收集了关于考试成绩 yy 随着学生每周学习数学的小时数 xx 如何变化的数据。7 名学生的数据如下:

  1. (i)

    绘制该数据的散点图,并确定线性积矩相关系数。评述在此处使用线性模型是否合适。

  2. (ii)

    以下是针对上述数据的两个可能模型:

    模型 A:y=a+bx2y = a + \dfrac{b}{x^2}

    模型 B:y=c+dxy = c + \dfrac{d}{\sqrt{x}},其中 aa、bb、cc 和 dd 是实常数。

    指出哪一个是更好的模型,并说明理由。

  3. (iii)

    针对你在 (ii) 中选择的模型,求出相应的最小二乘回归线方程。使用你的方程估计当 x=65x = 65 时 yy 的值。评述你所得答案的可靠性。

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完整解法

9(i)

1. Sketch the scatter diagram for the given data.

Plot the points (1,41)(1,41), (2,61)(2,61), (3,71)(3,71), (6,86)(6,86), (12,92)(12,92), (24,93)(24,93), and (36,95)(36,95) on a set of axes with xx (Study hours) on the horizontal axis and yy (Exam score) on the vertical axis.

[The resulting sketch should show the points forming a concave downwards curve that rises steeply at first and then levels off as xx increases.]

2. Determine the linear product-moment correlation coefficient, $r$, using a graphing calculator.

Entering the paired data (x,y)(x, y) into a graphing calculator:
n=7n = 7
∑x=84\sum x = 84, ∑y=539\sum y = 539
r≈0.729355r \approx 0.729355
r=0.729 (to 3 s.f.)r = 0.729 \text{ (to 3 s.f.)}

3. Comment on the appropriateness of a linear model based on the visual trend.

A linear model would not be appropriate here because the points on the scatter diagram do not follow a straight line; instead, they exhibit a clear non-linear trend, increasing at a decreasing rate and levelling off.

答案

[Scatter diagram sketched with xx-axis representing study hours and yy-axis representing exam scores, showing a curve that rises steeply then plateaus.]
r=0.729r = 0.729 (to 3 s.f.)
A linear model is not appropriate because the scatter diagram displays a clear curve (non-linear relationship).

9(ii)

1. Calculate the product-moment correlation coefficient for Model A.

For Model A (y=a+bx2y = a + \dfrac{b}{x^2}), the independent variable is X=1x2X = \dfrac{1}{x^2}.

Using a graphing calculator for the paired data (1x2,y)\left(\dfrac{1}{x^2}, y\right), the product-moment correlation coefficient is
rA≈−0.9077r_A \approx -0.9077

2. Calculate the product-moment correlation coefficient for Model B.

For Model B (y=c+dxy = c + \dfrac{d}{\sqrt{x}}), the independent variable is X=1xX = \dfrac{1}{\sqrt{x}}.

Using a graphing calculator for the paired data (1x,y)\left(\dfrac{1}{\sqrt{x}}, y\right), the product-moment correlation coefficient is
rB≈−0.9920r_B \approx -0.9920

3. Compare the $r$ values to conclude which model is better.

Comparing the absolute values of the correlation coefficients, ∣rB∣|r_B| is closer to 11 than ∣rA∣|r_A| (∣−0.9920∣>∣−0.9077∣|-0.9920| > |-0.9077|).

Thus, there is a stronger linear correlation between yy and 1x\dfrac{1}{\sqrt{x}} than between yy and 1x2\dfrac{1}{x^2}. Model B is the better model.

答案

Model B is a better model as its product-moment correlation coefficient, rr, has an absolute value closer to 11.

9(iii)

1. Find the equation of the least squares regression line for the chosen model.

Assuming Model B (y=c+dxy = c + \dfrac{d}{\sqrt{x}}) was chosen in (ii) as its linear product-moment correlation coefficient is closer to −1-1 (r≈−0.992r \approx -0.992 compared to −0.907-0.907 for Model A).

Let X=1xX = \dfrac{1}{\sqrt{x}}. Entering the paired data (X,y)(X, y) into a graphing calculator, the least squares regression line of yy on XX is:
y=108.93−66.682Xy = 108.93 - 66.682 X
y=109−66.7x (to 3 s.f.)y = 109 - \dfrac{66.7}{\sqrt{x}} \text{ (to 3 s.f.)}

2. Estimate the value of $y$ when $x = 65$.

Substitute x=65x = 65 into the unrounded regression equation:
y=108.93−66.68265y = 108.93 - \dfrac{66.682}{\sqrt{65}}
y=100.66≈101 (to 3 s.f.)y = 100.66 \approx 101 \text{ (to 3 s.f.)}

3. Comment on the reliability of the estimate.

The value x=65x = 65 lies outside the given data range of 1≤x≤361 \le x \le 36. Since this estimation involves extrapolation, the estimated value is unreliable (the linear relationship may not hold beyond the known data points).

答案

Equation: y=109−66.7xy = 109 - \dfrac{66.7}{\sqrt{x}}
Estimate: y≈101y \approx 101
Reliability: Unreliable, as the estimation involves extrapolation outside the given data range.

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