California course

Math III

Go further with polynomial and rational expressions, advanced functions, trigonometry, geometric modeling, and statistical inference.

Problem types
641
Practice variants
2,564
Problem types

Page 16 of 18

Each problem type has four distinct practice variants. Open a preview to move among all four.

N-CN.8 M3-045-A08-V01

Solve a polynomial over the complex numbers by factoring and using conjugate complex factors

Extend polynomial identities to complex numbers for higher-degree polynomial work.

Over the complex numbers, factoring is complete only when every nonconstant factor is linear. Use a familiar polynomial identity to break the higher-degree expression into quadratics, then split both differences …

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N-CN.8 M3-045-A09-V01

Compare complete factorization over R and C

Extend polynomial identities to complex numbers for higher-degree polynomial work.

The coefficient field determines what counts as a complete factorization. Over the reals, a quadratic with negative discriminant remains irreducible; over the complex numbers, its imaginary roots produce conjugate linear …

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N-CN.8 M3-045-A10-V01

List complex roots as repeated multiplicity records

Extend polynomial identities to complex numbers for higher-degree polynomial work.

Read each root from a factor of the form x minus r, and read its multiplicity from that factor's exponent. Add the multiplicities to recover the polynomial's degree rather than …

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N-CN.9 M3-046-A01-V01

Determine the total number of complex roots of a polynomial from its degree

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

The Fundamental Theorem of Algebra connects a polynomial's degree directly to its total number of complex roots. That total counts repeated roots according to multiplicity and includes real roots because …

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N-CN.9 M3-046-A02-V01

Count the complex roots of a factored polynomial, including multiplicity

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

Factored form records both root values and how often they occur. Set each distinct linear factor equal to zero, then attach the factor's exponent as that root's multiplicity. Keep the …

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N-CN.9 M3-046-A03-V01

Find missing polynomial roots from the degree, counting multiplicity

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

A polynomial with real coefficients cannot have an unpaired nonreal root. Complex conjugation keeps the real part and reverses only the imaginary sign, producing the companion root required by the …

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N-CN.9 M3-046-A04-V01

Find the conjugate root of a real-coefficient polynomial

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

Real coefficients force nonreal roots to occur in complex-conjugate pairs. Forming the conjugate preserves the real component and changes only the sign of the imaginary component; it does not negate …

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N-CN.9 M3-046-A05-V01

Build the monic polynomial from roots and multiplicities

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

Convert every root r into a factor x minus r, paying special attention to the double negative created by a negative root. Repeat factors when multiplicities are given and pair …

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N-CN.9 M3-046-A06-V01

Connect root records to visible graph behavior

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

Separate the root record into real and nonreal entries before interpreting the graph. Only real roots can become x-intercepts; odd multiplicity produces a crossing, while even multiplicity produces a touch, …

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N-CN.9 M3-046-A07-V01

Evaluate and classify a real or complex root candidate

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

The root test is exact: substitute the candidate for every occurrence of the variable and simplify the resulting polynomial value. Keep the complex number parenthesized and reduce powers of the …

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N-CN.9 M3-046-A08-V01

Infer the minimum number of nonreal roots from a polynomial graph and its degree

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

Use the degree as a budget of root occurrences, but remember that a graph shows distinct real-zero locations rather than every multiplicity. To find a minimum number of nonreal roots, …

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N-CN.9 M3-046-A09-V01

Enumerate polynomial root structures by degree slots

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

Treat the degree as a fixed number of multiplicity slots. Account for the stated simple roots first, then partition the remaining slots among additional real-root occurrences or nonreal roots. For …

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N-CN.9 M3-046-A10-V01

Relate concrete complete factorizations over R and C

Know the Fundamental Theorem of Algebra and connect it to polynomial roots.

Factorization depends on the number system allowed for the coefficients. A sum-of-squares quadratic has no real zeros and therefore remains irreducible over the reals, but solving the same zero equation …

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S-IC.1 M3-047-A01-V01

Separate population, frame, sample, unit, and size

Understand statistics as inference about population parameters from random samples.

Separate the study roles by asking four different questions: whom the conclusion is meant to describe, which list made people reachable, who actually supplied data, and what one row of …

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S-IC.1 M3-047-A02-V01

Separate parameter and statistic type, symbol, and value

Understand statistics as inference about population parameters from random samples.

First identify whether each quantity describes the full population or the observed sample. A population parameter is generally unknown and uses population notation, while the matching sample statistic is computed …

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S-IC.1 M3-047-A03-V01

Audit why a probability sample supports population inference

Understand statistics as inference about population parameters from random samples.

Audit a probability sample in order: compare the frame with the target, identify who had a known chance to enter, and name the selection bias that chance reduces. Then limit …

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S-IC.1 M3-047-A04-V01

Decide whether a described sampling method produces a random sample from a population

Understand statistics as inference about population parameters from random samples.

Classify the sampling method by its mechanism, not by whether a computer or roster happens to appear. A probability sample needs a defined frame and a chance-based rule that determines …

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S-IC.1 M3-047-A05-V01

Identify the type of sampling bias in a survey or study description

Understand statistics as inference about population parameters from random samples.

Locate when the distortion enters the study. If a target subgroup is absent from the reachable frame, the problem is undercoverage before contact occurs; nonresponse begins only after sampled people …

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S-IC.1 M3-047-A07-V01

Map a statistic to a closed population-estimate statement

Understand statistics as inference about population parameters from random samples.

A numerical summary computed from sampled units is a statistic, and its form determines the matching population parameter it estimates. Carry the same quantity type and operation from sample to …

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S-IC.1 M3-047-A08-V01

Bound generalization by the sampling frame

Understand statistics as inference about population parameters from random samples.

The largest design-supported population is bounded by the sampling frame: ask who had a genuine chance to enter under the actual procedure. Random sampling from a complete local roster supports …

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S-IC.1 M3-047-A09-V01

Choose which of two sampling plans gives better inference about a population

Understand statistics as inference about population parameters from random samples.

Judge inference quality by how participants enter the study before comparing sample sizes. Chance-based selection from a covering frame addresses systematic selection bias, while voluntary participation can overrepresent people with …

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S-IC.1 M3-047-A10-V01

Compute and interpret margin-of-error bounds

Understand statistics as inference about population parameters from random samples.

A margin of error is an additive distance on the same scale as the estimate. Subtract it for the lower endpoint and add it for the upper endpoint, taking special …

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S-IC.1 M3-047-A11-V01

Distinguish a sample description from a population inference

Understand statistics as inference about population parameters from random samples.

Classify the statement exactly as written by locating its scope words. A sample description reports a quantity only for observed units, while a population inference extends sample evidence to an …

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S-IC.2 M3-048-A01-V01

Build a complete simulation probability table

Use simulation to decide whether data are consistent with a proposed model.

Build a probability model by listing mutually exclusive outcomes that exhaust the entire sample space before assigning any probabilities. The stated random mechanism determines the weights, and a valid table …

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S-IC.2 M3-048-A03-V01

Define one simulation trial, statistic, and success rule

Use simulation to decide whether data are consistent with a proposed model.

One simulation trial must reproduce one full repetition of the real random process, including its probabilities and independence assumptions. Fix the stopping rule, the single statistic recorded from each trial, …

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S-IC.2 M3-048-A04-V01

Estimate probability from simulation results

Use simulation to decide whether data are consistent with a proposed model.

An empirical probability is the observed relative frequency of the event in repeated trials. Put the number of successes in the numerator and the total number of trials in the …

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S-IC.2 M3-048-A05-V01

Compare simulation evidence using a stated tail and alpha

Use simulation to decide whether data are consistent with a proposed model.

Use the supplied at-least-as-extreme count exactly according to the predeclared tail rule; do not double a count that already represents a two-sided definition. Divide that count by all simulations to …

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S-IC.2 M3-048-A09-V01

Compute and frame an empirical p-value

Use simulation to decide whether data are consistent with a proposed model.

Form the empirical p-value by dividing the count in the declared extreme tail by the total simulation count. Convert the same relative frequency consistently between decimal, percent, and a verbal …

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S-IC.2 M3-048-A10-V01

Make a simulation decision with explicit alpha

Use simulation to decide whether data are consistent with a proposed model.

Write the decision rule first, including whether its inequality is strict, and compare the p-value with alpha on the same scale. A p-value inside the stated rejection region leads to …

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S-IC.2 M3-048-A11-V01

Identify a limitation in a simulation setup

Use simulation to decide whether data are consistent with a proposed model.

Assess a simulation separately for model accuracy, independence, and number of repetitions. With only a small trial count, each additional success changes the relative frequency by a large step, so …

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S-IC.2 M3-048-R02-V01

Map a random device exactly to a probability model

Use simulation to decide whether data are consistent with a proposed model.

Start by counting the random device's equally likely outcomes and express the target probability as a fraction of that count. Assign exactly that many outcomes to success, map every remaining …

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S-IC.2 M3-048-R07-V01

Test a proposed population proportion by simulation

Use simulation to decide whether data are consistent with a proposed model.

A valid model test simulates the original sample size repeatedly under the proposed success proportion and records the same count statistic each time. Translate the stated direction into an extremeness …

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S-IC.2 M3-048-R08-V01

Match a simulation to random assignment or sampling

Use simulation to decide whether data are consistent with a proposed model.

A randomization test must reproduce what was random in the experiment. Under a no-effect explanation, keep the observed outcomes and fixed group sizes unchanged, reshuffle only the treatment labels, and …

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S-IC.3 M3-049-A01-V01

Recognize a sample survey

Distinguish sample surveys, experiments, and observational studies; explain randomization in each.

Classify the study by asking how the data are collected and whether researchers impose a condition. Here, focus on opinions obtained by questioning a group of adults and the absence …

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S-IC.3 M3-049-A02-V01

Recognize an observational study

Distinguish sample surveys, experiments, and observational studies; explain randomization in each.

Ask whether researchers imposed the explanatory condition or merely recorded conditions people already had or had chosen themselves. Without researcher assignment, comparing existing groups is observational even when the groups' …

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S-IC.3 M3-049-A03-V01

Classify a study as a survey, observational study, or experiment

Distinguish sample surveys, experiments, and observational studies; explain randomization in each.

Classify the study by tracing what researchers do before measuring the response. Determine whether they simply record existing patient choices or deliberately place patients into the drug and placebo groups, …

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