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