Logistic Regression
This assignment requires reading a primer on “Logistic Regression”
You will need to download an Excel Add-Ins – Real Statistics Resource Pack for this assignment. The site contains instructions on how to download and install the Add-Ins.
For information on how to use Real Statistics for this assignment see “Real Statistics Data Analysis Tool”
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Part 1 (30 points)
The data used in this assignment is from Andrew Ng’s Machine Learning course on Coursera. The data is provided here. The data consists of marks of two exams for 100 applicants. The target value (last column) takes on binary values 1 (“1” means the applicant was admitted to the university whereas “0” means the applicant didn’t get an admission. The objective is to build a classifier that can predict whether an application will be admitted to the university or not.
You will need to copy the dataset below into a text editor i.e. Notepad as a .csv (comma delimited value) file before you can load into Excel.
| 34.62365962451697,78.0246928153624,0 |
| 79.0327360507101,75.3443764369103,1 |
| 45.08327747668339,56.3163717815305,0 |
| 61.10666453684766,96.51142588489624,1 |
| 75.02474556738889,46.55401354116538,1 |
| 76.09878670226257,87.42056971926803,1 |
| 84.43281996120035,43.53339331072109,1 |
| 95.86155507093572,38.22527805795094,0 |
| 75.01365838958247,30.60326323428011,0 |
| 82.30705337399482,76.48196330235604,1 |
| 69.36458875970939,97.71869196188608,1 |
| 39.53833914367223,76.03681085115882,0 |
| 53.9710521485623,89.20735013750205,1 |
| 69.07014406283025,52.74046973016765,1 |
| 67.94685547711617,46.67857410673128,0 |
| 70.66150955499435,92.92713789364831,1 |
| 76.97878372747498,47.57596364975532,1 |
| 67.37202754570876,42.83843832029179,0 |
| 89.67677575072079,65.79936592745237,1 |
| 50.534788289883,48.85581152764205,0 |
| 34.21206097786789,44.20952859866288,0 |
| 77.9240914545704,68.9723599933059,1 |
| 62.27101367004632,69.95445795447587,1 |
| 80.1901807509566,44.82162893218353,1 |
| 93.114388797442,38.80067033713209,0 |
| 61.83020602312595,50.25610789244621,0 |
| 38.78580379679423,64.99568095539578,0 |
| 61.379289447425,72.80788731317097,1 |
| 85.40451939411645,57.05198397627122,1 |
| 52.10797973193984,63.12762376881715,0 |
| 52.04540476831827,69.43286012045222,1 |
| 40.23689373545111,71.16774802184875,0 |
| 54.63510555424817,52.21388588061123,0 |
| 33.91550010906887,98.86943574220611,0 |
| 64.17698887494485,80.90806058670817,1 |
| 74.78925295941542,41.57341522824434,0 |
| 34.1836400264419,75.2377203360134,0 |
| 83.90239366249155,56.30804621605327,1 |
| 51.54772026906181,46.85629026349976,0 |
| 94.44336776917852,65.56892160559052,1 |
| 82.36875375713919,40.61825515970618,0 |
| 51.04775177128865,45.82270145776001,0 |
| 62.22267576120188,52.06099194836679,0 |
| 77.19303492601364,70.45820000180959,1 |
| 97.77159928000232,86.7278223300282,1 |
| 62.07306379667647,96.76882412413983,1 |
| 91.56497449807442,88.69629254546599,1 |
| 79.94481794066932,74.16311935043758,1 |
| 99.2725269292572,60.99903099844988,1 |
| 90.54671411399852,43.39060180650027,1 |
| 34.52451385320009,60.39634245837173,0 |
| 50.2864961189907,49.80453881323059,0 |
| 49.58667721632031,59.80895099453265,0 |
| 97.64563396007767,68.86157272420604,1 |
| 32.57720016809309,95.59854761387875,0 |
| 74.24869136721598,69.82457122657193,1 |
| 71.79646205863379,78.45356224515052,1 |
| 75.3956114656803,85.75993667331619,1 |
| 35.28611281526193,47.02051394723416,0 |
| 56.25381749711624,39.26147251058019,0 |
| 30.05882244669796,49.59297386723685,0 |
| 44.66826172480893,66.45008614558913,0 |
| 66.56089447242954,41.09209807936973,0 |
| 40.45755098375164,97.53518548909936,1 |
| 49.07256321908844,51.88321182073966,0 |
| 80.27957401466998,92.11606081344084,1 |
| 66.74671856944039,60.99139402740988,1 |
| 32.72283304060323,43.30717306430063,0 |
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