3
分布を見つけて正規分布に変換する
1時間にイベントが発生する頻度(「1時間あたりの数」、nph)とイベントが持続する時間(「1秒あたりの秒数」、dph)を説明するデータがあります。 これは元のデータです: nph <- c(2.50000000003638, 3.78947368414551, 1.51456310682008, 5.84686774940732, 4.58823529414907, 5.59999999993481, 5.06666666666667, 11.6470588233699, 1.99999999998209, NA, 4.46153846149851, 18, 1.05882352939726, 9.21739130425452, 27.8399999994814, 15.3750000002237, NA, 6.00000000004109, 9.71428571436649, 12.4848484848485, 16.5034965037115, 20.6666666666667, 3.49999999997453, 4.65882352938624, 4.74999999996544, 3.99999999994522, 2.8, 14.2285714286188, 11.0000000000915, NA, 2.66666666666667, 3.76470588230138, 4.70588235287673, 13.2727272728677, 2.0000000000137, 18.4444444444444, 17.5555555555556, 14.2222222222222, 2.00000000001663, 4, 8.46153846146269, 19.2000000001788, 13.9024390245481, 13, 3.00000000004366, NA, …
8
normal-distribution
data-transformation
logistic
generalized-linear-model
ridge-regression
t-test
wilcoxon-signed-rank
paired-data
naive-bayes
distributions
logistic
goodness-of-fit
time-series
eviews
ecm
panel-data
reliability
psychometrics
validity
cronbachs-alpha
self-study
random-variable
expected-value
median
regression
self-study
multiple-regression
linear-model
forecasting
prediction-interval
normal-distribution
excel
bayesian
multivariate-analysis
modeling
predictive-models
canonical-correlation
rbm
time-series
machine-learning
neural-networks
fishers-exact
factorisation-theorem
svm
prediction
linear
reinforcement-learning
cdf
probability-inequalities
ecdf
time-series
kalman-filter
state-space-models
dynamic-regression
index-decomposition
sampling
stratification
cluster-sample
survey-sampling
distributions
maximum-likelihood
gamma-distribution