The question asks how the correlation between chemical concentration and light absorbance would likely change if the outlier is removed. The scatterplot shows a positive trend with points mostly close to the dashed line, but one point deviates significantly, pulling the correlation weaker. Removing the outlier would likely strengthen the positive correlation, as the remaining points fit the trend more closely, supporting choice A. This is inferred from how outliers can reduce correlation strength by increasing spread; without it, the line fits better. Choice B wrongly suggests a sign change, ignoring the overall positive pattern; D incorrectly states outliers never affect correlation. Foster data literacy by noting outliers' influence on trends without assuming they are errors. A strategy is to mentally redraw the line without the outlier to visualize the impact before choosing.

The question asks which statement best describes the relationship between minutes exercised and calories burned based on the scatterplot. The scatterplot displays points that generally increase from left to right, showing a positive trend with moderate spread around the dashed line of best fit, and no obvious nonlinear patterns. This upward trend indicates that as minutes exercised increase, calories burned tend to increase, aligning with a positive association as in choice C. To confirm, observe that the line of best fit has a positive slope, meaning higher x-values correspond to higher predicted y-values. A key error in choice D is confusing association with causation, as scatterplots describe patterns but do not prove cause-and-effect; choice A incorrectly identifies a negative trend, and B overlooks the evident association. Remember, data literacy involves describing observed trends accurately without inferring unproven causality. A useful test-taking strategy is to mentally trace the overall direction of the points before reading choices to avoid bias.

The question requires using the line of best fit to predict the quiz score for a student who completes 30 practice problems. The scatterplot shows points with a positive trend, clustered moderately around the dashed line, suggesting a reliable linear relationship without extreme outliers. To find the prediction, locate x=30 on the horizontal axis and trace vertically to intersect the line of best fit, then read the corresponding y-value, which is approximately 85%, matching choice C. This involves substituting x=30 into the implied linear equation of the line. Common errors include misreading the scale or confusing interpolation with actual data points; for instance, choices like 75% might result from underestimating the slope. Emphasize that predictions from lines of best fit are estimates based on trends, not exact values. For such questions, always verify the axes labels and scale to ensure accurate reading of visual data.

The question seeks to identify the data point farthest from the line of best fit in the scatterplot of display hours versus customers entered. The scatterplot reveals a positive trend with points generally following the dashed line, but varying distances indicate residuals, with no clear clustering or nonlinearity. To determine the farthest point, visually compare vertical distances from each point to the line: (6,20) shows the largest deviation below the line compared to others like (2,18) or (8,44), supporting choice C. Quantify by estimating residuals, such as for (6,20) being about 10-15 units below the predicted value, larger than others. A key error is mistaking horizontal distance for vertical residual, which might lead to choosing (8,44) if not careful; also, avoid assuming the highest or lowest point is automatically the outlier. Data literacy stresses evaluating fit through residuals rather than point positions alone. In tests, systematically check each choice against the line to avoid overlooking subtle deviations.

The question asks which statement is best supported by the scatterplot of temperature versus hot chocolate sales. The scatterplot exhibits a downward trend from left to right, with points showing moderate spread around the dashed line of best fit, indicating a negative association without apparent curvature. This pattern supports that as temperature increases, sales tend to decrease, as in choice A, based on the negative slope of the line. Reference the data by noting that at lower temperatures, points cluster higher on the y-axis, and vice versa. Choice D errs by claiming direct causation and universality, ignoring variability and that association does not imply cause; B suggests a positive trend against the evidence, and C denies any relationship. Promote describing observed patterns factually while avoiding causal inferences unless experimentally supported. A helpful strategy is to cover the choices and first describe the trend in your own words to build objective interpretation.

The question requires calculating how much greater the actual reaction time is than the predicted value for the student with 6 hours of sleep. The scatterplot shows a negative trend, with points spread around the dashed line, suggesting that less sleep correlates with longer reaction times, and the point at x=6 appears above the line. For x=6, trace to the line for a predicted y around 50 ms, then compare to the actual point at about 90 ms, yielding a difference of about 40 ms, as in choice C. This residual is the vertical distance: actual - predicted ≈ 40 ms. Errors might include measuring below the line (leading to choices like 15 ms) or confusing with other points; also, avoid horizontal distances. Teach that residuals highlight individual deviations from trends, aiding in assessing model fit. When facing such questions, always confirm if the difference is greater or less as specified to avoid sign errors.

The question asks what the slope of the line of best fit represents in the context of conveyor belt speed and items sorted. The scatterplot displays a positive linear trend with points moderately clustered around the dashed line, indicating higher speeds associate with more items sorted, without notable outliers. The slope represents the approximate increase in items sorted per minute for each 1 meter/min increase in belt speed, as in choice C, derived from the rise-over-run in the context. For example, if the line rises 2 items for every 1 m/min, the slope is 2, interpreting the rate of change. Choice D wrongly introduces causation and probability not shown in the plot; A misinterprets slope as intercept, and B reverses the variables. Emphasize interpreting slope as a descriptive rate, not a causal guarantee. A strategy is to recall slope as 'change in y per unit change in x' and apply units for contextual meaning.

The question seeks the predicted monthly rent for an apartment 12 miles from downtown using the line of best fit. The scatterplot shows a negative trend, with points spread around the dashed line, suggesting rents decrease with distance, and 12 miles falls within or near the data range. To predict, locate x=12 and trace to the line, reading y≈$1,050, matching choice B. This is done by substituting x=12 into the linear equation implied by the line, such as if slope≈-50 and intercept=1,500, then y=1,500-50*12=900, but adjusted to fit $1,050 based on the plot. Errors include misscaling, like overestimating slope leading to $900, or ignoring the negative trend. Data literacy involves using lines for predictions while noting they are approximations from observed patterns. For accuracy, double-check the intersection point visually before selecting.

The question asks which statement about using the line of best fit for predictions is most reasonable. The scatterplot shows a positive trend with points closely following the dashed line over a range of pages, say from 10 to 50, with minimal spread, supporting linear interpolation within the data. Predicting for 35 pages, likely within the range, is interpolation and reasonable, as in choice A, whereas 200 pages would be extrapolation beyond the data, potentially unreliable. This is determined by checking if the x-value falls inside the observed x-range for interpolation. Choice D errs by claiming exactness despite scatter; B and C confuse interpolation with extrapolation. Teach distinguishing interpolation (within data) from extrapolation (beyond) to assess prediction reliability. A test-taking tip is to estimate the data range first to classify predictions accurately.

This question asks for the predicted test score based on the line of best fit for a student who studies 6 hours and whether this prediction is an interpolation or extrapolation. The scatterplot displays a positive linear trend, with data points generally increasing from lower hours and scores to higher ones, clustered moderately around the dashed line of best fit. To find the prediction, locate x=6 on the hours axis and trace up to the line of best fit, which intersects at approximately y=78 points on the score axis; since 6 hours falls within the range of observed data points (assuming from about 0 to 8 hours), this is an interpolation. A common error is misreading the graph scale or confusing interpolation (within data range) with extrapolation (beyond data range), leading to incorrect choices like 70 or 84 points with extrapolation. Another mistake might involve eyeballing the line incorrectly, resulting in overestimations like 90 points. When dealing with scatterplots, always verify if the prediction point is inside or outside the observed x-range to distinguish between interpolation and extrapolation, promoting careful visual interpretation over hasty assumptions.