Hello!

I need assistance with this multi-layered question, including 7 parts (0-4, then 15 and 16 as seen in the screenshots below).

The question/prompt is listed in the first screenshot. Here it is again below.

Studies have shown that the frequency with which shoppers browse Internet retailers is related to the frequency with which they actually purchase products and/or services online. The following data show respondents' age and answer to the question "How many minutes do you browse online retailers per year?"

Note that this sheet includes questions 10-16 Age (X) Time (Y) 16 420 Studies have shown that the frequency with which shoppers browse Internet retailers is related to the frequency with which they actually purchase products and/or services online. The following data show 17 269 respondents age and answer to the question "How many minutes do you browse online retailers per year?" 19 315 22 337 22 243 22 459 22 414 28 224 28 381 28 412 28 576 30 333 33 551 34 548 35 626 35 521 35 562 699 39 643 455 666 553 459 525 559 507 612 710 378 566 652 725 6950l 1l 3l Use Data > Data Analysis > Correlation to compute the correlation checking the Labels checkbox. Use the Excel function =CORREL to compute the correlation. If answers for #1 and 2 do not agree, there is an error. The strength of the correlation motivates further examination. a) Insert Scatter (X, Y} plot linked to the data on this sheet with Age on the horizontal (X) axis. b) Add to your chart: the chart name, vertical axis label, and horizontal axis label. c) Complete the chart by adding Trendline and checking boxes [3'3 D'splly Equation on that [E Display B-squared value on dark Read directly from the chart: a) Intercept = b) Slope = o) R2 = Perform Data > Data Analysis > Regression. Highlight the Y-intercept with yellow. Highlight the X variable in blue. Highlight the R Square in orange 15) Use Excel to predict the number of minutes spent by a 22-year old shopper. Enter = followed by the regression formula. Enter the intercept and slope into the formula by clicking on the cells in the regression output with the results. 1.6) Is it appropriate to use this data to predict the amount of time that an 85-year-old will spend browsing online retailers ? If yes, what is the amount of time, if no, why