Situation. You, as a property financial backer, are keen on understanding which factor

(or then again factors) drives the costs of venture properties. A dataset is gathered which

contains the costs (in thousand dollars, as indicated by separated cost) for 50 one-

room lofts in city X, their comparing rents each week (in dollars, as

indicated by lease) and the expenses to hold every one of these properties each week (in dollars,

as meant by cost of property). Following the strategies beneath to dissect the

dataset 'assign2 data.csv' by utilizing Rstudio. If it's not too much trouble, just incorporate significant yields

from Rstudio in your answer and join the R codes as appendice ( for

''14''

join R codes).

(a). Import the information into Rstudio, draw two disperse plots: separated cost

versus lease and separated cost versus cost.

(b). Fit the accompanying two direct models:

Model 1: separated cost = b0 + b1 lease

Model 2: separated cost = c0 + c1 cost

Record the conditions of the two models with right coefficients.

(c). Recorded the p-values from the yield of your R codes. Com-

ment on the meaning of all coefficients acquired from (b) in view of the p-

values (from the yields of Rtudio). The importance level is 0.05.

(d). Produce leftover plots for each model in (b), remark on each plot.

(e). Produce ordinary qq plots for each model in (b), and remark on

each plot.

(f). Fit the accompanying straight model:

Model 3: separated cost = d0 + d1rent + d2cost

Record the condition of the model with right coefficients.

(g). Recorded the p-values from the yield of your R codes. Com-

ment on the meaning of all coefficients acquired from (f) in view of the p-

values (from the yields of Rtudio). The importance level is 0.05.

(h). Analyze Model 1 and Model 3, clarify which one is better.

(i).Given lease = 900 and cost = 650, foresee costs under Model 1 and

Model 3.

apart_price rent cost_of_property

500 277 55

505 282 60

515 297 65

520 309 70

525 313 72

535 319 75

540 326 72

550 333 71

555 342 70

570 358 68

585 371 63

590 379 60

595 396 71

600 401 73

615 407 78

620 414 80

625 426 82

630 434 85

635 441 81

645 443 80

660 455 91

665 473 93

670 479 95

675 483 97

685 487 96

695 508 95

710 519 93

720 523 92

725 531 91

730 537 94

735 546 97

750 557 99

755 563 100

765 572 104

770 582 103

775 597 101

785 602 101

795 608 97

800 618 90

805 627 95

810 634 101

815 641 103

825 653 104

835 662 96

840 665 98

845 673 91

860 686 87

875 697 110

890 711 112

905 728 115