• Study Resource
  • Explore
    • Arts & Humanities
    • Business
    • Engineering & Technology
    • Foreign Language
    • History
    • Math
    • Science
    • Social Science

    Top subcategories

    • Advanced Math
    • Algebra
    • Basic Math
    • Calculus
    • Geometry
    • Linear Algebra
    • Pre-Algebra
    • Pre-Calculus
    • Statistics And Probability
    • Trigonometry
    • other →

    Top subcategories

    • Astronomy
    • Astrophysics
    • Biology
    • Chemistry
    • Earth Science
    • Environmental Science
    • Health Science
    • Physics
    • other →

    Top subcategories

    • Anthropology
    • Law
    • Political Science
    • Psychology
    • Sociology
    • other →

    Top subcategories

    • Accounting
    • Economics
    • Finance
    • Management
    • other →

    Top subcategories

    • Aerospace Engineering
    • Bioengineering
    • Chemical Engineering
    • Civil Engineering
    • Computer Science
    • Electrical Engineering
    • Industrial Engineering
    • Mechanical Engineering
    • Web Design
    • other →

    Top subcategories

    • Architecture
    • Communications
    • English
    • Gender Studies
    • Music
    • Performing Arts
    • Philosophy
    • Religious Studies
    • Writing
    • other →

    Top subcategories

    • Ancient History
    • European History
    • US History
    • World History
    • other →

    Top subcategories

    • Croatian
    • Czech
    • Finnish
    • Greek
    • Hindi
    • Japanese
    • Korean
    • Persian
    • Swedish
    • Turkish
    • other →
 
Profile Documents Logout
Upload
Document
Document

Confidence Intervals Formulas
Confidence Intervals Formulas

Punkty ECTS - bw.pwsz.legnica.edu.pl
Punkty ECTS - bw.pwsz.legnica.edu.pl

... Introduction: Introduction to probabilistic methods-Kolmogorov concept of probability space: events space and probability as a set function on a sigma algebra of events. Classification of probabilistic models: discrete, conditional (dependence and independence, conditional probability, Bayes formula ...
Point Estimation of Parameters
Point Estimation of Parameters

Assignment I
Assignment I

... • Write a computer program to sample from fa, for a = 5, 10, 20, 50, and for  = .01 and repeat the following exercise for each value of a. – Generate a sample of size 100 and compute the sample mean µ̂ of the sample. Now compute the standard error of µ̂ two ways. First by assuming that the "parent ...
A Performance Assessment
A Performance Assessment

A brief introduction to maximum likelihood The key idea behind the
A brief introduction to maximum likelihood The key idea behind the

... We can use this probability density function to answer questions like "if an event has probability p = :6, and we have n = 10 trials, what is the probability of the event occurring x = 3 times"? This was a probability question, but when we collect data we have a statistical question such as "in n = ...
Standard error of estimate & Confidence interval
Standard error of estimate & Confidence interval

Bachelor of Science in Statistics Course Descriptions (Service Courses)
Bachelor of Science in Statistics Course Descriptions (Service Courses)

All confidence intervals follow the same formula:
All confidence intervals follow the same formula:

Explanation-of-a-recursive-formula-1
Explanation-of-a-recursive-formula-1

... example above gives the sequence of odd numbers 1, 3, 5, 7, ... . However, if the initial condition was modified to x1 = 2 or Start = 2, the recursive function would give the sequence of even numbers 2, 4, 6, 8, ... . Unlike a recursive formula, an explicit formula stands alone; that is, it has no a ...
Summary of Functions
Summary of Functions

Q SCI 381 Dr.Bare
Q SCI 381 Dr.Bare

... of -1.75 and 138 bushels corresponds to z = 1.75. From the z-table, the area under the z-curve between these two values = 0.9198. 1.b. Find the z-value with .0500 area to its left in the z-table to be -1.645. Similarly, the z-value with .9500 area to its left is found in the z-table to be 1.645. Usi ...
lecture7-confidence-intervals-for
lecture7-confidence-intervals-for

Estimating Population Mean NOTES
Estimating Population Mean NOTES

Oct 6
Oct 6

CENTRAL LIMIT THEOREM
CENTRAL LIMIT THEOREM

week 5 part 1
week 5 part 1

< 1 ... 97 98 99 100 101

German tank problem



In the statistical theory of estimation, the problem of estimating the maximum of a discrete uniform distribution from sampling without replacement is known in English as the German tank problem, due to its application in World War II to the estimation of the number of German tanks.The analyses illustrate the difference between frequentist inference and Bayesian inference.Estimating the population maximum based on a single sample yields divergent results, while the estimation based on multiple samples is an instructive practical estimation question whose answer is simple but not obvious.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report