California course

Math I

Build connected understanding across quantities, equations, functions, coordinate geometry, congruence, and data.

Problem types
659
Practice variants
2,636
Problem types

Page 18 of 19

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

S-ID.6.b M1-055-A03-V01

Interpret a residual, \(observed - predicted\), to judge model fit informally

Assess model fit informally using residuals.

Residual sign and residual size answer different questions. Use the sign to identify underprediction or overprediction, but judge size with the absolute residual divided by the supplied reference scale; only …

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S-ID.6.b M1-055-A04-V01

Create a residual table

Assess model fit informally using residuals.

A residual table is reliable only when every observation stays paired with its own prediction. Work row by row using observed minus predicted, preserve negative signs, and check each entry …

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S-ID.6.b M1-055-A05-V01

Plot a residual plot from given inputs and residuals

Assess model fit informally using residuals.

A residual plot uses the original input horizontally and its paired residual vertically. Plot every ordered pair without swapping or negating coordinates, draw the zero reference line, and count points …

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S-ID.6.b M1-055-A06-V01

Interpret a random residual plot

Assess model fit informally using residuals.

A useful residual plot should look like unstructured noise around zero, not perfect predictions. Check for balance above and below the line, then look for curves, trends, clusters, or changing …

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S-ID.6.b M1-055-A07-V01

Interpret a patterned residual plot

Assess model fit informally using residuals.

Having residuals on both sides of zero is not enough; their order across x matters. Trace the sign pattern from low to high inputs, and if the points form a …

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S-ID.6.b M1-055-A08-V01

Compare models using residual summaries

Assess model fit informally using residuals.

Compare residual models with the stated priorities rather than one convenient statistic. Address systematic pattern first, then use mean absolute error for typical miss size; a signed mean near zero …

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S-ID.6.b M1-055-A09-V01

Identify whether there is a residual outlier from a list of residuals

Assess model fit informally using residuals.

A residual outlier is separated by error magnitude, not by sign or list position. Keep each label attached to its residual, compare absolute distances from zero, and identify whether one …

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S-ID.6.b M1-055-A10-V01

Decide whether a residual is acceptable in context

Assess model fit informally using residuals.

Practical acceptability depends on error magnitude and context, not on whether the residual is exactly zero. Compare the absolute residual with the allowed tolerance in matching units, remembering that “up …

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S-ID.6.c M1-056-A01-V01

Decide whether a scatter plot suggests a linear association

Fit a linear function to data when a scatter plot suggests a linear association.

A scatter plot can have an overall direction without having a linear form. Look past small point-to-point variation and ask whether a single straight band would capture the cloud without …

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S-ID.6.c M1-056-A03-V01

Estimate the slope of a line of fit from two points on the line

Fit a linear function to data when a scatter plot suggests a linear association.

Slope is a rate built from two points on the same fitted line, not from the nearest data dots or the line’s visual steepness alone. Read each coordinate on the …

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S-ID.6.c M1-056-A04-V01

Estimate the y-intercept of a line of fit

Fit a linear function to data when a scatter plot suggests a linear association.

The intercept answers a boundary question: what does the fitted model predict when the input is zero? Trace the line to the vertical axis and interpret that value in context, …

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S-ID.6.c M1-056-A05-V01

Write a linear model from the slope and y-intercept of a fitted line

Fit a linear function to data when a scatter plot suggests a linear association.

A fitted-line equation packages two separate features: the rate of change and the value at zero. Put the slope with the input variable and the intercept as the constant, using …

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S-ID.6.c M1-056-A06-V01

Interpret slope in context

Fit a linear function to data when a scatter plot suggests a linear association.

Units turn a slope from a number into a statement. Read it as output units per one input unit, let its sign tell whether the predicted output rises or falls, …

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S-ID.6.c M1-056-A07-V01

Interpret intercept in context

Fit a linear function to data when a scatter plot suggests a linear association.

An intercept can be algebraically defined yet contextually misleading if zero input is excluded or unrealistic. First check whether zero belongs to the stated domain; if it does, interpret the …

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S-ID.6.c M1-056-A08-V01

Use a linear model to interpolate when a scatter plot shows an approximate straight-line pattern

Fit a linear function to data when a scatter plot suggests a linear association.

Reliability depends on where the requested input sits relative to the data used to fit the line. Compare that input with the observed range before calculating: work inside the range …

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S-ID.6.c M1-056-A09-V01

Use a linear model to extrapolate cautiously

Fit a linear function to data when a scatter plot suggests a linear association.

Extending a fitted line is a mathematical calculation and an evidence question at the same time. Measure how far the requested input lies beyond the observed range, then describe the …

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S-ID.6.c M1-056-A10-V01

Compare possible lines of fit

Fit a linear function to data when a scatter plot suggests a linear association.

Matching the trend’s direction is only the first test for a fitted line. A stronger fit runs through the center of the point cloud and leaves residuals small and roughly …

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S-ID.7 M1-057-A01-V01

Interpret the slope of a fitted linear model

Interpret slope and intercept of a linear model in the data context.

Slope describes how the model’s predicted output changes when the input increases by one unit. Translate its sign and magnitude with output-units-per-input-unit language, and keep “predicted” in the statement: a …

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S-ID.7 M1-057-A02-V01

Interpret the y-intercept of a fitted linear model

Interpret slope and intercept of a linear model in the data context.

The constant term tells what the model predicts at zero input, but a prediction can exist where no data were collected. Interpret the intercept with output units, then compare zero …

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S-ID.7 M1-057-A03-V01

Identify the units of the slope and intercept in a linear model from context

Interpret slope and intercept of a linear model in the data context.

Dimensional consistency gives a reliable shortcut for parameter units. Because slope multiplies the input to produce an output change, its units must be output units divided by input units; the …

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S-ID.7 M1-057-A04-V01

Compare slopes of fitted models

Interpret slope and intercept of a linear model in the data context.

Two slopes are directly comparable only when they describe the same output per the same input unit. Their signs determine direction, their ordered values determine which change is faster, and …

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S-ID.7 M1-057-A05-V01

Compare the y-intercepts of two linear models in context

Interpret slope and intercept of a linear model in the data context.

Comparing intercepts means comparing both models at the shared input of zero. Confirm that zero has the same contextual meaning, identify which predicted starting value is larger, and subtract the …

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S-ID.7 M1-057-A06-V01

Write a contextual sentence for a model parameter

Interpret slope and intercept of a linear model in the data context.

A good contextual parameter sentence should reveal whether the number is a rate or a starting value before naming its magnitude. For a slope, describe the predicted output change for …

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S-ID.7 M1-057-A07-V01

Decide whether an intercept interpretation is reasonable

Interpret slope and intercept of a linear model in the data context.

Reasonableness has more than one layer: zero input may be meaningful, the predicted output may be physically plausible, and yet the fitted data may still provide no support there. Check …

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S-ID.7 M1-057-A08-V01

Interpret the slope of a linear model as predicted change per unit input

Interpret slope and intercept of a linear model in the data context.

A negative slope does not make the predicted output itself negative; it describes the direction of change. Separate sign from magnitude, attach output units per input unit, and translate a …

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S-ID.7 M1-057-A09-V01

Interpret the slope of a linear model as predicted change per 1 unit of input

Interpret slope and intercept of a linear model in the data context.

An equation and a graph encode the same line in different ways. Read the coefficient of the input as the equation’s slope, compute signed rise over run from the graph, …

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S-ID.7 M1-057-A10-V01

Choose the correct interpretation of a model parameter

Interpret slope and intercept of a linear model in the data context.

Start by matching the question’s language to a parameter role: “per additional input” calls for slope, while “at zero input” calls for intercept. Then attach output-per-input units to the rate …

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S-ID.8 M1-058-A01-V01

Interpret the sign of the correlation coefficient r for a linear fit

Use technology to compute and interpret the correlation coefficient of a linear fit.

Correlation sign and correlation strength answer different questions. After checking whether the magnitude is large enough to count as a meaningful linear signal under the stated rule, use the sign …

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S-ID.8 M1-058-A02-V01

Interpret the sign and magnitude of the correlation coefficient r for a linear fit

Use technology to compute and interpret the correlation coefficient of a linear fit.

Read correlation in two passes rather than treating its signed decimal as one label. The sign gives the direction of the linear tendency, while the absolute value is compared with …

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S-ID.8 M1-058-A03-V01

Match the direction and strength of a linear scatter plot to a likely correlation coefficient

Use technology to compute and interpret the correlation coefficient of a linear fit.

Estimate a correlation from a scatter plot by separating direction from tightness. The cloud’s upward or downward tilt determines the sign, while how closely the points hug a straight line …

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S-ID.8 M1-058-A04-V01

Interpret correlation coefficients to identify the direction and strength of a linear association

Use technology to compute and interpret the correlation coefficient of a linear fit.

Correlation strength is distance from zero, so compare absolute values before restoring each coefficient’s sign. A negative coefficient can be stronger than a positive one; its minus sign changes only …

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S-ID.8 M1-058-A05-V01

Read and interpret the correlation coefficient r from linear regression technology output

Use technology to compute and interpret the correlation coefficient of a linear fit.

Regression output contains several numbers with different jobs. Use the labels before interpreting the decimals: the line’s coefficient and constant are slope and intercept, while the separately labeled correlation coefficient …

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S-ID.8 M1-058-A06-V01

Interpret correlation in context without implying causation

Use technology to compute and interpret the correlation coefficient of a linear fit.

A correlation coefficient is unitless, so it cannot be read as output units gained per input unit. Translate sign and magnitude into a contextual tendency between the two variables, then …

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S-ID.8 M1-058-A07-V01

Determine whether correlation is appropriate

Use technology to compute and interpret the correlation coefficient of a linear fit.

Before using Pearson correlation, inspect what kind of pattern the scatter actually has. It is designed for two quantitative variables with an approximately straight form and no point that dominates …

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S-ID.8 M1-058-A08-V01

Assess an outlier's effect on correlation

Use technology to compute and interpret the correlation coefficient of a linear fit.

Influence is diagnosed by asking how much the statistic changes when one point is removed, while also comparing the two scatter patterns. A far-out point aligned with the trend can …

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S-ID.8 M1-058-A09-V01

Compare two correlation coefficients by direction and strength of linear association

Use technology to compute and interpret the correlation coefficient of a linear fit.

Compare correlation coefficients on two independent axes. Signs tell whether their linear directions agree or differ, and absolute values tell which association is stronger; being numerically smaller because of a …

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