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Inferential Statistics · Paper 1 — Free Maths AI SL Practice Questions

1FoundationSAQ-SRegression analysis5 marksPaper 1~8 min
A researcher studies the relationship between the number of hours a plant is exposed to sunlight each day, xx, and the height of the plant after 30 days, yy cm. Data collected for six plants is shown below. x (hours): 1, 2, 3, 4, 5, 6 y (cm): 12, 18, 25, 30, 36, 40
(a)
State the type of regression model that would be most appropriate for this data. [1 mark]
(b)
Calculate the equation of the regression line of yy on xx, giving the values of aa and bb in y=ax+by = ax + b correct to three significant figures. [2 marks]
(c)
A plant receives x=8x = 8 hours of sunlight per day. A student uses the regression equation to predict its height after 30 days and states: "The model predicts a height of 52.552.5 cm, so this is a reliable estimate." Evaluate the reliability of this prediction. [2 marks]
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2FoundationSAQ-SRegression analysis4 marksPaper 1~6 min
A student investigates the relationship between the age of a car, xx years, and its selling price, yy thousand dollars. Data for five cars are shown in the table below. Age xx (years) — 1 — 2 — 3 — 4 — 5 Price yy (thousand dollars) — 18 — 15 — 12 — 9 — 6 The regression line of yy on xx is y=21.0−3.00xy = 21.0 - 3.00x.
(a)
State the type of correlation between the age and selling price of a car. [1 mark]
(b)
A car is 4.5 years old. Calculate its predicted selling price in dollars. [2 marks]
(c)
The actual selling price of the 4.5-year-old car is USD 6000. (i) Calculate the residual for this car in dollars. [1]
(ii) State what the sign of the residual indicates about the regression line's prediction for this car. [1 mark]
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3MasterySAQ-SRegression analysis5 marksPaper 1~8 min
A biologist studies the relationship between temperature (TT, in °C) and chirping rate (CC, in chirps per minute) of a species of cricket. She collects data for n=8n = 8 temperatures and obtains the following summary statistics: ∑T=160,∑C=140,∑T2=3600,∑C2=2800,∑TC=3100\sum T = 160, \quad \sum C = 140, \quad \sum T^2 = 3600, \quad \sum C^2 = 2800, \quad \sum TC = 3100
(a)
Show that the Pearson correlation coefficient r≈0.8r \approx 0.8, correct to 1 decimal place. [3 marks]
(b)
Calculate the coefficient of determination r2r^2. [1 mark]
(c)
Hence explain what the value of r2r^2 indicates about the relationship between temperature and chirping rate. [1 mark]
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4ChallengeSAQ-LRegression analysis8 marksPaper 1~12 min
A marine biologist studies the relationship between water temperature (TT, in °C) and diving depth (DD, in metres) for a species of seal. Data from n=12n = 12 seals give the following summary statistics: ∑T=96.0,∑D=240,∑T2=824.0,∑D2=5400,∑TD=2100\sum T = 96.0, \quad \sum D = 240, \quad \sum T^2 = 824.0, \quad \sum D^2 = 5400, \quad \sum TD = 2100
(a)
Calculate the Pearson correlation coefficient rr, correct to three significant figures. [2 marks]
(b)
Determine the equation of the regression line of DD on TT in the form D=a+bTD = a + bT, with aa and bb correct to three significant figures. [2 marks]
(c)
Use your regression line to estimate the diving depth when T=9.5 °CT = 9.5\,°\text{C}, giving your answer correct to the nearest metre. [1 mark]
(d)
The biologist claims: "Temperature is a good predictor of diving depth because the correlation is strong." Evaluate this claim. In your response, refer to your value of rr, the reliability of the regression model within the data range, and one limitation of using this model. The following formulae are provided: r=n∑TD−(∑T) ⁣(∑D)n∑T2−(∑T)2  n∑D2−(∑D)2r = \frac{n\sum TD - \left(\sum T\right)\!\left(\sum D\right)}{\sqrt{n\sum T^2 - \left(\sum T\right)^2}\;\sqrt{n\sum D^2 - \left(\sum D\right)^2}} b=n∑TD−(∑T) ⁣(∑D)n∑T2−(∑T)2,a=Dˉ−bTˉb = \frac{n\sum TD - \left(\sum T\right)\!\left(\sum D\right)}{n\sum T^2 - \left(\sum T\right)^2}, \qquad a = \bar{D} - b\bar{T} [3 marks]
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Solutions

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