### Data Analysis and MOOC’s

### Topics

### Tags

5-number summary
addition rule
Adjusted R-squared
AIC
Akaike Information Criterion
Analysis of variance
ANOVA
backward model selection
bagging
basic least squares
bayes decision boundary
bayes rule
bias
bias-variance tradeoff
big data
binary outcome
boosting
Bootstrapping
Central limit theorem
classification
classifier
clustering
coefficient of determination
coefficient of variation
compaing means
comparing proportions
conditional probability
confidence interval
confidence level
confounders
continuous random variable
correlation
correlation coefficient
covariance
covariant
critical value
cross validation
data analysis
data collection
Decision tree
degrees of freedom
dendogram
dependent samples
dependent variable
deviance
df
dichotomous
digital signature
dimensionality reduction
discrete random variable
Distributions
dummy variables
ensemble
error rate
error term
ESS
estimate
estimator
event
Expected value
experiment
Explained sum of squares
explanatory variable
extraneous factor
factors
false negative
false positive
F distribution
forward model selection
F Ratio
F Score
F Test
Glossary
gold standard
goodness of fit
hadoop
hdfs
hierarchical clustering
hold-out
https
image compression
independent events
independent samples
independent variable
input variables
intercept
interpreting a linear model
k-fold
k-means clustering
law of large numbers
leave-one-out
linear regression
line of best fit
loess
logistic regression
logistic regression prediction
logit
log odds
lowess
lurking variables
Machine learning
margin of error
matched pairs
mean
mean squared error
median
misclassification error rate
models
model selection
Moving average
mse
multifactor regression
Multiple R-squared
multiplication rule
Mutually Exclusive Events
naive bayes classifier
negative predicted value
non-parametric method
nonparametric test
Normal distribution
Null hypothesis
Observational study
odds
omitted variable bias
one sample
one sided
one tailed
one way anova
OSEMN
outcome
output variables
Overfitting
overlearning
P-value
paired
parameter
parametric method
parametric test
pca
population
population mean
population variance
positive predicted value
power
prediction
prediction error
prediction with regression
predictor
predictor variable
principal component analysis
Probability
Probability density function
Probability distribution
quality of fit
Quartile
random forest
random variable
regression
regression with factors
relevel
Resampling
residual
residual analysis
Residual mean deviance
Residual sum of squares
response variable
resubstitution error
R Square
RSS
sample
sample size
sample space
Sampling
Sampling bias
sampling distribution
sampling variability
sampling without replacement
sampling with replacement
sensitivity
Singular value decomposition
slope
smoothing
specificity
splines
SSE
SSR
SST
Standard Deviation
statistic
statistical learning
Statistical significance
subsampling
sum of squares
Supervised learning
SVD
t-test
testing proportions
test selection
test set
test statistic
training set
trainĀing set
transformation
treatment
trimmed mean
true negative
true positive
tukey
two sample
two sided
two tailed
two way anova
type I error
type II error
types of data
types of hypothesis test
unpaired
Unsupervised learning
validation set
variability
Variables
variable selection
Variance
visualize
z-score
z-test
z-value

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