• 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
University of Pittsburgh Statistics Curriculum
University of Pittsburgh Statistics Curriculum

Repeated Measures ANOVA
Repeated Measures ANOVA

Transformations. Non-parametric tests
Transformations. Non-parametric tests

Chapter 3. POPULATION DISTRIBUTIONS
Chapter 3. POPULATION DISTRIBUTIONS

1 Confidence intervals
1 Confidence intervals

bme stats workshop
bme stats workshop

習題 - OoCities
習題 - OoCities

... b. The plant manager is worried that the drums of Chemical ZX-900 are underfilled . Because of this. she decides to draw a sample of 100 drums from each daily shipment and will reject the shipment (send it back to the supplier) if the average fill for the 100 drums is less than 49.85 gallons. Suppos ...
null hypothesis
null hypothesis

Ch9
Ch9

... Select the test statistic Determine the critical value rule for rejecting H0 Collect the sample data and calculate the value of the test statistic 6. Decide whether to reject H0 by using the test statistic and the critical value rule 7. Interpret the statistical results in managerial terms and asses ...
Similar Figures 1
Similar Figures 1

templar_AQA_AS_stats_5831 - Hertfordshire Grid for Learning
templar_AQA_AS_stats_5831 - Hertfordshire Grid for Learning

... and to independent events to apply the rule P(AB)=P(A)P(B). Solve simple probability problems using tree diagrams or laws of probability. To recognise when to use the binomial distribution To state any assumptions necessary to use the binomial distribution To apply the binomial distribution to a v ...
STAT 217 Assignment #1 Note: answers may vary slightly due to
STAT 217 Assignment #1 Note: answers may vary slightly due to

... (a) Using the sample data, a confidence interval for the proportion of all such scanned items that are overcharges was found to be from 0.00915 to 0.02325. What was the level of confidence that was used? [~95% level of confidence] (b) Find the sample size necessary to estimate the proportion of scan ...
Chapter 5: Discrete Probability Distributions
Chapter 5: Discrete Probability Distributions

... Theoretical Probability: using the probability of an event occurring based on what would happen in theory Empirical Probability: using real sample data to find the probability of an event occuring ...
Unit 2: Statistics
Unit 2: Statistics

... Unit 2: Statistics 3-1: Distributions Probability Distribution: - a table or a graph that displays the theoretical probability for each outcome of an experiment. - P (any particular outcome) is between 0 and 1 - the sum of all the probabilities is always 1. a. Uniform Probability Distribution: - a p ...
Why is it There? Spatial Analysis: Descriptive
Why is it There? Spatial Analysis: Descriptive

... The extremes of an attribute are the highest and lowest values, and the range is the difference between them in the units of the attribute. A histogram is a two-dimensional plot of attribute values grouped by magnitude and the frequency of records in that group, shown as a variablelength bar. For a ...
Tests of Hypothesis - KFUPM Faculty List
Tests of Hypothesis - KFUPM Faculty List

... Example 7.8 A random sample of 12 bulbs produced by an old machine was tested and found to have a mean life span of 40 hours with variance 24 hours. Also, a random sample of 10 bulbs produced by a new machine was found to have a life span of 45 hours with variance 30 hours. Assume that the life span ...
Chapter 9 Input Modeling
Chapter 9 Input Modeling

Fall 2015
Fall 2015

Bayesian two-stage design for phase II clinical trials
Bayesian two-stage design for phase II clinical trials

Uncertainty and Errors in Hypothesis Testing
Uncertainty and Errors in Hypothesis Testing

Notes Pages - Adult Basic Skills Professional Development
Notes Pages - Adult Basic Skills Professional Development

BASIC COUNTING - Mathematical sciences
BASIC COUNTING - Mathematical sciences

... Comparison To Chebyshev • Similarly X~normal(,2) has probability 0.9544 of falling within two standard deviations of its mean and probability 0.9974 of falling within three standard deviations of its mean. Note that these values are much higher than the lower bounds from Chebyshev’s theorem. It i ...
U1.2-GraphicsBasicStats
U1.2-GraphicsBasicStats

8 Independent and Dependent t
8 Independent and Dependent t

cowan_autrans_1 - Centre for Particle Physics
cowan_autrans_1 - Centre for Particle Physics

... In frequentist statistics, probabilities are associated only with the data, i.e., outcomes of repeatable observations. Probability = limiting frequency Probabilities such as P (Higgs boson exists), P (0.117 < as < 0.121), etc. are either 0 or 1, but we don’t know which. The tools of frequentist stat ...
< 1 ... 120 121 122 123 124 125 126 127 128 ... 269 >

Foundations of statistics

Foundations of statistics is the usual name for the epistemological debate in statistics over how one should conduct inductive inference from data. Among the issues considered in statistical inference are the question of Bayesian inference versus frequentist inference, the distinction between Fisher's ""significance testing"" and Neyman-Pearson ""hypothesis testing"", and whether the likelihood principle should be followed. Some of these issues have been debated for up to 200 years without resolution.Bandyopadhyay & Forster describe four statistical paradigms: ""(1) classical statistics or error statistics, (ii) Bayesian statistics, (iii) likelihood-based statistics, and (iv) the Akaikean-Information Criterion-based statistics"".Savage's text Foundations of Statistics has been cited over 10000 times on Google Scholar. It tells the following.It is unanimously agreed that statistics depends somehow on probability. But, as to what probability is and how it is connected with statistics, there has seldom been such complete disagreement and breakdown of communication since the Tower of Babel. Doubtless, much of the disagreement is merely terminological and would disappear under sufficiently sharp analysis.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report