20 medium SAT Two-variable data: models & scatterplots questions
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Math · Problem-Solving and Data Analysis~2 per testMedium tier
The scatterplot shows the relationship between the number of hours studied, x, and test scores, y, for 10 students. The line of best fit for the data passes through approximately (2, 68) and (6, 84). For a student who studied 5 hours, the actual test score was 78. What is the sign of the residual for this data point?
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Why B is right
The line has slope (84 - 68)/(6 - 2) = 16/4 = 4. Using point-slope form: y−68=4(x−2), so y=4x+60. At x=5, the predicted value is 4(5) + 60 = 80. The residual is actual minus predicted: 78 - 80 = -2, which is negative.
Why the others are wrong
AThis incorrectly compares the actual value to a wrong predicted value, possibly from using an incorrect slope calculation.
CThis confuses the relationship between x and y values with the concept of residual, which is the difference between actual and predicted y-values.
DThis misunderstands that residual sign depends on actual versus predicted values, not on the slope of the line itself.
Question 2Medium
The scatterplot shows the relationship between two variables, x and y. The scatterplot has 12 data points in a linear pattern trending down from left to right. A line of best fit passes through the approximate coordinates (3, 45) and (11, 21). Which of the following equations best represents the line of best fit?
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Why A is right
The slope is (21 - 45)/(11 - 3) = -24/8 = -3. Using point-slope form with (3, 45): y−45=−3(x−3), which simplifies to y=−3x+54 or y=54−3x.
Why the others are wrong
BThis incorrectly uses a positive slope instead of the negative slope indicated by the downward trend.
CThis uses the y-value from one data point as the y-intercept without proper calculation.
DThis uses an incorrect slope derived from extrapolating incorrectly beyond the given data range.
Question 3Medium
The scatterplot shows the relationship between study hours per week, x, and test scores, y, for 16 students. The data points range from (5, 62) to (20, 94). A line of best fit passes through approximately (8, 70) and (17, 88).
According to the line of best fit, what is the meaning of the slope in this context?
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Why B is right
The slope of the line of best fit is (88-70)/(17-8) = 18/9 = 2. Since x represents study hours and y represents test scores, the slope indicates that for each additional hour studied per week, the test score is predicted to increase by 2 points.
Why the others are wrong
AThis describes the y-intercept of the line, not the slope.
CThis incorrectly assigns a negative interpretation to a positive slope, reversing the relationship direction.
DThis confuses the slope with an endpoint or maximum value from the data set rather than the rate of change.
Question 4Medium
The table shows the temperature T, in degrees Celsius, of a cooling liquid at time t minutes after it was removed from heat.
t | T
0 | 100
2 | 61
4 | 37
6 | 23
8 | 14
Which of the following equations best models the relationship between t and T?
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Why B is right
The temperature decreases rapidly at first then levels off, indicating exponential decay approaching an ambient temperature. Testing T=10+90(0.78t): when t=2, T=10+90(0.6084)≈10+55=65 (approximately 61); when t=4, T=10+90(0.37)≈43 (close to 37). This exponential approach to 10 degrees matches the cooling pattern.
Why the others are wrong
AThis linear model assumes constant cooling rate. When t = 8, this gives T = 100 - 88 = 12, which is close to 14 but fails at t = 2 where it gives 78, not 61.
CThis exponential model decays too quickly and approaches zero instead of room temperature. When t = 2, this gives T = 100(0.25) = 25, not 61.
DThis quadratic model fails to match the data. When t = 2, this gives T = -4 + 100 = 96, not 61.
Question 5Medium
A scatterplot shows the relationship between study hours per week, x, and test scores, y, for 14 students. The data points follow a roughly linear pattern trending upward from left to right. A line of best fit passes through approximately (5, 68) and (15, 88), with x-values ranging from 3 to 18.
The scatterplot shows the relationship between study hours per week and test scores for a group of students. A line of best fit is also shown. At x=12, which of the following is closest to the y-value predicted by the line of best fit?
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Why B is right
The line of best fit has a slope of 2 points per hour (rise of 20 over run of 10). Starting from (5, 68), moving to x=12 represents 7 additional hours, yielding y=68+7(2) = 82, which is closest to 80.
Why the others are wrong
AThis results from using an incorrect slope calculation or confusing the relationship between the variables.
CThis incorrectly uses the x-value itself as the predicted y-value, misreading which axis represents the predicted outcome.
DThis value lies beyond the reasonable range of the line of best fit for the given data, representing an overestimation from extrapolating incorrectly.
Question 6Medium
The scatterplot shows the relationship between two variables, x and y. The scatterplot has 9 data points in a linear pattern trending upward from left to right. A line of best fit is shown passing through approximately (2, 15) and (10, 47). The data points range from x=1 to x=11. At x=6, which of the following is closest to the y-value predicted by the line of best fit?
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Why B is right
The line of best fit passes through (2, 15) and (10, 47), giving a slope of (47 - 15)/(10 - 2) = 32/8 = 4. Using point-slope form with (2, 15): y−15=4(x−2), so y=4x+7. At x=6, y=4(6) + 7 = 31.
Why the others are wrong
AThis results from using an incorrect slope calculation, possibly confusing the change in y with the change in x.
CThis incorrectly uses the x-value as the predicted y-value, confusing which axis represents which variable.
DThis value is beyond the reasonable range of the data and results from incorrectly extrapolating using the endpoint value rather than computing from the line equation.
Question 7Medium
A scatterplot shows 9 data points representing the relationship between advertising spending in thousands of dollars, x, and monthly revenue in thousands of dollars, y. The points follow a linear pattern trending upward from left to right, with approximate coordinates (2, 40), (3, 47), (4, 54), (5, 61), (6, 68), (7, 75), (8, 82), (9, 89), and (10, 96). A line of best fit passes through approximately (2, 40) and (10, 96).
The scatterplot shows the relationship between two variables, x and y. A line of best fit for the data is also shown. Which of the following best interprets the slope of the line of best fit in context?
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Why A is right
The slope of the line of best fit is (96 - 40)/(10 - 2) = 56/8 = 7. This means that for every increase of 1 unit in x (1 thousand dollars in advertising spending), y (revenue) increases by 7 units (7 thousand dollars).
Why the others are wrong
BThis may result from incorrectly computing the slope as the change in x rather than properly dividing changes.
CThis represents the y-intercept value of the line rather than the slope.
DThis represents the total change in y across the entire data range rather than the change per unit of x.
Question 8Medium
The table shows several values of x and their corresponding values of y.
x | y
1 | 3
2 | 6
3 | 12
4 | 24
5 | 48
Which of the following equations best models the relationship between x and y?
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Why B is right
The y-values double each time x increases by 1, indicating exponential growth with base 2. Testing the equation y=1.5(2x): when x=1, y=1.5(2)=3; when x=2, y=1.5(4)=6; when x=3, y=1.5(8)=12. This pattern continues and matches all data points.
Why the others are wrong
AThis linear model incorrectly assumes a constant additive relationship. While y = 3(1) = 3 works for the first point, y = 3(2) = 6 works for the second, but y = 3(5) = 15 does not equal 48.
CThis quadratic model fits the first two points but fails thereafter. When x = 3, this gives y = 3(9) = 27, not 12.
DThis linear model only fits the first two data points. When x = 3, this gives y = 6(3) - 3 = 15, not 12.
Question 9Medium
A scatterplot shows 10 data points relating the number of hours studied, x, and test score, y. The line of best fit passes through approximately (3, 68) and (7, 84). The data points range from x=2 to x=9.
The scatterplot shows the relationship between hours studied and test score for 10 students. A line of best fit is also shown. At x=5, which of the following is closest to the test score predicted by the line of best fit?
Show the answer and explanation
Why B is right
The line passes through (3, 68) and (7, 84), giving slope (84 - 68) / (7 - 3) = 4. Using point-slope form with (3, 68): y−68=4(x−3). At x=5: y−68=4(2) = 8, so y=76.
Why the others are wrong
AThis results from using an incorrect slope calculation or averaging the y-values incorrectly.
CThis incorrectly uses the y-value from one of the given points without performing the interpolation.
DThis results from extrapolating beyond the reasonable range or using an incorrect calculation method.
Question 10Medium
The scatterplot shows the relationship between elevation above sea level in meters, x, and average temperature in degrees Celsius, y, for 15 weather stations. The data points range from (200, 22) to (1400, 10). A line of best fit passes through approximately (400, 20) and (1200, 12).
Based on the line of best fit, what is the predicted average temperature, in degrees Celsius, for an elevation of 700 meters above sea level?
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Why B is right
The line of best fit passes through (400, 20) and (1200, 12), giving a slope of (12-20)/(1200-400) = -8/800 = -0.01. Using point-slope form with (400, 20): y−20=−0.01(x−400), so y=−0.01x+24. At x=700, y=−0.01(700) + 24 = 17.
Why the others are wrong
AThis results from using an incorrect slope of approximately -0.02, leading to y = -0.02(700) + 28 = 14.
CThis results from reading the y-value at x = 400 instead of calculating the predicted value at x = 700.
DThis results from incorrectly extrapolating from a point near x = 1400, beyond the target value.
Question 11Medium
A study of Daily Revenue vs. Hours Open recorded the relationship between the number of hours a coffee shop is open each day, x, and its daily revenue in dollars, y. The line of best fit for the data is given by the equation y=340x+120.
According to the model, what is the predicted increase in daily revenue, in dollars, for each additional hour the coffee shop is open?
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Why B is right
The equation y=340x+120 is in slope-intercept form, where the slope 340 represents the change in y for each unit increase in x. Since x represents hours open and y represents daily revenue in dollars, the slope indicates that daily revenue increases by $340 for each additional hour the shop is open.
Why the others are wrong
AThis is the y-intercept of the model, which represents the predicted revenue when the shop is open 0 hours, not the rate of change per hour.
CThis incorrectly adds the slope and y-intercept (340 + 120), but these represent different aspects of the model and should not be combined to find the rate of change.
DThis incorrectly doubles the slope, possibly by treating the increase as applying to both opening and closing or by miscalculating the rate of change.
Question 12Medium
The table shows the distance d, in feet, traveled by a car t seconds after the brakes were applied.
t | d
0 | 0
1 | 19
2 | 36
3 | 51
4 | 64
Which of the following equations best models the relationship between t and d?
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Why A is right
The rate of distance increase slows over time, indicating a quadratic model with deceleration. Testing d=20t - t2: when t=1, d=20−1=19; when t=3, d=60−9=51; when t=4, d=80−16=64. This matches all data points.
Why the others are wrong
BThis linear model assumes constant speed and does not account for deceleration. When t = 4, this gives d = 16(4) = 64, which coincidentally matches but fails at t = 1 where it gives 16, not 19.
CThis exponential model assumes accelerating growth. When t = 1, this gives d = 10(1.8) = 18, not 19, and the pattern diverges further.
DThis quadratic model uses incorrect coefficients. When t = 1, this gives d = 25 - 2 = 23, not 19.
Question 13Medium
A scatterplot shows the relationship between elevation in hundreds of meters, x, and average temperature in degrees Celsius, y, for 13 weather stations. The data points follow a linear pattern trending downward. A line of best fit passes through approximately (3, 22) and (11, 14), with x-values ranging from 2 to 12.
The scatterplot shows the relationship between elevation and average temperature. A line of best fit is also shown. Based on the line of best fit, what is the predicted decrease in average temperature, in degrees Celsius, for each 1 hundred meter increase in elevation?
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Why D is right
The slope of the line is (14 - 22)/(11 - 3) = -8/8 = -1 degree Celsius per hundred meters. Since the question asks for the decrease, the magnitude is 1 degree Celsius per hundred meter increase in elevation.
Why the others are wrong
AThis incorrectly uses the total change in temperature rather than the rate of change per unit elevation.
BThis results from incorrectly calculating the slope, possibly by using wrong values or making computational errors.
CThis value does not match the actual slope and may result from confusing the calculation or extrapolating incorrectly.
Question 14Medium
The scatterplot shows the relationship between altitude, x, in thousands of feet, and temperature, y, in degrees Fahrenheit, for 12 weather stations. The data points follow a roughly linear pattern trending downward from left to right. A line of best fit passes through the approximate coordinates (1, 54) and (7, 18). The scatterplot has x-values ranging from 0 to 9.
The scatterplot shows the relationship between altitude and temperature for 12 weather stations. A line of best fit is also shown. Based on the line of best fit, what is the predicted decrease in temperature, in degrees Fahrenheit, for each increase of 1 thousand feet in altitude?
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Why B is right
The slope of the line of best fit represents the rate of change in temperature per thousand feet. Using points (1, 54) and (7, 18), the slope is (18-54)/(7-1) = -36/6 = -6. The magnitude of the slope is 6, indicating a decrease of 6 degrees Fahrenheit per thousand feet.
Why the others are wrong
AThis results from incorrectly calculating the slope, possibly by dividing the temperature change by the wrong interval.
CThis incorrectly uses the y-coordinate value from one of the given points rather than calculating the rate of change.
DThis incorrectly uses an x-coordinate value rather than calculating the slope from the two points.
Question 15Medium
A study of Plant Height vs. Days After Planting recorded the relationship between the number of days after planting, x, and the height of a plant in centimeters, y. The line of best fit for the data is given by the equation y=2.5x+8.
According to the model, what is the predicted height of the plant, in centimeters, at the time of planting?
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Why C is right
At the time of planting, the number of days after planting is 0, so x=0. Substituting into the equation y=2.5(0) + 8 gives y=8. Therefore, the predicted height at planting is 8 centimeters.
Why the others are wrong
AThis is the slope of the model, which represents the rate of change in height per day, not the initial height at planting.
BThis incorrectly doubles the slope, possibly by misinterpreting the rate of change as applying to the initial condition.
DThis incorrectly adds the slope and y-intercept (2.5 + 8), or evaluates the model at x = 1 instead of x = 0.
Question 16Medium
The table shows the profit P, in thousands of dollars, for a company in year t, where t=0 corresponds to 2015.
t | P
0 | 50
1 | 58
2 | 74
3 | 98
4 | 130
Which of the following equations best models the relationship between t and P?
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Why C is right
The profit increases at an accelerating rate, suggesting a quadratic model. Testing P=50+4t2+4t: when t=1, P=50+4+4=58; when t=2, P=50+16+8=74; when t=3, P=50+36+12=98. This matches all data points.
Why the others are wrong
AThis linear model only approximates early growth. When t = 4, this gives P = 50 + 80 = 130, which coincidentally matches but fails at t = 2 where it gives 90, not 74.
BThis exponential model assumes constant percentage growth. When t = 2, this gives P = 50(1.69) = 84.5, not 74.
DThis quadratic model omits the linear term. When t = 1, this gives P = 50 + 8 = 58, which matches, but when t = 2, it gives P = 50 + 32 = 82, not 74.
Question 17Medium
A scatterplot shows the relationship between temperature in degrees Celsius, x, and ice cream sales in dollars, y, for 12 days. The data points follow a linear pattern trending upward. A line of best fit passes through approximately (10, 200) and (30, 400), with x-values ranging from 8 to 35.
The scatterplot shows the relationship between temperature and ice cream sales. A line of best fit is also shown. Based on the line of best fit, what is the predicted increase in ice cream sales, in dollars, for each 1 degree Celsius increase in temperature?
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Why C is right
The slope of the line of best fit is the change in y divided by the change in x. From (10, 200) to (30, 400), the slope is (400 - 200)/(30 - 10) = 200/20 = 10 dollars per degree Celsius.
Why the others are wrong
AThis results from incorrectly calculating the slope, possibly by inverting the rise and run or using wrong coordinates.
BThis incorrectly interprets the change in x as the rate of change, confusing the independent and dependent variables.
DThis value does not correspond to the actual slope and may result from extrapolating to an incorrect region of the data.
Question 18Medium
The scatterplot shows the relationship between two variables, x and y. The scatterplot has 9 data points roughly along a line with positive slope. The line of best fit passes through approximately (2, 15) and (10, 47). At x=6, which of the following is closest to the y-value predicted by the line of best fit?
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Why B is right
The line of best fit passes through (2, 15) and (10, 47), giving a slope of (47 - 15)/(10 - 2) = 32/8 = 4. Using point-slope form with (2, 15): y−15=4(x−2), so y=4x+7. At x=6, y=4(6) + 7 = 31.
Why the others are wrong
AThis results from incorrectly calculating the slope as 5 instead of 4, leading to y = 5x + 5 and predicting y = 35 at x = 6.
CThis confusion treats the x-value as the predicted y-value, misreading which axis represents the prediction.
DThis value exceeds what the line predicts even at x = 10, representing an extrapolation error beyond the actual trend.
Question 19Medium
The scatterplot shows the relationship between hours of sleep per night, x, and reaction time in milliseconds, y, for 15 participants. The data points range from (4, 380) to (9, 255). A line of best fit passes through approximately (5, 365) and (8, 290).
Based on the line of best fit, what is the predicted reaction time, in milliseconds, for a participant who sleeps 7 hours per night?
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Why B is right
The line of best fit passes through (5, 365) and (8, 290), giving a slope of (290-365)/(8-5) = -75/3 = -25. Using point-slope form with (5, 365): y−365=−25(x−5), so y=−25x+490. At x=7, y=−25(7) + 490 = 315.
Why the others are wrong
AThis results from reading the y-value at x = 8 instead of calculating the predicted value at x = 7.
CThis results from using an incorrect slope of approximately -12.5, leading to y = -12.5(7) + 427.5 ≈ 340.
DThis results from incorrectly extrapolating from the data point at x = 9 rather than using the line of best fit.
Question 20Medium
The scatterplot shows the relationship between distance from city center in kilometers, x, and apartment rental price in hundreds of dollars per month, y, for 13 apartments. The data points range from (2, 26) to (18, 10). A line of best fit passes through approximately (5, 23) and (14, 14).
According to the line of best fit, what is the predicted rental price, in hundreds of dollars per month, for an apartment located 11 kilometers from the city center?
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Why B is right
The line of best fit passes through (5, 23) and (14, 14), giving a slope of (14-23)/(14-5) = -9/9 = -1. Using point-slope form with (5, 23): y−23=−1(x−5), so y=−x+28. At x=11, y=−11+28=17.
Why the others are wrong
AThis results from reading the y-value at x = 14 instead of calculating the predicted value at x = 11.
CThis results from using an incorrect slope of approximately -0.5, leading to y = -0.5(11) + 25.5 = 20.
DThis results from incorrectly extrapolating from a data point near x = 18 rather than using the line of best fit equation.
What to do after medium
Medium is the tier that decides most scores. If these are landing, the hard set is where the remaining points are.
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