TI-83/84 Quick Guide

This guide collects every TI-83/84 keystroke sequence used across MATH 1209 in one place, grouped by the kind of task. Menu keys are shown in MONOSPACE; the symbol ▸ means “then choose.” Bring a TI-83/84 (or the equivalent your instructor names) with fresh batteries — phone calculators are not allowed on quizzes or exams. Each procedure pairs with a formula on the Course Formula Sheet and an R function in the weekly units.

1Entering and clearing data (every week)¶

You put data into a list (L1, L2, …) before almost any calculation.

1. Press STAT, then choose 1: Edit.

2. Move the cursor into L1 and type each value, pressing ENTER after every entry.

3. To empty a list first, move the cursor onto the list name (e.g., L1) at the top and press CLEAR, then ENTER. Do not press DEL — that deletes the whole list column instead of emptying it.

2Numerical summaries: 1-Var Stats¶

Use when you need the mean, standard deviation, or five-number summary of a data set (Ch 2).

1. Enter the data in L1 (above).

2. STAT ▸ CALC ▸ 1: 1-Var Stats ▸ L1 ▸ ENTER.

3. Read the screen: $\bar{x}$ = mean; Sx = sample standard deviation (this is the $s$ you want — ignore σx, the population version); n = sample size. Press the down arrow ▼ to reach minX, Q1, Med, Q3, maxX — the five-number summary.

Weighted version — expected value $E(X)$ and $SD(X)$ (Ch 4–5). Put the values in L1 and their probabilities $P(x)$ in L2, then run STAT ▸ CALC ▸ 1-Var Stats L1,L2 (type the comma before L2). The calculator reads L2 as weights, so the reported $\bar{x}$ is $E(X)$ and σx is $SD(X)$.

3Pictures: STAT PLOT (histogram and boxplot)¶

Use when you want to see the shape of a distribution (Ch 2).

1. Enter data in L1.

2. 2ND ▸ Y= (this is STAT PLOT) ▸ Plot1 ▸ turn On.

3. Choose the histogram icon, or the modified boxplot icon (which shows outliers as separate dots). Set Xlist: L1.

4. ZOOM ▸ 9: ZoomStat auto-fits the window ▸ GRAPH.

4Normal-model areas and cutoffs (Ch 4)¶

All three live under 2ND ▸ VARS (this is the DISTR menu).

4.1normalcdf(lower, upper, μ, σ) — find an area (probability)¶

Use when the question asks “what proportion / probability is below, above, or between” given values.

• “At most” (left tail): use -1E99 as the lower bound. Get -1E99 by pressing (-) 2ND , (the EE key) 99.

• “At least” (right tail): use 1E99 as the upper bound.

• Example: $P(X < 7)$ for $N(10, 1.8)$ → normalcdf(-1E99, 7, 10, 1.8) → 0.0478.

• Example: $P(8 < X < 12)$ → normalcdf(8, 12, 10, 1.8) → 0.7335.

4.2invNorm(area, μ, σ) — find a cutoff value¶

Use when the question gives a percentage and asks for the value at that boundary. It expects the area to the left.

• For a “top $p$%” cutoff, enter invNorm(1 - p, μ, σ).

• Example: the top-15% cutoff for $N(72, 9)$ → invNorm(0.85, 72, 9) → 81.33.

• Example: the bottom-10% cutoff → invNorm(0.10, 72, 9) → 60.47.

4.3ShadeNorm(lower, upper, μ, σ) — draw the shaded region¶

Found under 2ND ▸ VARS ▸ DRAW. It draws the curve with the requested region shaded and prints the area — use it to picture any normalcdf call before trusting the number.

5Inference for a proportion (Ch 6)¶

Both are under STAT ▸ TESTS. Enter x = number of successes (a whole count), not the proportion.

5.11-PropZInt — confidence interval for $p$¶

Use when you are estimating an unknown proportion.

STAT ▸ TESTS ▸ A: 1-PropZInt ▸ enter x, n, and C-Level ▸ Calculate. The screen reports the interval, $\hat{p}$, and n.

5.21-PropZTest — hypothesis test for $p$¶

Use when you are testing a claim about a proportion.

STAT ▸ TESTS ▸ 5: 1-PropZTest ▸ enter the claimed p0, x, n, choose the alternative (≠p0, <p0, or >p0) ▸ Calculate. The screen reports z, the p-value, and $\hat{p}$.

6Inference for a mean (Ch 5, 7)¶

Under STAT ▸ TESTS. Choose Stats to type in $\bar{x}$, Sx, n, or Data to read from a list in L1.

6.1ZInterval — mean interval when $\sigma$ is known¶

Use when the population standard deviation $\sigma$ is given (mostly a Ch 5 preview). STAT ▸ TESTS ▸ 7: ZInterval ▸ enter $\sigma$, $\bar{x}$ (or the data list), n, and C-Level ▸ Calculate.

6.2TInterval — confidence interval for a mean $\mu$¶

Use when you are estimating a mean and $\sigma$ is unknown (the usual case). STAT ▸ TESTS ▸ 8: TInterval ▸ Stats ▸ enter $\bar{x}$, Sx, n, and C-Level ▸ Calculate. It reports the interval directly.

6.3Z-Test — mean test when $\sigma$ is known¶

STAT ▸ TESTS ▸ 1: Z-Test ▸ Stats ▸ enter $\mu_0$, $\sigma$, $\bar{x}$, n, choose the alternative ▸ Calculate. Reports z and the p-value.

6.4T-Test — hypothesis test for a mean $\mu$¶

Use when you are testing a claim about a mean and $\sigma$ is unknown. STAT ▸ TESTS ▸ 2: T-Test ▸ Stats ▸ enter $\mu_0$ (the null value), $\bar{x}$, Sx, n, choose the alternative (≠, <, or >) ▸ Calculate. Reports t, df, and the p-value.

7Which procedure? A cross-reference¶

Reading a problem for its variable type (categorical → a proportion; numerical → a mean) and its verb (estimate → an interval; test → a hypothesis test) points you at the right menu item. This table maps each task to its TI menu and its R equivalent from the weekly units.

In R, adding correct = FALSE to prop.test() skips the continuity correction it applies by default, so its p-value matches the TI’s 1-PropZTest and the by-hand formula.

8A note on standard errors¶

There is no dedicated menu for a standard error by itself — key the formula in on the home screen, watching your parentheses so the square root wraps the whole fraction.

• $SE_{\hat{p}}$ with $\hat{p} = 0.55$, $n = 50$: 2ND [√] ( .55 × ( 1 − .55 ) ÷ 50 ) ENTER.

• $SE_{\bar{x}}$ with $\sigma = 1.10$, $n = 30$: 1.10 ÷ 2ND [√] ( 30 ) ENTER.

The inference menus above (1-PropZInt, TInterval, and so on) compute the standard error for you automatically once you enter the summary numbers.