is carried to required site of body cell.? what will be the carrier material and how can be detected that correct delivery of drug is done Rafiq
analytical skills graphene is prepared to kill any type viruses .
what is Nano technology ?
Bob Reply
write examples of Nano molecule?
The nanotechnology is as new science, to scale nanometric
nanotechnology is the study, desing, synthesis, manipulation and application of materials and functional systems through control of matter at nanoscale
Is there any normative that regulates the use of silver nanoparticles?
Damian Reply
what king of growth are you checking .?
What fields keep nano created devices from performing or assimulating ? Magnetic fields ? Are do they assimilate ?
Stoney Reply
why we need to study biomolecules, molecular biology in nanotechnology?
Adin Reply
yes I'm doing my masters in nanotechnology, we are being studying all these domains as well..
what school?
biomolecules are e building blocks of every organics and inorganic materials.
anyone know any internet site where one can find nanotechnology papers?
Damian Reply
sciencedirect big data base
Introduction about quantum dots in nanotechnology
Praveena Reply
what does nano mean?
Anassong Reply
nano basically means 10^(-9). nanometer is a unit to measure length.
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Source:  OpenStax, Collaborative statistics for mt230. OpenStax CNX. Aug 18, 2011 Download for free at http://legacy.cnx.org/content/col11345/1.2
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