9154AM ThuAprZO "- - ?OB3%1!' B myopenmath53.s3.amazonawscom Step 4: Average rate of Change (4 pts) Compute the average rate of change per year of the cost over each time period. Just fill in the result; don't' show the calculation. Time Line Price(d0llars) Average rate of change 1 970 $0 .20 XXXXXXXXXXXXX 1980 $0.50 2003 $2.00 2009 $2.25 2013 $2.50 2015 $2.75 Do these values suggest a linear trend? Explain. Step 5: Linear Modeling (6 pts) Assuming that the trend is linear, generate a linear model. To make the calculation easier, rescale the time values for 2009 through 2015 in the above table. Let 2009 be the year 0. t I P( dollars) 0 | 4 10:07 AM Thu Apr 20 . . . 1 78% myopenmaths3.s3.amazonaws.com Step 5: Linear Modeling (6 pts) Assuming that the trend is linear, generate a linear model. To make the calculation easier, rescale the time values for 2009 through 2015 in the above table. Let 2009 be the year 0. P( dollars) 210:40 AM Thu Apr 20 . @ 73% myopenmaths3.s3.amazonaws.com Using any two data points from the rescaled table develop a possible linear model of the above data by filling in the blanks in the following calculation. (0, 2. 25) and ( , ) P = at + b P = at + ? a = ( - )( - ) a = a= P = ?t + ? (a) Predict the price in 2020 For 208, t = ? P = ? ( ? ) + ? P =10:41 AM Thu Apr 20 . . . . @(7 72% myopenmaths3.$3.amazonaws.com a= P = ?t + ? (a) Predict the price in 2020 For 208, t = ? P = ?( ? ) + ? P = According to this model, the Price is expected to be about $ in the year 2018. Step 6: Using EXCEL (6 pts) (b) Obtain a scatter plot of the data with the "best-fit" line