Lets consider a similar problem where we actually keep up with the bed-time and the arrival time simultaneously. Both of these variables are continuous, so you should use regression to try and show if there is a relationship between the two variables. Use the data below to create a regression equation.

A) Interpret the coefficients of the equation. B) Interpret the R-square value. C) Do the results indicate that going to bed late has an influence on the arrival time? Explain your answer

\begin{tabular}{|c|c|} \hline Bed Time & Arrival Time \\ \hline 558 & 436 \\ \hline 595 & 445 \\ \hline 623 & 470 \\ \hline 627 & 433 \\ \hline 562 & 453 \\ \hline 541 & 432 \\ \hline 575 & 449 \\ \hline 652 & 448 \\ \hline 588 & 460 \\ \hline 651 & 451 \\ \hline 558 & 432 \\ \hline 587 & 445 \\ \hline 610 & 434 \\ \hline 646 & 458 \\ \hline 654 & 448 \\ \hline 620 & 454 \\ \hline \end{tabular} \begin{tabular}{|c|c|} \hline Bed Time & Arrival Time \\ \hline 598 & 442 \\ \hline 581 & 457 \\ \hline 611 & 457 \\ \hline 608 & 452 \\ \hline 544 & 437 \\ \hline 591 & 444 \\ \hline 577 & 440 \\ \hline 551 & 439 \\ \hline 555 & 440 \\ \hline 615 & 459 \\ \hline 585 & 440 \\ \hline 658 & 466 \\ \hline 606 & 453 \\ \hline 636 & 453 \\ \hline 578 & 465 \\ \hline 588 & 425 \\ \hline 585 & 460 \\ \hline \end{tabular} \begin{tabular}{|c|c|} \hline Bed Time & Arrival Time \\ \hline 584 & 455 \\ \hline 617 & 456 \\ \hline 578 & 429 \\ \hline 609 & 468 \\ \hline 541 & 438 \\ \hline 610 & 461 \\ \hline 652 & 459 \\ \hline 546 & 440 \\ \hline 593 & 464 \\ \hline 544 & 437 \\ \hline 635 & 441 \\ \hline 615 & 447 \\ \hline 552 & 446 \\ \hline 649 & 460 \\ \hline 648 & 462 \\ \hline 624 & 452 \\ \hline 626 & 450 \\ \hline \end{tabular} The regression equation is Arrival Time =352.8+0.1605 Bed Time Model Summary Analysis of Variance