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Details for: Foundations of statistics for data scientists
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Foundations of statistics for data scientists with R and Python
By:
Alan Agresti
.
Contributor(s):
Maria Kateri
.
Publisher:
Boca Raton :
Chapman and Hall/CRC,
2022
Description:
xvii, 467 p. ; ill. , 26 cm
.
ISBN:
9780367748456.
Subject(s):
Python
|
AIC (Akaike information criterion)
|
Bayesian inference
|
Bonferroni method
|
Chi-squared distribution
|
Conditional association
|
Delta method
|
Estimation
|
Explanatory variables
|
Generalized linear models
|
Histogram
|
Inteval estimate
|
Likehood ratio test
|
M-estimation
|
Monte Carlo methods
|
Normal distribution
|
Overdistribution
|
Overdispersion
|
P-value
|
Poisson distribution
|
Quasi-complete separation
|
Random variable
|
Sampling test
|
Significance test
|
Skewed distributions
|
T statistic
|
Wilcoxon test
DDC classification:
519.50285536
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519.50285536 AGR (
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032969
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