Predicting Subscriber Growth Models

The video tutorial explains how to analyze data on US cell phone subscribers using a scatter plot and a linear model. It demonstrates how to use the model to predict the number of subscribers in a given year, specifically 2016, and estimates when the subscriber count will reach 333 million. The tutorial emphasizes the importance of understanding the model's fit to the data and using it for accurate predictions.

What does the first column in the data table represent?

• Annual growth rate

• Total population

• Years since 1995

• Number of subscribers in millions

What is the purpose of creating a scatter plot in this analysis?

• To calculate the average number of subscribers

• To visualize the relationship between years and subscribers

• To predict future population growth

• To determine the exact number of subscribers each year

What does the linear model C(T) = 8.997T + 17711 represent?

• The total number of subscribers each year

• A prediction of future subscribers based on past data

• The average number of subscribers over the years

• The maximum number of subscribers possible

Why is the linear model considered a good fit for the data?

• The points on the scatter plot are close to the line

• It passes through all data points exactly

• It has the highest possible slope

• It predicts the exact number of subscribers each year

How do you calculate the value of T for the year 2016?

• Divide 2016 by 1995

• Multiply 1995 by 2016

• Subtract 1995 from 2016

• Add 1995 to 2016

What is the estimated number of subscribers in 2016 according to the model?

• 416 million

• 416,648 million

• 46.648 million

• 648 million

What equation is used to predict when subscribers will reach 333 million?

• T = 333 / 18.997

• C(T) = 18.997T + 333

• 333 = 18.997T + 17.711

• C(T) = 333

What is the predicted year when subscribers will reach 333 million?

• 2013

• 2012

• 2011

• 2010

Why is it important not to round the value of T to 17 in the prediction?

• It would make the model inaccurate

• It would predict the wrong year

• It would increase the number of subscribers

• It would decrease the number of subscribers

What is the significance of the interval 16 ≤ T < 17 in the prediction?

• It shows the range of years for the prediction

• It defines the model's accuracy

• It represents the number of subscribers

• It indicates the model's error margin