The table below shows experimental values of two variables xx and yy. It is known that xx and yy are related by an equation of the form y=h(x+1)ky = h(x+1)^k, where hh and kk are constants.
xx123456
yy5.27.29.110.912.614.2

  1. (i)

    Draw the graph of lg⁡y\lg y plotted against lg⁡(1+x)\lg(1+x), using a scale of 2 cm to 0.1 unit on both axes.

  2. (ii)

    Use your graph to estimate the value of each of the constants hh and kk.

  3. (iii)

    Explain how the value of yy when x=1.5x = 1.5 can be estimated using the straight line graph.

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Worked solution

3(i)

1. Calculate the values of $X = \lg(1+x)$ and $Y = \lg y$ for each data point.

For x=1,y=5.2x=1, y=5.2: X=lg⁡(2)≈0.301X = \lg(2) \approx 0.301, Y=lg⁡(5.2)≈0.716Y = \lg(5.2) \approx 0.716
For x=2,y=7.2x=2, y=7.2: X=lg⁡(3)≈0.477X = \lg(3) \approx 0.477, Y=lg⁡(7.2)≈0.857Y = \lg(7.2) \approx 0.857
For x=3,y=9.1x=3, y=9.1: X=lg⁡(4)≈0.602X = \lg(4) \approx 0.602, Y=lg⁡(9.1)≈0.959Y = \lg(9.1) \approx 0.959
For x=4,y=10.9x=4, y=10.9: X=lg⁡(5)≈0.699X = \lg(5) \approx 0.699, Y=lg⁡(10.9)≈1.037Y = \lg(10.9) \approx 1.037
For x=5,y=12.6x=5, y=12.6: X=lg⁡(6)≈0.778X = \lg(6) \approx 0.778, Y=lg⁡(12.6)≈1.100Y = \lg(12.6) \approx 1.100
For x=6,y=14.2x=6, y=14.2: X=lg⁡(7)≈0.845X = \lg(7) \approx 0.845, Y=lg⁡(14.2)≈1.152Y = \lg(14.2) \approx 1.152

2. Plot the points on the grid and draw the line of best fit.

Plot the points (0.301,0.716)(0.301, 0.716), (0.477,0.857)(0.477, 0.857), (0.602,0.959)(0.602, 0.959), (0.699,1.037)(0.699, 1.037), (0.778,1.100)(0.778, 1.100), (0.845,1.152)(0.845, 1.152) on axes with scale 2 cm to 0.1 unit.
Draw a straight line of best fit passing through or close to these points.

Answer

A straight line graph plotted with points (0.301,0.716)(0.301, 0.716), (0.477,0.857)(0.477, 0.857), (0.602,0.959)(0.602, 0.959), (0.699,1.037)(0.699, 1.037), (0.778,1.100)(0.778, 1.100), (0.845,1.152)(0.845, 1.152).

3(ii)

1. Convert the non-linear equation to linear form using logarithms

y=h(x+1)ky = h(x+1)^k
lg⁡y=lg⁡(h(x+1)k)\lg y = \lg(h(x+1)^k)
lg⁡y=lg⁡h+klg⁡(x+1)\lg y = \lg h + k \lg(x+1)

2. Identify the gradient and $Y$-intercept from the linear form

Comparing lg⁡y=klg⁡(x+1)+lg⁡h\lg y = k \lg(x+1) + \lg h with Y=mX+cY = mX + c:
Gradient m=km = k
YY-intercept c=lg⁡hc = \lg h

3. Calculate the gradient to find $k$

Using points from the line of best fit, e.g., (0.30,0.72)(0.30, 0.72) and (0.85,1.00)(0.85, 1.00):
k=1.00−0.720.85−0.30=0.280.55≈0.51k = \frac{1.00 - 0.72}{0.85 - 0.30} = \frac{0.28}{0.55} \approx 0.51
(Acceptable range for kk: 0.500.50 to 0.540.54)

4. Find the $Y$-intercept to calculate $h$

From the graph, the YY-intercept is approximately 0.560.56.
lg⁡h=0.56\lg h = 0.56
h=100.56≈3.63h = 10^{0.56} \approx 3.63
(Acceptable range for hh: 3.53.5 to 3.83.8)

Answer

h≈3.63h \approx 3.63, k≈0.52k \approx 0.52

3(iii)

1. Explain the process of finding the corresponding $y$ value from the graph

Calculate the value on the horizontal axis: lg⁡(1+1.5)=lg⁡2.5\lg(1+1.5) = \lg 2.5.
Read the corresponding value on the vertical axis from the graph, which gives lg⁡y\lg y.
Calculate yy using y=10lg⁡yy = 10^{\lg y}.

Answer

Calculate lg⁡(1+1.5)=lg⁡2.5\lg(1+1.5) = \lg 2.5. Find this value on the horizontal axis and read the corresponding value on the vertical axis to get lg⁡y\lg y. Finally, calculate y=10lg⁡yy = 10^{\lg y}.

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