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