TI-84 Calculator

Chapter guide for MATH 1501 (which topic below maps to which chapter of The Textbook)

Ch. 2	Descriptive Statistics
Ch. 3	Combinations & Permutations
Ch. 4.3	Binomial Distribution
Ch. 4.6	Poisson Distribution
Ch. 5.2–5.3	Uniform & Exponential
Ch. 6	Normal Distribution
Ch. 7	Central Limit Theorem
Ch. 8	Confidence Intervals
Ch. 9	Hypothesis Testing
Ch. 12	Linear Regression

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Combinations & Permutations

nCr — combinations

n → MATH → ◀◀ PROB → 3:nCr → r → ENTER

n = total items in the set, r = items chosen.

nPr — permutations

n → MATH → ◀◀ PROB → 2:nPr → r → ENTER

n = total items in the set, r = items chosen, order matters.

n! — factorial

n → MATH → ◀◀ PROB → 4:! → ENTER

Descriptive Statistics

1-Var Stats — mean, median, standard deviation

STAT → 1:Edit → enter data into L1 → STAT → ▶ CALC → 1:1‑Var Stats → L1 → ENTER

x̄ = sample mean | Sx = standard deviation of your sample data (divides by n−1) | σx = standard deviation treating your data as the entire population (divides by n) | n = how many data points you entered | Q1/Med/Q3 = quartiles.

Box Plot / Histogram

2nd → Y= (STAT PLOT) → 1:Plot1 → On → choose Type → Xlist: L1 → ZOOM → 9:ZoomStat

Binomial Distribution

binompdf(n, p, x) — P(X = x)

2nd → VARS (DISTR) → A:binompdf( → n , p , x ) → ENTER

n = number of trials, p = probability of success per trial, x = number of successes.

binomcdf(n, p, x) — P(X ≤ x)

2nd → VARS (DISTR) → B:binomcdf( → n , p , x ) → ENTER

Poisson Distribution

poissonpdf(μ, x) — P(X = x)

2nd → VARS (DISTR) → C:poissonpdf( → μ , x ) → ENTER

μ = average rate of occurrence over the interval, x = number of occurrences.

poissoncdf(μ, x) — P(X ≤ x)

2nd → VARS (DISTR) → D:poissoncdf( → μ , x ) → ENTER

Uniform & Exponential

No built-in TI-84 function for either distribution — compute directly from the formulas.

Uniform: f(x) = 1 / (b − a), a ≤ x ≤ b

a = lower bound, b = upper bound.

Exponential: f(x) = m·e^−mx, P(X ≤ x) = 1 − e^−mx

e^x key: 2nd → LN

m = rate parameter (events per unit time/space).

Normal Distribution

normalpdf(x, μ, σ) — height of curve at x

2nd → VARS (DISTR) → 1:normalpdf( → x , μ , σ ) → ENTER

normalcdf(lower, upper, μ, σ) — P(lower ≤ X ≤ upper)

2nd → VARS (DISTR) → 2:normalcdf( → lower , upper , μ , σ ) → ENTER

Use −1E99 / 1E99 for −∞ / ∞ (2nd → , → then EE).

invNorm(area, μ, σ) — x for a given left-tail area

2nd → VARS (DISTR) → 3:invNorm( → area , μ , σ ) → ENTER

μ = mean, σ = standard deviation, of the distribution being evaluated.

Central Limit Theorem

Sampling distribution of x̄

2nd → VARS (DISTR) → 2:normalcdf( → lower , upper , μ , σ/√(n) ) → ENTER

Same as Normal, above — just replace σ with σ/√n, where n = sample size.

Confidence Intervals

ZInterval — mean, σ known

STAT → ▶▶ TESTS → 7:ZInterval → Stats or Data → enter values → Calculate → ENTER

TInterval — mean, σ unknown

STAT → ▶▶ TESTS → 8:TInterval → Stats or Data → enter values → Calculate → ENTER

1-PropZInt — proportion

STAT → ▶▶ TESTS → A:1‑PropZInt → x , n , C‑Level → Calculate → ENTER

n = sample size, x = number of successes in that sample.

Hypothesis Testing

Z-Test — mean, σ known

STAT → ▶▶ TESTS → 1:Z‑Test → Stats or Data → enter μ₀, σ → Calculate → ENTER

T-Test — mean, σ unknown

STAT → ▶▶ TESTS → 2:T‑Test → Stats or Data → enter μ₀ → Calculate → ENTER

1-PropZTest — proportion

STAT → ▶▶ TESTS → 5:1‑PropZTest → enter p₀, x, n → Calculate → ENTER

μ₀ / p₀ = the value in your null hypothesis, n = sample size.

Output includes the test statistic (z or t) and the p-value.

Linear Regression

Enter data

STAT → 1:Edit → x-values into L1, y-values into L2

Turn on r, r² (one time only)

2nd → 0 (CATALOG) → ▼ to DiagnosticOn → ENTER → ENTER

LinReg(a+bx) — least-squares line

STAT → ▶ CALC → 8:LinReg(a+bx) → L1 , L2 → ENTER

Returns y = a + bx, plus a (intercept), b (slope), r², r.

Variable Glossary

Grouped by topic — the same letter (like n) means different things in different rows above, so it's defined per group below rather than once.

Counting

n: total number of items in the set
r: number of items chosen from the set

Binomial & Poisson

n: number of trials (Binomial)
p: probability of success on one trial
x: number of successes / occurrences
μ: mean rate of occurrence (Poisson)

Normal & Sampling

μ: population mean
σ: population standard deviation
x̄: sample mean
Sx: sample standard deviation (from data, n−1 divisor)
σx: population standard deviation (from data, n divisor)

Regression

a, b: intercept, slope of y = a + bx
r: correlation coefficient
r²: coefficient of determination

Confidence Intervals & Hypothesis Tests

n: sample size
x: number of successes in the sample
C-Level: confidence level (e.g. 0.95)
df: degrees of freedom
μ₀ / p₀: the null-hypothesis value being tested against

Gregory Barco