# Fetch Data
qb_stats <- read.csv("../data/qb_stats.csv")
# Grab the college predictors
predictors <- c("height", "weight", "age", "c_avg_cmpp", "c_rate", "c_pct",
"c_avg_inter", "c_avg_tds", "c_avg_yds", "c_numyrs", "c_avg_att")
college_stats = qb_stats[, predictors]
# Set the resopnse variables
cpct = qb_stats["completion_percentage"]
# Generate clean data set
data.log.no_combine.for_cpct = data.frame(log(na.omit(cbind(cpct, college_stats)) +
0.1))
# Generate the linear model
lm.log.no_combine.cpct <- lm(formula = completion_percentage ~ ., data = data.log.no_combine.for_cpct)
# Find optimum linear regression model for cpct
step_reg.log.no_combine.cpct <- stepAIC(lm.log.no_combine.cpct, direction = "both")
## Start: AIC=-393
## completion_percentage ~ height + weight + age + c_avg_cmpp +
## c_rate + c_pct + c_avg_inter + c_avg_tds + c_avg_yds + c_numyrs +
## c_avg_att
##
## Df Sum of Sq RSS AIC
## - c_numyrs 1 0.000 40.3 -395
## - height 1 0.000 40.3 -395
## - c_rate 1 0.007 40.3 -395
## - c_avg_tds 1 0.011 40.3 -395
## - c_avg_inter 1 0.044 40.4 -395
## - c_avg_yds 1 0.061 40.4 -395
## - weight 1 0.177 40.5 -394
## - c_pct 1 0.267 40.6 -393
## - c_avg_cmpp 1 0.286 40.6 -393
## - c_avg_att 1 0.321 40.6 -393
## <none> 40.3 -393
## - age 1 0.348 40.7 -393
##
## Step: AIC=-395
## completion_percentage ~ height + weight + age + c_avg_cmpp +
## c_rate + c_pct + c_avg_inter + c_avg_tds + c_avg_yds + c_avg_att
##
## Df Sum of Sq RSS AIC
## - height 1 0.000 40.3 -397
## - c_rate 1 0.008 40.3 -397
## - c_avg_tds 1 0.012 40.3 -397
## - c_avg_inter 1 0.046 40.4 -397
## - c_avg_yds 1 0.062 40.4 -397
## - weight 1 0.181 40.5 -396
## - c_pct 1 0.281 40.6 -395
## - c_avg_cmpp 1 0.299 40.6 -395
## - c_avg_att 1 0.333 40.7 -395
## <none> 40.3 -395
## - age 1 0.348 40.7 -395
## + c_numyrs 1 0.000 40.3 -393
##
## Step: AIC=-397
## completion_percentage ~ weight + age + c_avg_cmpp + c_rate +
## c_pct + c_avg_inter + c_avg_tds + c_avg_yds + c_avg_att
##
## Df Sum of Sq RSS AIC
## - c_rate 1 0.008 40.3 -399
## - c_avg_tds 1 0.012 40.3 -399
## - c_avg_inter 1 0.046 40.4 -399
## - c_avg_yds 1 0.062 40.4 -399
## - weight 1 0.285 40.6 -397
## - c_pct 1 0.287 40.6 -397
## - c_avg_cmpp 1 0.306 40.6 -397
## - c_avg_att 1 0.341 40.7 -397
## <none> 40.3 -397
## - age 1 0.352 40.7 -397
## + height 1 0.000 40.3 -395
## + c_numyrs 1 0.000 40.3 -395
##
## Step: AIC=-398.9
## completion_percentage ~ weight + age + c_avg_cmpp + c_pct + c_avg_inter +
## c_avg_tds + c_avg_yds + c_avg_att
##
## Df Sum of Sq RSS AIC
## - c_avg_tds 1 0.005 40.3 -401
## - c_avg_inter 1 0.243 40.6 -400
## - c_avg_yds 1 0.286 40.6 -399
## - c_pct 1 0.310 40.6 -399
## - weight 1 0.313 40.6 -399
## - c_avg_cmpp 1 0.317 40.6 -399
## - c_avg_att 1 0.339 40.7 -399
## <none> 40.3 -399
## - age 1 0.355 40.7 -399
## + c_rate 1 0.008 40.3 -397
## + c_numyrs 1 0.000 40.3 -397
## + height 1 0.000 40.3 -397
##
## Step: AIC=-400.9
## completion_percentage ~ weight + age + c_avg_cmpp + c_pct + c_avg_inter +
## c_avg_yds + c_avg_att
##
## Df Sum of Sq RSS AIC
## - c_avg_inter 1 0.239 40.6 -402
## - weight 1 0.313 40.6 -401
## - c_pct 1 0.325 40.7 -401
## - c_avg_cmpp 1 0.330 40.7 -401
## <none> 40.3 -401
## - age 1 0.351 40.7 -401
## - c_avg_att 1 0.354 40.7 -401
## - c_avg_yds 1 0.483 40.8 -400
## + c_avg_tds 1 0.005 40.3 -399
## + c_numyrs 1 0.001 40.3 -399
## + c_rate 1 0.000 40.3 -399
## + height 1 0.000 40.3 -399
##
## Step: AIC=-401.5
## completion_percentage ~ weight + age + c_avg_cmpp + c_pct + c_avg_yds +
## c_avg_att
##
## Df Sum of Sq RSS AIC
## - c_pct 1 0.159 40.7 -403
## - c_avg_cmpp 1 0.161 40.7 -403
## - c_avg_att 1 0.177 40.8 -402
## - weight 1 0.204 40.8 -402
## - age 1 0.332 40.9 -402
## <none> 40.6 -402
## - c_avg_yds 1 0.405 41.0 -401
## + c_avg_inter 1 0.239 40.3 -401
## + c_rate 1 0.108 40.5 -400
## + c_numyrs 1 0.003 40.6 -400
## + c_avg_tds 1 0.001 40.6 -400
## + height 1 0.001 40.6 -400
##
## Step: AIC=-402.6
## completion_percentage ~ weight + age + c_avg_cmpp + c_avg_yds +
## c_avg_att
##
## Df Sum of Sq RSS AIC
## - c_avg_cmpp 1 0.005 40.7 -405
## - age 1 0.309 41.0 -403
## - weight 1 0.313 41.0 -403
## - c_avg_yds 1 0.321 41.1 -403
## - c_avg_att 1 0.334 41.1 -403
## <none> 40.7 -403
## + c_pct 1 0.159 40.6 -402
## + c_avg_inter 1 0.074 40.7 -401
## + c_rate 1 0.033 40.7 -401
## + c_numyrs 1 0.012 40.7 -401
## + height 1 0.006 40.7 -401
## + c_avg_tds 1 0.003 40.7 -401
##
## Step: AIC=-404.6
## completion_percentage ~ weight + age + c_avg_yds + c_avg_att
##
## Df Sum of Sq RSS AIC
## - age 1 0.332 41.1 -405
## <none> 40.7 -405
## - weight 1 0.350 41.1 -405
## - c_avg_yds 1 0.557 41.3 -403
## - c_avg_att 1 0.585 41.3 -403
## + c_avg_inter 1 0.062 40.7 -403
## + c_numyrs 1 0.010 40.7 -403
## + c_rate 1 0.009 40.7 -403
## + height 1 0.008 40.7 -403
## + c_avg_cmpp 1 0.005 40.7 -403
## + c_pct 1 0.003 40.7 -403
## + c_avg_tds 1 0.003 40.7 -403
##
## Step: AIC=-404.6
## completion_percentage ~ weight + c_avg_yds + c_avg_att
##
## Df Sum of Sq RSS AIC
## - weight 1 0.239 41.3 -405
## <none> 41.1 -405
## + age 1 0.332 40.7 -405
## - c_avg_yds 1 0.561 41.6 -403
## - c_avg_att 1 0.606 41.7 -403
## + c_avg_inter 1 0.049 41.0 -403
## + c_avg_cmpp 1 0.027 41.0 -403
## + c_pct 1 0.022 41.0 -403
## + height 1 0.021 41.1 -403
## + c_numyrs 1 0.011 41.1 -403
## + c_rate 1 0.006 41.1 -403
## + c_avg_tds 1 0.002 41.1 -403
##
## Step: AIC=-405.3
## completion_percentage ~ c_avg_yds + c_avg_att
##
## Df Sum of Sq RSS AIC
## <none> 41.3 -405
## + weight 1 0.239 41.1 -405
## + age 1 0.221 41.1 -405
## + height 1 0.181 41.1 -404
## - c_avg_yds 1 0.603 41.9 -404
## - c_avg_att 1 0.617 41.9 -404
## + c_avg_cmpp 1 0.068 41.2 -404
## + c_pct 1 0.058 41.3 -404
## + c_rate 1 0.004 41.3 -403
## + c_avg_inter 1 0.001 41.3 -403
## + c_avg_tds 1 0.000 41.3 -403
## + c_numyrs 1 0.000 41.3 -403
summary(step_reg.log.no_combine.cpct)
##
## Call:
## lm(formula = completion_percentage ~ c_avg_yds + c_avg_att, data = data.log.no_combine.for_cpct)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.154 -0.035 0.027 0.104 0.326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.484 0.355 9.82 <2e-16 ***
## c_avg_yds 0.347 0.188 1.84 0.066 .
## c_avg_att -0.389 0.208 -1.87 0.063 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.421 on 233 degrees of freedom
## Multiple R-squared: 0.0147, Adjusted R-squared: 0.00628
## F-statistic: 1.74 on 2 and 233 DF, p-value: 0.177
plot(step_reg.log.no_combine.cpct)
leaps.log.no_combine.cpct <- regsubsets(completion_percentage ~ ., data = data.log.no_combine.for_cpct,
nbest = 10)
subsets(leaps.log.no_combine.cpct, statistic = "rsq")
## Error: invalid coordinate lengths
cv.lm(df = data.log.no_combine.for_cpct, step_reg.log.no_combine.cpct, m = 5) # 5 fold cross-validation
## Analysis of Variance Table
##
## Response: completion_percentage
## Df Sum Sq Mean Sq F value Pr(>F)
## c_avg_yds 1 0.0 0.001 0.00 0.951
## c_avg_att 1 0.6 0.617 3.48 0.063 .
## Residuals 233 41.3 0.177
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning:
##
## As there is >1 explanatory variable, cross-validation predicted values for
## a fold are not a linear function of corresponding overall predicted
## values. Lines that are shown for the different folds are approximate
##
## fold 1
## Observations in test set: 47
## 3 15 18 24 26 35 37 39
## Predicted 3.985 3.953 3.912 3.909 3.98228 3.9118 3.85 3.9908
## cvpred 3.984 3.993 3.988 3.987 3.98275 3.9823 3.99 3.9803
## completion_percentage 4.052 4.109 4.109 4.114 3.98713 4.0271 -2.30 3.9435
## CV residual 0.068 0.116 0.122 0.127 0.00438 0.0448 -6.29 -0.0368
## 41 45 52 56 62 71 72
## Predicted 3.9748 3.966 3.961 3.9782 3.8687 3.9676 3.966
## cvpred 3.9939 3.978 3.995 3.9847 3.9847 3.9781 3.970
## completion_percentage 4.0413 4.113 4.197 3.9982 3.9493 4.0254 4.159
## CV residual 0.0474 0.134 0.202 0.0135 -0.0354 0.0473 0.189
## 76 79 81 85 88 89 103
## Predicted 3.9139 3.9293 3.9854 3.903 3.9628 3.9542 3.948
## cvpred 3.9702 3.9844 3.9863 3.977 3.9889 3.9804 3.962
## completion_percentage 4.0055 3.9982 4.0091 4.083 4.0395 4.0466 4.064
## CV residual 0.0353 0.0138 0.0229 0.106 0.0506 0.0662 0.101
## 116 118 119 121 133 136 139
## Predicted 3.9329 3.9391 3.879 3.947 3.93409 3.964 3.9994
## cvpred 3.9874 3.9853 3.975 3.969 3.96665 3.977 3.9350
## completion_percentage 3.8959 3.9396 4.098 4.142 3.97218 3.789 4.0236
## CV residual -0.0915 -0.0457 0.123 0.173 0.00552 -0.188 0.0886
## 149 152 159 165 166 170 173
## Predicted 3.9988 3.9565 3.8810 3.95566 3.940 3.9568 3.971
## cvpred 3.9716 3.9906 3.9726 3.97838 3.986 3.9787 3.970
## completion_percentage 3.8939 3.9140 3.9220 3.98713 3.863 3.9180 4.086
## CV residual -0.0778 -0.0766 -0.0506 0.00875 -0.123 -0.0607 0.115
## 176 189 194 200 207 213 222
## Predicted 3.9504 3.9464 4.0479 3.9290 3.8852 3.980 3.6479
## cvpred 3.9576 3.9682 3.9011 3.9777 3.9789 3.961 3.9684
## completion_percentage 3.9871 4.0466 3.9815 4.0019 3.8857 3.686 3.9299
## CV residual 0.0295 0.0784 0.0804 0.0242 -0.0932 -0.275 -0.0385
## 225 226 232 239
## Predicted 3.9631 3.9952 3.9423 3.89330
## cvpred 3.9748 3.9652 3.9581 3.94526
## completion_percentage 3.8877 3.9040 4.0289 3.94739
## CV residual -0.0871 -0.0612 0.0708 0.00213
##
## Sum of squares = 40 Mean square = 0.85 n = 47
##
## fold 2
## Observations in test set: 48
## 4 5 7 14 17 33 36
## Predicted 3.938 3.959 4.0871 4.0205 3.9527 3.836 3.94980
## cvpred 3.931 3.956 4.1105 4.0303 3.9483 3.807 3.94474
## completion_percentage 4.067 4.135 4.0960 4.0960 3.9778 4.066 3.95316
## CV residual 0.137 0.179 -0.0145 0.0657 0.0295 0.259 0.00842
## 42 46 47 66 67 70 74 78
## Predicted 3.943 3.9188 3.9521 4.05 3.97 3.9104 3.932 4.0085
## cvpred 3.936 3.9073 3.9475 4.07 3.97 3.8972 3.923 4.0158
## completion_percentage 4.165 3.9455 4.0342 4.01 4.17 3.9299 4.062 4.0622
## CV residual 0.229 0.0382 0.0868 -0.06 0.20 0.0327 0.139 0.0464
## 99 100 102 114 122 126 128
## Predicted 4.0298 3.9857 3.9472 3.897 3.9103 3.9789 4.0009
## cvpred 4.0412 3.9882 3.9413 3.881 3.8969 3.9798 4.0065
## completion_percentage 3.9703 3.9570 4.0325 4.162 3.9665 3.9646 3.9703
## CV residual -0.0709 -0.0312 0.0912 0.281 0.0696 -0.0152 -0.0362
## 131 138 141 144 147 153 156
## Predicted 3.834 3.9503 3.9328 3.976 3.889 3.8803 3.9158
## cvpred 3.805 3.9454 3.9242 3.977 3.871 3.8607 3.9035
## completion_percentage 3.859 4.0073 3.9589 4.086 4.043 3.9455 3.9908
## CV residual 0.054 0.0619 0.0347 0.109 0.172 0.0847 0.0873
## 158 161 163 164 171 172 174
## Predicted 4.0117 4.0062 3.924 3.932 3.968 4.06 4.0238
## cvpred 4.0194 4.0127 3.914 3.923 3.967 4.07 4.0343
## completion_percentage 4.0091 3.9741 4.069 4.078 3.859 3.96 4.0893
## CV residual -0.0102 -0.0387 0.155 0.155 -0.108 -0.11 0.0551
## 177 179 184 185 188 192 201
## Predicted 3.9809 3.99062 3.9384 4.011 4.002 3.941 3.945
## cvpred 3.9821 3.99396 3.9309 4.018 4.007 3.934 3.939
## completion_percentage 3.9759 3.99268 3.9532 3.630 3.775 3.811 3.857
## CV residual -0.0062 -0.00128 0.0222 -0.389 -0.232 -0.123 -0.083
## 203 206 209 215 216
## Predicted 3.9542 3.944 3.9403 3.882 3.8409
## cvpred 3.9502 3.938 3.9330 3.862 3.8129
## completion_percentage 3.8774 3.831 3.8544 3.643 3.8816
## CV residual -0.0727 -0.107 -0.0786 -0.219 0.0687
##
## Sum of squares = 0.79 Mean square = 0.02 n = 48
##
## fold 3
## Observations in test set: 47
## 2 6 25 29 34 43 44
## Predicted 3.9923 3.902 3.770 3.911 3.893 4.0011 3.9345
## cvpred 4.0086 3.880 3.671 3.895 3.866 4.0184 3.9269
## completion_percentage 3.9927 4.034 4.096 4.029 4.029 3.9741 3.9778
## CV residual -0.0159 0.154 0.425 0.134 0.163 -0.0443 0.0509
## 48 50 51 55 59 60 65
## Predicted 3.9156 3.908 3.943 3.9326 3.9018 3.9392 3.906
## cvpred 3.8983 3.889 3.939 3.9241 3.8806 3.9305 3.887
## completion_percentage 3.8177 4.114 4.111 3.8774 3.9741 3.9684 4.109
## CV residual -0.0806 0.225 0.172 -0.0467 0.0934 0.0379 0.222
## 77 80 86 87 90 94 101
## Predicted 3.94647 3.9740 3.940 3.9535 3.9539 3.9582 4.0232
## cvpred 3.94270 3.9811 3.935 3.9490 3.9543 3.9591 4.0528
## completion_percentage 3.93574 4.0483 4.108 3.9815 4.0466 3.9722 4.0860
## CV residual -0.00696 0.0672 0.173 0.0326 0.0923 0.0131 0.0332
## 106 107 108 112 123 124 134
## Predicted 3.9995 3.9751 3.986 3.940 3.952 3.96546 3.923
## cvpred 4.0176 3.9804 3.996 3.935 3.950 3.96842 3.908
## completion_percentage 3.9908 3.9338 3.816 4.047 3.995 3.97781 4.016
## CV residual -0.0268 -0.0466 -0.181 0.111 0.045 0.00939 0.108
## 137 148 157 178 180 181 182
## Predicted 3.946 3.9852 3.926 3.869 3.9550 4.016 4.030
## cvpred 3.939 3.9977 3.902 3.833 3.9507 4.038 4.054
## completion_percentage 3.721 3.9853 4.004 4.022 4.0395 3.871 3.844
## CV residual -0.218 -0.0124 0.102 0.188 0.0888 -0.166 -0.211
## 186 193 195 199 202 210 217
## Predicted 4.058 3.8651 3.97566 3.946 3.89254 3.9330 3.969
## cvpred 4.092 3.8256 3.98056 3.941 3.86803 3.9222 3.970
## completion_percentage 3.918 3.8089 3.97218 3.798 3.87743 3.9834 3.709
## CV residual -0.174 -0.0167 -0.00838 -0.143 0.00941 0.0612 -0.261
## 218 219 234 236 238
## Predicted 3.9469 3.8857 3.9486 4.063 4.063
## cvpred 3.9377 3.8508 3.9376 4.099 4.100
## completion_percentage 3.8670 3.9279 4.0360 3.859 3.991
## CV residual -0.0706 0.0771 0.0984 -0.241 -0.109
##
## Sum of squares = 0.9 Mean square = 0.02 n = 47
##
## fold 4
## Observations in test set: 47
## 9 10 11 13 16 22 27 28
## Predicted 3.948 3.933 4.025 3.893 3.9253 3.969 3.926 3.931
## cvpred 3.932 3.922 4.035 3.863 3.9049 3.961 3.916 3.913
## completion_percentage 4.104 3.996 3.842 3.996 3.9627 4.154 4.142 4.067
## CV residual 0.172 0.074 -0.193 0.133 0.0578 0.194 0.226 0.155
## 31 32 38 49 53 58 64 68
## Predicted 3.916 3.923 3.944 4.017 3.880 3.9764 3.959 3.949
## cvpred 3.897 3.909 3.925 4.030 3.860 3.9722 3.949 3.944
## completion_percentage 4.154 4.197 4.124 3.932 4.047 4.0483 4.130 3.916
## CV residual 0.257 0.288 0.199 -0.098 0.187 0.0761 0.182 -0.028
## 75 84 92 93 95 97 104
## Predicted 4.0038 3.912 3.9961 3.9927 3.9961 3.9056 3.992
## cvpred 4.0064 3.888 4.0020 3.9995 4.0057 3.8931 4.003
## completion_percentage 3.9964 4.025 3.9318 3.9834 4.0091 3.9100 3.902
## CV residual -0.0101 0.138 -0.0702 -0.0161 0.0035 0.0169 -0.101
## 105 109 110 113 117 120 125
## Predicted 3.8991 3.9775 3.9394 3.9384 3.9144 3.9949 4.0242
## cvpred 3.8834 3.9776 3.9246 3.9241 3.8933 4.0206 4.0427
## completion_percentage 3.9200 3.9338 3.9120 4.0164 3.8816 3.9608 4.0570
## CV residual 0.0366 -0.0438 -0.0126 0.0923 -0.0117 -0.0598 0.0143
## 127 130 143 145 146 162 183
## Predicted 3.968 4.029 4.093 3.9307 3.9232 3.9460 3.9332
## cvpred 3.974 4.046 4.142 3.9206 3.9072 3.9403 3.9202
## completion_percentage 4.076 3.865 3.934 3.9815 3.8918 3.9140 3.9778
## CV residual 0.102 -0.181 -0.208 0.0609 -0.0154 -0.0263 0.0576
## 190 196 198 211 214 224 227
## Predicted 3.8739 3.8770 3.9332 3.975 3.9382 4.002 3.959
## cvpred 3.8443 3.8488 3.9359 3.986 3.9319 4.029 3.979
## completion_percentage 3.8586 3.8836 3.9474 3.829 3.9703 3.804 3.721
## CV residual 0.0143 0.0349 0.0115 -0.158 0.0384 -0.225 -0.258
## 229 233 237
## Predicted 3.909 3.9206 4.050
## cvpred 3.920 3.9111 4.086
## completion_percentage 3.638 3.8286 3.816
## CV residual -0.282 -0.0824 -0.271
##
## Sum of squares = 0.93 Mean square = 0.02 n = 47
##
## fold 5
## Observations in test set: 47
## 1 8 12 19 20 21 23 30
## Predicted 3.949 3.953 3.965 3.99581 3.908 3.9220 4.001 3.928
## cvpred 3.936 3.941 3.952 3.98986 3.893 3.9074 4.012 3.914
## completion_percentage 4.162 4.064 4.057 3.98713 4.000 3.9741 4.151 4.036
## CV residual 0.226 0.123 0.105 -0.00273 0.107 0.0667 0.139 0.122
## 40 54 57 61 69 73 82 83
## Predicted 3.958 3.950 3.94 3.9688 3.9871 3.925 4.007 3.977
## cvpred 3.949 3.935 3.92 3.9597 3.9793 3.915 3.999 3.966
## completion_percentage 4.108 4.111 4.08 4.0271 3.9627 4.057 3.791 4.140
## CV residual 0.158 0.175 0.16 0.0674 -0.0166 0.142 -0.208 0.174
## 91 96 98 111 115 129 132
## Predicted 3.984 3.9554 3.984 4.0114 3.9715 3.9509 3.9033
## cvpred 3.974 3.9460 3.983 4.0058 3.9632 3.9399 3.8903
## completion_percentage 3.816 3.9741 4.140 4.0342 3.9060 3.9684 3.9608
## CV residual -0.158 0.0281 0.157 0.0284 -0.0572 0.0285 0.0705
## 135 140 142 150 151 154 155 160
## Predicted 3.9699 4.0017 3.9683 3.94 3.8899 3.985 3.937 3.923
## cvpred 3.9588 3.9942 3.9600 3.93 3.8729 3.993 3.922 3.913
## completion_percentage 3.9416 4.0236 3.9834 4.07 3.9040 3.906 4.098 4.055
## CV residual -0.0172 0.0294 0.0234 0.14 0.0311 -0.087 0.175 0.142
## 167 168 169 187 191 204 205
## Predicted 3.9972 4.0098 3.9156 3.8999 3.931 4.0182 3.93
## cvpred 3.9953 4.0018 3.9046 3.8876 3.922 4.0125 3.92
## completion_percentage 3.9416 4.0466 3.9180 3.9140 3.995 4.0910 3.70
## CV residual -0.0537 0.0447 0.0134 0.0264 0.073 0.0785 -0.22
## 208 212 220 221 223 228 230
## Predicted 3.9491 3.90629 3.841 3.95065 3.9669 3.976 3.989
## cvpred 3.9429 3.89532 3.829 3.94704 3.9609 3.974 3.986
## completion_percentage 3.9357 3.88773 3.944 3.94932 3.8774 4.057 4.121
## CV residual -0.0072 -0.00759 0.115 0.00228 -0.0835 0.083 0.135
## 235 240
## Predicted 3.91718 4.035
## cvpred 3.90514 4.041
## completion_percentage 3.91002 3.920
## CV residual 0.00489 -0.121
##
## Sum of squares = 0.57 Mean square = 0.01 n = 47
##
## Overall (Sum over all 47 folds)
## ms
## 0.183