Pick one of the quantitative independent variables from the training data set (train.csv) , and define that variable as X. Pick SalePrice as the dependent variable, and define it as Y for the next analysis.

train.data <- read.csv("https://raw.githubusercontent.com/komotunde/DATA605/master/train.csv", header  = TRUE)
head(train.data)
##   Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape
## 1  1         60       RL          65    8450   Pave  <NA>      Reg
## 2  2         20       RL          80    9600   Pave  <NA>      Reg
## 3  3         60       RL          68   11250   Pave  <NA>      IR1
## 4  4         70       RL          60    9550   Pave  <NA>      IR1
## 5  5         60       RL          84   14260   Pave  <NA>      IR1
## 6  6         50       RL          85   14115   Pave  <NA>      IR1
##   LandContour Utilities LotConfig LandSlope Neighborhood Condition1
## 1         Lvl    AllPub    Inside       Gtl      CollgCr       Norm
## 2         Lvl    AllPub       FR2       Gtl      Veenker      Feedr
## 3         Lvl    AllPub    Inside       Gtl      CollgCr       Norm
## 4         Lvl    AllPub    Corner       Gtl      Crawfor       Norm
## 5         Lvl    AllPub       FR2       Gtl      NoRidge       Norm
## 6         Lvl    AllPub    Inside       Gtl      Mitchel       Norm
##   Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt
## 1       Norm     1Fam     2Story           7           5      2003
## 2       Norm     1Fam     1Story           6           8      1976
## 3       Norm     1Fam     2Story           7           5      2001
## 4       Norm     1Fam     2Story           7           5      1915
## 5       Norm     1Fam     2Story           8           5      2000
## 6       Norm     1Fam     1.5Fin           5           5      1993
##   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType
## 1         2003     Gable  CompShg     VinylSd     VinylSd    BrkFace
## 2         1976     Gable  CompShg     MetalSd     MetalSd       None
## 3         2002     Gable  CompShg     VinylSd     VinylSd    BrkFace
## 4         1970     Gable  CompShg     Wd Sdng     Wd Shng       None
## 5         2000     Gable  CompShg     VinylSd     VinylSd    BrkFace
## 6         1995     Gable  CompShg     VinylSd     VinylSd       None
##   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure
## 1        196        Gd        TA      PConc       Gd       TA           No
## 2          0        TA        TA     CBlock       Gd       TA           Gd
## 3        162        Gd        TA      PConc       Gd       TA           Mn
## 4          0        TA        TA     BrkTil       TA       Gd           No
## 5        350        Gd        TA      PConc       Gd       TA           Av
## 6          0        TA        TA       Wood       Gd       TA           No
##   BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF
## 1          GLQ        706          Unf          0       150         856
## 2          ALQ        978          Unf          0       284        1262
## 3          GLQ        486          Unf          0       434         920
## 4          ALQ        216          Unf          0       540         756
## 5          GLQ        655          Unf          0       490        1145
## 6          GLQ        732          Unf          0        64         796
##   Heating HeatingQC CentralAir Electrical X1stFlrSF X2ndFlrSF LowQualFinSF
## 1    GasA        Ex          Y      SBrkr       856       854            0
## 2    GasA        Ex          Y      SBrkr      1262         0            0
## 3    GasA        Ex          Y      SBrkr       920       866            0
## 4    GasA        Gd          Y      SBrkr       961       756            0
## 5    GasA        Ex          Y      SBrkr      1145      1053            0
## 6    GasA        Ex          Y      SBrkr       796       566            0
##   GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr
## 1      1710            1            0        2        1            3
## 2      1262            0            1        2        0            3
## 3      1786            1            0        2        1            3
## 4      1717            1            0        1        0            3
## 5      2198            1            0        2        1            4
## 6      1362            1            0        1        1            1
##   KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu
## 1            1          Gd            8        Typ          0        <NA>
## 2            1          TA            6        Typ          1          TA
## 3            1          Gd            6        Typ          1          TA
## 4            1          Gd            7        Typ          1          Gd
## 5            1          Gd            9        Typ          1          TA
## 6            1          TA            5        Typ          0        <NA>
##   GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual
## 1     Attchd        2003          RFn          2        548         TA
## 2     Attchd        1976          RFn          2        460         TA
## 3     Attchd        2001          RFn          2        608         TA
## 4     Detchd        1998          Unf          3        642         TA
## 5     Attchd        2000          RFn          3        836         TA
## 6     Attchd        1993          Unf          2        480         TA
##   GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch X3SsnPorch
## 1         TA          Y          0          61             0          0
## 2         TA          Y        298           0             0          0
## 3         TA          Y          0          42             0          0
## 4         TA          Y          0          35           272          0
## 5         TA          Y        192          84             0          0
## 6         TA          Y         40          30             0        320
##   ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold
## 1           0        0   <NA>  <NA>        <NA>       0      2   2008
## 2           0        0   <NA>  <NA>        <NA>       0      5   2007
## 3           0        0   <NA>  <NA>        <NA>       0      9   2008
## 4           0        0   <NA>  <NA>        <NA>       0      2   2006
## 5           0        0   <NA>  <NA>        <NA>       0     12   2008
## 6           0        0   <NA> MnPrv        Shed     700     10   2009
##   SaleType SaleCondition SalePrice
## 1       WD        Normal    208500
## 2       WD        Normal    181500
## 3       WD        Normal    223500
## 4       WD       Abnorml    140000
## 5       WD        Normal    250000
## 6       WD        Normal    143000
#I will go ahead and use Sale Price as my dependent variable, as requested so it'll be my Y. For my X variable (independent), I will use Total Basement Square Footage.

X <- train.data$TotalBsmtSF
Y <- train.data$SalePrice

Probability. Calculate as a minimum the below probabilities a through c. Assume the small letter “x” is estimated as the 4th quartile of the X variable, and the small letter “y” is estimated as the 2nd quartile of the Y variable. Interpret the meaning of all probabilities.

  1. P(X > x |Y > y) = .4519231
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
x <- quantile(X, .75) #x is the estimated 4th quartile of X
y <- quantile(Y, .5) #y is the estimated 2nd quartile of Y 

num <- filter(train.data, SalePrice > y & TotalBsmtSF > x) %>% tally()/nrow(train.data)
den <- filter(train.data, SalePrice > y) %>% tally()/nrow(train.data)
p1 <- num/den
p1 
##           n
## 1 0.4519231

Below is how I arrived at the above code.

\[P(X > x |Y > y) = P(X > .75 |Y > .50)\] \[P(A|B) = P(B and A)/P(B)\] b. P(X>x, Y>y) = 0.1246575

num.b <- filter(train.data, TotalBsmtSF > x) %>% tally()/nrow(train.data)
den.b <- filter(train.data, SalePrice > y) %>% tally()/nrow(train.data)
p2 <- num.b * den.b
p2
##           n
## 1 0.1246575
  1. P(Xy) = 0.5480769
num.c <- filter(train.data, SalePrice > y & TotalBsmtSF < x) %>% tally()/nrow(train.data)
den.c <- filter(train.data, SalePrice > y) %>% tally()/nrow(train.data)
p3 <- num.c/den.c
p3
##           n
## 1 0.5480769

Does splitting the training data in this fashion make them independent? In other words, does P(X|Y)=P(X)P(Y))? Check mathematically, and then evaluate by running a Chi Square test for association. You might have to research this.

filter(train.data, SalePrice <= y & TotalBsmtSF <=x) %>% tally() #696
##     n
## 1 696
filter(train.data, SalePrice > y & TotalBsmtSF <=x) %>% tally() #399
##     n
## 1 399
filter(train.data, TotalBsmtSF <=x) %>% tally() #1095
##      n
## 1 1095
filter(train.data, SalePrice <= y & TotalBsmtSF > x) %>% tally() #36
##    n
## 1 36
filter(train.data, SalePrice > y & TotalBsmtSF > x) %>% tally() #329
##     n
## 1 329
filter(train.data, TotalBsmtSF > x) %>% tally()#365
##     n
## 1 365
filter(train.data, SalePrice <= y) %>% tally() #732
##     n
## 1 732
filter(train.data, SalePrice > y) %>% tally() #728
##     n
## 1 728
nrow(train.data)#1460
## [1] 1460
P.A = 365/1460  #.25
P.A
## [1] 0.25
P.B = 728/1460 #.5
P.B
## [1] 0.4986301
P.AB = P.A * P.B
P.AB
## [1] 0.1246575
#P(X > x |Y > y) = .4519231

#We can see mathematically that these two variables are dependent.

It does not appear that splitting up the data makes them independent. We will perform th Chi Square test to verify our mathematical findings

chi.test <- table(train.data$SalePrice, train.data$TotalBsmtSF)
#now we will do the chi test to find our signifance level

chisq.test(chi.test)
## Warning in chisq.test(chi.test): Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  chi.test
## X-squared = 509710, df = 476640, p-value < 2.2e-16
#Since our p-value is significantly less than .05, we can reject the null hypothesis stating that X and Y are independent. That is to say that there is dependence between the Sale Price and the Total Basement Square Foot.

Descriptive and Inferential Statistics. Provide univariate descriptive statistics and appropriate plots for both variables(SalePrice and TotalBsmtSF). * TotalBsmtSF: Total square feet of basement area * SalePrice: Price house sold for + I assumed on this description as it was not on the data description list.

require(ggplot2)
## Loading required package: ggplot2
summary(train.data$SalePrice)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   34900  129975  163000  180921  214000  755000
summary(train.data$TotalBsmtSF)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   795.8   991.5  1057.4  1298.2  6110.0
ggplot(data=train.data, aes(SalePrice)) + geom_histogram(binwidth = 1000) 

#This plot looks slightly skewed to the right.

ggplot(data=train.data, aes(TotalBsmtSF)) + geom_histogram(binwidth = 100) 

#This plot also looks slightly skewed to the right.

Provide a scatterplot of X and Y.

ggplot(train.data, aes(x = TotalBsmtSF, y = SalePrice)) + geom_point(shape = 1) + geom_smooth(method = lm) + labs(x = "Basement Total Sq. Footage", y = "Total Sale Price", title = "Basement Square Footage vs. Sale Price")

#I used geom_smooth to add a linear regression line which is by default a 95% confidence interval region

Transform both variables simultaneously using Box-Cox transformations.You might have to research this.

#I found a lot of useful information on the following website:  http://rstudio-pubs-static.s3.amazonaws.com/63893_9f6bc9cd73ad47aab3aa85d0193244d9.html

require(MASS)
## Loading required package: MASS
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
bc <- lm(train.data$SalePrice ~ train.data$TotalBsmtSF)
trans <- boxcox(bc, lambda=seq(-2,2,.1), plotit = FALSE, interp = TRUE)
trans.test.data <- as.data.frame(trans)
lamda <- trans.test.data[which.max(trans$y),1]
lamda #0.02020202
## [1] 0.02020202
new.data <- cbind(X, Y, train.data$SalePrice^lamda, train.data$TotalBsmtSF^lamda)
colnames(new.data) <- c("TotalBsmtSF", "SalePrice", "Trans.SalePrice", "Trans.TB")
head(new.data)
##      TotalBsmtSF SalePrice Trans.SalePrice Trans.TB
## [1,]         856    208500        1.280727 1.146151
## [2,]        1262    181500        1.277144 1.155175
## [3,]         920    223500        1.282526 1.147822
## [4,]         756    140000        1.270463 1.143278
## [5,]        1145    250000        1.285433 1.152906
## [6,]         796    143000        1.271008 1.144470

Using the transformed variables, run a correlation analysis and interpret. Test the hypothesis that the correlation between these variables is 0 and provide a 99% confidence interval.Discuss the meaning of your analysis.

#For some odd reason, it was not easy for me to subset the data frame for just the columns I need. I used this site for assistance: https://stackoverflow.com/questions/10085806/extracting-specific-columns-from-a-data-frame

trans.data <- new.data[,c("Trans.SalePrice", "Trans.TB")]
head(trans.data)
##      Trans.SalePrice Trans.TB
## [1,]        1.280727 1.146151
## [2,]        1.277144 1.155175
## [3,]        1.282526 1.147822
## [4,]        1.270463 1.143278
## [5,]        1.285433 1.152906
## [6,]        1.271008 1.144470
require(corrplot)
## Loading required package: corrplot
require(stats)
corr. <- cor(trans.data)
corrplot(corr., method = "ellipse")

corrplot(corr., method = "number")

#https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html
TSP <- new.data[,"Trans.SalePrice"]
TTB <- new.data[,"Trans.TB"]
TBSF <- new.data[,"TotalBsmtSF"]
SP <- new.data[,"SalePrice"]

c.test <- cor.test(TSP,TTB,  method = "pearson", conf.level = .99)
c.test
## 
##  Pearson's product-moment correlation
## 
## data:  TSP and TTB
## t = 8.844, df = 1458, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 99 percent confidence interval:
##  0.1607066 0.2886342
## sample estimates:
##       cor 
## 0.2256429
#https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.test.html

From both the numerical and ellipse correlation plot, we see that there is a correlation betweeen the two variables. The confidence interval is [0.1607066, 0.2886342]. From this, we reject our null hypothesis, which we proved is incorrect when we showed that there is a strong correlation between Total Basement SF and Total Sale Price.

prec <- solve(corr.)

ans <- corr. * prec
ans2 <- prec * corr.

ans
##                 Trans.SalePrice    Trans.TB
## Trans.SalePrice      1.05364608 -0.05364608
## Trans.TB            -0.05364608  1.05364608
ans2
##                 Trans.SalePrice    Trans.TB
## Trans.SalePrice      1.05364608 -0.05364608
## Trans.TB            -0.05364608  1.05364608

Many times, it makes sense to fit a closed form distribution to data. For your non-transformed independent variable, location shift it so that the minimum value is above zero. Then load the MASS package and run fitdistr to fit a density function of your choice. (See https://stat.ethz.ch/R-manual/R-devel/library/MASS/html/fitdistr.html ). Find the optimal value of the parameters for this distribution, and then take 1000 samples from this distribution (e.g., rexp(1000, ???) for an exponential). Plot a histogram and compare it with a histogram of your non-transformed original variable.

min(train.data$TotalBsmtSF)
## [1] 0
fit.data <- cbind(SP, TBSF)
zero_sub = apply(fit.data, 1, function(row) all(row !=0 ))

FD <- fit.data[zero_sub,]
FD
##             SP TBSF
##    [1,] 208500  856
##    [2,] 181500 1262
##    [3,] 223500  920
##    [4,] 140000  756
##    [5,] 250000 1145
##    [6,] 143000  796
##    [7,] 307000 1686
##    [8,] 200000 1107
##    [9,] 129900  952
##   [10,] 118000  991
##   [11,] 129500 1040
##   [12,] 345000 1175
##   [13,] 144000  912
##   [14,] 279500 1494
##   [15,] 157000 1253
##   [16,] 132000  832
##   [17,] 149000 1004
##   [18,] 159000 1114
##   [19,] 139000 1029
##   [20,] 325300 1158
##   [21,] 139400  637
##   [22,] 230000 1777
##   [23,] 129900 1040
##   [24,] 154000 1060
##   [25,] 256300 1566
##   [26,] 134800  900
##   [27,] 306000 1704
##   [28,] 207500 1484
##   [29,]  68500  520
##   [30,]  40000  649
##   [31,] 149350 1228
##   [32,] 179900 1234
##   [33,] 165500 1398
##   [34,] 277500 1561
##   [35,] 309000 1117
##   [36,] 145000 1097
##   [37,] 153000 1297
##   [38,] 109000 1057
##   [39,] 160000 1088
##   [40,] 170000 1350
##   [41,] 144000  840
##   [42,] 130250  938
##   [43,] 141000 1150
##   [44,] 319900 1752
##   [45,] 239686 1434
##   [46,] 249700 1656
##   [47,] 113000  736
##   [48,] 127000  955
##   [49,] 177000  794
##   [50,] 114500  816
##   [51,] 110000  816
##   [52,] 385000 1842
##   [53,] 130000  384
##   [54,] 180500 1425
##   [55,] 172500  970
##   [56,] 196500  860
##   [57,] 438780 1410
##   [58,] 124900  780
##   [59,] 158000 1158
##   [60,] 101000  530
##   [61,] 202500 1370
##   [62,] 140000  576
##   [63,] 219500 1057
##   [64,] 317000 1143
##   [65,] 180000 1947
##   [66,] 226000 1453
##   [67,]  80000  747
##   [68,] 225000 1304
##   [69,] 244000 2223
##   [70,] 129500  845
##   [71,] 185000  832
##   [72,] 144900 1086
##   [73,] 107400  840
##   [74,]  91000  462
##   [75,] 135750  952
##   [76,] 127000  672
##   [77,] 136500 1768
##   [78,] 110000  440
##   [79,] 193500  896
##   [80,] 153500 1237
##   [81,] 245000 1563
##   [82,] 126500 1065
##   [83,] 168500  384
##   [84,] 260000 1288
##   [85,] 174000  684
##   [86,] 164500  612
##   [87,]  85000 1013
##   [88,] 123600  990
##   [89,]  98600 1235
##   [90,] 163500  876
##   [91,] 133900 1214
##   [92,] 204750  824
##   [93,] 185000  680
##   [94,] 214000 1588
##   [95,]  94750  960
##   [96,]  83000  458
##   [97,] 128950  950
##   [98,] 205000 1610
##   [99,] 178000  741
##  [100,] 198900 1226
##  [101,] 169500 1040
##  [102,] 250000 1053
##  [103,] 100000  641
##  [104,] 115000  789
##  [105,] 115000  793
##  [106,] 190000 1844
##  [107,] 136900  994
##  [108,] 180000  384
##  [109,] 383970 1264
##  [110,] 217000 1809
##  [111,] 259500 1028
##  [112,] 176000  729
##  [113,] 139000 1092
##  [114,] 155000 1125
##  [115,] 320000 1673
##  [116,] 163990  728
##  [117,] 180000  938
##  [118,] 100000  732
##  [119,] 136000 1080
##  [120,] 153900 1199
##  [121,] 181000 1362
##  [122,]  84500  520
##  [123,] 128000 1078
##  [124,]  87000  672
##  [125,] 155000  660
##  [126,] 150000 1008
##  [127,] 226000  924
##  [128,] 244000  992
##  [129,] 150750 1063
##  [130,] 220000 1267
##  [131,] 180000 1461
##  [132,] 174000 1304
##  [133,] 143000 1214
##  [134,] 171000 1907
##  [135,] 230000 1004
##  [136,] 231500  928
##  [137,] 115000  864
##  [138,] 260000 1734
##  [139,] 166000  910
##  [140,] 204000 1490
##  [141,] 125000 1728
##  [142,] 130000  970
##  [143,] 105000  715
##  [144,] 222500  884
##  [145,] 141000 1080
##  [146,] 115000  896
##  [147,] 122000  969
##  [148,] 372402 1710
##  [149,] 190000  825
##  [150,] 235000 1602
##  [151,] 125000 1200
##  [152,]  79000  572
##  [153,] 269500  774
##  [154,] 254900  991
##  [155,] 320000 1392
##  [156,] 162500 1232
##  [157,] 412500 1572
##  [158,] 220000 1541
##  [159,] 103200  882
##  [160,] 152000 1149
##  [161,] 127500  644
##  [162,] 190000 1617
##  [163,] 325624 1582
##  [164,] 183500  840
##  [165,] 228000 1686
##  [166,] 128500  720
##  [167,] 215000 1080
##  [168,] 239000 1064
##  [169,] 163000 1362
##  [170,] 184000 1606
##  [171,] 243000 1202
##  [172,] 211000 1151
##  [173,] 172500 1052
##  [174,] 501837 2216
##  [175,] 100000  968
##  [176,] 177000  756
##  [177,] 200100  793
##  [178,] 200000 1362
##  [179,] 127000  504
##  [180,] 475000 1107
##  [181,] 173000 1188
##  [182,] 135000  660
##  [183,] 153337 1086
##  [184,] 286000 1593
##  [185,] 315000  853
##  [186,] 184000  725
##  [187,] 192000 1431
##  [188,] 130000  970
##  [189,] 127000  864
##  [190,] 148500  855
##  [191,] 311872 1726
##  [192,] 235000 1360
##  [193,] 104000  755
##  [194,] 274900 1713
##  [195,] 140000 1121
##  [196,] 171500 1196
##  [197,] 112000  617
##  [198,] 149000  848
##  [199,] 110000  720
##  [200,] 180500 1424
##  [201,] 143900 1140
##  [202,] 141000 1100
##  [203,] 277000 1157
##  [204,] 145000 1092
##  [205,]  98000  864
##  [206,] 186000 1212
##  [207,] 252678  900
##  [208,] 156000  990
##  [209,] 161750  689
##  [210,] 134450 1070
##  [211,] 210000 1436
##  [212,] 107000  686
##  [213,] 311500  798
##  [214,] 167240 1248
##  [215,] 204900 1498
##  [216,] 200000 1010
##  [217,] 179900  713
##  [218,]  97000  864
##  [219,] 386250 2392
##  [220,] 112000  630
##  [221,] 290000 1203
##  [222,] 106000  483
##  [223,] 125000  912
##  [224,] 192500 1373
##  [225,] 148000 1194
##  [226,] 403000 1462
##  [227,]  94500  483
##  [228,] 128200  894
##  [229,] 216500  860
##  [230,]  89500  483
##  [231,] 185500 1414
##  [232,] 194500  996
##  [233,] 318000 1694
##  [234,] 113000  735
##  [235,] 262500 1566
##  [236,] 110500  686
##  [237,]  79000  540
##  [238,] 120000  626
##  [239,] 205000  948
##  [240,] 241500 1845
##  [241,] 137000 1020
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##  [905,] 176000  832
##  [906,] 236500 1753
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##  [909,] 117500  925
##  [910,] 320000 1905
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##  [912,] 242000  585
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##  [915,] 253000 1113
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## [1421,] 266500 1152
## [1422,] 142125 1078
## [1423,] 147500 1256
min(FD)
## [1] 105

I removed the 0’s from both the independent and dependent variable as I could not figure out how to remove from just one.

require(MASS)
F.TBSF <- FD[,"TBSF"]
dist <- fitdistr(F.TBSF, "normal")
opt.lamda <- dist$estimate
samp <- rexp(1200, opt.lamda)
hist(samp, breaks = 25)

The two histograms look quite different. This second histogram is more right skewed, and although our initial one was right skewed as well, it was not to the degree of this one.

Build some type of regression model and submit your model to the competition board. Provide your complete model summary and results with analysis. Report your Kaggle.com user name and score.

#I would like to preface this by saying that I decided on the multiple regression model as it was the most straightforward and easy for me to handle. 

model <- lm(formula = train.data$SalePrice ~ train.data$OverallQual + train.data$GrLivArea + train.data$TotalBsmtSF + train.data$GarageArea, data=train.data)
summary(model) 
## 
## Call:
## lm(formula = train.data$SalePrice ~ train.data$OverallQual + 
##     train.data$GrLivArea + train.data$TotalBsmtSF + train.data$GarageArea, 
##     data = train.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -513806  -19624   -1361   16128  286272 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            -99838.483   4670.871 -21.375   <2e-16 ***
## train.data$OverallQual  24954.711   1044.735  23.886   <2e-16 ***
## train.data$GrLivArea       45.628      2.507  18.199   <2e-16 ***
## train.data$TotalBsmtSF     30.126      2.912  10.347   <2e-16 ***
## train.data$GarageArea      58.246      6.094   9.558   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39190 on 1455 degrees of freedom
## Multiple R-squared:  0.7573, Adjusted R-squared:  0.7566 
## F-statistic:  1135 on 4 and 1455 DF,  p-value: < 2.2e-16

From the summary of the linear model done, we can say our equation is:

\[SalePrice = -98436.050 + (26988.854*OverallQuality) + (49.573*GrLivArea) + (11317.522*GarageCars) + ( 30.126*TotalBsmtSF)\] I would like to check my equation by checking on the test data. I will load and compare my calculated SalePrice with the one provided in the test data.

test.data <- read.csv("https://raw.githubusercontent.com/komotunde/DATA605/master/test.csv", header  = TRUE)
head(test.data)
##     Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape
## 1 1461         20       RH          80   11622   Pave  <NA>      Reg
## 2 1462         20       RL          81   14267   Pave  <NA>      IR1
## 3 1463         60       RL          74   13830   Pave  <NA>      IR1
## 4 1464         60       RL          78    9978   Pave  <NA>      IR1
## 5 1465        120       RL          43    5005   Pave  <NA>      IR1
## 6 1466         60       RL          75   10000   Pave  <NA>      IR1
##   LandContour Utilities LotConfig LandSlope Neighborhood Condition1
## 1         Lvl    AllPub    Inside       Gtl        NAmes      Feedr
## 2         Lvl    AllPub    Corner       Gtl        NAmes       Norm
## 3         Lvl    AllPub    Inside       Gtl      Gilbert       Norm
## 4         Lvl    AllPub    Inside       Gtl      Gilbert       Norm
## 5         HLS    AllPub    Inside       Gtl      StoneBr       Norm
## 6         Lvl    AllPub    Corner       Gtl      Gilbert       Norm
##   Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt
## 1       Norm     1Fam     1Story           5           6      1961
## 2       Norm     1Fam     1Story           6           6      1958
## 3       Norm     1Fam     2Story           5           5      1997
## 4       Norm     1Fam     2Story           6           6      1998
## 5       Norm   TwnhsE     1Story           8           5      1992
## 6       Norm     1Fam     2Story           6           5      1993
##   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType
## 1         1961     Gable  CompShg     VinylSd     VinylSd       None
## 2         1958       Hip  CompShg     Wd Sdng     Wd Sdng    BrkFace
## 3         1998     Gable  CompShg     VinylSd     VinylSd       None
## 4         1998     Gable  CompShg     VinylSd     VinylSd    BrkFace
## 5         1992     Gable  CompShg     HdBoard     HdBoard       None
## 6         1994     Gable  CompShg     HdBoard     HdBoard       None
##   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure
## 1          0        TA        TA     CBlock       TA       TA           No
## 2        108        TA        TA     CBlock       TA       TA           No
## 3          0        TA        TA      PConc       Gd       TA           No
## 4         20        TA        TA      PConc       TA       TA           No
## 5          0        Gd        TA      PConc       Gd       TA           No
## 6          0        TA        TA      PConc       Gd       TA           No
##   BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF
## 1          Rec        468          LwQ        144       270         882
## 2          ALQ        923          Unf          0       406        1329
## 3          GLQ        791          Unf          0       137         928
## 4          GLQ        602          Unf          0       324         926
## 5          ALQ        263          Unf          0      1017        1280
## 6          Unf          0          Unf          0       763         763
##   Heating HeatingQC CentralAir Electrical X1stFlrSF X2ndFlrSF LowQualFinSF
## 1    GasA        TA          Y      SBrkr       896         0            0
## 2    GasA        TA          Y      SBrkr      1329         0            0
## 3    GasA        Gd          Y      SBrkr       928       701            0
## 4    GasA        Ex          Y      SBrkr       926       678            0
## 5    GasA        Ex          Y      SBrkr      1280         0            0
## 6    GasA        Gd          Y      SBrkr       763       892            0
##   GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr
## 1       896            0            0        1        0            2
## 2      1329            0            0        1        1            3
## 3      1629            0            0        2        1            3
## 4      1604            0            0        2        1            3
## 5      1280            0            0        2        0            2
## 6      1655            0            0        2        1            3
##   KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu
## 1            1          TA            5        Typ          0        <NA>
## 2            1          Gd            6        Typ          0        <NA>
## 3            1          TA            6        Typ          1          TA
## 4            1          Gd            7        Typ          1          Gd
## 5            1          Gd            5        Typ          0        <NA>
## 6            1          TA            7        Typ          1          TA
##   GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual
## 1     Attchd        1961          Unf          1        730         TA
## 2     Attchd        1958          Unf          1        312         TA
## 3     Attchd        1997          Fin          2        482         TA
## 4     Attchd        1998          Fin          2        470         TA
## 5     Attchd        1992          RFn          2        506         TA
## 6     Attchd        1993          Fin          2        440         TA
##   GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch X3SsnPorch
## 1         TA          Y        140           0             0          0
## 2         TA          Y        393          36             0          0
## 3         TA          Y        212          34             0          0
## 4         TA          Y        360          36             0          0
## 5         TA          Y          0          82             0          0
## 6         TA          Y        157          84             0          0
##   ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold
## 1         120        0   <NA> MnPrv        <NA>       0      6   2010
## 2           0        0   <NA>  <NA>        Gar2   12500      6   2010
## 3           0        0   <NA> MnPrv        <NA>       0      3   2010
## 4           0        0   <NA>  <NA>        <NA>       0      6   2010
## 5         144        0   <NA>  <NA>        <NA>       0      1   2010
## 6           0        0   <NA>  <NA>        <NA>       0      4   2010
##   SaleType SaleCondition
## 1       WD        Normal
## 2       WD        Normal
## 3       WD        Normal
## 4       WD        Normal
## 5       WD        Normal
## 6       WD        Normal
test.subset <- test.data[,c("Id", "OverallQual", "GrLivArea", "GarageCars", "TotalBsmtSF", "GarageArea")]
head(test.subset)
##     Id OverallQual GrLivArea GarageCars TotalBsmtSF GarageArea
## 1 1461           5       896          1         882        730
## 2 1462           6      1329          1        1329        312
## 3 1463           5      1629          2         928        482
## 4 1464           6      1604          2         926        470
## 5 1465           8      1280          2        1280        506
## 6 1466           6      1655          2         763        440
#I will find the sale price from the above equation and combine with our test subset data frame.


Sale.Price <- (-98436.050 + (26988.854*test.subset$OverallQual) + (49.573*test.subset$GrLivArea) + (11317.522*test.subset$GarageCars) + (  30.126*test.subset$TotalBsmtSF))
head(Sale.Price)
## [1] 118814.3 180734.6 167854.6 193543.9 242124.5 191161.6
require(knitr)
## Loading required package: knitr
new.df <- cbind(test.subset, Sale.Price)
kable(new.df)
Id OverallQual GrLivArea GarageCars TotalBsmtSF GarageArea Sale.Price
1461 5 896 1 882 730 118814.282
1462 6 1329 1 1329 312 180734.567
1463 5 1629 2 928 482 167854.609
1464 6 1604 2 926 470 193543.886
1465 8 1280 2 1280 506 242124.546
1466 6 1655 2 763 440 191161.571
1467 6 1187 2 1168 420 180162.437
1468 6 1465 2 789 393 182525.977
1469 7 1341 2 1300 506 218762.165
1470 4 882 2 882 525 102448.928
1471 7 1337 2 1405 511 221727.103
1472 6 987 1 483 264 138294.005
1473 5 1092 1 525 320 117775.608
1474 6 1456 2 855 440 184068.136
1475 7 836 1 836 308 168431.814
1476 9 2334 3 1590 751 342019.924
1477 8 1544 3 1544 868 274482.604
1478 9 1698 3 1698 730 313745.104
1479 8 1822 3 1822 678 296638.926
1480 9 2696 3 2846 958 397803.606
1481 8 2250 3 1671 756 313307.144
1482 8 1370 2 1370 484 249297.456
1483 6 1324 2 1324 430 191653.594
1484 7 1145 2 1145 437 204376.327
1485 7 1374 2 384 400 192802.658
1486 7 1733 2 847 433 224547.703
1487 8 2475 3 1629 962 323195.777
1488 8 1595 3 1595 880 278547.253
1489 7 1218 2 1218 676 210194.354
1490 6 1468 2 1468 528 203130.250
1491 7 1659 2 831 484 220397.285
1492 5 1012 1 816 429 122576.434
1493 6 1494 2 1208 461 196586.388
1494 8 2349 3 1231 762 304959.431
1495 8 2225 3 1390 713 303602.413
1496 7 1488 2 1488 506 231713.084
1497 7 1680 2 840 588 221709.452
1498 7 1200 2 600 480 190684.172
1499 7 1200 2 600 480 190684.172
1500 6 1236 2 600 480 165479.946
1501 6 1512 2 756 440 183861.750
1502 7 1080 2 530 496 182626.592
1503 8 1418 3 1642 852 271188.754
1504 8 1848 2 975 592 261093.580
1505 7 1492 2 1492 596 232031.880
1506 6 1829 2 1829 535 231901.589
1507 6 2495 2 1280 660 248378.033
1508 6 1891 2 1224 678 216748.885
1509 6 1645 2 715 441 189219.793
1510 5 1232 2 1232 490 157332.432
1511 5 1209 2 1209 504 155499.355
1512 5 1510 2 1510 517 179488.754
1513 6 1775 2 533 480 190181.351
1514 5 1728 0 1728 0 174228.092
1515 5 2461 2 1140 400 215486.057
1516 6 1556 2 782 470 186826.238
1517 6 1128 1 1080 315 163269.020
1518 5 1604 2 1604 576 186980.460
1519 7 1480 2 1480 620 231075.492
1520 5 1143 1 1143 308 138921.699
1521 5 1206 1 1206 312 143942.736
1522 6 1580 2 1244 440 201934.202
1523 4 1337 1 832 263 112180.821
1524 5 1064 1 864 318 126600.278
1525 5 972 1 972 305 125293.170
1526 5 988 1 988 264 126568.354
1527 4 985 2 576 676 98336.391
1528 6 1224 1 816 280 160074.764
1529 5 1175 2 1175 484 152789.589
1530 6 1395 2 1395 440 197312.223
1531 4 1844 1 709 240 133608.834
1532 5 936 0 936 0 111106.484
1533 5 1347 2 1347 551 166497.817
1534 6 1251 1 827 240 161744.621
1535 5 1633 1 1027 240 159717.853
1536 5 1245 1 1008 315 139911.135
1537 2 832 2 678 780 39846.866
1538 8 1566 1 930 288 234440.802
1539 8 2268 2 346 624 262964.986
1540 5 2256 0 840 0 173650.748
1541 6 1470 2 738 624 181237.416
1542 5 1612 1 988 363 157501.906
1543 7 2068 1 1108 315 237700.022
1544 4 765 1 765 200 81806.623
1545 6 1132 1 608 240 149247.840
1546 6 1196 2 572 576 162653.498
1547 5 1453 1 835 240 145010.521
1548 6 1416 1 780 312 168508.244
1549 5 1040 2 528 624 126605.712
1550 5 1536 2 928 480 163244.320
1551 6 1068 1 1124 288 161620.184
1552 4 1962 2 888 572 156168.524
1553 6 1560 0 780 0 164329.234
1554 6 1324 1 662 180 160392.660
1555 5 1675 1 1422 365 173699.689
1556 5 1224 1 689 180 129259.908
1557 5 1392 0 1212 0 142026.548
1558 6 919 1 600 231 138447.783
1559 4 1884 0 707 0 124213.980
1560 5 1680 2 856 450 168213.760
1561 4 1832 0 1832 0 155527.934
1562 5 892 1 864 416 118073.722
1563 5 864 1 864 280 116685.678
1564 5 1373 2 1319 591 166943.187
1565 5 1440 2 720 480 152219.104
1566 7 1483 2 1420 690 229416.651
1567 4 756 2 660 440 89514.758
1568 6 1981 2 1117 522 217986.973
1569 3 1610 1 972 480 102943.036
1570 6 1074 1 663 467 148029.536
1571 4 1531 1 756 209 119508.407
1572 5 1172 1 1172 366 141232.970
1573 7 1508 2 1508 572 233307.064
1574 5 1298 2 1250 504 161146.518
1575 7 1433 2 1433 528 227329.639
1576 6 1802 4 946 1017 226596.904
1577 7 1222 2 1222 615 210513.150
1578 6 1445 2 676 484 178130.279
1579 5 965 2 870 580 133190.829
1580 7 1692 2 878 513 223449.116
1581 5 1026 1 1026 308 129596.916
1582 5 876 2 876 484 128959.588
1583 8 1978 3 1978 850 309071.970
1584 7 2098 2 1040 621 248456.166
1585 6 848 2 848 420 153716.870
1586 3 640 1 0 240 25574.754
1587 5 992 1 381 319 108480.164
1588 5 1196 1 1196 336 143145.746
1589 4 1120 2 744 480 110089.914
1590 6 1096 1 1008 352 159513.612
1591 4 960 0 960 0 86030.406
1592 5 1296 1 1107 260 145421.832
1593 6 856 2 856 399 154354.462
1594 4 2650 0 0 0 140887.816
1595 5 1666 0 894 0 146029.482
1596 7 2133 1 1528 312 253575.187
1597 5 2177 2 1032 484 198153.717
1598 7 1652 2 423 473 207758.866
1599 8 1034 2 982 598 220952.040
1600 8 1191 2 1191 531 235031.335
1601 2 540 1 480 200 8089.080
1602 6 1107 1 629 625 148641.161
1603 4 952 1 756 330 90805.640
1604 8 1646 2 1594 525 269727.828
1605 8 1916 3 1049 741 278011.390
1606 6 1285 2 1243 473 187280.041
1607 6 2048 2 1958 776 246644.330
1608 7 1346 3 1336 660 231412.088
1609 7 1214 2 1214 520 209875.558
1610 5 1444 2 384 400 142295.060
1611 5 1264 2 1068 528 153978.104
1612 5 1430 2 1430 624 173112.834
1613 5 1344 2 1344 686 166258.720
1614 5 945 1 945 253 123141.297
1615 4 1092 0 546 0 80101.878
1616 4 1092 0 546 0 80101.878
1617 4 1092 1 546 286 91419.400
1618 5 874 1 864 352 117181.408
1619 5 833 2 833 495 125532.531
1620 5 2432 2 1216 616 216338.016
1621 5 1274 2 624 576 141097.890
1622 5 1479 1 864 275 147173.073
1623 8 1803 2 1679 482 280071.499
1624 6 1797 2 1152 616 209919.951
1625 6 882 1 882 502 145109.114
1626 6 1434 2 1434 528 200420.484
1627 6 1608 2 945 470 194314.572
1628 8 2283 3 1008 632 294969.515
1629 7 1628 2 384 390 205394.200
1630 8 2522 2 2208 564 331651.140
1631 8 1478 2 1418 495 256097.388
1632 8 1734 2 1587 528 273879.370
1633 7 1382 2 384 396 193199.242
1634 6 1636 2 952 440 195913.498
1635 6 1516 2 707 409 182583.868
1636 6 1190 2 1181 430 180702.794
1637 5 1934 2 1122 567 188818.818
1638 7 2050 2 832 529 239810.454
1639 6 1671 2 1427 484 211958.403
1640 7 2673 2 1043 441 277051.019
1641 6 1707 2 1140 479 205096.869
1642 7 1884 2 926 581 234413.180
1643 8 1874 2 1058 588 264882.936
1644 8 1811 2 972 565 259169.001
1645 5 1621 2 1621 478 188335.343
1646 6 1116 2 1116 528 175076.202
1647 7 1193 2 1176 506 207689.737
1648 5 1180 2 1180 477 153188.084
1649 5 1050 1 1043 336 131298.810
1650 4 864 2 864 576 101014.346
1651 4 864 2 864 576 101014.346
1652 6 987 1 483 264 138294.005
1653 6 987 1 483 288 138294.005
1654 6 1548 2 804 440 187092.426
1655 7 1055 2 1057 440 197263.669
1656 6 1456 2 855 440 184068.136
1657 6 1548 2 804 440 187092.426
1658 6 1456 2 855 440 184068.136
1659 7 836 1 836 345 168431.814
1660 5 1120 2 864 656 140693.888
1661 9 2772 3 1776 754 369336.334
1662 9 2690 3 1365 864 352889.562
1663 9 2020 3 2020 896 339408.182
1664 10 2674 3 2630 762 417194.638
1665 9 1736 3 1736 834 316773.666
1666 8 1782 3 1782 932 293450.966
1667 8 2520 3 1508 640 321781.316
1668 9 1743 3 1739 927 317211.055
1669 9 1531 3 1504 700 299621.969
1670 9 1808 3 1774 850 321487.710
1671 7 1760 2 1760 583 253391.212
1672 9 2452 3 2452 886 373838.150
1673 8 2400 3 1082 730 302998.880
1674 8 1606 3 1598 871 279182.934
1675 6 1358 2 1358 484 194363.360
1676 6 1306 2 1306 624 190219.012
1677 7 1358 2 1350 484 221111.206
1678 10 2492 3 2492 949 404014.964
1679 8 2200 3 2200 685 326765.148
1680 9 1884 2 1884 649 317251.596
1681 9 1456 2 1451 539 282989.794
1682 8 1712 3 1712 701 287872.036
1683 7 1405 2 1405 478 225098.067
1684 7 1456 2 728 390 207230.988
1685 6 1490 2 745 392 182439.758
1686 7 1220 2 1220 397 210353.752
1687 7 1374 2 384 400 192802.658
1688 7 1630 2 868 436 220074.330
1689 7 1594 2 846 434 217626.930
1690 7 1489 2 782 400 210493.701
1691 7 1342 2 698 393 200675.886
1692 8 2004 3 870 644 276981.260
1693 7 1374 2 384 400 192802.658
1694 6 1514 2 794 394 185105.684
1695 7 1430 2 384 400 195578.746
1696 7 2312 3 1177 658 274509.572
1697 7 1430 2 384 410 195578.746
1698 8 2687 3 1455 810 328463.329
1699 8 2063 3 2024 815 314671.471
1700 7 2061 2 1070 647 247525.745
1701 8 2232 2 1173 623 286094.560
1702 8 1696 3 1696 972 286596.852
1703 8 1658 3 1614 726 282242.746
1704 8 1702 3 1702 844 287075.046
1705 8 1432 2 1422 492 253937.534
1706 9 2490 3 2458 795 375902.680
1707 7 1436 2 1436 529 227568.736
1708 7 1402 2 1402 625 224858.970
1709 9 1530 3 1530 984 300355.672
1710 8 1448 2 1372 692 253224.402
1711 8 1795 3 900 782 267524.283
1712 8 1836 2 1836 517 286437.190
1713 7 1662 3 1652 711 256596.972
1714 7 1553 2 1553 588 236893.519
1715 7 1653 2 812 628 219527.453
1716 7 1218 2 1218 576 210194.354
1717 7 1141 2 1141 484 204057.531
1718 6 1158 0 1158 0 155788.516
1719 7 1812 2 835 478 228102.458
1720 8 1512 3 1512 845 271932.236
1721 7 1114 2 1114 576 201905.658
1722 5 1114 0 1114 0 125292.906
1723 5 1114 2 1114 576 147927.950
1724 7 1450 3 1450 788 240002.044
1725 7 2122 2 913 559 245819.916
1726 6 1730 2 816 528 196476.224
1727 6 1332 2 1332 542 192291.186
1728 7 1540 2 754 495 212178.396
1729 6 1400 2 676 465 175899.494
1730 6 1882 2 0 612 179428.504
1731 5 980 2 980 400 137248.284
1732 5 864 2 864 732 128003.200
1733 5 864 1 864 440 116685.678
1734 5 1020 1 1024 288 129239.226
1735 5 912 1 912 300 120511.230
1736 5 912 1 912 252 120511.230
1737 8 2014 3 2014 864 311941.134
1738 7 1755 2 975 524 229494.437
1739 7 3005 3 1376 704 314858.735
1740 6 1726 2 1726 561 223692.592
1741 6 1256 2 1145 641 182890.076
1742 6 1512 2 756 440 183861.750
1743 7 1452 2 691 506 205918.034
1744 8 1694 2 1694 642 275119.932
1745 7 1740 2 1740 540 251797.232
1746 8 2499 2 392 527 275802.145
1747 7 2067 2 752 784 238243.115
1748 6 2640 2 932 515 245082.270
1749 5 1336 2 1288 502 164175.080
1750 6 1216 1 1216 336 171728.580
1751 7 2288 2 908 520 253898.404
1752 5 864 1 864 312 116685.678
1753 5 1568 2 1568 440 184111.296
1754 7 2061 2 1165 498 250387.715
1755 6 1320 2 1232 495 188683.710
1756 4 894 2 894 396 103405.316
1757 5 864 1 864 308 116685.678
1758 4 1362 3 1040 768 142321.398
1759 5 1728 2 1048 576 176377.456
1760 5 1313 2 1313 610 163788.051
1761 5 1292 2 1292 520 162114.372
1762 5 2140 2 744 549 187643.228
1763 6 1576 2 1208 368 200651.374
1764 5 960 1 960 330 124336.782
1765 6 1691 2 1680 550 220571.741
1766 6 1453 2 1453 530 201934.765
1767 6 1567 2 1562 542 210869.821
1768 5 1144 1 1169 286 139754.548
1769 7 1329 2 1329 441 219040.943
1770 6 988 1 988 297 153557.208
1771 5 1202 1 1202 304 143623.940
1772 5 1382 1 1382 350 157969.760
1773 4 1200 1 1200 294 116475.688
1774 5 1866 2 1866 495 207861.598
1775 6 1062 1 1062 297 159454.934
1776 5 1112 2 631 480 133277.946
1777 5 793 1 793 240 111027.049
1778 5 1031 1 1031 230 129995.411
1779 5 1210 1 0 616 107809.072
1780 7 1527 2 699 410 209877.017
1781 5 1200 1 1200 246 143464.542
1782 3 792 1 792 366 56969.642
1783 5 1352 2 676 720 146531.136
1784 4 1039 1 405 281 84544.265
1785 5 1078 1 1054 264 133018.240
1786 4 2377 2 1313 528 189544.869
1787 7 1690 2 560 624 213769.902
1788 2 599 0 416 0 -2231.699
1789 5 846 2 720 576 122772.742
1790 3 725 1 630 320 48767.839
1791 6 2544 3 1248 907 261160.600
1792 6 1380 1 1380 364 184799.216
1793 6 1040 2 1040 480 169019.078
1794 6 951 2 951 480 161925.867
1795 6 1105 1 1105 308 162881.991
1796 6 1142 1 160 384 136247.122
1797 6 1133 2 795 900 166248.497
1798 5 1041 1 1041 294 130792.401
1799 5 732 1 732 240 106165.410
1800 5 1183 1 528 288 122377.129
1801 4 1461 1 832 384 118327.873
1802 6 1495 2 864 576 186272.617
1803 6 1806 2 780 483 199159.236
1804 5 941 2 941 564 134140.023
1805 6 1045 1 1045 264 158100.051
1806 6 1378 1 901 162 170269.716
1807 6 1944 2 972 324 211784.502
1808 5 1306 2 240 472 131115.842
1809 5 1464 0 504 0 124266.596
1810 5 1558 2 690 400 157164.938
1811 4 1701 0 600 0 111918.639
1812 5 1447 0 801 0 132371.277
1813 5 1328 1 768 308 136795.454
1814 4 861 2 861 288 100775.249
1815 2 612 1 0 308 -2802.144
1816 5 792 1 624 287 105886.182
1817 4 1510 2 677 720 127404.942
1818 6 2007 1 917 357 201933.149
1819 6 1288 1 624 280 157463.244
1820 4 816 0 624 0 68769.558
1821 6 1480 1 972 207 177465.108
1822 6 1521 3 741 640 195173.539
1823 3 797 0 245 0 29421.063
1824 6 1432 1 1022 216 176591.904
1825 5 1654 1 297 336 138766.906
1826 5 1142 1 468 320 118537.076
1827 5 995 1 960 264 126071.837
1828 5 1582 1 952 250 154930.180
1829 5 1072 5 1072 1184 178533.158
1830 6 1768 2 1048 576 205349.230
1831 7 1944 1 672 216 218418.034
1832 5 2128 0 1040 0 173330.604
1833 6 1930 1 720 316 192181.206
1834 6 1427 1 483 379 160106.125
1835 7 1864 0 1020 0 213618.520
1836 6 1666 1 1204 384 193674.918
1837 4 892 0 858 0 79586.590
1838 6 1403 1 698 308 165393.463
1839 4 704 1 560 220 72606.840
1840 5 1200 0 1200 0 132147.020
1841 5 1152 1 1152 252 139638.990
1842 5 1112 1 585 226 120574.628
1843 5 1052 1 1052 668 131669.090
1844 6 1034 2 456 504 151128.056
1845 5 1774 1 552 384 152397.796
1846 6 1138 2 1138 480 176829.580
1847 5 2071 1 825 336 175345.375
1848 2 660 0 0 0 -11740.162
1849 4 1383 2 0 498 100713.869
1850 5 1073 1 1073 340 133342.769
1851 6 1639 1 747 240 178568.865
1852 5 1089 1 1089 252 134617.953
1853 5 1049 1 1049 266 131429.993
1854 7 1061 2 1054 462 197470.729
1855 6 1338 2 520 576 168126.312
1856 7 1879 2 1079 473 238774.593
1857 5 2016 2 0 576 159082.432
1858 5 2228 2 0 720 169591.908
1859 5 1535 2 0 400 135237.819
1860 5 1229 2 1094 672 153026.325
1861 5 1513 2 0 400 134147.213
1862 7 2787 4 1168 820 309103.135
1863 7 2787 4 1168 820 309103.135
1864 7 2787 4 1168 820 309103.135
1865 9 1680 3 1555 1138 308544.772
1866 9 1720 3 1720 846 315498.482
1867 8 1468 3 1468 904 268425.480
1868 8 1838 2 1838 524 286596.588
1869 7 1290 2 1282 662 215691.674
1870 8 1254 3 1254 810 251369.894
1871 8 1498 3 1498 844 270816.450
1872 6 1422 2 704 440 177833.628
1873 7 1759 2 1449 525 243972.453
1874 5 990 1 990 440 126727.752
1875 7 1463 2 1463 539 229720.609
1876 6 1772 2 928 492 201932.402
1877 7 1444 2 1431 577 227814.690
1878 7 1492 2 1492 608 232031.880
1879 5 907 1 907 343 120112.735
1880 5 914 2 914 444 131988.150
1881 7 1611 3 1569 1231 251568.291
1882 8 2184 2 1068 570 280551.826
1883 7 1725 2 840 550 223940.237
1884 7 1870 2 944 608 234261.426
1885 8 1513 2 1462 521 259157.987
1886 8 1828 2 1822 523 285618.842
1887 7 1417 2 1417 511 226054.455
1888 8 1602 3 1578 810 278382.122
1889 6 1396 2 608 440 173652.634
1890 4 1149 1 1014 544 108344.029
1891 6 1072 2 547 525 155753.296
1892 4 876 1 876 320 90653.212
1893 5 1368 1 1078 195 148117.434
1894 5 1678 0 1678 0 170243.142
1895 5 1560 1 1150 313 159804.522
1896 5 1298 1 720 256 133862.216
1897 5 1268 1 832 250 135749.138
1898 6 1242 1 583 180 153947.720
1899 6 1232 2 816 440 171788.870
1900 7 1228 1 600 215 180754.694
1901 5 1567 2 840 440 162129.995
1902 5 1273 1 1273 307 149282.569
1903 7 2480 2 920 400 263777.932
1904 5 1112 1 1112 390 136451.030
1905 6 1561 2 1561 463 210542.257
1906 5 1523 1 1067 295 155469.863
1907 6 1906 2 1310 576 220083.316
1908 6 1032 2 516 452 152836.470
1909 6 1229 2 561 462 163958.021
1910 6 1229 2 561 462 163958.021
1911 6 1982 2 572 501 201617.876
1912 7 2365 2 1803 551 284678.295
1913 5 2168 2 760 576 189513.288
1914 4 572 1 572 200 66424.716
1915 8 1648 2 1596 482 269887.226
1916 2 810 1 0 280 7013.310
1917 8 2052 3 1107 642 286500.626
1918 6 926 1 902 351 147892.846
1919 6 1287 2 957 541 178763.151
1920 5 1595 2 1582 672 185871.531
1921 9 2036 3 2190 780 345322.770
1922 9 1641 3 1641 885 309202.261
1923 6 2237 2 712 440 218476.631
1924 7 1479 2 1645 578 235996.709
1925 7 2014 2 912 486 240435.906
1926 9 1978 3 1978 920 336060.824
1927 5 1008 1 1008 384 128162.334
1928 5 1404 2 1188 504 164533.444
1929 5 796 1 796 336 111266.146
1930 6 1091 1 384 429 140467.123
1931 5 883 2 828 698 127860.551
1932 5 1287 2 624 440 141742.339
1933 5 1632 2 784 898 163665.184
1934 6 1604 2 926 470 193543.886
1935 6 1470 2 680 420 179490.108
1936 6 1604 2 926 472 193543.886
1937 6 1636 2 916 386 194828.962
1938 5 1384 2 1348 404 168362.144
1939 8 1682 2 1664 528 273621.276
1940 8 1280 2 1280 506 242124.546
1941 6 1633 2 932 460 195162.259
1942 6 1709 2 836 416 196037.711
1943 8 1337 2 1337 462 246667.389
1944 8 2500 3 1168 683 310547.016
1945 9 1884 3 1884 670 328569.118
1946 6 1474 2 1632 495 208368.352
1947 9 1710 2 1710 557 303383.970
1948 6 1488 2 1488 738 204724.230
1949 7 1688 2 1675 528 247261.246
1950 7 1260 2 1260 598 213541.712
1951 7 2064 2 2048 550 277137.692
1952 7 1782 2 1782 551 255144.590
1953 6 1211 2 1211 461 182647.607
1954 7 2044 2 738 489 236681.172
1955 5 1308 2 720 484 145675.468
1956 7 2840 4 1420 1314 319322.256
1957 6 1444 2 1444 473 201217.474
1958 8 2340 3 1236 787 304663.904
1959 5 1792 2 896 480 174970.976
1960 4 936 1 936 288 95435.152
1961 5 864 2 864 576 128003.200
1962 6 987 1 483 264 138294.005
1963 6 987 1 483 352 138294.005
1964 5 1302 1 630 264 131349.168
1965 6 1456 2 855 460 184068.136
1966 7 1055 2 1055 440 197203.417
1967 8 1582 3 1582 905 277511.166
1968 8 2464 3 2418 650 346419.888
1969 9 1950 3 1950 706 333829.252
1970 9 2748 3 1850 850 370375.906
1971 10 2790 4 1620 1150 403835.368
1972 9 2331 3 1414 1003 336569.029
1973 8 2088 3 948 656 283495.220
1974 8 2332 3 1148 756 301616.232
1975 10 2470 3 2535 789 404219.776
1976 8 1575 3 1603 732 277796.801
1977 8 2649 3 1479 746 327302.579
1978 8 2690 3 1765 795 337951.108
1979 9 1866 3 1858 870 326893.528
1980 7 1367 2 1367 484 222069.505
1981 9 1800 3 1800 944 321874.402
1982 8 1342 2 1342 550 247065.884
1983 8 1342 2 1342 550 247065.884
1984 7 1626 2 764 474 216742.934
1985 7 1455 3 1415 644 239195.499
1986 7 1576 3 1393 668 244531.060
1987 7 1246 2 1146 428 209413.326
1988 6 1515 2 789 393 185004.627
1989 7 1720 2 840 400 223692.372
1990 7 1986 2 847 434 237089.672
1991 7 1358 2 1350 484 221111.206
1992 7 1892 2 982 431 236496.820
1993 7 1414 2 707 403 204516.276
1994 7 2322 2 1037 400 259470.140
1995 7 1651 2 831 450 220000.701
1996 8 2199 2 1173 516 284458.651
1997 9 2172 3 1519 687 331850.152
1998 8 2006 3 1982 938 310580.518
1999 7 2125 2 1064 576 250517.661
2000 8 2501 3 1242 751 312825.913
2001 7 2197 3 1080 783 265886.455
2002 7 1578 3 1578 642 250203.516
2003 8 1861 3 1341 851 284081.667
2004 8 1874 3 955 880 273097.480
2005 8 1460 2 1460 480 256470.366
2006 8 1372 2 1363 588 249185.720
2007 8 1660 3 1660 660 283727.688
2008 7 1218 2 1218 462 210194.354
2009 6 1696 2 835 542 195363.136
2010 7 1663 2 835 478 220716.081
2011 6 1175 0 1175 0 157143.399
2012 7 1162 2 1162 483 205731.210
2013 6 1609 2 754 525 188610.079
2014 6 1680 2 886 474 196106.394
2015 7 1657 2 832 483 220328.265
2016 6 1677 2 827 627 194180.241
2017 7 1737 2 836 506 224414.609
2018 5 984 1 984 384 126249.558
2019 5 864 1 864 420 116685.678
2020 5 890 1 890 308 118757.852
2021 5 864 1 864 276 116685.678
2022 6 1430 2 1430 484 200101.688
2023 5 1641 2 1001 490 170648.683
2024 6 2683 2 930 473 247153.657
2025 9 2786 2 1848 636 360881.906
2026 7 1245 2 1235 495 212044.967
2027 6 1200 2 600 480 163695.318
2028 7 1392 2 689 540 202883.402
2029 6 1549 2 744 440 185334.439
2030 9 1638 2 1614 495 296922.618
2031 9 1310 2 1310 545 271504.370
2032 9 1419 2 1419 588 280191.561
2033 9 1557 2 1557 484 291190.023
2034 7 1404 2 672 462 202966.136
2035 7 1789 2 858 546 227655.177
2036 8 1586 2 960 480 247653.564
2037 8 1607 2 960 480 248694.597
2038 10 2393 2 970 846 341937.943
2039 8 1239 2 1230 477 238585.753
2040 9 2944 3 994 864 354304.358
2041 8 1671 2 1426 550 265905.985
2042 7 1812 2 952 469 231627.200
2043 6 1427 2 1427 516 199862.591
2044 6 1740 2 1740 512 224808.378
2045 7 1620 2 1620 578 242233.352
2046 5 1625 2 1625 484 188654.139
2047 5 1464 2 732 470 153770.368
2048 5 925 2 912 576 132473.201
2049 5 1728 2 1728 576 196863.136
2050 7 1670 2 825 464 220761.832
2051 5 1014 1 0 267 98092.764
2052 5 1114 2 1114 451 147927.950
2053 5 1118 1 1118 264 136929.224
2054 4 906 1 348 231 76233.874
2055 5 1496 2 1296 450 172347.768
2056 5 1337 1 572 264 131336.915
2057 5 1036 1 1036 312 130393.906
2058 6 1988 2 1104 480 217942.346
2059 5 1176 1 1176 292 141551.766
2060 5 1440 1 1248 480 156808.110
2061 6 1570 2 1395 441 205987.498
2062 5 1104 1 1104 384 135813.438
2063 5 882 1 720 240 113239.848
2064 5 1152 2 1152 636 150956.512
2065 5 950 2 984 400 135881.598
2066 6 1790 2 998 540 204933.536
2067 5 1764 1 0 301 135272.514
2068 5 1824 2 1824 484 204514.240
2069 4 869 1 0 390 63915.825
2070 5 1159 1 747 288 127784.971
2071 4 672 1 672 308 74394.616
2072 5 1436 4 892 1488 179837.528
2073 5 1044 2 912 372 138372.388
2074 7 1312 2 1312 495 217686.060
2075 6 1081 1 1081 401 160969.215
2076 5 876 2 876 576 128959.588
2077 5 1256 1 1256 311 147927.686
2078 5 1027 1 1027 299 129676.615
2079 6 1320 2 768 576 174705.246
2080 6 984 1 936 280 151792.364
2081 6 1278 1 854 240 163896.494
2082 5 1800 0 1800 0 179966.420
2083 5 1588 2 768 480 161001.956
2084 5 825 1 825 350 113577.417
2085 5 1117 1 1117 264 136849.525
2086 6 1133 1 192 308 136764.997
2087 5 1323 2 780 400 148226.623
2088 5 1360 1 680 330 135730.702
2089 5 672 1 672 256 101383.470
2090 6 1456 1 728 308 168924.612
2091 4 1594 0 828 0 113483.056
2092 4 1656 2 801 440 138378.224
2093 5 1740 1 936 225 162280.698
2094 7 1027 0 931 0 169444.705
2095 6 1436 2 884 828 183950.330
2096 5 899 1 481 200 106882.475
2097 5 1080 0 684 0 110653.244
2098 5 1499 3 904 869 172004.617
2099 2 407 1 407 297 -703.327
2100 3 1588 0 448 0 74748.884
2101 5 1627 2 797 420 163808.957
2102 7 1450 1 624 288 192482.924
2103 5 1017 1 346 308 108665.079
2104 5 2350 2 973 393 204952.412
2105 5 1540 0 572 0 130082.712
2106 2 1086 2 723 400 53794.078
2107 7 2495 2 1313 342 276361.045
2108 5 984 1 984 308 126249.558
2109 4 1093 1 869 308 101199.671
2110 5 1143 1 1008 288 134854.689
2111 7 1668 1 672 252 204735.886
2112 5 1738 1 1024 240 164832.640
2113 5 1210 1 672 240 128053.744
2114 6 1290 1 554 200 155453.570
2115 6 1672 1 981 240 187254.258
2116 5 949 2 949 370 134777.615
2117 6 1497 1 825 672 173879.327
2118 6 1342 1 739 240 163604.676
2119 6 1013 1 992 160 154917.037
2120 5 1216 1 565 355 125127.700
2121 4 896 1 NA 280 NA
2122 4 1136 1 704 336 98360.520
2123 5 808 1 0 164 87880.726
2124 7 2009 2 989 400 242507.743
2125 5 1902 2 1100 576 186569.710
2126 6 1716 2 810 672 195601.446
2127 6 1984 1 1242 360 210583.920
2128 6 1609 1 796 228 178557.849
2129 6 768 2 768 440 147340.950
2130 6 1536 1 768 308 174095.492
2131 6 1969 2 1272 400 222061.627
2132 5 1308 2 880 400 150495.628
2133 6 1040 2 1040 320 169019.078
2134 6 1236 1 768 384 159223.592
2135 5 759 2 451 576 110355.997
2136 3 1344 0 536 0 65304.160
2137 5 1054 1 1046 240 131587.480
2138 5 1075 2 981 440 141987.845
2139 6 1096 2 1008 484 170831.134
2140 5 992 1 992 294 126887.150
2141 6 1034 2 450 504 150947.300
2142 5 1073 1 1073 270 133342.769
2143 5 1126 2 1060 506 146896.022
2144 5 1140 2 1300 400 154820.284
2145 5 960 1 960 300 124336.782
2146 6 1188 2 988 621 174789.330
2147 5 1721 2 988 626 174222.885
2148 5 1350 3 576 627 154736.912
2149 5 904 3 864 912 141303.642
2150 7 1524 2 1524 478 234582.248
2151 7 1079 1 570 249 172464.537
2152 5 1518 0 1580 0 159359.114
2153 5 1509 1 1509 322 168091.533
2154 5 864 0 864 0 105368.156
2155 6 1269 1 621 280 156430.979
2156 6 2814 2 1077 614 258076.242
2157 7 1626 2 1602 534 241988.522
2158 7 2200 2 812 453 246643.884
2159 7 2037 2 672 472 234345.845
2160 7 1356 2 628 484 199261.088
2161 8 1615 3 1615 864 280141.233
2162 9 2276 3 2271 1348 359660.496
2163 9 1766 3 1751 874 318712.746
2164 8 1511 3 1401 811 268538.677
2165 6 1643 2 1182 438 203189.489
2166 5 990 2 990 528 138045.274
2167 7 1418 2 1418 558 226134.154
2168 6 1771 2 864 600 199954.765
2169 7 1652 2 782 532 218574.100
2170 7 1823 2 928 626 231449.479
2171 5 1174 2 864 528 143370.830
2172 5 1076 2 1076 576 144899.388
2173 5 1558 2 864 440 162406.862
2174 7 2161 2 1257 570 258116.607
2175 9 1947 3 1357 725 315815.815
2176 8 1786 3 1778 715 293528.754
2177 7 2327 2 1129 596 262489.597
2178 8 1764 2 850 560 253163.698
2179 6 848 2 848 420 153716.870
2180 6 1838 3 1838 721 243936.402
2181 7 1445 2 1445 470 228286.027
2182 7 1564 3 1564 814 249087.730
2183 7 1361 2 1351 610 221290.051
2184 5 1092 1 1092 264 134857.050
2185 5 1033 2 456 504 124089.629
2186 6 1127 2 1127 480 175952.891
2187 6 1117 2 1029 542 172504.813
2188 6 1398 2 684 440 176041.356
2189 5 3820 2 0 624 248512.124
2190 4 1152 0 0 0 66627.462
2191 4 1152 0 0 0 66627.462
2192 4 784 0 784 0 72003.382
2193 5 1053 0 585 0 106332.299
2194 5 1137 0 0 0 92872.721
2195 4 930 1 911 286 94384.564
2196 4 1204 1 416 312 93055.196
2197 4 1292 1 949 205 113474.778
2198 7 1424 1 1228 312 209390.130
2199 6 1920 2 960 480 210233.238
2200 6 1316 1 644 369 159453.808
2201 6 1264 2 676 400 169157.566
2202 8 1512 1 798 180 227787.228
2203 6 1603 2 784 599 189216.421
2204 5 1938 1 942 240 172276.908
2205 5 1374 1 884 225 142570.428
2206 6 1091 1 1091 344 161766.205
2207 8 1873 2 608 786 251276.663
2208 7 2161 2 832 506 245313.057
2209 5 1898 2 1898 484 210411.966
2210 6 1032 2 516 462 152836.470
2211 6 919 1 894 195 147304.827
2212 6 1090 1 720 240 150539.886
2213 5 1200 0 780 0 119494.100
2214 5 1656 1 624 288 148717.254
2215 5 912 1 520 360 108701.838
2216 5 1955 1 1151 356 179415.983
2217 1 733 2 0 487 -12475.143
2218 4 1361 1 173 185 93517.539
2219 4 1049 1 356 195 83563.821
2220 4 864 1 592 216 81502.552
2221 8 1648 2 1596 525 269887.226
2222 7 1646 2 1594 482 242738.974
2223 7 2032 3 1840 786 280602.670
2224 7 1820 3 910 816 242076.014
2225 5 1872 2 0 484 151943.920
2226 4 1689 2 1680 432 166494.887
2227 7 1501 2 1277 512 226000.947
2228 8 1537 3 1518 788 273352.317
2229 8 1780 2 920 612 256065.686
2230 5 1442 2 352 400 141231.882
2231 7 1612 3 796 666 228330.466
2232 6 1495 2 1495 438 205282.123
2233 6 1256 2 1256 578 186234.062
2234 8 1440 2 1432 467 254635.378
2235 6 1675 2 1666 435 219356.809
2236 8 1728 2 1884 520 282529.354
2237 8 1964 3 1964 892 307956.184
2238 7 1344 4 1344 784 242871.472
2239 5 1092 0 1092 0 123539.528
2240 6 1189 2 1189 392 180894.229
2241 4 1200 3 1200 850 139110.732
2242 5 1040 2 1040 499 142030.224
2243 5 1475 1 550 336 137515.217
2244 4 988 1 526 297 85661.288
2245 4 988 1 462 297 83733.224
2246 5 1160 1 1113 257 138860.660
2247 6 1092 0 546 0 134079.586
2248 5 816 1 816 264 112860.126
2249 5 845 1 845 264 115171.397
2250 5 889 2 864 484 129242.525
2251 5 1836 1 686 288 159508.206
2252 6 1587 2 1587 525 212614.431
2253 6 1384 2 384 390 166309.534
2254 6 1694 2 392 398 181918.172
2255 6 1714 2 923 451 198906.538
2256 7 1553 2 691 420 210924.907
2257 6 2299 2 938 482 228358.633
2258 7 1187 2 1168 420 207151.291
2259 6 1642 2 903 392 194734.762
2260 6 1128 2 1302 480 181274.514
2261 8 1179 2 1166 480 233683.309
2262 8 1321 2 1312 484 245121.071
2263 8 2541 3 1149 729 312007.115
2264 9 2338 3 2660 1110 374453.036
2265 5 1424 3 1100 828 174191.338
2266 9 1612 2 1612 556 295573.468
2267 8 2234 2 2220 724 317735.628
2268 10 2042 3 1742 724 359112.614
2269 6 1284 2 1284 480 188465.634
2270 7 1479 2 725 484 208280.789
2271 7 1664 2 1529 663 241673.098
2272 7 1930 2 780 481 232295.142
2273 6 1177 2 1153 495 179214.817
2274 6 1353 2 1259 478 191133.021
2275 6 1220 2 1625 944 195565.928
2276 7 1324 2 1228 585 215750.352
2277 6 1877 2 1084 488 211837.223
2278 5 1422 2 925 576 157502.620
2279 5 914 1 914 368 120670.628
2280 4 914 1 914 270 93681.774
2281 6 1337 2 1337 511 192689.681
2282 6 1337 2 1405 522 194738.249
2283 6 1092 1 525 264 144764.462
2284 6 1218 1 672 264 155439.182
2285 7 1055 1 1061 319 186066.651
2286 5 988 1 988 360 126568.354
2287 9 1816 3 1790 730 322366.310
2288 8 1694 3 1694 856 286437.454
2289 8 2122 3 2108 938 320126.862
2290 9 2656 3 1934 1040 368345.774
2291 8 2550 3 1108 670 311218.106
2292 10 2046 3 1994 878 366902.658
2293 9 2552 3 2552 932 381808.050
2294 9 2758 3 1704 814 366473.240
2295 10 2290 3 2320 1174 388819.546
2296 8 2152 2 1582 728 294450.254
2297 8 2100 3 918 786 283186.316
2298 9 1802 3 1802 843 322033.800
2299 8 2956 3 1706 916 349360.092
2300 8 2385 3 1317 818 309334.895
2301 8 1818 3 895 774 268513.832
2302 7 1614 3 1614 878 253072.680
2303 8 1721 2 1721 554 277271.805
2304 8 1828 3 1298 876 281150.340
2305 6 1302 2 1302 631 189900.216
2306 6 1302 2 1302 631 189900.216
2307 6 1362 2 1362 460 194682.156
2308 8 1554 2 1554 627 263962.072
2309 9 1577 2 1577 564 292784.003
2310 8 1324 2 1324 550 245631.302
2311 7 1405 2 1405 478 225098.067
2312 6 1496 2 768 572 183430.094
2313 6 1536 2 768 400 185413.014
2314 7 1458 2 608 454 203715.014
2315 6 1495 2 738 440 182476.741
2316 6 1746 3 698 350 205032.046
2317 6 1326 2 1326 388 191812.992
2318 6 1504 2 752 440 183344.662
2319 7 1456 2 728 400 207230.988
2320 7 1258 2 1246 462 213020.802
2321 8 1589 3 1406 630 272556.001
2322 7 1266 2 1266 388 214019.906
2323 7 1119 2 835 437 193748.369
2324 7 1374 2 384 400 192802.658
2325 7 1525 2 1510 534 234210.057
2326 7 1394 2 384 400 193794.118
2327 7 1948 2 847 434 235205.898
2328 7 1995 2 854 435 237746.711
2329 6 1690 2 915 442 197475.778
2330 6 1644 2 941 460 195978.696
2331 8 2551 3 1779 925 331482.225
2332 9 3078 3 1408 806 373419.304
2333 8 2582 3 1389 758 321269.848
2334 7 2385 3 1066 600 274784.415
2335 8 2202 2 1105 517 282558.802
2336 8 2538 3 1325 933 317160.572
2337 7 1369 2 1369 605 222228.903
2338 8 1542 3 1542 852 274323.206
2339 8 1534 2 1524 484 262066.832
2340 10 1966 3 1966 1092 362093.290
2341 7 1528 2 1528 480 234901.044
2342 8 1538 2 1538 484 262686.888
2343 7 1506 2 1494 672 232786.154
2344 7 1977 3 982 574 252028.047
2345 8 1830 3 1302 859 281369.990
2346 8 1338 2 1338 598 246747.088
2347 7 1335 2 1335 575 219519.137
2348 8 1792 2 896 590 255937.538
2349 5 1588 2 725 561 159706.538
2350 8 1880 3 955 880 273394.918
2351 8 1584 2 1574 594 266051.782
2352 8 1685 3 1685 658 285720.163
2353 6 2443 3 1158 744 253442.387
2354 6 1100 0 1100 0 151165.974
2355 7 1143 0 1143 0 181581.885
2356 5 1094 2 1094 576 146333.970
2357 7 1486 2 1461 566 230800.536
2358 6 1820 2 864 492 202383.842
2359 6 1266 1 630 283 156553.394
2360 5 894 1 894 308 119076.648
2361 5 1040 2 1040 686 142030.224
2362 7 2503 2 1223 564 274046.289
2363 5 1037 2 1037 431 141791.127
2364 6 1055 2 1044 542 169883.177
2365 8 1378 2 1378 540 249935.048
2366 8 1151 2 1141 484 231542.115
2367 8 1565 2 1511 476 263211.957
2368 7 1352 2 1352 466 220874.020
2369 8 1550 2 1550 528 263643.276
2370 6 1501 2 744 440 182954.935
2371 6 1573 2 756 440 186885.703
2372 7 1358 2 1339 625 220779.820
2373 8 2048 2 1054 552 273388.134
2374 8 2362 2 1220 1105 293954.972
2375 8 1494 2 1494 478 259180.132
2376 7 2362 2 2002 546 290524.650
2377 8 2497 2 2461 676 338033.693
2378 6 1152 2 912 412 170715.126
2379 6 2411 2 1008 570 236019.629
2380 6 1082 2 973 480 169082.702
2381 6 1295 2 1295 528 189342.323
2382 7 1610 2 1124 515 226795.126
2383 7 1594 2 1594 472 240161.178
2384 7 2075 2 1148 473 250569.595
2385 6 1093 2 1093 484 173243.125
2386 6 1052 1 1052 311 158657.944
2387 5 1107 1 949 308 131292.627
2388 4 1224 3 0 530 104149.284
2389 5 1074 2 894 396 139317.310
2390 5 1187 2 864 440 144015.279
2391 5 964 2 964 784 135973.100
2392 5 894 1 894 312 119076.648
2393 7 1200 2 1174 440 207976.496
2394 6 1042 2 1042 440 169178.476
2395 6 2154 2 845 539 218368.830
2396 6 1374 1 1130 286 176970.278
2397 6 1652 2 1592 510 215987.306
2398 5 908 2 822 512 128919.120
2399 3 666 0 666 0 35610.046
2400 3 670 0 370 0 26891.042
2401 5 808 1 808 308 112222.534
2402 5 1150 1 1078 288 137310.520
2403 5 1560 3 1560 792 194791.226
2404 5 1280 2 793 432 146486.622
2405 6 1254 2 1218 525 184990.128
2406 6 936 1 936 315 149412.860
2407 5 1008 1 1008 308 128162.334
2408 5 1053 2 1053 692 143066.311
2409 5 1144 1 1144 336 139001.398
2410 6 1721 2 1331 464 211544.957
2411 5 922 1 922 308 121308.220
2412 5 1411 1 1204 310 154044.949
2413 5 1216 1 1216 336 144739.726
2414 5 1154 1 864 336 131061.848
2415 5 1560 2 1560 484 183473.704
2416 6 948 2 948 410 161686.770
2417 6 1040 1 1040 293 157701.556
2418 5 925 1 925 252 121547.317
2419 4 1540 1 1190 352 133029.248
2420 5 925 1 923 390 121487.065
2421 4 1647 1 1105 280 135772.849
2422 5 924 1 924 420 121467.618
2423 4 1544 0 925 0 113926.628
2424 5 1728 1 1216 371 170121.102
2425 5 3086 3 666 1200 243506.980
2426 4 1281 2 1151 580 130332.449
2427 5 1534 0 736 0 134725.938
2428 7 1651 1 725 276 205489.823
2429 5 888 1 920 240 119562.486
2430 6 952 1 952 288 150688.044
2431 5 1238 1 864 357 135225.980
2432 5 1040 1 1040 286 130712.702
2433 5 1170 1 1170 338 141073.572
2434 5 1242 1 1242 324 146811.900
2435 5 1377 1 1377 351 157571.265
2436 5 925 1 0 300 93680.767
2437 5 864 1 864 294 116685.678
2438 5 936 1 896 288 121218.966
2439 5 960 2 850 576 132340.444
2440 6 1296 2 845 576 175835.196
2441 5 1022 1 1022 184 129278.120
2442 5 967 1 967 180 124894.675
2443 5 1072 2 808 379 136627.328
2444 5 1174 2 680 576 137827.646
2445 4 1141 1 570 252 94571.501
2446 4 1798 2 1075 342 153672.114
2447 8 1772 2 725 816 249794.532
2448 6 1642 1 728 374 178145.190
2449 5 1232 2 616 480 138774.816
2450 6 1650 2 926 468 195824.244
2451 5 1358 1 687 336 135842.438
2452 7 2454 2 819 576 259446.308
2453 4 968 1 0 331 68823.552
2454 4 1382 1 0 384 89346.774
2455 5 1060 1 756 308 123148.378
2456 5 1435 1 910 308 146377.657
2457 5 1274 1 728 224 132913.472
2458 6 1232 1 600 217 153964.132
2459 5 884 1 884 240 118279.658
2460 6 1409 2 861 528 181918.961
2461 5 1322 1 672 280 133605.920
2462 5 1426 1 966 230 147618.556
2463 5 1281 2 756 379 145421.533
2464 6 2264 1 1100 408 220186.468
2465 6 1376 2 768 576 177481.334
2466 5 1316 2 750 576 146975.832
2467 7 1344 2 672 456 199991.756
2468 5 1173 1 592 240 123809.463
2469 5 1214 1 969 216 137199.458
2470 6 2294 2 741 658 222175.946
2471 7 1952 1 976 299 227972.922
2472 6 2180 2 1138 720 228484.646
2473 4 1315 2 808 436 121684.713
2474 4 1484 1 771 264 117630.366
2475 7 2267 2 1005 498 255779.593
2476 5 1282 1 672 240 131623.000
2477 5 999 1 925 308 125215.719
2478 5 1452 2 1420 572 173902.180
2479 4 1005 2 1005 440 112251.905
2480 5 1020 2 980 528 139231.204
2481 5 1040 1 1040 264 130712.702
2482 5 868 2 768 576 125309.396
2483 5 897 1 876 264 118683.099
2484 5 943 2 943 528 134299.421
2485 5 912 1 912 315 120511.230
2486 5 1375 1 1240 323 153344.857
2487 5 2654 2 671 638 210924.552
2488 7 1302 1 680 224 186833.176
2489 6 1299 2 967 494 179659.287
2490 5 1176 1 1158 303 141009.498
2491 4 998 2 0 460 81628.264
2492 6 1522 2 1190 552 197432.164
2493 5 1325 2 409 576 137149.023
2494 5 1630 2 1073 649 172272.452
2495 4 1242 1 484 336 96987.538
2496 6 2422 2 1288 527 245000.212
2497 6 1626 1 988 332 185184.782
2498 5 864 1 864 399 116685.678
2499 4 943 1 0 308 67584.227
2500 5 1038 1 1038 264 130553.304
2501 5 1342 1 1342 256 154781.800
2502 5 1480 1 1080 253 153729.862
2503 4 1362 1 951 280 117005.140
2504 6 1822 2 1105 515 209743.354
2505 7 1958 2 779 499 233653.060
2506 8 1651 3 1643 870 282769.389
2507 8 2140 3 2140 894 321983.208
2508 8 1651 3 1643 870 282769.389
2509 7 1546 3 1546 796 247653.148
2510 8 1500 2 1489 674 259326.940
2511 7 1270 2 1270 524 214338.702
2512 7 1795 2 879 578 228585.261
2513 7 1873 2 929 619 233958.255
2514 8 1743 2 966 529 255617.281
2515 5 1022 2 1022 747 140595.642
2516 6 1308 2 635 497 170103.612
2517 5 990 1 990 384 126727.752
2518 5 1097 1 1097 242 135255.545
2519 7 1873 2 988 597 235735.689
2520 7 1753 2 853 534 225719.919
2521 7 1690 2 845 517 222355.812
2522 7 1842 2 985 486 234108.548
2523 5 894 2 864 440 129490.390
2524 5 1025 2 1015 370 140533.479
2525 5 1009 2 995 576 139137.791
2526 5 1040 2 1040 748 142030.224
2527 5 907 1 876 308 119178.829
2528 5 879 2 864 440 128746.795
2529 5 864 2 864 576 128003.200
2530 4 875 2 385 728 87129.295
2531 7 1673 2 1673 583 246457.399
2532 7 1932 2 979 610 238389.362
2533 7 1729 2 860 542 224741.049
2534 8 1592 2 1556 484 265906.098
2535 8 2439 2 1531 560 307141.279
2536 7 1992 2 860 608 237778.748
2537 8 1341 2 1341 482 246986.185
2538 7 1476 2 738 552 208523.708
2539 7 1190 2 1169 578 207330.136
2540 6 1330 2 1330 437 192131.788
2541 7 1491 2 738 484 209267.303
2542 5 1536 2 768 400 158424.160
2543 5 936 1 936 384 122424.006
2544 4 1088 2 1088 520 118866.922
2545 5 1351 2 901 576 153259.913
2546 6 1179 2 1179 622 180097.239
2547 6 1044 2 960 528 166807.290
2548 5 2233 2 0 579 169839.773
2549 5 1408 2 734 489 151054.532
2550 10 5095 3 5095 1154 611471.461
2551 6 1072 2 547 525 155753.296
2552 5 960 1 960 392 124336.782
2553 4 1152 0 0 0 66627.462
2554 5 1195 0 1195 0 131748.525
2555 6 865 1 660 216 137578.401
2556 4 768 1 768 355 82045.720
2557 4 864 2 864 528 101014.346
2558 5 2592 0 1296 0 204044.732
2559 5 1422 1 1290 352 157181.088
2560 6 1298 1 651 240 158772.376
2561 7 1098 1 531 216 172231.510
2562 6 1436 1 585 228 163625.134
2563 6 1461 1 686 225 167907.185
2564 7 1718 2 851 264 223924.612
2565 5 1226 2 0 400 119919.762
2566 4 1755 1 930 231 135854.683
2567 5 1355 2 728 528 148246.407
2568 6 1560 2 1560 580 210462.558
2569 6 1488 2 1488 552 204724.230
2570 6 1045 2 516 462 153480.919
2571 4 1680 2 1680 628 166048.730
2572 8 1020 2 1004 509 220920.790
2573 7 1696 2 1696 625 248290.476
2574 8 2726 2 279 691 283650.978
2575 6 1215 2 720 720 168054.033
2576 5 1601 0 936 0 144072.529
2577 5 1828 NA 859 NA NA
2578 5 816 1 816 100 112860.126
2579 2 845 1 0 256 8748.365
2580 5 1991 0 957 0 164038.645
2581 4 1073 2 1073 720 117671.437
2582 5 1001 1 837 216 122663.777
2583 8 1625 2 1573 495 268054.149
2584 6 1299 2 1001 486 180683.571
2585 5 1392 3 1441 650 182877.968
2586 7 1409 2 1409 576 225416.863
2587 7 1478 2 1454 506 230193.070
2588 7 918 1 852 360 172978.816
2589 6 1026 2 970 528 166216.236
2590 7 1501 2 1501 512 232749.171
2591 6 2279 2 585 461 216732.695
2592 7 1689 2 1689 433 247732.583
2593 8 1564 2 1546 502 264216.794
2594 7 1240 2 1265 528 212700.882
2595 8 1312 2 1267 471 243319.244
2596 8 1922 3 1922 692 304608.826
2597 6 1491 2 1491 571 204963.327
2598 7 2486 2 1122 452 270160.822
2599 10 1824 3 1824 932 350776.032
2600 3 2034 4 0 1041 128632.082
2601 6 936 2 920 460 160248.366
2602 4 1092 1 546 253 91419.400
2603 4 992 1 530 297 85980.084
2604 4 1092 0 546 0 80101.878
2605 4 1092 1 546 286 91419.400
2606 5 1008 2 923 678 136919.146
2607 6 1356 2 1244 528 190829.850
2608 5 1676 2 1160 672 177173.772
2609 5 1432 2 716 531 151702.016
2610 5 796 0 796 0 99948.624
2611 4 1608 1 1396 444 142606.168
2612 5 1178 2 1090 502 150377.598
2613 5 816 1 816 264 112860.126
2614 5 887 1 864 288 117825.857
2615 6 1293 2 1178 452 185718.435
2616 6 1024 1 1008 313 155944.356
2617 5 1797 3 1617 963 208257.209
2618 6 1390 2 1705 550 206403.418
2619 7 1851 2 1851 506 260643.821
2620 6 1525 2 813 400 186223.381
2621 6 1671 2 781 423 192497.007
2622 6 1776 2 821 443 198907.212
2623 8 2064 2 1074 527 274783.822
2624 7 2212 3 1700 773 285308.170
2625 8 2687 2 2062 618 335432.289
2626 6 1169 2 1160 402 179029.115
2627 8 1204 2 1191 461 235675.784
2628 9 2798 3 1398 670 359237.604
2629 10 3390 3 1528 758 419490.054
2630 9 2473 3 1094 675 333968.075
2631 10 2698 3 1850 736 394886.110
2632 9 2795 3 1390 660 358847.877
2633 9 1714 2 1714 517 303702.766
2634 9 2000 3 1910 722 335102.862
2635 6 1102 2 988 582 170526.052
2636 7 1857 2 945 482 233647.103
2637 7 1083 2 1047 596 198350.453
2638 7 2318 2 960 541 256952.146
2639 6 1875 2 675 485 199416.543
2640 6 1103 2 1103 462 174040.115
2641 4 874 2 864 576 101510.076
2642 6 1419 2 1487 543 201273.567
2643 6 1092 2 525 440 156081.984
2644 5 1365 2 765 440 149856.799
2645 6 1030 1 494 264 140757.030
2646 6 948 1 483 264 136360.658
2647 6 1092 1 525 264 144764.462
2648 6 1069 2 1069 440 171330.349
2649 5 1387 1 943 300 144992.311
2650 7 1055 1 1055 319 185885.895
2651 6 1456 2 855 460 184068.136
2652 9 2589 3 1738 831 359119.687
2653 8 1618 3 1618 880 280380.330
2654 7 1740 3 1721 874 262542.360
2655 9 1868 3 1868 1085 327293.934
2656 9 2206 3 1090 670 320611.580
2657 9 2091 2 1606 521 319138.179
2658 8 2253 2 1209 575 288220.129
2659 8 2389 3 1054 672 301610.049
2660 8 2358 3 1524 784 314232.506
2661 9 1792 3 1792 925 321236.810
2662 9 1780 3 1780 816 320280.422
2663 8 1914 3 1420 746 289088.990
2664 9 1565 2 1560 556 291676.985
2665 9 1686 3 1686 899 312788.716
2666 9 1666 2 1666 575 299877.214
2667 6 1456 2 728 390 180242.134
2668 7 1492 2 738 440 209316.876
2669 6 1326 2 1326 427 191812.992
2670 8 2373 3 1052 632 300756.629
2671 7 1492 2 738 440 209316.876
2672 7 1364 2 1220 437 217492.264
2673 7 1511 2 1368 398 229238.143
2674 7 1548 2 1365 388 230981.966
2675 7 1142 2 1142 440 204137.230
2676 7 1598 2 848 433 217885.474
2677 6 1889 2 982 431 209359.247
2678 7 2322 3 1035 617 270727.410
2679 8 1976 3 1044 885 280835.140
2680 8 2234 3 1160 768 297119.590
2681 9 2855 3 1540 774 366341.157
2682 8 2726 3 1342 725 326992.438
2683 9 3500 3 1733 959 404130.060
2684 8 2494 3 1290 803 313924.950
2685 8 2799 3 1286 704 328924.211
2686 8 1964 2 1100 760 270609.798
2687 8 1670 3 1670 928 284524.678
2688 8 1504 2 1504 510 259977.122
2689 8 1278 2 1278 584 241965.148
2690 9 2640 3 2036 792 370625.458
2691 6 1716 2 858 615 197047.494
2692 6 1142 0 1142 0 154513.332
2693 7 1400 2 1400 612 224699.572
2694 6 1131 0 1131 0 153636.643
2695 7 1686 2 891 462 223543.316
2696 7 1585 2 784 449 215312.961
2697 6 1837 2 941 688 205546.285
2698 6 1731 2 836 462 197128.317
2699 6 1398 2 1398 542 197551.320
2700 6 1217 2 1217 484 183125.801
2701 6 1320 2 636 472 170728.614
2702 5 988 2 988 624 137885.876
2703 6 1654 2 0 528 168125.860
2704 5 1211 2 864 576 145205.031
2705 5 984 1 984 310 126249.558
2706 5 909 1 882 294 119458.731
2707 5 925 2 925 484 132864.839
2708 5 1024 1 1024 308 129437.518
2709 5 912 0 912 0 109193.708
2710 5 941 1 912 288 121948.847
2711 7 2646 2 1748 550 296951.378
2712 8 2826 3 1334 888 331708.730
2713 7 1143 2 1143 588 204216.929
2714 7 1223 2 600 480 191824.351
2715 6 1524 2 756 440 184456.626
2716 7 1080 2 530 496 182626.592
2717 7 1694 1 569 434 202921.806
2718 8 1568 2 1568 564 265077.858
2719 5 1193 2 1153 501 153019.131
2720 7 1334 2 1334 477 219439.438
2721 5 1051 2 1051 504 142906.913
2722 5 1770 2 900 530 174000.874
2723 6 976 2 976 504 163918.342
2724 5 898 1 833 326 117437.254
2725 5 1051 1 1051 264 131589.391
2726 5 1141 1 1141 568 138762.301
2727 6 1565 1 1187 299 188155.903
2728 6 1488 2 1008 430 190263.750
2729 5 1440 2 720 480 152219.104
2730 5 1248 1 1248 286 147290.094
2731 6 816 1 816 240 139848.980
2732 5 1043 1 938 273 127788.569
2733 5 1433 2 1251 441 167868.999
2734 5 1624 1 1444 240 171834.238
2735 5 1216 1 1056 280 139919.566
2736 5 1728 1 704 234 154696.590
2737 5 936 1 936 240 122424.006
2738 5 1584 2 1584 506 185386.480
2739 6 1246 2 1246 441 185437.072
2740 6 1008 2 1008 430 166468.710
2741 5 1364 1 1179 331 150961.868
2742 6 1336 2 1121 488 186132.892
2743 5 1370 1 1058 300 147614.060
2744 6 1124 1 1124 353 164396.272
2745 6 1050 1 1050 286 158498.546
2746 5 1008 1 1008 280 128162.334
2747 6 1575 2 216 400 170716.809
2748 5 1145 2 301 684 124972.275
2749 5 1005 1 1005 319 127923.237
2750 5 1056 1 1056 300 131987.886
2751 5 884 1 884 270 118279.658
2752 7 2039 3 929 791 253504.895
2753 5 1384 2 1015 896 158330.186
2754 5 2640 3 2171 1008 266737.052
2755 6 1312 2 784 649 174790.678
2756 3 713 1 713 371 50673.421
2757 3 715 2 715 660 62150.341
2758 4 720 1 448 280 70025.896
2759 6 1595 2 663 528 185174.591
2760 4 1760 2 1077 648 151848.592
2761 6 1146 1 1083 294 164251.712
2762 6 1207 1 1135 264 168842.217
2763 6 1773 2 1313 418 213580.485
2764 7 1472 2 0 484 186092.428
2765 7 2448 2 1438 441 277796.864
2766 4 1521 1 910 597 123652.081
2767 3 1040 2 0 400 56721.476
2768 5 1556 0 1556 0 160519.864
2769 5 1150 1 1150 288 139479.592
2770 7 1045 2 1045 528 196406.427
2771 5 864 1 864 336 116685.678
2772 5 1025 0 1025 0 118199.695
2773 5 2014 2 716 624 180553.502
2774 5 1668 1 1212 240 167026.218
2775 4 1657 1 1147 162 137533.871
2776 6 1416 2 780 400 179825.766
2777 7 1428 2 712 576 205360.928
2778 7 1004 1 502 200 166697.994
2779 4 1951 2 938 576 157129.521
2780 6 1032 1 1032 280 157063.964
2781 5 844 1 844 216 115091.698
2782 4 864 1 576 528 81020.536
2783 3 1376 1 738 216 84293.470
2784 5 960 2 960 576 135654.304
2785 5 1566 2 816 450 161357.398
2786 3 492 1 492 200 33059.942
2787 6 1182 1 770 378 156606.902
2788 3 840 1 798 250 59529.902
2789 7 2104 2 1226 432 254357.040
2790 5 1248 0 624 0 117173.948
2791 5 960 2 960 624 135654.304
2792 3 1020 0 1020 0 63823.492
2793 6 1827 1 848 240 190931.315
2794 3 1162 1 728 258 73383.588
2795 5 1324 1 741 180 135783.760
2796 5 816 1 816 210 112860.126
2797 6 2486 2 1595 576 257421.566
2798 4 1430 2 596 370 120998.896
2799 5 1330 1 396 390 125687.728
2800 5 819 0 610 0 95485.367
2801 6 984 1 984 308 153238.412
2802 5 1422 1 806 288 142600.104
2803 6 1921 2 1921 576 239233.897
2804 5 1640 2 0 394 140442.984
2805 5 1032 1 0 260 98985.078
2806 5 879 1 830 180 116404.989
2807 7 1073 1 1073 246 187320.477
2808 5 1064 2 1064 528 143943.000
2809 6 934 1 912 336 148590.690
2810 5 1059 1 1059 286 132226.983
2811 5 1458 2 1176 512 166848.874
2812 6 1040 2 960 616 166608.998
2813 5 1967 2 1967 580 215911.197
2814 5 1949 2 1949 586 214476.615
2815 5 872 1 872 322 117323.270
2816 7 1830 2 1865 521 260024.552
2817 5 1000 2 1000 575 138842.264
2818 5 810 2 782 576 122855.926
2819 7 1700 1 430 450 199031.730
2820 4 1350 2 967 504 128209.802
2821 5 1150 1 799 215 128905.366
2822 6 2009 3 672 795 217286.469
2823 6 3672 2 1836 836 323475.510
2824 5 1560 2 1776 528 189980.920
2825 5 1488 2 0 569 132907.888
2826 5 1057 1 1057 288 132067.585
2827 6 1609 1 1000 305 184703.553
2828 6 2559 2 868 506 239138.793
2829 6 1440 4 1344 920 220641.626
2830 7 1876 3 1308 848 256842.250
2831 7 1208 2 1208 632 209397.364
2832 6 1846 2 1836 495 232955.212
2833 8 1590 3 1570 754 277546.238
2834 7 1809 2 945 638 231267.599
2835 7 1614 2 1614 576 241755.158
2836 7 1596 2 784 610 215858.264
2837 6 1388 2 1388 522 196754.330
2838 5 1100 2 1100 462 146812.164
2839 6 1499 2 781 473 183970.451
2840 7 1425 2 1425 591 226692.047
2841 7 1749 2 842 515 225190.241
2842 7 1779 2 951 586 229961.165
2843 6 1388 1 675 317 163956.970
2844 6 1282 3 995 672 190977.596
2845 5 864 1 864 297 116685.678
2846 7 1762 2 826 591 225352.674
2847 6 1755 2 840 530 198438.573
2848 8 1358 2 1348 418 248039.808
2849 7 1909 2 872 529 234025.701
2850 8 2214 3 1090 646 294019.310
2851 7 2049 2 1065 467 246780.239
2852 7 1939 3 1054 555 252313.345
2853 7 1995 2 930 610 240036.287
2854 6 848 2 848 420 153716.870
2855 7 1390 2 1390 545 223902.582
2856 7 1737 2 851 578 224866.499
2857 7 1611 2 784 572 216601.859
2858 7 1336 2 1336 502 219598.836
2859 4 1436 2 676 528 123706.414
2860 6 1012 0 976 0 143067.926
2861 5 1176 1 1176 360 141551.766
2862 7 1724 2 796 616 222565.120
2863 6 914 0 914 0 136341.960
2864 7 2314 2 1150 502 262477.794
2865 6 1072 2 547 525 155753.296
2866 7 1709 2 970 380 227063.449
2867 4 936 1 624 265 86035.840
2868 6 1338 2 832 528 177525.624
2869 4 1669 1 1093 288 136501.943
2870 4 1482 2 954 609 134361.800
2871 4 1414 0 864 0 105644.452
2872 2 498 1 498 216 6549.282
2873 4 1273 1 616 275 102500.933
2874 5 1551 1 1058 240 156586.773
2875 6 1340 1 780 440 164740.696
2876 7 1479 1 624 312 193920.541
2877 6 1510 1 755 216 172414.956
2878 7 1636 1 818 288 207547.946
2879 5 1465 1 992 240 150335.179
2880 5 1288 1 824 240 136499.590
2881 6 1550 1 817 318 176265.688
2882 5 1717 1 901 410 160086.109
2883 7 1671 2 1022 451 226746.227
2884 7 1609 3 637 579 223391.713
2885 6 1801 2 697 365 196410.913
2886 5 2315 1 1168 342 197774.405
2887 5 976 1 976 215 125611.966
2888 6 1285 2 861 506 175771.909
2889 4 672 0 432 0 55846.854
2890 4 641 1 641 272 71923.947
2891 6 1638 1 967 384 185147.012
2892 3 729 0 0 0 18669.229
2893 5 1396 0 660 0 125595.288
2894 3 936 0 216 0 35438.056
2895 8 1778 2 1573 495 275638.818
2896 8 1646 2 1594 525 269727.828
2897 6 1625 2 1625 576 215642.993
2898 6 1664 2 1664 616 218751.254
2899 8 1491 2 1491 490 258941.035
2900 6 1210 2 1128 528 180097.576
2901 6 1650 2 1632 518 217093.200
2902 6 1403 2 1381 470 197287.043
2903 8 1960 3 1728 714 300648.156
2904 9 1838 3 1838 682 324902.964
2905 1 1600 1 0 270 19187.126
2906 7 1368 4 1288 784 242374.168
2907 5 1304 1 264 336 120422.198
2908 5 874 1 864 288 117181.408
2909 5 1652 3 1652 928 202123.534
2910 4 630 0 630 0 59729.736
2911 4 1092 1 546 253 91419.400
2912 5 1360 1 1104 336 148504.126
2913 4 1092 1 546 286 91419.400
2914 4 1092 0 546 0 80101.878
2915 4 1092 0 546 0 80101.878
2916 4 1092 1 546 286 91419.400
2917 5 1224 2 1224 576 156694.840
2918 5 970 0 912 0 112068.942
2919 7 2000 3 996 650 253589.990 
write.csv(new.df, file = "kagglesubmission.csv")