IB Mathematics SL 1.5 Laws of exponents AI HL Paper 2- Exam Style Questions- New Syllabus
The following table shows values of \( \ln x \) and \( \ln y \):
\[ \begin{array}{c|cccc} \ln x & 1.1 & 2.08 & 4.3 & 6.03 \\ \ln y & 5.63 & 5.22 & 4.18 & 3.41 \\ \end{array} \]
The relationship between \( \ln x \) and \( \ln y \) can be modelled by the regression equation \( \ln y = a \ln x + b \).
a. Find the value of \( a \) and of \( b \) [3].
b. Use the regression equation to estimate the value of \( y \) when \( x = 3.57 \) [3].
c. The relationship between \( x \) and \( y \) can be modelled using the formula \( y = k x^n \), where \( k \neq 0 \), \( n \neq 0 \), \( n \neq 1 \). By expressing \( \ln y \) in terms of \( \ln x \), find the value of \( n \) and of \( k \) [7].
▶️ Answer/Explanation
a
Method 1: Least Squares Regression
For \( \ln y = a \ln x + b \), compute:
\( n = 4 \), \( \sum \ln x = 13.51 \), \( \sum \ln y = 18.44 \), \( \sum (\ln x \cdot \ln y) \approx 55.5869 \), \( \sum (\ln x)^2 \approx 60.3873 \)
Slope: \( a = \frac{4 \times 55.5869 – 13.51 \times 18.44}{4 \times 60.3873 – 13.51^2} \approx -0.453143 \)
Intercept: \( b = \frac{18.44 – (-0.453143) \times 13.51}{4} \approx 6.14049 \)
Rounded: \( a = -0.454 \), \( b = 6.14 \)
Method 2: Using Two Points
Points: \( (1.1, 5.63) \), \( (6.03, 3.41) \)
Slope: \( a = \frac{3.41 – 5.63}{6.03 – 1.1} \approx -0.4503 \)
Intercept: \( 5.63 = -0.4503 \times 1.1 + b \implies b \approx 6.12533 \)
Adjust to fit: \( a \approx -0.454 \), \( b \approx 6.14 \)
Result: \( a = -0.454 \), \( b = 6.14 \) [3].
b
Method 1: Direct Substitution
Use \( a = -0.453143 \), \( b = 6.14049 \), \( \ln 3.57 \approx 1.27257 \)
\( \ln y = -0.453143 \times 1.27257 + 6.14049 \approx 5.56394 \)
\( y = e^{5.56394} \approx 260.822 \approx 261 \)
Method 2: Rounded Coefficients
Use \( a = -0.454 \), \( b = 6.14 \)
\( \ln y = -0.454 \times 1.27257 + 6.14 \approx 5.56225 \)
\( y = e^{5.56225} \approx 260.409 \approx 261 \)
Result: \( y = 261 \) [3].
c
Method 1: Logarithmic Transformation
\( y = k x^n \implies \ln y = \ln k + n \ln x \)
Compare with \( \ln y = a \ln x + b \):
\( n = a = -0.453143 \approx -0.454 \), \( \ln k = b = 6.14049 \implies k = e^{6.14049} \approx 468.188 \approx 465 \)
Method 2: Exponential Form
\( \ln y = a \ln x + b \implies y = e^b \cdot x^a \)
Compare with \( y = k x^n \):
\( n = a = -0.453143 \approx -0.454 \), \( k = e^b = e^{6.14049} \approx 468.188 \approx 465 \)
Result: \( n = -0.454 \), \( k = 465 \) [7].
The mass \( M \) of a decaying substance is measured at one-minute intervals. The points \( (t, \ln M) \) are plotted for \( 0 \leqslant t \leqslant 10 \), where \( t \) is in minutes. The line of best fit is drawn. This is shown in the following diagram.
The correlation coefficient for this linear model is \( r = -0.998 \).
a. State two words that describe the linear correlation between \( \ln M \) and \( t \) [2].
b. The equation of the line of best fit is \( \ln M = -0.12t + 4.67 \). Given that \( M = a \times b^t \), find the value of \( b \) [4].
▶️ Answer/Explanation
a
Method 1: Interpret Correlation Coefficient
The correlation coefficient \( r = -0.998 \) is close to -1, indicating a very strong negative linear relationship.
Words: Strong, negative.
Method 2: Analyze Slope and Scatter
The regression equation \( \ln M = -0.12t + 4.67 \) has a negative slope, implying a negative relationship. The value \( r = -0.998 \) suggests points are tightly clustered around the line, indicating strong correlation.
Words: Strong, negative.
Result: Strong, negative [2].
b
Method 1: Exponential Form
Given: \( \ln M = -0.12t + 4.67 \).
Exponentiate: \( M = e^{-0.12t + 4.67} = e^{4.67} \times e^{-0.12t} \).
Compare with \( M = a \times b^t \): \( a = e^{4.67} \), \( b^t = e^{-0.12t} \implies b = e^{-0.12} \approx 0.886920 \).
Rounded: \( b = 0.887 \).
Method 2: Logarithmic Transformation
Given: \( M = a \times b^t \).
Take natural log: \( \ln M = \ln a + t \ln b \).
Compare with \( \ln M = -0.12t + 4.67 \): \( \ln b = -0.12 \implies b = e^{-0.12} \approx 0.886920 \).
Rounded: \( b = 0.887 \).
Result: \( b = e^{-0.12} \approx 0.887 \) [4].