Fitting Exponential Models to Data · Try It 2

Sales of a video game released in the year 2000 took off at first, but then steadily slowed as time moved on. The table shows the number of games sold, in thousands, from the years 2000–2010.

$$\begin{array}{ccccccc}\text{Year}& \text{2000}& \text{2001}& \text{2002}& \text{2003}& \text{2004}& \text{2005}\\ \text{Number Sold (thousands)}& \text{142}& \text{149}& \text{154}& \text{155}& \text{159}& \text{161}\\ \text{Year}& \text{2006}& \text{2007}& \text{2008}& \text{2009}& \text{2010}& \text{-}\\ \text{Number Sold (thousands)}& \text{163}& \text{164}& \text{164}& \text{166}& \text{167}& \text{-}\end{array}$$

ⓐ Let $x$ represent time in years starting with $x=1$ for the year 2000. Let $y$ represent the number of games sold in thousands. Use logarithmic regression to fit a model to these data.
ⓑ If games continue to sell at this rate, how many games will sell in 2015? Round to the nearest thousand.

OpenStax, Algebra and Trigonometry 2e · §6.8 Fitting Exponential Models to Data · Try It 2