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Blank Jeopardy
Blank Jeopardy

Research Methods I
Research Methods I

Pol 600: Research Methods
Pol 600: Research Methods

... several measures of central tendency, each with different pros and cons: expected values (sometimes called expectations, means or averages), medians, and modes. Expected values (usually denoted as E (X) or x̄) are most commonly used in practice, but there are applications where medians (denoted x̃) ...
notes
notes

Review: Inference for a Population Mean Part 1
Review: Inference for a Population Mean Part 1

Powerpoint slides
Powerpoint slides

The T Distribution
The T Distribution

1 - JustAnswer
1 - JustAnswer

Locating a Shift in the Mean of a Time Series
Locating a Shift in the Mean of a Time Series

1. Use the confidence level and sample data to find a confidence
1. Use the confidence level and sample data to find a confidence

Document
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... if and only if for any other statistics Y2=u2(X1,…Xn), … , Yn=un(X1,…Xn) the conditional pdf h(y2,…,yn | y1 ) of Y2,…,Yn given Y1=y1 does not depend upon  no matter what the value of y1 is. So given that we know Y1=y1 , it isn’t possible to use any other statistic Y2 to make any inference about . ...
How do we choose which measures of center and spread to use
How do we choose which measures of center and spread to use

... Group data like histograms Still have original values (unlike histograms) Two columns. Left column: Stem Right column: Leaf (includes only the final digit) ...
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here

Simple Linear Regression
Simple Linear Regression

class notes - rivier.instructure.com.
class notes - rivier.instructure.com.

... (symbol P may also be used to represent personal totals) ...
Chapter 7.1
Chapter 7.1

Inference on Least Squares and Multiple Regression
Inference on Least Squares and Multiple Regression

economic freedom leads
economic freedom leads

... 60% of the variability in logged per capita GDP, a reasonably large amount. Once again the intercept is meaningless, but what about the slope? The slope coefficient is −.764; this says that a one-unit increase in the economic freedom index is associated with an expected .764 unit decrease in the log ...
Kleinbaum, D.G. and S. John; (1969)A central tolerance region for the multivariate normal distribution, II."
Kleinbaum, D.G. and S. John; (1969)A central tolerance region for the multivariate normal distribution, II."

... n X. 2p (l-a)/tp (Q))~ +{N(n-p+l)}-:t (np)"r{Fp,n-p+l(l-Q)}~ ...
9.3 Notes for SUB
9.3 Notes for SUB

Chapter 24 - TeacherWeb
Chapter 24 - TeacherWeb

... 1. Independence Assumption: the data in each group must be drawn independently. A) Randomization condition: Data must arise from a random sample. B) 10% condition: The sample is less than 10% of the population. 2. Normal Population Assumption: the underlying populations are each Normally distributed ...
Measures of Variability
Measures of Variability

View/Open
View/Open

... mean but a standard deviation 50% smaller than the standard deviation under the mediumvariance scenario. Analogously, the distribution of e~y under the high-variance scenario is ey,q, with probabilities π y,H q ≡ π(q + 0.5; φ1H , φ2H ) − π(q − 0.5; φ1H , φ2H ) for [ φ1H , φ2H ] = [0.93, 1.73]. Note ...
CHI-SQUARED - UT Mathematics
CHI-SQUARED - UT Mathematics

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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.""
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