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