Number and Algebra
Functions
Geometry and Trigonometry
Statistics and Probability
Calculus
Convert the masses 50 g50\text{ g}50 g, 1500 g1500\text{ g}1500 g and 25000 g25000\text{ g}25000 g to kilograms. Define M=m1000M=\dfrac{m}{1000}M=1000m where mmm is in grams. What are the values of MMM?
A process takes times t=1800 t=1800\,t=1800s, 5400 5400\,5400s and 9000 9000\,9000s. Define the scaled variable T=t3600T=\dfrac{t}{3600}T=3600t in hours. Calculate the values of TTT.
If variable yyy ranges from 000 to 250002500025000 mm and you define Y=y1000Y = \frac{y}{1000}Y=1000y in metres, find the range of YYY.
The exponential model is y=5erx.y = 5e^{rx}.y=5erx. Show that plotting ln(y)\ln(y)ln(y) versus xxx yields the straight‐line equation ln(y)=ln(5)+rx\ln(y) = \ln(5) + rxln(y)=ln(5)+rx.
Express the exponential model y=10e2xy = 10e^{2x}y=10e2x as a linear relationship between ln(y)\ln(y)ln(y) and xxx.
A company’s monthly revenue ranges from $200,000 to $1,500,000. If R=revenue106R=\dfrac{\text{revenue}}{10^6}R=106revenue in millions of dollars, find the minimum and maximum values of RRR.
Convert the model y=5erxy = 5e^{rx}y=5erx into a linear model involving log10(y)\log_{10}(y)log10(y) and xxx.
Express the model y=12e−0.5xy = 12e^{-0.5x}y=12e−0.5x in the form ln(y)=mx+c\ln(y)=mx+cln(y)=mx+c and identify mmm and ccc.
An experiment yields the best‐fit line ln(P)=−0.2t+3.5.\ln(P) = -0.2t + 3.5.ln(P)=−0.2t+3.5. Write down the exponential decay model for PPP as a function of ttt.
A best‐fit line for ln(y)\ln(y)ln(y) versus xxx is given by ln(y)=0.693+0.05x.\ln(y) = 0.693 + 0.05x.ln(y)=0.693+0.05x. Determine the parameters AAA and rrr of the equivalent exponential model y=Aerxy = Ae^{rx}y=Aerx.
Given the exponential decay model N=40e−0.5t,N = 40e^{-0.5t},N=40e−0.5t, find the half‐life T1/2T_{1/2}T1/2 of NNN.
Given the straight‐line fit log10(y)=1.301+0.02x,\log_{10}(y) = 1.301 + 0.02x,log10(y)=1.301+0.02x, find the equivalent exponential model y=Aerxy = Ae^{rx}y=Aerx.
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Question Type 2: Converting exponential and logarithmic models into linearized relations