Business Analytics Lab Worksheet 03

CME Group Foundation Business Analytics Lab

About

Quantitative Descriptive Analytics aims to gather an in-depth understanding of the underlying reasons and motivations for an event or observation. It is typically represented with visuals or charts.

Qualitative Descriptive Analytics focuses on investigating a phenomenon via statistical, mathematical, and computationaly techniques. It aims to quantify an event with metrics and numbers.

In this lab, we will explore the marketing data set and understand it better through simple statistics.

Setup

Make sure to download the folder titled ‘bsad_lab03’ zip folder and extract the folder to unzip it. Next, we must set this folder as the working directory. The way to do this is to open R Studio, go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Now, follow the directions to complete the lab.

---

Task 1

First begin by reading in the data from the ‘marketing.csv’ file, and viewing it to make sure we see it being read in correctly.

mydata = read.csv(file="data/rottentomatoes.csv")
head(mydata)

Now calculate the Range, Min, Max, Mean, STDEV, and Variance for each variable. Below is an example of how to compute the items for the variable ‘sales’. Follow the example and do the same for radio, paper, tv, and pos.

str(mydata)

'data.frame':   5043 obs. of  21 variables:
 $ ï..title         : Factor w/ 4917 levels "#Horror ",..: 398 2731 3279 3707 3332 1961 3289 3459 399 1631 ...
 $ genres           : Factor w/ 914 levels "Action","Action|Adventure",..: 107 101 128 288 754 126 120 308 126 447 ...
 $ director         : Factor w/ 2399 levels "","A. Raven Cruz",..: 929 801 2027 380 606 109 2030 1652 1228 554 ...
 $ actor1           : Factor w/ 2098 levels "","50 Cent","A.J. Buckley",..: 305 983 355 1968 528 443 787 223 338 35 ...
 $ actor2           : Factor w/ 3033 levels "","50 Cent","A. Michael Baldwin",..: 1408 2218 2489 534 2433 2549 1228 801 2440 653 ...
 $ actor3           : Factor w/ 3522 levels "","50 Cent","A.J. Buckley",..: 3442 1393 3134 1771 1 2714 1970 2163 3018 2941 ...
 $ length           : int  178 169 148 164 NA 132 156 100 141 153 ...
 $ budget           : num  2.37e+08 3.00e+08 2.45e+08 2.50e+08 NA ...
 $ director_fb_likes: int  0 563 0 22000 131 475 0 15 0 282 ...
 $ actor1_fb_likes  : int  1000 40000 11000 27000 131 640 24000 799 26000 25000 ...
 $ actor2_fb_likes  : int  936 5000 393 23000 12 632 11000 553 21000 11000 ...
 $ actor3_fb_likes  : int  855 1000 161 23000 NA 530 4000 284 19000 10000 ...
 $ total_cast_likes : int  4834 48350 11700 106759 143 1873 46055 2036 92000 58753 ...
 $ fb_likes         : int  33000 0 85000 164000 0 24000 0 29000 118000 10000 ...
 $ critic_reviews   : int  723 302 602 813 NA 462 392 324 635 375 ...
 $ users_reviews    : int  3054 1238 994 2701 NA 738 1902 387 1117 973 ...
 $ users_votes      : int  886204 471220 275868 1144337 8 212204 383056 294810 462669 321795 ...
 $ score            : num  7.9 7.1 6.8 8.5 7.1 6.6 6.2 7.8 7.5 7.5 ...
 $ aspect_ratio     : num  1.78 2.35 2.35 2.35 NA 2.35 2.35 1.85 2.35 2.35 ...
 $ gross            : int  760505847 309404152 200074175 448130642 NA 73058679 336530303 200807262 458991599 301956980 ...
 $ year             : int  2009 2007 2015 2012 NA 2012 2007 2010 2015 2009 ...

summary(mydata)

                         ï..title                     genres                 director   
 Ben-Hur                  :   3   Drama               : 236                   : 104  
 Halloween                :   3   Comedy              : 209   Steven Spielberg:  26  
 Home                     :   3   Comedy|Drama        : 191   Woody Allen     :  22  
 King Kong                :   3   Comedy|Drama|Romance: 187   Clint Eastwood  :  20  
 Pan                      :   3   Comedy|Romance      : 158   Martin Scorsese :  20  
 The Fast and the Furious :   3   Drama|Romance       : 152   Ridley Scott    :  17  
 (Other)                     :5025   (Other)             :3910   (Other)         :4834  
               actor1                 actor2                actor3         length          budget         
 Robert De Niro   :  49   Morgan Freeman :  20                 :  23   Min.   :  7.0   Min.   :2.180e+02  
 Johnny Depp      :  41   Charlize Theron:  15   Ben Mendelsohn:   8   1st Qu.: 93.0   1st Qu.:6.000e+06  
 Nicolas Cage     :  33   Brad Pitt      :  14   John Heard    :   8   Median :103.0   Median :2.000e+07  
 J.K. Simmons     :  31                  :  13   Steve Coogan  :   8   Mean   :107.2   Mean   :3.975e+07  
 Bruce Willis     :  30   James Franco   :  11   Anne Hathaway :   7   3rd Qu.:118.0   3rd Qu.:4.500e+07  
 Denzel Washington:  30   Meryl Streep   :  11   Jon Gries     :   7   Max.   :511.0   Max.   :1.222e+10  
 (Other)          :4829   (Other)        :4959   (Other)       :4982   NA's   :15      NA's   :492        
 director_fb_likes actor1_fb_likes  actor2_fb_likes  actor3_fb_likes   total_cast_likes    fb_likes     
 Min.   :    0.0   Min.   :     0   Min.   :     0   Min.   :    0.0   Min.   :     0   Min.   :     0  
 1st Qu.:    7.0   1st Qu.:   614   1st Qu.:   281   1st Qu.:  133.0   1st Qu.:  1411   1st Qu.:     0  
 Median :   49.0   Median :   988   Median :   595   Median :  371.5   Median :  3090   Median :   166  
 Mean   :  686.5   Mean   :  6560   Mean   :  1652   Mean   :  645.0   Mean   :  9699   Mean   :  7526  
 3rd Qu.:  194.5   3rd Qu.: 11000   3rd Qu.:   918   3rd Qu.:  636.0   3rd Qu.: 13756   3rd Qu.:  3000  
 Max.   :23000.0   Max.   :640000   Max.   :137000   Max.   :23000.0   Max.   :656730   Max.   :349000  
 NA's   :104       NA's   :7        NA's   :13       NA's   :23                                         
 critic_reviews  users_reviews     users_votes          score        aspect_ratio       gross          
 Min.   :  1.0   Min.   :   1.0   Min.   :      5   Min.   :1.600   Min.   : 1.18   Min.   :      162  
 1st Qu.: 50.0   1st Qu.:  65.0   1st Qu.:   8594   1st Qu.:5.800   1st Qu.: 1.85   1st Qu.:  5340988  
 Median :110.0   Median : 156.0   Median :  34359   Median :6.600   Median : 2.35   Median : 25517500  
 Mean   :140.2   Mean   : 272.8   Mean   :  83668   Mean   :6.442   Mean   : 2.22   Mean   : 48468408  
 3rd Qu.:195.0   3rd Qu.: 326.0   3rd Qu.:  96309   3rd Qu.:7.200   3rd Qu.: 2.35   3rd Qu.: 62309438  
 Max.   :813.0   Max.   :5060.0   Max.   :1689764   Max.   :9.500   Max.   :16.00   Max.   :760505847  
 NA's   :50      NA's   :21                                         NA's   :329     NA's   :884        
      year     
 Min.   :1916  
 1st Qu.:1999  
 Median :2005  
 Mean   :2002  
 3rd Qu.:2011  
 Max.   :2016  
 NA's   :108   

gross = gross[!is.na(gross)]
gross

   [1] 760505847 309404152 200074175 448130642  73058679 336530303 200807262 458991599 301956980 330249062
  [11] 200069408 168368427 423032628  89289910 291021565 141614023 623279547 241063875 179020854 255108370
  [21] 262030663 105219735 258355354  70083519 218051260 658672302 407197282  65173160 652177271 304360277
  [31] 373377893 408992272 334185206 234360014 268488329 402076689 245428137 234903076 202853933 172051787
  [41] 191450875 116593191 414984497 125320003 350034110 202351611 233914986 228756232  65171860 144812796
  [51]  90755643 101785482 352358779 317011114 123070338 237282182 130468626 223806889 140080850 166112167
  [61] 137850096  47375327 124051759 291709845 154985087 533316061 292979556 198332128 318298180  73820094
  [71] 113745408 102176165 161087183 100289690 100189501  88246220 150167630 356454367 362645141 312057433
  [81] 155111815 241407328 208543795  38297305 259746958 238371987  93417865 222487711 189412677    665426
  [91] 102315545 217387997 150350192 333130696 187991439 292568851 303001229 144512310 127490802 146405371
 [101] 281666058  63143812  60655503  76846624 320706665  46978995  89732035 104383624 198539855 318759914
 [111]  34293771 292000866 289994397 227946274 256386216 206456431 206435493 205343774 179982968 177243721
 [121] 179883016 139259759 400736600 281492479 206360018 153629485 133375846 181015141 114053579 119420252
 [131]  83640426  79711678 195000874  61937495 124051759 126597121 165230261 131564731 133382309  73103784
 [141]  21379315  64459316  34964818 111505642 133228348 216366733 160201106 118099659 201573391 190418803
 [151]  82161969 143523463 209364921 103400692 110332737 111110575  65007045 257704099 403706375 176997107
 [161]  31141074  31704416 107503316 129734803 132122995 122512052  68642452  32131830 176636816 126930660
 [171]  93926386 292298923  63992328 134518390  52792307 183635922  83024900 123207194  83348920 227137090
 [181] 215395021 180191634 424645577 292298923 177343675 234277056 138396624 149234747 118311368 101160529
 [191]  77564037 249358727  49551662  60522097 137748063 113733726 148337537 317557891  33592415 305388685
 [201] 337103873 217536138 131536019 214948780 209805005 186830669 163192114 119412921  32694788 113165635
 [211] 107285004 260031035 186739919 215397307 182618434 131920333 124976634 115802596 108521835 100685880
 [221] 126464904  64736114  93050117  57637485  58607007  43929341  30212620  76418654  89021735 380262555
 [231] 310675583 289907418 132550960 474544677 187165546  40911830  47952020 190871240 274084951  67155742
 [241]  81638674  56114221 250863268 155181732 125332007 113330342 125531634 186336103 129995817 102608827
 [251]  42776259  98780042 106369117 142614158  50026353  66002193  85463309  71017784  48068396  61656849
 [261] 134520804 313837577  24004159  58183966 100446895 144795350  47396698 140015224 104374107 228430993
 [271]  35799026   6712451 101643008 187670866 132014112 261970615 167007184 180011740 204843350  97030725
 [281] 130127620 146282411  65452312 148383780 119219978 101228120 162804648 100117603  89296573  85017401
 [291] 173005002  75030163  77222184  34964818 107515297  67631157  66862068  57366262 116866727 184031112
 [301]  54700065  27098580  55673333  40198710  72660029  38120554  49392095  39292022  28772222  17010646
 [311]  24985612   4411102  35024475 130174897  10200000 202007640  77679638      9213  58867694  59475623
 [321] 108638745  86897182  63540020  95328937  50802661 161317423 201148159  43982842 380838870 377019252
 [331] 340478898  17176900 131144183  23014504 181166115 176740650  71148699  67344392  22406362 261437578
 [341]  11000000  88761720 250147615 245823397  81557479 226138454 155370362 124870275 196573705  58229120
 [351] 125305545 132373442 120618403 110416702 102515793 100012500 209019489  84037039  85884815  83077470
 [361] 100018837  78747585  78616689  75817994 100853835  73209340  72515360  68558662  65653758  64685359
 [371]  61355436     26871  60874615 143618384  58220776  47474112  42877165  35168677  56114221  37567440
 [381]  61644321    190562 120147445 241688385 144512310 233630478 197992827 176049130 172620724 183405771
 [391]  20315324 148313048 127706877 126149655  66941559  78009155  63224849 111544445 112703470 117144465
 [401]  84303558 150832203  51396781  47592825  50016394  57010853  62494975  46440491  44606335  40048332
 [411]  64933670  31494270  31111260 123307945 153288182  13401683 137340146  43575716  80170146  75754670
 [421]  33048353  34543701 242589580 102981571 180965237 407999255 254455986 162831698 155019340 145771527
 [431]  82506325 140459099  53215979 158115031 133103929 133668525 130313314 124590960 127968405 120136047
 [441] 128200012 112225777 109993847 104054514 103028109 101087161 101111837  95632614  94822707  92969824
 [451]  91188905  90443603  82226474  79363785  76081498  85707116  74329966 100169068  73215310  80360866
 [461]  69102910  65948711    821997 169692572  60507228  56684819  50628009  69772969  45356386  55350897
 [471]  39442871  37899638  37754208  27779888  38542418  34566746  32885565  36073232  21471685  20950820
 [481]  19673424  19480739  17593391  18318000  27356090  17473245  15131330  19406406   1891821  23219748
 [491] 170708996 422783777 103812241 119793567  92930005  67286731  74158157 127083765   1339152  15071514
 [501]  26000610 323505540  66462600 368049635 306124059 229074524 193136719  35286428 157299717 134568845
 [511] 134006721 195329763 120776832 118823091  41814863  97360069 117698894 162001186  77032279  73023275
 [521]  68473360  66636385 160762022 103338338  55808744  47379090  43426961  47000485  45434443  42044321
 [531]  73661010  41523271  37600435  39251128  83503161  34636443  22751979  30013346  14567883     90820
 [541]   5409517  21009180  94999143 336029560  36381716  55585389  36976367 107225164  70224196  51814190
 [551]  47456450 148213377 112950721  75600000  62647540 183132370  27796042  32616869  18947630 114195633
 [561] 144156464 227965690 436471036 244052771 152149590 141204016 162495848 136448821 120523073 119654900
 [571]  72660029 117541000 116643346 100614858  42272747  80281096 219613391  78120196  98895417  70117571
 [581]  83552429  66257002  65012000  79883359  78031620  54222000  52474616  55942830  40932372  38345403
 [591]  37901509  48430355  30157016  28031250  33105600  62321039  38509342  19076815  25093607  18990542
 [601]  14294842  19819494  13596911   8460990   7097125  37760080   5851188  25121291  18821279 118471320
 [611] 300523113  71069884 251501645  35324232  81257500    617840  29655590  45045037  28965197  27550735
 [621]  39380442  72980108  37516013  87704396  83892374   5932060 216119491  43568507 182805123 176387405
 [631]  33685268 182204440 171383253 172071312 119412921 139225854 148775460 115731542 100468793  93771072
 [641] 100448498 115603980  90454043  84049211  70450000  69688384  70236496  63695760  59617068  55637680
 [651]  85911262  53846915  54758461  52397389  38966057  42345531  36064910  33328051  32598931  28045540
 [661]  37023395  43532294  17218080  10014234  19059018   1987287  24407944  13750556  31054924  43247140
 [671]   2208939 213079163  19548064 356784000  25052000 122012710     72413  58255287  77086030  65000000
 [681]  32178777  15738632  54116191 118153533 108012170 210592590 279167575 143151473 136801374 168213584
 [691] 135381507 167735396 121468960 106635996 102678089 125603360 101217900 104148781  75573300  93375151
 [701] 106126012  93307796  90646554 109176215  82670733  82569532  81687587  80574010  75764085  90356857
 [711]  75530832  75370763 100003492  90341670  74540762  80033643  73648142  71844424  75638743  66734992
 [721]  75280058  64505912  77862546  61112916  88200225  60573641  59035104  56702901  55994557  54910560
 [731]  53789313  51045801  50818750  50189179  50024083  50549107  56443482  62401264  47748610  46975183
 [741]  50807639  46611204 257756197  48472213  43060566  45996718  43337279  37479778  36965395  40559930
 [751]  36830057  36279230  42194060  43119879  35096190  35754555  43290977  33927476  32122249  40076438
 [761]  32940507  31670931  30695227  32522352  28424210  26082914  29136626  26288320  26616590 623279547
 [771]  30063805  22518325  13082288  18208078  14218868     22451  31165421  11802056  25472967  22362500
 [781]  17281832  19781879   7605668   4535117   4426297  10166502 363024263  12065985 350123553  80021740
 [791]  48291624  35231365  53715611  31199215  29580087  44665963  60128566  49875589  60984028  36931089
 [801]  51317350  28328132  51774002  25528495 113006880  45860039 329691196 217326336 166225040 141600000
 [811] 134218018 128769345 177575142 105263257 104354205 107100855  98711404 100328194 101530738  93815117
 [821]  91400000 162586036  89706988  83000000  78745923  70098138  66365290  66207920  63408614  58422650
 [831]  56932305  68750000  68218041  25040293  55747724  55473600  49994804  41609593  38553833  76137505
 [841]  34350553  34238611  34098563  33828318  33472850  31051126  35707327  20550712  18573791  51225796
 [851]  16264475  25857987  12870569  11466088  16088610  51178893   6768055  39440655   6167817  81645152
 [861]  69951824   9483821  66676062  26838389  75604320 108200000   5660084   7221458  70327868  58297830
 [871]  57386369  45207112  62563543  33574332  73343413  25031037  22843047   5755286 164435221  95720716
 [881] 118683135 143704210 110476776  80270227  36385763  37035845  34580635  42438300  23324666  23020488
 [891]  90567722  72601713  35092918 296623634 267652016  62453315 165500000 153620822 218628680 147637474
 [901] 135014968   2175312 126203320 126975169 125548685 105807520 191616238 105264608  97680195 126088877
 [911]  91030827 150315155 127997349  88504640  81517441  81022333  75621915  79948113  88658172  75888270
 [921]  84244877  75367693  73701902  75605492  67823573  91439400  67128202  70496802  60470220  58336565
 [931]  66002004  54997476  55682070  52752475  55092830  50815288  52822418  50150619  48745150  50007168
 [941]  48154732  48265581  46982632  44737059  56724080  44484065  47553512  42610000  41482207  47105085
 [951]  41256277  50740078  40203020  40905277  38590500  39177541  39778599  37486138  38105077  35168395
 [961]  32800000  33643461  32741596  31874869  30306268  27667947  27067160  26616999  26536120  26199517
 [971]  25450527  25407250  23159305  24006726  20389967  19593740  19118247  26442251  17114882  18472363
 [981]  14131298  21557240  21283440  10556196  16671505  10400000   9528092  10137232   9795017  20488579
 [991]  19445217   8355815  28837115   6471394   6291602  10706786   8742261  43905746  21413502   7994115
 [ reached getOption("max.print") -- omitted 3159 entries ]

gross

gross = mydata$gross
#Max gross
max = max(gross,na.rm = TRUE)
max

[1] 760505847

#Min gross
min = min(gross,na.rm = TRUE)
min

[1] 162

#Range
max-min

[1] 760505685

#Mean
mean_gross = mean(gross,na.rm = TRUE)
#Standard Deviation
sd(gross,na.rm = TRUE)

[1] 68452990

#Variance
var(gross,na.rm = TRUE)

[1] 4.685812e+15

Follow the example and do the same for radio, paper, tv, and pos. Use the worksheet given to try the different commands to find the max, min, and range.

---

Task 2

An easy way to calculate all of these statistics of all of these variables is with the summary function. Below is an example.

summary(mydata)

                         ï..title                     genres                 director   
 Ben-Hur                  :   3   Drama               : 236                   : 104  
 Halloween                :   3   Comedy              : 209   Steven Spielberg:  26  
 Home                     :   3   Comedy|Drama        : 191   Woody Allen     :  22  
 King Kong                :   3   Comedy|Drama|Romance: 187   Clint Eastwood  :  20  
 Pan                      :   3   Comedy|Romance      : 158   Martin Scorsese :  20  
 The Fast and the Furious :   3   Drama|Romance       : 152   Ridley Scott    :  17  
 (Other)                     :5025   (Other)             :3910   (Other)         :4834  
               actor1                 actor2                actor3         length          budget         
 Robert De Niro   :  49   Morgan Freeman :  20                 :  23   Min.   :  7.0   Min.   :2.180e+02  
 Johnny Depp      :  41   Charlize Theron:  15   Ben Mendelsohn:   8   1st Qu.: 93.0   1st Qu.:6.000e+06  
 Nicolas Cage     :  33   Brad Pitt      :  14   John Heard    :   8   Median :103.0   Median :2.000e+07  
 J.K. Simmons     :  31                  :  13   Steve Coogan  :   8   Mean   :107.2   Mean   :3.975e+07  
 Bruce Willis     :  30   James Franco   :  11   Anne Hathaway :   7   3rd Qu.:118.0   3rd Qu.:4.500e+07  
 Denzel Washington:  30   Meryl Streep   :  11   Jon Gries     :   7   Max.   :511.0   Max.   :1.222e+10  
 (Other)          :4829   (Other)        :4959   (Other)       :4982   NA's   :15      NA's   :492        
 director_fb_likes actor1_fb_likes  actor2_fb_likes  actor3_fb_likes   total_cast_likes    fb_likes     
 Min.   :    0.0   Min.   :     0   Min.   :     0   Min.   :    0.0   Min.   :     0   Min.   :     0  
 1st Qu.:    7.0   1st Qu.:   614   1st Qu.:   281   1st Qu.:  133.0   1st Qu.:  1411   1st Qu.:     0  
 Median :   49.0   Median :   988   Median :   595   Median :  371.5   Median :  3090   Median :   166  
 Mean   :  686.5   Mean   :  6560   Mean   :  1652   Mean   :  645.0   Mean   :  9699   Mean   :  7526  
 3rd Qu.:  194.5   3rd Qu.: 11000   3rd Qu.:   918   3rd Qu.:  636.0   3rd Qu.: 13756   3rd Qu.:  3000  
 Max.   :23000.0   Max.   :640000   Max.   :137000   Max.   :23000.0   Max.   :656730   Max.   :349000  
 NA's   :104       NA's   :7        NA's   :13       NA's   :23                                         
 critic_reviews  users_reviews     users_votes          score        aspect_ratio       gross          
 Min.   :  1.0   Min.   :   1.0   Min.   :      5   Min.   :1.600   Min.   : 1.18   Min.   :      162  
 1st Qu.: 50.0   1st Qu.:  65.0   1st Qu.:   8594   1st Qu.:5.800   1st Qu.: 1.85   1st Qu.:  5340988  
 Median :110.0   Median : 156.0   Median :  34359   Median :6.600   Median : 2.35   Median : 25517500  
 Mean   :140.2   Mean   : 272.8   Mean   :  83668   Mean   :6.442   Mean   : 2.22   Mean   : 48468408  
 3rd Qu.:195.0   3rd Qu.: 326.0   3rd Qu.:  96309   3rd Qu.:7.200   3rd Qu.: 2.35   3rd Qu.: 62309438  
 Max.   :813.0   Max.   :5060.0   Max.   :1689764   Max.   :9.500   Max.   :16.00   Max.   :760505847  
 NA's   :50      NA's   :21                                         NA's   :329     NA's   :884        
      year     
 Min.   :1916  
 1st Qu.:1999  
 Median :2005  
 Mean   :2002  
 3rd Qu.:2011  
 Max.   :2016  
 NA's   :108   

There are some statistics not captured here like standard deviation and variance, but there is an easy and quick way to find most of your basic statistics.

Now, we will produce a basic blot of the ‘sales’ variable . Here we utilize the plot function and within the plot function we call the variable we want to plot.

plot(order(gross,decreasing = TRUE))

When looking at this graph we cannot truly capture the data or see a clear pattern. A better way to visualize this plot would be to re-order the data based on increasing sales.

rottentomatoes = read.csv("data/rottentomatoes.csv")
gross = rottentomatoes$gross

layout(matrix(1:4,2,2))
#xlab labels the x axis, ylab labels the y axis
plot(gross, type="b", xlab = "score", ylab = "budget") 

There are further ways to customize plots, such as changing the colors of the lines, adding a heading, or even making them interactive.

Now, lets plot the sales graph, alongside radio, paper, and tv which you will code. Make sure to run the code in the same chunk so they are on the same layout.

#Layout allows us to see all 4 graphs on one screen
layout(matrix(1:4,2,2))
#Example of how to plot the sales variable
plot(gross, type="b", xlab = "Case Number", ylab = "Sales in $1,000") 

#Plot of Radio
#Plot of Paper
#Plot of TV

The 20 months of case_number are in no particular order and not related to a chronological time sequence. They are simply 20 independent use case studies. Since each case is independent, we can reorder them. To reveal a potential trend, consider reordering the sales column from low to high and see how the other four variables behave.

newdata = mydata[order(sales),]
newsales = newdata$sales
newradio = newdata$radio
newtv = newdata$tv
newpaper = newdata$paper

#Layout allows us to see all 4 graphs on one screen
layout(matrix(1:4,2,2))

#Example of how to plot the sales variable
plot(newsales, type="b", xlab = "Case Number", ylab = "Sales in $1,000") 

---

Task 3

Given a sales value of 25000, calculate the corresponding z-value or z-score using the mean and standard deviation calculations conducted in task 1.

We know that the z-score = (x - mean)/sd. So, input this into the R code where x=25000, mean=16717.2, and stdev = 2617.0521 which we found above.

Based on the z-values, how would you rate a $25000 sales value: poor, average, good, or very good performance? Explain your logic.