A) The number of values in the final calculation of a statistic that are free to vary B) The constant value used in a hypothesis test C) The total sample size minus the standard deviation D) The number of variables ignored in a linear model
A) Number of independent scores minus the number of parameters used as intermediate steps B) Number of parameters minus the sample mean C) Sum of all observations divided by two D) Total sample size multiplied by the number of variables
A) N B) N - 2 C) N + 1 D) N - 1
A) The total length of a random vector B) The probability of a type I error C) The number of dimensions of the domain of a random vector D) The sum of squares of a sample
A) Discrete probability trees B) Non-linear calculus C) Bayesian prior estimation D) Linear models
A) The sample size B) The variance estimate C) The number of degrees of freedom D) The mean of the distribution
A) Sample variance formulas B) Hypothesis testing rules C) Introductory textbooks D) The underlying geometry
A) Ronald Fisher B) William Sealy Gosset C) Student's teacher D) Carl Friedrich Gauss
A) 1950 B) 1922 C) 1821 D) 1908
A) Biometrika B) Fisher C) Gauss D) Student
A) Christensen B) William Sealy Gosset C) Carl Friedrich Gauss D) Ronald Fisher
A) μ B) σ C) ν (lowercase Greek letter nu) D) n
A) dfom B) fre C) deg D) d.f.
A) Degrees of freedom B) Variance C) Sample size D) Number of parameters
A) Multiplied by the sample variance B) Beside the test statistic as a subscript or in parentheses C) At the very beginning of the research paper D) As an exponent of the mean
A) n B) n - 1 C) n + 1 D) 1
A) They must equal N minus one B) They must equal the sample mean C) They must sum up to zero D) They must be strictly positive
A) n - 1 B) n C) 1 D) n - 2
A) A Student's t-distribution with n degrees of freedom B) An F-distribution with 1 degree of freedom C) A standard normal distribution with n degrees of freedom D) A scaled chi-squared distribution with n - 1 degrees of freedom
A) When the sample size is less than three B) When the data is not normally distributed C) When the variance is equal to zero D) When the hypothesized mean is correct
A) 3n - 3 B) 3n C) n D) n - 1
A) 3 B) 3n C) 1 D) 2
A) 1 B) 3 C) 2 D) n - 1
A) 3n - 3 B) 2 C) n - 1 D) 3n
A) The author of Christensen (2002) B) The creator of Student's t-distribution C) An English statistician who popularized the term D) A German astronomer and mathematician whose work recognized the basic concept as early as 1821
A) A multiple of the vector of 1's B) Oblique projection onto the subspace spanned by the vector of 1's C) Least-squares projection onto the (n - 1)-dimensional orthogonal complement D) A vector pointing randomly in n-dimensional space
A) a B) ei C) Yi D) xi
A) p - 1 B) n C) n - p D) p
A) The first slope B) The intercept C) The dependent variable D) The error term
A) Squared lengths B) Mean values C) Standard deviations D) Sample sizes
A) ANOVA B) SST C) SSM D) SSE
A) One-way Analysis of Variance B) Balanced ANOVA models C) Unbalanced split-plot designs D) Simple linear regression
A) Only under the null hypothesis B) Yes C) Only in one-way ANOVA D) No
A) The population variance B) The total sum-of-squares C) The sample mean D) The residual sum-of-squares
A) Multivariate analysis B) Non-linear classification C) Non-standard regression methods D) Ordinary least squares
A) Xy B) By C) Jy D) Hy
A) tr(H'Σ) B) tr(I - H) C) tr(H) D) tr(2I - H)
A) tr(HH'H) B) tr(Σ - H) C) tr(I - H') D) tr(H'H)
A) tr(H – 2H'H) B) tr(2H – H H') C) tr(H + HH') D) tr(2I – H H')
A) Satterthwaite approximation B) Chi-squared approximation C) Bonferroni approximation D) Gaussian approximation
A) X(X 'X)X ' B) X(X 'X)−1X ' C) X'(X 'X)−1X D) (X 'X)−1X 'X
A) x_ij B) h_ii C) r_i D) h_ij
A) A chi-squared matrix B) A linear smoother C) A polynomial filter D) A Gaussian blur
A) The Satterthwaite approximation B) A simple smoothing matrix like a Gaussian blur C) An observation covariance matrix D) A standard linear regression hat matrix
A) tr((I − H)'(I − H)) B) tr(I - 2H) C) tr(H'H) D) tr(2H - HH')
A) The number of observations n B) The error variance itself C) The trace of the hat matrix D) The objective function being minimized
A) An observation covariance matrix B) An identity matrix C) A smoothing blur matrix D) A quadratic form matrix
A) The number of primary sampling units B) The known input variables C) The degrees of freedom in structural equation models D) The unknown parameters' a posteriori standard deviation
A) 10 B) 4 C) 8 D) 6
A) 10 B) 8 C) 4 D) 6
A) Nearly half B) About ten percent C) Nearly all D) Zero
A) L stratum means B) m_hi stratum means C) n_h stratum means D) One stratum mean |