A technology specialist studied the relationship between time spent on social media and age. One observation found that a 40-year-old person spends 6 hour per week on social media. A second observation showed that a 20- year-old person spends 14 hours per week on social media. Assuming time spent per week on social media T (in hr) is a linear function of age a (in yr), find a linear model of this event. T = Use the model

A popular television show recently released a video previewing the upcoming season on his website as fans of the show discovered the video the number of views of the preview video has grown each day during the last two weeks the number of days in the release of the video and the natural log of the number of video views as shown in the scatterplot. based on the scatterplot and residual plot, what type of model would be appropriate for summarizing the relationship between days and numbers of views? an exponential model is appropriate because the Residual plot does not show a clear pattern A linear model is appropriate because of scatterplot shows a strong positive linear relationship A power model is appropriate because the scatterplot of days a natural log of use is strong and linear an exponential model is appropriate because the relationship between the days in the natural log of views in linear and the residual plot does not show a clear pattern

A box office analyst seeks to predict opening weekend box office gross for movies. Toward this​ goal, the analyst plans to use online trailer views as a predictor. For each of the 66 ​movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross​ (in millions of​ dollars) are collected and stored in the accompanying table. A linear regression was performed on these​ data, and the result is the linear regression equation Yi=−0.840+1.4108Xi. Determine the coefficient of​ determination,r2​,and interpret its meaning. Determine the standard error of the estimate. How useful do you think this regression model is for predicting opening weekend box office​ gross? Can you think of other variables that might explain the variation in opening weekend box office​ gross?

A box office analyst seeks to predict opening weekend box office gross for movies. Toward this​ goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross​ (in millions of​ dollars) are collected and stored in the accompanying table. A linear regression was performed on these​ data, and the result is the linear regression equation Yi=−1.254+1.3968Xi.   Complete parts​ (a) through​ (d). a. Determine the coefficient of​ determination,r2​,and interpret its meaning. b. Determine the standard error of the estimate.   c. How useful do you think this regression model is for predicting opening weekend box office​ gross?   d. Can you think of other variables that might explain the variation in opening weekend box office​ gross?