Use the table, answer the questions, please include the result of the answers(number,algebra).

1.What is the median value for Balance?

2. If we want to understand the relationship between variables, a good place to start is to explore the correlation. What is the correlation between Limit and Balance?

3. Now, let's explore the relationship between limit and balance in a bit more detail. Build a simple linear regression model, using Balance as your outcome variable and Limit as your input variable. Run this line first: options(scipen=999). Give your model the object name model, using the code below as a template/starting point: scipen(options=999) #avoids scientific notation model

4. What is the multiple r-squared value for your model? Remember to enter the multiple r-squared value here -- not the adjusted r-squared value.

5. Now it's time to predict the future! What Balance would your model predict for a consumer with a Limit of 5000? (Note: You do not necessarily need to use an R function to solve this).

6. Now let's try some multiple linear regression. Make a new model now, using two input values: Limit and age, in order to predict the outcome variable Balance. Below is an example/template, but remember, you will need to change some things here to make this work with your data: model

7. Take a look at your dataset. What Balance would your model predict for the person in the third row?

8. Let's make another prediction. What Balance would your model predict for a fictional person with a Limit of 6000 and an Age of 30?

9. Now, let's take a look at a residual value from this model. A residual is found by taking the observed value (in other words, the actual value) and subtracting the expected value (in other words, the value your model predicted). The actual value in this dataset for any customer is the Balance value for that customer's row. What is the residual value here for the consumer in the third row?

10. What is the residual value for the consumer in the 10th row?

A B C D E F G H K M N O ID Income Limit Rating Cards Age Education Gender Student Married Ethnicity Balance 139 21. 374 2073 175 74 11 Female No Yes Caucasian 0 AWN 228 66. 989 5614 430 47 14 Female No Yes Caucasian 182 307 45. 12 3762 287 80 8 Male No Yes Caucasian 70 400 18. 701 5524 415 64 7 Female No No Asian 966 135 34. 664 5835 452 77 15 Female No Yes African An 835 371 35. 61 6135 466 40 12 Male No No Caucasian 992 50 00 263 10. 588 4049 296 66 13 Female No Yes Caucasian 465 373 19. 782 3782 293 46 16 Female Yes No Caucasian 840 10 273 30. 413 3690 299 25 15 Female Yes No Asian 728 11 292 24. 088 3665 287 56 13 Female No Yes Caucasian 309 12 24 64. 027 5179 398 48 8 Male No Yes African An 411 13 72 58. 781 7402 538 81 12 Female No Yes Asian 1103 14 227 59. 879 6906 527 78 15 Female No No Caucasian 1032 15 347 21. 551 5380 420 51 18 Male No Yes Asian 907 16 25 10. 742 1757 156 57 15 Female No No Caucasian 0 17 188 23. 365 2179 167 75 15 Male No No Asian 0 18 2 106. 025 6645 483 82 15 Female Yes Yes Asian 903 19 301 21. 786 4632 355 50 17 Male No Yes Caucasian 580 20 262 180. 379 9310 665 67 8 Female Yes Yes Asian 1050 21 208 10. 842 4391 358 37 10 Female Yes Yes Caucasian 1216 22 381 115. 123 7760 538 83 14 Female No No African An 661 23 348 160. 231 10748 754 69 17 Male No No Caucasian 1192 24 12 15. 045 1311 138 64 16 Male No No Caucasian 0 25 51 36. 362 5183 376 49 15 Male No Yes African An 654 26 211 24. 543 3206 243 62 12 Female No Yes Caucasian 95 27 159 42. 357 5550 406 83 12 Female No Yes Asian 653 28 256 58. 063 4221 304 50 8 Male No No African An 118 29 255 36. 508 6386 469 79 6 Female No Yes Caucasian 104830 239 26. 532 2910 236 58 19 Female No Yes Caucasian 52 31 391 135. 118 10578 747 81 15 Female No Yes Asian 1393 32 226 15. 079 5673 411 28 15 Female No Yes Asian 33 1075 103 68. 713 7582 531 56 16 Male Yes No Caucasian 34 1587 306 24. 46 1924 165 50 14 Female No Yes Asian 0 35 33 134. 181 7838 563 48 13 Female No No Caucasian 36 526 65 32. 164 2937 223 79 15 Female No Yes 37 African An 0 19 49. 57 6384 448 28 9 Female No Yes Asian 891 38 246 34. 509 2001 189 80 18 Female No Yes African An 39 0 203 21. 038 1448 145 58 13 Female No Yes Caucasian 0 40 193 28. 508 3933 287 56 14 Male No Yes 41 Asian 336 351 30. 002 1561 155 70 13 Female No Yes 42 Caucasian 0 396 12. 096 4100 307 32 13 Male No Yes 43 Caucasian 560 96 14. 084 855 120 46 17 Female No Yes 44 African An 0 179 28. 316 4391 316 29 10 Female No No Caucasian 453 45 1 14. 891 3606 283 34 11 Male No Yes 46 Caucasian 333 279 128. 04 6982 518 78 11 Female No Yes 47 Caucasian 250 89 15. 56 4307 352 57 8 Male No Yes African An 579 48 297 65. 896 5140 370 49 17 Female No Yes Caucasian 293 49 171 34. 772 2021 167 57 9 Male No No 50 Asian 0 48 44. 646 4431 320 49 15 Male Yes Yes Caucasian 797 51 354 41. 868 4716 342 47 18 Male No No Caucasian 425 52 147 17. 055 5110 371 55 15 Female No Yes 53 Caucasian 805 247 19. 588 3211 265 59 14 Female No No Asian 54 199 77 30. 622 3293 251 68 16 Male Yes No 55 Caucasian 532 205 57. 337 5310 392 45 7 Female No No Caucasian 56 456 356 180. 682 11966 832 58 8 Female No Yes African An 1405\f\f