Online entertainment streaming services have gained in popularity in recent years as an alternative to traditional television. One such company has seen steady growth in each period of 3 months, called a quarter, over the past 4 years. The scatterplot shows the relationship between the number of quarters since January 2014 and the log of the number of members to the streaming service. A least-squares equation that summarizes this relationship is Log subscribers hat = 0.026 (quarters) minus 1.299. A graph titled log (subscribers) versus quarters since January 2011 has quarters since January 2011 on the x-axis, and log (subscribers) on the y-axis. Points form a line with positive slope. A graph titled log (subscribers) versus quarters since January 2011 has quarters since January 2011 on the x-axis, and residual on the y-axis. Points are scattered throughout the graph. Based on the scatterplot and residual plot, what type of model is appropriate for comparing time and subscribers? A linear model is appropriate because the residual plot shows a random scatter of points. A logarithmic model is appropriate because the log of the number of subscribers was taken. A power model is appropriate because the relationship between period and the log of subscribers is roughly linear. An exponential model is appropriate because the relationship between period and the log of subscribers is roughly linear and the residual plot shows no distinct pattern.