• Study Resource
  • Explore Categories
    • 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
A Simplified Introduction to Correlation and Regression K. L. Weldon
A Simplified Introduction to Correlation and Regression K. L. Weldon

... regression line back to the original units. First we need to record the mean and SD of each variable: mean(V)=619 SD(V)=71; mean(M)=649, SD(M)=65. Then substituting directly into zV=r zM, one gets (V-619)/71=0.5 (M-649)/65. For example, if M=700, the right side is 0.5*51/65 = .39 so that the predict ...
Review of Part VI – Learning About the World
Review of Part VI – Learning About the World

... Randomization condition: Subjects were randomized with respect to whether they did the scented trial first or second. 10% condition: We are testing the effects of the scent, not the subjects, so this condition doesn’t need to be checked. Nearly Normal condition: The histogram of differences between ...
Confidence Intervals for the mean
Confidence Intervals for the mean

Elements of the R Language - the Centre for Cognitive Ageing and
Elements of the R Language - the Centre for Cognitive Ageing and

... 11 You can have multiple data sets open at the same time, each in its own data frame. 12 The filename extension is the “.txt” (or similar) after the filename. This has to be given. Windows may hide filename extensions so you may need to take action to show them. Use the menu item: Tools > Folder Opt ...
Empirical Rule
Empirical Rule

Chapter 6 Statistical inference for the population mean
Chapter 6 Statistical inference for the population mean

... sample was drawn. What exactly does the sample, often a tiny subset, tell us of the population? We can never observe the whole population, even if it is finite, except at enormous expense, and so the population mean and variance (or indeed any aspect of the population distribution) can never be know ...
T - Ohio Dominican University
T - Ohio Dominican University

Mind on Statistics Test Bank
Mind on Statistics Test Bank

... 10. To determine if there is a statistically significant relationship between two quantitative variables, one test that can be conducted is A. a t-test of the null hypotheses that the slope of the regression line is zero. B. a t-test of the null hypotheses that the intercept of the regression line i ...
Document
Document

Statistical Foundations: Hypothesis Testing
Statistical Foundations: Hypothesis Testing

Sampling Distribution of Sample Mean
Sampling Distribution of Sample Mean

Bell-shaped distribution
Bell-shaped distribution

Slide 1
Slide 1

Chapter 1 Looking at Data – Distributions
Chapter 1 Looking at Data – Distributions

Document
Document

Fundamentals of Statistics I
Fundamentals of Statistics I

File
File

chap03 - Kent State University
chap03 - Kent State University

Chapter 5 Important Probability Distributions - Full
Chapter 5 Important Probability Distributions - Full

P201 Lecture Notes06 Chapter 5
P201 Lecture Notes06 Chapter 5

... Measures of Central Tendency The Frozen Broccoli Example A truck carrying 10,000 packages of frozen broccoli overturns on the interstate between mile marker 158 and 159. The driver is unhurt. He calls for help. The first question asked is “Where is the broccoli?” ...
Course 52558: Problem Set 1 Solution
Course 52558: Problem Set 1 Solution

... convince us that the true value of θ is close to 0.4. Although this is higher than our original estimate, it is still less than the majority and we are willing to let the sample surprise us to that extent. (d) What guidelines for statistical inference do your answers suggest? Solution: The above sug ...
4. Inferences about a single quantitative predictor
4. Inferences about a single quantitative predictor

Ch6-Sec6.1
Ch6-Sec6.1

ExtraExercise from the book - Center for Statistical Sciences
ExtraExercise from the book - Center for Statistical Sciences

document
document

... Except in the case of small samples, the assumption that the data are an SRS from the population of interest is more important than the assumption that the population distribution is Normal.  Sample size less than 15: Use t procedures if the data appear close to Normal (symmetric, single peak, no o ...
< 1 ... 19 20 21 22 23 24 25 26 27 ... 114 >

Degrees of freedom (statistics)

In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary.The number of independent ways by which a dynamic system can move, without violating any constraint imposed on it, is called number of degrees of freedom. In other words, the number of degrees of freedom can be defined as the minimum number of independent coordinates that can specify the position of the system completely.Estimates of statistical parameters can be based upon different amounts of information or data. The number of independent pieces of information that go into the estimate of a parameter are called the degrees of freedom. In general, the degrees of freedom of an estimate of a parameter are equal to the number of independent scores that go into the estimate minus the number of parameters used as intermediate steps in the estimation of the parameter itself (i.e. the sample variance has N-1 degrees of freedom, since it is computed from N random scores minus the only 1 parameter estimated as intermediate step, which is the sample mean).Mathematically, degrees of freedom is the number of dimensions of the domain of a random vector, or essentially the number of ""free"" components (how many components need to be known before the vector is fully determined).The term is most often used in the context of linear models (linear regression, analysis of variance), where certain random vectors are constrained to lie in linear subspaces, and the number of degrees of freedom is the dimension of the subspace. The degrees of freedom are also commonly associated with the squared lengths (or ""sum of squares"" of the coordinates) of such vectors, and the parameters of chi-squared and other distributions that arise in associated statistical testing problems.While introductory textbooks may introduce degrees of freedom as distribution parameters or through hypothesis testing, it is the underlying geometry that defines degrees of freedom, and is critical to a proper understanding of the concept. Walker (1940) has stated this succinctly as ""the number of observations minus the number of necessary relations among these observations.""
  • studyres.com © 2026
  • DMCA
  • Privacy
  • Terms
  • Report