From cc9590cb6eeaf722415f09d0ec0dafe9dce4e3a4 Mon Sep 17 00:00:00 2001
From: Fredrik Ekre <fredrik.ekre@chalmers.se>
Date: Fri, 18 May 2018 16:23:08 +0200
Subject: [PATCH] rm stats

---
 base/deprecated.jl                        |  28 +-
 base/exports.jl                           |  22 +-
 base/range.jl                             |   2 -
 base/statistics.jl                        | 699 ----------------------
 base/sysimg.jl                            |   1 -
 doc/src/base/math.md                      |  11 -
 doc/src/manual/interfaces.md              |   6 +-
 stdlib/LinearAlgebra/docs/src/index.md    |   1 -
 stdlib/LinearAlgebra/src/LinearAlgebra.jl |   1 -
 stdlib/LinearAlgebra/src/generic.jl       |  40 --
 stdlib/LinearAlgebra/test/generic.jl      |  47 --
 stdlib/SparseArrays/src/linalg.jl         |  23 -
 stdlib/SparseArrays/src/sparsematrix.jl   |  61 --
 stdlib/SparseArrays/test/sparse.jl        |  62 +-
 test/dimensionful.jl                      |   6 +-
 test/docs.jl                              |   1 -
 test/offsetarray.jl                       |   9 +-
 test/ranges.jl                            |  10 +-
 test/reducedim.jl                         |   6 -
 test/statistics.jl                        | 427 +------------
 20 files changed, 41 insertions(+), 1422 deletions(-)

diff --git a/base/deprecated.jl b/base/deprecated.jl
index 49decc44e4c3e..54456accb98ad 100644
--- a/base/deprecated.jl
+++ b/base/deprecated.jl
@@ -296,12 +296,6 @@ deprecate(Base, :DSP, 2)
 using .DSP
 export conv, conv2, deconv, filt, filt!, xcorr
 
-# PR #21709
-@deprecate cov(x::AbstractVector, corrected::Bool) cov(x, corrected=corrected)
-@deprecate cov(x::AbstractMatrix, vardim::Int, corrected::Bool) cov(x, dims=vardim, corrected=corrected)
-@deprecate cov(X::AbstractVector, Y::AbstractVector, corrected::Bool) cov(X, Y, corrected=corrected)
-@deprecate cov(X::AbstractVecOrMat, Y::AbstractVecOrMat, vardim::Int, corrected::Bool) cov(X, Y, dims=vardim, corrected=corrected)
-
 # PR #22325
 # TODO: when this replace is removed from deprecated.jl:
 # 1) rename the function replace_new from strings/util.jl to replace
@@ -1347,14 +1341,6 @@ export readandwrite
 @deprecate findmin(A::AbstractArray, dims)    findmin(A, dims=dims)
 
 @deprecate mean(A::AbstractArray, dims)                              mean(A, dims=dims)
-@deprecate varm(A::AbstractArray, m::AbstractArray, dims; kwargs...) varm(A, m; kwargs..., dims=dims)
-@deprecate var(A::AbstractArray, dims; kwargs...)                    var(A; kwargs..., dims=dims)
-@deprecate std(A::AbstractArray, dims; kwargs...)                    std(A; kwargs..., dims=dims)
-@deprecate cov(X::AbstractMatrix, dim::Int; kwargs...)               cov(X; kwargs..., dims=dim)
-@deprecate cov(x::AbstractVecOrMat, y::AbstractVecOrMat, dim::Int; kwargs...) cov(x, y; kwargs..., dims=dim)
-@deprecate cor(X::AbstractMatrix, dim::Int)                          cor(X, dims=dim)
-@deprecate cor(x::AbstractVecOrMat, y::AbstractVecOrMat, dim::Int)   cor(x, y, dims=dim)
-@deprecate median(A::AbstractArray, dims; kwargs...)                 median(A; kwargs..., dims=dims)
 
 @deprecate mapreducedim(f, op, A::AbstractArray, dims)     mapreduce(f, op, A, dims=dims)
 @deprecate mapreducedim(f, op, A::AbstractArray, dims, v0) mapreduce(f, op, v0, A, dims=dims)
@@ -1664,6 +1650,20 @@ end
 # when this is removed, `isbitstype(typeof(x))` can be replaced with `isbits(x)`
 @deprecate isbits(@nospecialize(t::Type)) isbitstype(t)
 
+# statistics to StatsBase (#27152)
+@deprecate_moved cor "StatsBase"
+@deprecate_moved cov "StatsBase"
+@deprecate_moved median "StatsBase"
+@deprecate_moved median! "StatsBase"
+@deprecate_moved middle "StatsBase"
+@deprecate_moved quantile "StatsBase"
+@deprecate_moved quantile! "StatsBase"
+@deprecate_moved std "StatsBase"
+@deprecate_moved stdm "StatsBase"
+@deprecate_moved var "StatsBase"
+@deprecate_moved varm "StatsBase"
+@deprecate_moved linreg "StatsBase"
+
 # Special string deprecation
 @deprecate start(s::AbstractString) firstindex(s)
 @deprecate next(s::AbstractString, i::Integer) iterate(s, i)
diff --git a/base/exports.jl b/base/exports.jl
index 1204c0b252e7f..c61cd5bef4e85 100644
--- a/base/exports.jl
+++ b/base/exports.jl
@@ -633,19 +633,19 @@ export
     set_zero_subnormals,
 
 # statistics
-    cor,
-    cov,
+    # cor,
+    # cov,
     mean!,
     mean,
-    median!,
-    median,
-    middle,
-    quantile!,
-    quantile,
-    std,
-    stdm,
-    var,
-    varm,
+    # median!,
+    # median,
+    # middle,
+    # quantile!,
+    # quantile,
+    # std,
+    # stdm,
+    # var,
+    # varm,
 
 # iteration
     done,
diff --git a/base/range.jl b/base/range.jl
index 5806242dfa11c..62ff08b110dc8 100644
--- a/base/range.jl
+++ b/base/range.jl
@@ -854,8 +854,6 @@ function mean(r::AbstractRange{<:Real})
     (first(r) + last(r)) / 2
 end
 
-median(r::AbstractRange{<:Real}) = mean(r)
-
 function _in_range(x, r::AbstractRange)
     if step(r) == 0
         return !isempty(r) && first(r) == x
diff --git a/base/statistics.jl b/base/statistics.jl
index a5310c3fc5d2c..783c2335a6c50 100644
--- a/base/statistics.jl
+++ b/base/statistics.jl
@@ -78,702 +78,3 @@ mean(A::AbstractArray; dims=:) = _mean(A, dims)
 
 _mean(A::AbstractArray{T}, region) where {T} = mean!(reducedim_init(t -> t/2, +, A, region), A)
 _mean(A::AbstractArray, ::Colon) = sum(A) / _length(A)
-
-##### variances #####
-
-# faster computation of real(conj(x)*y)
-realXcY(x::Real, y::Real) = x*y
-realXcY(x::Complex, y::Complex) = real(x)*real(y) + imag(x)*imag(y)
-
-var(iterable; corrected::Bool=true, mean=nothing) = _var(iterable, corrected, mean)
-
-function _var(iterable, corrected::Bool, mean)
-    y = iterate(iterable)
-    if y === nothing
-        throw(ArgumentError("variance of empty collection undefined: $(repr(iterable))"))
-    end
-    count = 1
-    value, state = y
-    y = iterate(iterable, state)
-    if mean === nothing
-        # Use Welford algorithm as seen in (among other places)
-        # Knuth's TAOCP, Vol 2, page 232, 3rd edition.
-        M = value / 1
-        S = real(zero(M))
-        while y !== nothing
-            value, state = y
-            y = iterate(iterable, state)
-            count += 1
-            new_M = M + (value - M) / count
-            S = S + realXcY(value - M, value - new_M)
-            M = new_M
-        end
-        return S / (count - Int(corrected))
-    elseif isa(mean, Number) # mean provided
-        # Cannot use a compensated version, e.g. the one from
-        # "Updating Formulae and a Pairwise Algorithm for Computing Sample Variances."
-        # by Chan, Golub, and LeVeque, Technical Report STAN-CS-79-773,
-        # Department of Computer Science, Stanford University,
-        # because user can provide mean value that is different to mean(iterable)
-        sum2 = abs2(value - mean::Number)
-        while y !== nothing
-            value, state = y
-            y = iterate(iterable, state)
-            count += 1
-            sum2 += abs2(value - mean)
-        end
-        return sum2 / (count - Int(corrected))
-    else
-        throw(ArgumentError("invalid value of mean, $(mean)::$(typeof(mean))"))
-    end
-end
-
-centralizedabs2fun(m) = x -> abs2.(x - m)
-centralize_sumabs2(A::AbstractArray, m) =
-    mapreduce(centralizedabs2fun(m), +, A)
-centralize_sumabs2(A::AbstractArray, m, ifirst::Int, ilast::Int) =
-    mapreduce_impl(centralizedabs2fun(m), +, A, ifirst, ilast)
-
-function centralize_sumabs2!(R::AbstractArray{S}, A::AbstractArray, means::AbstractArray) where S
-    # following the implementation of _mapreducedim! at base/reducedim.jl
-    lsiz = check_reducedims(R,A)
-    isempty(R) || fill!(R, zero(S))
-    isempty(A) && return R
-
-    if has_fast_linear_indexing(A) && lsiz > 16
-        nslices = div(_length(A), lsiz)
-        ibase = first(LinearIndices(A))-1
-        for i = 1:nslices
-            @inbounds R[i] = centralize_sumabs2(A, means[i], ibase+1, ibase+lsiz)
-            ibase += lsiz
-        end
-        return R
-    end
-    indsAt, indsRt = safe_tail(axes(A)), safe_tail(axes(R)) # handle d=1 manually
-    keep, Idefault = Broadcast.shapeindexer(indsRt)
-    if reducedim1(R, A)
-        i1 = first(indices1(R))
-        @inbounds for IA in CartesianIndices(indsAt)
-            IR = Broadcast.newindex(IA, keep, Idefault)
-            r = R[i1,IR]
-            m = means[i1,IR]
-            @simd for i in axes(A, 1)
-                r += abs2(A[i,IA] - m)
-            end
-            R[i1,IR] = r
-        end
-    else
-        @inbounds for IA in CartesianIndices(indsAt)
-            IR = Broadcast.newindex(IA, keep, Idefault)
-            @simd for i in axes(A, 1)
-                R[i,IR] += abs2(A[i,IA] - means[i,IR])
-            end
-        end
-    end
-    return R
-end
-
-function varm!(R::AbstractArray{S}, A::AbstractArray, m::AbstractArray; corrected::Bool=true) where S
-    if isempty(A)
-        fill!(R, convert(S, NaN))
-    else
-        rn = div(_length(A), _length(R)) - Int(corrected)
-        centralize_sumabs2!(R, A, m)
-        R .= R .* (1 // rn)
-    end
-    return R
-end
-
-"""
-    varm(v, m; dims, corrected::Bool=true)
-
-Compute the sample variance of a collection `v` with known mean(s) `m`,
-optionally over the given dimensions. `m` may contain means for each dimension of
-`v`. If `corrected` is `true`, then the sum is scaled with `n-1`,
-whereas the sum is scaled with `n` if `corrected` is `false` where `n = length(x)`.
-
-!!! note
-    Julia does not ignore `NaN` values in the computation. Use the [`missing`](@ref) type
-    to represent missing values, and the [`skipmissing`](@ref) function to omit them.
-"""
-varm(A::AbstractArray, m::AbstractArray; corrected::Bool=true, dims=:) = _varm(A, m, corrected, dims)
-
-_varm(A::AbstractArray{T}, m, corrected::Bool, region) where {T} =
-    varm!(reducedim_init(t -> abs2(t)/2, +, A, region), A, m; corrected=corrected)
-
-varm(A::AbstractArray, m; corrected::Bool=true) = _varm(A, m, corrected, :)
-
-function _varm(A::AbstractArray{T}, m, corrected::Bool, ::Colon) where T
-    n = _length(A)
-    n == 0 && return typeof((abs2(zero(T)) + abs2(zero(T)))/2)(NaN)
-    return centralize_sumabs2(A, m) / (n - Int(corrected))
-end
-
-
-"""
-    var(v; dims, corrected::Bool=true, mean=nothing)
-
-Compute the sample variance of a vector or array `v`, optionally along the given dimensions.
-The algorithm will return an estimator of the generative distribution's variance
-under the assumption that each entry of `v` is an IID drawn from that generative
-distribution. This computation is equivalent to calculating `sum(abs2, v - mean(v)) /
-(length(v) - 1)`. If `corrected` is `true`, then the sum is scaled with `n-1`,
-whereas the sum is scaled with `n` if `corrected` is `false` where `n = length(x)`.
-The mean `mean` over the region may be provided.
-
-!!! note
-    Julia does not ignore `NaN` values in the computation. Use the [`missing`](@ref) type
-    to represent missing values, and the [`skipmissing`](@ref) function to omit them.
-"""
-var(A::AbstractArray; corrected::Bool=true, mean=nothing, dims=:) = _var(A, corrected, mean, dims)
-
-_var(A::AbstractArray, corrected::Bool, mean, dims) =
-    varm(A, coalesce(mean, Base.mean(A, dims=dims)); corrected=corrected, dims=dims)
-
-_var(A::AbstractArray, corrected::Bool, mean, ::Colon) =
-    real(varm(A, coalesce(mean, Base.mean(A)); corrected=corrected))
-
-varm(iterable, m; corrected::Bool=true) = _var(iterable, corrected, m)
-
-## variances over ranges
-
-varm(v::AbstractRange, m::AbstractArray) = range_varm(v, m)
-varm(v::AbstractRange, m) = range_varm(v, m)
-
-function range_varm(v::AbstractRange, m)
-    f  = first(v) - m
-    s  = step(v)
-    l  = length(v)
-    vv = f^2 * l / (l - 1) + f * s * l + s^2 * l * (2 * l - 1) / 6
-    if l == 0 || l == 1
-        return typeof(vv)(NaN)
-    end
-    return vv
-end
-
-function var(v::AbstractRange)
-    s  = step(v)
-    l  = length(v)
-    vv = abs2(s) * (l + 1) * l / 12
-    if l == 0 || l == 1
-        return typeof(vv)(NaN)
-    end
-    return vv
-end
-
-
-##### standard deviation #####
-
-function sqrt!(A::AbstractArray)
-    for i in eachindex(A)
-        @inbounds A[i] = sqrt(A[i])
-    end
-    A
-end
-
-stdm(A::AbstractArray, m; corrected::Bool=true) =
-    sqrt.(varm(A, m; corrected=corrected))
-
-"""
-    std(v; corrected::Bool=true, mean=nothing, dims)
-
-Compute the sample standard deviation of a vector or array `v`, optionally along the given
-dimensions. The algorithm returns an estimator of the generative distribution's standard
-deviation under the assumption that each entry of `v` is an IID drawn from that generative
-distribution. This computation is equivalent to calculating `sqrt(sum((v - mean(v)).^2) /
-(length(v) - 1))`. A pre-computed `mean` may be provided. If `corrected` is `true`,
-then the sum is scaled with `n-1`, whereas the sum is scaled with `n` if `corrected` is
-`false` where `n = length(x)`.
-
-!!! note
-    Julia does not ignore `NaN` values in the computation. Use the [`missing`](@ref) type
-    to represent missing values, and the [`skipmissing`](@ref) function to omit them.
-"""
-std(A::AbstractArray; corrected::Bool=true, mean=nothing, dims=:) = _std(A, corrected, mean, dims)
-
-_std(A::AbstractArray, corrected::Bool, mean, dims) =
-    sqrt.(var(A; corrected=corrected, mean=mean, dims=dims))
-
-_std(A::AbstractArray, corrected::Bool, mean, ::Colon) =
-    sqrt.(var(A; corrected=corrected, mean=mean))
-
-_std(A::AbstractArray{<:AbstractFloat}, corrected::Bool, mean, dims) =
-    sqrt!(var(A; corrected=corrected, mean=mean, dims=dims))
-
-_std(A::AbstractArray{<:AbstractFloat}, corrected::Bool, mean, ::Colon) =
-    sqrt.(var(A; corrected=corrected, mean=mean))
-
-std(iterable; corrected::Bool=true, mean=nothing) =
-    sqrt(var(iterable, corrected=corrected, mean=mean))
-
-"""
-    stdm(v, m; corrected::Bool=true)
-
-Compute the sample standard deviation of a vector `v`
-with known mean `m`. If `corrected` is `true`,
-then the sum is scaled with `n-1`, whereas the sum is
-scaled with `n` if `corrected` is `false` where `n = length(x)`.
-
-!!! note
-    Julia does not ignore `NaN` values in the computation. Use the [`missing`](@ref) type
-    to represent missing values, and the [`skipmissing`](@ref) function to omit them.
-"""
-stdm(iterable, m; corrected::Bool=true) =
-    std(iterable, corrected=corrected, mean=m)
-
-
-###### covariance ######
-
-# auxiliary functions
-
-_conj(x::AbstractArray{<:Real}) = x
-_conj(x::AbstractArray) = conj(x)
-
-_getnobs(x::AbstractVector, vardim::Int) = _length(x)
-_getnobs(x::AbstractMatrix, vardim::Int) = size(x, vardim)
-
-function _getnobs(x::AbstractVecOrMat, y::AbstractVecOrMat, vardim::Int)
-    n = _getnobs(x, vardim)
-    _getnobs(y, vardim) == n || throw(DimensionMismatch("dimensions of x and y mismatch"))
-    return n
-end
-
-_vmean(x::AbstractVector, vardim::Int) = mean(x)
-_vmean(x::AbstractMatrix, vardim::Int) = mean(x, dims=vardim)
-
-# core functions
-
-unscaled_covzm(x::AbstractVector{<:Number})    = sum(abs2, x)
-unscaled_covzm(x::AbstractVector)              = sum(t -> t*t', x)
-unscaled_covzm(x::AbstractMatrix, vardim::Int) = (vardim == 1 ? _conj(x'x) : x * x')
-
-unscaled_covzm(x::AbstractVector, y::AbstractVector) = sum(conj(y[i])*x[i] for i in eachindex(y, x))
-unscaled_covzm(x::AbstractVector, y::AbstractMatrix, vardim::Int) =
-    (vardim == 1 ? *(transpose(x), _conj(y)) : *(transpose(x), transpose(_conj(y))))
-unscaled_covzm(x::AbstractMatrix, y::AbstractVector, vardim::Int) =
-    (c = vardim == 1 ? *(transpose(x), _conj(y)) :  x * _conj(y); reshape(c, length(c), 1))
-unscaled_covzm(x::AbstractMatrix, y::AbstractMatrix, vardim::Int) =
-    (vardim == 1 ? *(transpose(x), _conj(y)) : *(x, adjoint(y)))
-
-# covzm (with centered data)
-
-covzm(x::AbstractVector; corrected::Bool=true) = unscaled_covzm(x) / (_length(x) - Int(corrected))
-function covzm(x::AbstractMatrix, vardim::Int=1; corrected::Bool=true)
-    C = unscaled_covzm(x, vardim)
-    T = promote_type(typeof(first(C) / 1), eltype(C))
-    A = convert(AbstractMatrix{T}, C)
-    b = 1//(size(x, vardim) - corrected)
-    A .= A .* b
-    return A
-end
-covzm(x::AbstractVector, y::AbstractVector; corrected::Bool=true) =
-    unscaled_covzm(x, y) / (_length(x) - Int(corrected))
-function covzm(x::AbstractVecOrMat, y::AbstractVecOrMat, vardim::Int=1; corrected::Bool=true)
-    C = unscaled_covzm(x, y, vardim)
-    T = promote_type(typeof(first(C) / 1), eltype(C))
-    A = convert(AbstractArray{T}, C)
-    b = 1//(_getnobs(x, y, vardim) - corrected)
-    A .= A .* b
-    return A
-end
-
-# covm (with provided mean)
-## Use map(t -> t - xmean, x) instead of x .- xmean to allow for Vector{Vector}
-## which can't be handled by broadcast
-covm(x::AbstractVector, xmean; corrected::Bool=true) =
-    covzm(map(t -> t - xmean, x); corrected=corrected)
-covm(x::AbstractMatrix, xmean, vardim::Int=1; corrected::Bool=true) =
-    covzm(x .- xmean, vardim; corrected=corrected)
-covm(x::AbstractVector, xmean, y::AbstractVector, ymean; corrected::Bool=true) =
-    covzm(map(t -> t - xmean, x), map(t -> t - ymean, y); corrected=corrected)
-covm(x::AbstractVecOrMat, xmean, y::AbstractVecOrMat, ymean, vardim::Int=1; corrected::Bool=true) =
-    covzm(x .- xmean, y .- ymean, vardim; corrected=corrected)
-
-# cov (API)
-"""
-    cov(x::AbstractVector; corrected::Bool=true)
-
-Compute the variance of the vector `x`. If `corrected` is `true` (the default) then the sum
-is scaled with `n-1`, whereas the sum is scaled with `n` if `corrected` is `false` where `n = length(x)`.
-"""
-cov(x::AbstractVector; corrected::Bool=true) = covm(x, Base.mean(x); corrected=corrected)
-
-"""
-    cov(X::AbstractMatrix; dims::Int=1, corrected::Bool=true)
-
-Compute the covariance matrix of the matrix `X` along the dimension `dims`. If `corrected`
-is `true` (the default) then the sum is scaled with `n-1`, whereas the sum is scaled with `n`
-if `corrected` is `false` where `n = size(X, dims)`.
-"""
-cov(X::AbstractMatrix; dims::Int=1, corrected::Bool=true) =
-    covm(X, _vmean(X, dims), dims; corrected=corrected)
-
-"""
-    cov(x::AbstractVector, y::AbstractVector; corrected::Bool=true)
-
-Compute the covariance between the vectors `x` and `y`. If `corrected` is `true` (the
-default), computes ``\\frac{1}{n-1}\\sum_{i=1}^n (x_i-\\bar x) (y_i-\\bar y)^*`` where
-``*`` denotes the complex conjugate and `n = length(x) = length(y)`. If `corrected` is
-`false`, computes ``\\frac{1}{n}\\sum_{i=1}^n (x_i-\\bar x) (y_i-\\bar y)^*``.
-"""
-cov(x::AbstractVector, y::AbstractVector; corrected::Bool=true) =
-    covm(x, Base.mean(x), y, Base.mean(y); corrected=corrected)
-
-"""
-    cov(X::AbstractVecOrMat, Y::AbstractVecOrMat; dims::Int=1, corrected::Bool=true)
-
-Compute the covariance between the vectors or matrices `X` and `Y` along the dimension
-`dims`. If `corrected` is `true` (the default) then the sum is scaled with `n-1`, whereas
-the sum is scaled with `n` if `corrected` is `false` where `n = size(X, dims) = size(Y, dims)`.
-"""
-cov(X::AbstractVecOrMat, Y::AbstractVecOrMat; dims::Int=1, corrected::Bool=true) =
-    covm(X, _vmean(X, dims), Y, _vmean(Y, dims), dims; corrected=corrected)
-
-##### correlation #####
-
-"""
-    clampcor(x)
-
-Clamp a real correlation to between -1 and 1, leaving complex correlations unchanged
-"""
-clampcor(x::Real) = clamp(x, -1, 1)
-clampcor(x) = x
-
-# cov2cor!
-
-function cov2cor!(C::AbstractMatrix{T}, xsd::AbstractArray) where T
-    nx = length(xsd)
-    size(C) == (nx, nx) || throw(DimensionMismatch("inconsistent dimensions"))
-    for j = 1:nx
-        for i = 1:j-1
-            C[i,j] = adjoint(C[j,i])
-        end
-        C[j,j] = oneunit(T)
-        for i = j+1:nx
-            C[i,j] = clampcor(C[i,j] / (xsd[i] * xsd[j]))
-        end
-    end
-    return C
-end
-function cov2cor!(C::AbstractMatrix, xsd, ysd::AbstractArray)
-    nx, ny = size(C)
-    length(ysd) == ny || throw(DimensionMismatch("inconsistent dimensions"))
-    for (j, y) in enumerate(ysd)   # fixme (iter): here and in all `cov2cor!` we assume that `C` is efficiently indexed by integers
-        for i in 1:nx
-            C[i,j] = clampcor(C[i, j] / (xsd * y))
-        end
-    end
-    return C
-end
-function cov2cor!(C::AbstractMatrix, xsd::AbstractArray, ysd)
-    nx, ny = size(C)
-    length(xsd) == nx || throw(DimensionMismatch("inconsistent dimensions"))
-    for j in 1:ny
-        for (i, x) in enumerate(xsd)
-            C[i,j] = clampcor(C[i,j] / (x * ysd))
-        end
-    end
-    return C
-end
-function cov2cor!(C::AbstractMatrix, xsd::AbstractArray, ysd::AbstractArray)
-    nx, ny = size(C)
-    (length(xsd) == nx && length(ysd) == ny) ||
-        throw(DimensionMismatch("inconsistent dimensions"))
-    for (i, x) in enumerate(xsd)
-        for (j, y) in enumerate(ysd)
-            C[i,j] = clampcor(C[i,j] / (x * y))
-        end
-    end
-    return C
-end
-
-# corzm (non-exported, with centered data)
-
-corzm(x::AbstractVector{T}) where {T} = one(real(T))
-function corzm(x::AbstractMatrix, vardim::Int=1)
-    c = unscaled_covzm(x, vardim)
-    return cov2cor!(c, collect(sqrt(c[i,i]) for i in 1:min(size(c)...)))
-end
-corzm(x::AbstractVector, y::AbstractMatrix, vardim::Int=1) =
-    cov2cor!(unscaled_covzm(x, y, vardim), sqrt(sum(abs2, x)), sqrt!(sum(abs2, y, dims=vardim)))
-corzm(x::AbstractMatrix, y::AbstractVector, vardim::Int=1) =
-    cov2cor!(unscaled_covzm(x, y, vardim), sqrt!(sum(abs2, x, dims=vardim)), sqrt(sum(abs2, y)))
-corzm(x::AbstractMatrix, y::AbstractMatrix, vardim::Int=1) =
-    cov2cor!(unscaled_covzm(x, y, vardim), sqrt!(sum(abs2, x, dims=vardim)), sqrt!(sum(abs2, y, dims=vardim)))
-
-# corm
-
-corm(x::AbstractVector{T}, xmean) where {T} = one(real(T))
-corm(x::AbstractMatrix, xmean, vardim::Int=1) = corzm(x .- xmean, vardim)
-function corm(x::AbstractVector, mx, y::AbstractVector, my)
-    n = length(x)
-    length(y) == n || throw(DimensionMismatch("inconsistent lengths"))
-    n > 0 || throw(ArgumentError("correlation only defined for non-empty vectors"))
-
-    @inbounds begin
-        # Initialize the accumulators
-        xx = zero(sqrt(abs2(x[1])))
-        yy = zero(sqrt(abs2(y[1])))
-        xy = zero(x[1] * y[1]')
-
-        @simd for i in eachindex(x, y)
-            xi = x[i] - mx
-            yi = y[i] - my
-            xx += abs2(xi)
-            yy += abs2(yi)
-            xy += xi * yi'
-        end
-    end
-    return clampcor(xy / max(xx, yy) / sqrt(min(xx, yy) / max(xx, yy)))
-end
-
-corm(x::AbstractVecOrMat, xmean, y::AbstractVecOrMat, ymean, vardim::Int=1) =
-    corzm(x .- xmean, y .- ymean, vardim)
-
-# cor
-"""
-    cor(x::AbstractVector)
-
-Return the number one.
-"""
-cor(x::AbstractVector) = one(real(eltype(x)))
-
-"""
-    cor(X::AbstractMatrix; dims::Int=1)
-
-Compute the Pearson correlation matrix of the matrix `X` along the dimension `dims`.
-"""
-cor(X::AbstractMatrix; dims::Int=1) = corm(X, _vmean(X, dims), dims)
-
-"""
-    cor(x::AbstractVector, y::AbstractVector)
-
-Compute the Pearson correlation between the vectors `x` and `y`.
-"""
-cor(x::AbstractVector, y::AbstractVector) = corm(x, Base.mean(x), y, Base.mean(y))
-
-"""
-    cor(X::AbstractVecOrMat, Y::AbstractVecOrMat; dims=1)
-
-Compute the Pearson correlation between the vectors or matrices `X` and `Y` along the dimension `dims`.
-"""
-cor(x::AbstractVecOrMat, y::AbstractVecOrMat; dims::Int=1) =
-    corm(x, _vmean(x, dims), y, _vmean(y, dims), dims)
-
-##### median & quantiles #####
-
-"""
-    middle(x)
-
-Compute the middle of a scalar value, which is equivalent to `x` itself, but of the type of `middle(x, x)` for consistency.
-"""
-middle(x::Union{Bool,Int8,Int16,Int32,Int64,Int128,UInt8,UInt16,UInt32,UInt64,UInt128}) = Float64(x)
-# Specialized functions for real types allow for improved performance
-middle(x::AbstractFloat) = x
-middle(x::Real) = (x + zero(x)) / 1
-
-"""
-    middle(x, y)
-
-Compute the middle of two reals `x` and `y`, which is
-equivalent in both value and type to computing their mean (`(x + y) / 2`).
-"""
-middle(x::Real, y::Real) = x/2 + y/2
-
-"""
-    middle(range)
-
-Compute the middle of a range, which consists of computing the mean of its extrema.
-Since a range is sorted, the mean is performed with the first and last element.
-
-```jldoctest
-julia> middle(1:10)
-5.5
-```
-"""
-middle(a::AbstractRange) = middle(a[1], a[end])
-
-"""
-    middle(a)
-
-Compute the middle of an array `a`, which consists of finding its
-extrema and then computing their mean.
-
-```jldoctest
-julia> a = [1,2,3.6,10.9]
-4-element Array{Float64,1}:
-  1.0
-  2.0
-  3.6
- 10.9
-
-julia> middle(a)
-5.95
-```
-"""
-middle(a::AbstractArray) = ((v1, v2) = extrema(a); middle(v1, v2))
-
-"""
-    median!(v)
-
-Like [`median`](@ref), but may overwrite the input vector.
-"""
-function median!(v::AbstractVector)
-    isempty(v) && throw(ArgumentError("median of an empty array is undefined, $(repr(v))"))
-    if eltype(v)<:AbstractFloat
-        @inbounds for x in v
-            isnan(x) && return x
-        end
-    end
-    inds = axes(v, 1)
-    n = length(inds)
-    mid = div(first(inds)+last(inds),2)
-    if isodd(n)
-        return middle(partialsort!(v,mid))
-    else
-        m = partialsort!(v, mid:mid+1)
-        return middle(m[1], m[2])
-    end
-end
-median!(v::AbstractArray) = median!(vec(v))
-
-"""
-    median(v; dims)
-
-Compute the median of an entire array `v`, or, optionally,
-along the given dimensions. For an even number of
-elements no exact median element exists, so the result is
-equivalent to calculating mean of two median elements.
-
-!!! note
-    Julia does not ignore `NaN` values in the computation. Use the [`missing`](@ref) type
-    to represent missing values, and the [`skipmissing`](@ref) function to omit them.
-"""
-median(v::AbstractArray; dims=:) = _median(v, dims)
-
-_median(v::AbstractArray, dims) = mapslices(median!, v, dims)
-
-_median(v::AbstractArray{T}, ::Colon) where {T} = median!(copyto!(Array{T,1}(undef, _length(v)), v))
-
-# for now, use the R/S definition of quantile; may want variants later
-# see ?quantile in R -- this is type 7
-"""
-    quantile!([q, ] v, p; sorted=false)
-
-Compute the quantile(s) of a vector `v` at the probability or probabilities `p`, which
-can be given as a single value, a vector, or a tuple. If `p` is a vector, an optional
-output array `q` may also be specified. (If not provided, a new output array is created.)
-The keyword argument `sorted` indicates whether `v` can be assumed to be sorted; if
-`false` (the default), then the elements of `v` may be partially sorted.
-
-The elements of `p` should be on the interval [0,1], and `v` should not have any `NaN`
-values.
-
-Quantiles are computed via linear interpolation between the points `((k-1)/(n-1), v[k])`,
-for `k = 1:n` where `n = length(v)`. This corresponds to Definition 7 of Hyndman and Fan
-(1996), and is the same as the R default.
-
-!!! note
-    Julia does not ignore `NaN` values in the computation: `quantile!` will
-    throw an `ArgumentError` in the presence of `NaN` values in the data array.
-    Use the [`missing`](@ref) type to represent missing values, and the
-    [`skipmissing`](@ref) function to omit them.
-
-* Hyndman, R.J and Fan, Y. (1996) "Sample Quantiles in Statistical Packages",
-  *The American Statistician*, Vol. 50, No. 4, pp. 361-365
-"""
-function quantile!(q::AbstractArray, v::AbstractVector, p::AbstractArray;
-                   sorted::Bool=false)
-    if size(p) != size(q)
-        throw(DimensionMismatch("size of p, $(size(p)), must equal size of q, $(size(q))"))
-    end
-    isempty(q) && return q
-
-    minp, maxp = extrema(p)
-    _quantilesort!(v, sorted, minp, maxp)
-
-    for (i, j) in zip(eachindex(p), eachindex(q))
-        @inbounds q[j] = _quantile(v,p[i])
-    end
-    return q
-end
-
-quantile!(v::AbstractVector, p::AbstractArray; sorted::Bool=false) =
-    quantile!(similar(p,float(eltype(v))), v, p; sorted=sorted)
-
-quantile!(v::AbstractVector, p::Real; sorted::Bool=false) =
-    _quantile(_quantilesort!(v, sorted, p, p), p)
-
-function quantile!(v::AbstractVector, p::Tuple{Vararg{Real}}; sorted::Bool=false)
-    isempty(p) && return ()
-    minp, maxp = extrema(p)
-    _quantilesort!(v, sorted, minp, maxp)
-    return map(x->_quantile(v, x), p)
-end
-
-# Function to perform partial sort of v for quantiles in given range
-function _quantilesort!(v::AbstractArray, sorted::Bool, minp::Real, maxp::Real)
-    isempty(v) && throw(ArgumentError("empty data vector"))
-
-    if !sorted
-        lv = length(v)
-        lo = floor(Int,1+minp*(lv-1))
-        hi = ceil(Int,1+maxp*(lv-1))
-
-        # only need to perform partial sort
-        sort!(v, 1, lv, Sort.PartialQuickSort(lo:hi), Base.Sort.Forward)
-    end
-    isnan(v[end]) && throw(ArgumentError("quantiles are undefined in presence of NaNs"))
-    return v
-end
-
-# Core quantile lookup function: assumes `v` sorted
-@inline function _quantile(v::AbstractVector, p::Real)
-    0 <= p <= 1 || throw(ArgumentError("input probability out of [0,1] range"))
-
-    lv = length(v)
-    f0 = (lv - 1)*p # 0-based interpolated index
-    t0 = trunc(f0)
-    h  = f0 - t0
-    i  = trunc(Int,t0) + 1
-
-    T  = promote_type(eltype(v), typeof(v[1]*h))
-
-    if h == 0
-        return convert(T, v[i])
-    else
-        a = v[i]
-        b = v[i+1]
-        if isfinite(a) && isfinite(b)
-            return convert(T, a + h*(b-a))
-        else
-            return convert(T, (1-h)*a + h*b)
-        end
-    end
-end
-
-
-"""
-    quantile(v, p; sorted=false)
-
-Compute the quantile(s) of a vector `v` at a specified probability or vector or tuple of
-probabilities `p`. The keyword argument `sorted` indicates whether `v` can be assumed to
-be sorted.
-
-The `p` should be on the interval [0,1], and `v` should not have any `NaN` values.
-
-Quantiles are computed via linear interpolation between the points `((k-1)/(n-1), v[k])`,
-for `k = 1:n` where `n = length(v)`. This corresponds to Definition 7 of Hyndman and Fan
-(1996), and is the same as the R default.
-
-!!! note
-    Julia does not ignore `NaN` values in the computation: `quantile` will
-    throw an `ArgumentError` in the presence of `NaN` values in the data array.
-    Use the [`missing`](@ref) type to represent missing values, and the
-    [`skipmissing`](@ref) function to omit them.
-
-- Hyndman, R.J and Fan, Y. (1996) "Sample Quantiles in Statistical Packages",
-  *The American Statistician*, Vol. 50, No. 4, pp. 361-365
-"""
-quantile(v::AbstractVector, p; sorted::Bool=false) =
-    quantile!(sorted ? v : copymutable(v), p; sorted=sorted)
diff --git a/base/sysimg.jl b/base/sysimg.jl
index c4fcbd7049eee..a15a6897ef32c 100644
--- a/base/sysimg.jl
+++ b/base/sysimg.jl
@@ -750,7 +750,6 @@ end
     # @deprecate_stdlib kron        LinearAlgebra true
     @deprecate_stdlib ldltfact    LinearAlgebra true
     @deprecate_stdlib ldltfact!   LinearAlgebra true
-    @deprecate_stdlib linreg      LinearAlgebra true
     @deprecate_stdlib logabsdet   LinearAlgebra true
     @deprecate_stdlib logdet      LinearAlgebra true
     @deprecate_stdlib lu          LinearAlgebra true
diff --git a/doc/src/base/math.md b/doc/src/base/math.md
index df67d02159969..a3bf3e345818d 100644
--- a/doc/src/base/math.md
+++ b/doc/src/base/math.md
@@ -189,15 +189,4 @@ Base.FastMath.@fastmath
 ```@docs
 Base.mean
 Base.mean!
-Base.std
-Base.stdm
-Base.var
-Base.varm
-Base.middle
-Base.median
-Base.median!
-Base.quantile
-Base.quantile!
-Base.cov
-Base.cor
 ```
diff --git a/doc/src/manual/interfaces.md b/doc/src/manual/interfaces.md
index 9441ae1e9303b..11d5897f5d23a 100644
--- a/doc/src/manual/interfaces.md
+++ b/doc/src/manual/interfaces.md
@@ -87,7 +87,7 @@ julia> for i in Squares(7)
 49
 ```
 
-We can use many of the builtin methods that work with iterables, like [`in`](@ref), [`mean`](@ref) and [`std`](@ref):
+We can use many of the builtin methods that work with iterables, like [`in`](@ref), [`mean`](@ref) and [`sum`](@ref):
 
 ```jldoctest squaretype
 julia> 25 in Squares(10)
@@ -96,8 +96,8 @@ true
 julia> mean(Squares(100))
 3383.5
 
-julia> std(Squares(100))
-3024.355854282583
+julia> sum(Squares(100))
+338350
 ```
 
 There are a few more methods we can extend to give Julia more information about this iterable
diff --git a/stdlib/LinearAlgebra/docs/src/index.md b/stdlib/LinearAlgebra/docs/src/index.md
index 8ccffe52ee8de..69bff5e12c051 100644
--- a/stdlib/LinearAlgebra/docs/src/index.md
+++ b/stdlib/LinearAlgebra/docs/src/index.md
@@ -379,7 +379,6 @@ Base.inv(::AbstractMatrix)
 LinearAlgebra.pinv
 LinearAlgebra.nullspace
 Base.kron
-LinearAlgebra.linreg
 LinearAlgebra.exp(::StridedMatrix{<:LinearAlgebra.BlasFloat})
 LinearAlgebra.log(::StridedMatrix)
 LinearAlgebra.sqrt(::StridedMatrix{<:Real})
diff --git a/stdlib/LinearAlgebra/src/LinearAlgebra.jl b/stdlib/LinearAlgebra/src/LinearAlgebra.jl
index eeef2b912be53..80e9b6fd9a979 100644
--- a/stdlib/LinearAlgebra/src/LinearAlgebra.jl
+++ b/stdlib/LinearAlgebra/src/LinearAlgebra.jl
@@ -104,7 +104,6 @@ export
     ldiv!,
     ldltfact!,
     ldltfact,
-    linreg,
     logabsdet,
     logdet,
     lowrankdowndate,
diff --git a/stdlib/LinearAlgebra/src/generic.jl b/stdlib/LinearAlgebra/src/generic.jl
index 2ecd4220095b4..2fc52c3bd49aa 100644
--- a/stdlib/LinearAlgebra/src/generic.jl
+++ b/stdlib/LinearAlgebra/src/generic.jl
@@ -1121,46 +1121,6 @@ isdiag(A::AbstractMatrix) = isbanded(A, 0, 0)
 isdiag(x::Number) = true
 
 
-"""
-    linreg(x, y)
-
-Perform simple linear regression using Ordinary Least Squares. Returns `a` and `b` such
-that `a + b*x` is the closest straight line to the given points `(x, y)`, i.e., such that
-the squared error between `y` and `a + b*x` is minimized.
-
-# Examples
-```julia
-using PyPlot
-x = 1.0:12.0
-y = [5.5, 6.3, 7.6, 8.8, 10.9, 11.79, 13.48, 15.02, 17.77, 20.81, 22.0, 22.99]
-a, b = linreg(x, y)          # Linear regression
-plot(x, y, "o")              # Plot (x, y) points
-plot(x, a + b*x)             # Plot line determined by linear regression
-```
-
-See also:
-
-`\\`, [`cov`](@ref), [`std`](@ref), [`mean`](@ref).
-
-"""
-function linreg(x::AbstractVector, y::AbstractVector)
-    # Least squares given
-    # Y = a + b*X
-    # where
-    # b = cov(X, Y)/var(X)
-    # a = mean(Y) - b*mean(X)
-    if size(x) != size(y)
-        throw(DimensionMismatch("x has size $(size(x)) and y has size $(size(y)), " *
-            "but these must be the same size"))
-    end
-    mx = mean(x)
-    my = mean(y)
-    # don't need to worry about the scaling (n vs n - 1) since they cancel in the ratio
-    b = Base.covm(x, mx, y, my)/Base.varm(x, mx)
-    a = my - b*mx
-    return (a, b)
-end
-
 # BLAS-like in-place y = x*α+y function (see also the version in blas.jl
 #                                          for BlasFloat Arrays)
 function axpy!(α, x::AbstractArray, y::AbstractArray)
diff --git a/stdlib/LinearAlgebra/test/generic.jl b/stdlib/LinearAlgebra/test/generic.jl
index 05118849e9a8a..82569cfdb05bd 100644
--- a/stdlib/LinearAlgebra/test/generic.jl
+++ b/stdlib/LinearAlgebra/test/generic.jl
@@ -89,53 +89,6 @@ n = 5 # should be odd
     end
 end
 
-@testset "linrange" begin
-    # make sure unequal input arrays throw an error
-    x = [2; 5; 6]
-    y = [3; 7; 10; 10]
-    @test_throws DimensionMismatch linreg(x, y)
-    x = [2 5 6]
-    y = [3; 7; 10]
-    @test_throws MethodError linreg(x, y)
-
-    # check (UnitRange, Array)
-    x = 1:12
-    y = [5.5; 6.3; 7.6; 8.8; 10.9; 11.79; 13.48; 15.02; 17.77; 20.81; 22.0; 22.99]
-    @test [linreg(x,y)...] ≈ [2.5559090909090867, 1.6960139860139862]
-    @test [linreg(view(x,1:6),view(y,1:6))...] ≈ [3.8366666666666642,1.3271428571428574]
-
-    # check (LinRange, UnitRange)
-    x = range(1.0, stop=12.0, length=100)
-    y = -100:-1
-    @test [linreg(x, y)...] ≈ [-109.0, 9.0]
-
-    # check (UnitRange, UnitRange)
-    x = 1:12
-    y = 12:-1:1
-    @test [linreg(x, y)...] ≈ [13.0, -1.0]
-
-    # check (LinRange, LinRange)
-    x = range(-5, stop=10, length=100)
-    y = range(50, stop=200, length=100)
-    @test [linreg(x, y)...] ≈ [100.0, 10.0]
-
-    # check (Array, Array)
-    # Anscombe's quartet (https://en.wikipedia.org/wiki/Anscombe%27s_quartet)
-    x123 = [10.0; 8.0; 13.0; 9.0; 11.0; 14.0; 6.0; 4.0; 12.0; 7.0; 5.0]
-    y1 = [8.04; 6.95; 7.58; 8.81; 8.33; 9.96; 7.24; 4.26; 10.84; 4.82; 5.68]
-    @test [linreg(x123,y1)...] ≈ [3.0,0.5] atol=15e-5
-
-    y2 = [9.14; 8.14; 8.74; 8.77; 9.26; 8.10; 6.12; 3.10; 9.13; 7.26; 4.74]
-    @test [linreg(x123,y2)...] ≈ [3.0,0.5] atol=10e-3
-
-    y3 = [7.46; 6.77; 12.74; 7.11; 7.81; 8.84; 6.08; 5.39; 8.15; 6.42; 5.73]
-    @test [linreg(x123,y3)...] ≈ [3.0,0.5] atol=10e-3
-
-    x4 = [8.0; 8.0; 8.0; 8.0; 8.0; 8.0; 8.0; 19.0; 8.0; 8.0; 8.0]
-    y4 = [6.58; 5.76; 7.71; 8.84; 8.47; 7.04; 5.25; 12.50; 5.56; 7.91; 6.89]
-    @test [linreg(x4,y4)...] ≈ [3.0,0.5] atol=10e-3
-end
-
 @testset "diag" begin
     A = Matrix(1.0I, 4, 4)
     @test diag(A) == fill(1, 4)
diff --git a/stdlib/SparseArrays/src/linalg.jl b/stdlib/SparseArrays/src/linalg.jl
index c304d3c3821fa..32c667a421914 100644
--- a/stdlib/SparseArrays/src/linalg.jl
+++ b/stdlib/SparseArrays/src/linalg.jl
@@ -1012,26 +1012,3 @@ chol(A::SparseMatrixCSC) = error("Use cholfact() instead of chol() for sparse ma
 lu(A::SparseMatrixCSC) = error("Use lufact() instead of lu() for sparse matrices.")
 eig(A::SparseMatrixCSC) = error("Use IterativeEigensolvers.eigs() instead of eig() for sparse matrices.")
 
-function Base.cov(X::SparseMatrixCSC; dims::Int=1, corrected::Bool=true)
-    vardim = dims
-    a, b = size(X)
-    n, p = vardim == 1 ? (a, b) : (b, a)
-
-    # The covariance can be decomposed into two terms
-    # 1/(n - 1) ∑ (x_i - x̄)*(x_i - x̄)' = 1/(n - 1) (∑ x_i*x_i' - n*x̄*x̄')
-    # which can be evaluated via a sparse matrix-matrix product
-
-    # Compute ∑ x_i*x_i' = X'X using sparse matrix-matrix product
-    out = Matrix(Base.unscaled_covzm(X, vardim))
-
-    # Compute x̄
-    x̄ᵀ = mean(X, dims=vardim)
-
-    # Subtract n*x̄*x̄' from X'X
-    @inbounds for j in 1:p, i in 1:p
-        out[i,j] -= x̄ᵀ[i] * x̄ᵀ[j]' * n
-    end
-
-    # scale with the sample size n or the corrected sample size n - 1
-    return rmul!(out, inv(n - corrected))
-end
diff --git a/stdlib/SparseArrays/src/sparsematrix.jl b/stdlib/SparseArrays/src/sparsematrix.jl
index 6986b12cd2cf5..47fe84a7cd194 100644
--- a/stdlib/SparseArrays/src/sparsematrix.jl
+++ b/stdlib/SparseArrays/src/sparsematrix.jl
@@ -3489,67 +3489,6 @@ function hash(A::SparseMatrixCSC{T}, h::UInt) where T
     hashrun(0, length(A)-lastidx, h)  # Hash zeros at end
 end
 
-## Statistics
-
-# This is the function that does the reduction underlying var/std
-function Base.centralize_sumabs2!(R::AbstractArray{S}, A::SparseMatrixCSC{Tv,Ti}, means::AbstractArray) where {S,Tv,Ti}
-    lsiz = Base.check_reducedims(R,A)
-    size(means) == size(R) || error("size of means must match size of R")
-    isempty(R) || fill!(R, zero(S))
-    isempty(A) && return R
-
-    colptr = A.colptr
-    rowval = A.rowval
-    nzval = A.nzval
-    m = size(A, 1)
-    n = size(A, 2)
-
-    if size(R, 1) == size(R, 2) == 1
-        # Reduction along both columns and rows
-        R[1, 1] = Base.centralize_sumabs2(A, means[1])
-    elseif size(R, 1) == 1
-        # Reduction along rows
-        @inbounds for col = 1:n
-            mu = means[col]
-            r = convert(S, (m-colptr[col+1]+colptr[col])*abs2(mu))
-            @simd for j = colptr[col]:colptr[col+1]-1
-                r += abs2(nzval[j] - mu)
-            end
-            R[1, col] = r
-        end
-    elseif size(R, 2) == 1
-        # Reduction along columns
-        rownz = fill(convert(Ti, n), m)
-        @inbounds for col = 1:n
-            @simd for j = colptr[col]:colptr[col+1]-1
-                row = rowval[j]
-                R[row, 1] += abs2(nzval[j] - means[row])
-                rownz[row] -= 1
-            end
-        end
-        for i = 1:m
-            R[i, 1] += rownz[i]*abs2(means[i])
-        end
-    else
-        # Reduction along a dimension > 2
-        @inbounds for col = 1:n
-            lastrow = 0
-            @simd for j = colptr[col]:colptr[col+1]-1
-                row = rowval[j]
-                for i = lastrow+1:row-1
-                    R[i, col] = abs2(means[i, col])
-                end
-                R[row, col] = abs2(nzval[j] - means[row, col])
-                lastrow = row
-            end
-            for i = lastrow+1:m
-                R[i, col] = abs2(means[i, col])
-            end
-        end
-    end
-    return R
-end
-
 ## Uniform matrix arithmetic
 
 (+)(A::SparseMatrixCSC, J::UniformScaling) = A + sparse(J, size(A)...)
diff --git a/stdlib/SparseArrays/test/sparse.jl b/stdlib/SparseArrays/test/sparse.jl
index 9decccac8a18e..20fdbd70e6462 100644
--- a/stdlib/SparseArrays/test/sparse.jl
+++ b/stdlib/SparseArrays/test/sparse.jl
@@ -489,7 +489,7 @@ end
     pA = sparse(rand(3, 7))
 
     for arr in (se33, sA, pA)
-        for f in (sum, prod, minimum, maximum, var)
+        for f in (sum, prod, minimum, maximum)
             farr = Array(arr)
             @test f(arr) ≈ f(farr)
             @test f(arr, dims=1) ≈ f(farr, dims=1)
@@ -518,9 +518,8 @@ end
         @test prod(sparse(Int[])) === 1
         @test_throws ArgumentError minimum(sparse(Int[]))
         @test_throws ArgumentError maximum(sparse(Int[]))
-        @test var(sparse(Int[])) === NaN
 
-        for f in (sum, prod, var)
+        for f in (sum, prod)
             @test isequal(f(spzeros(0, 1), dims=1), f(Matrix{Int}(I, 0, 1), dims=1))
             @test isequal(f(spzeros(0, 1), dims=2), f(Matrix{Int}(I, 0, 1), dims=2))
             @test isequal(f(spzeros(0, 1), dims=(1, 2)), f(Matrix{Int}(I, 0, 1), dims=(1, 2)))
@@ -2034,63 +2033,6 @@ end
     @test issymmetric(B)
 end
 
-# Faster covariance function for sparse matrices
-# Prevents densifying the input matrix when subtracting the mean
-# Test against dense implementation
-# PR https://github.com/JuliaLang/julia/pull/22735
-# Part of this test needed to be hacked due to the treatment
-# of Inf in sparse matrix algebra
-# https://github.com/JuliaLang/julia/issues/22921
-# The issue will be resolved in
-# https://github.com/JuliaLang/julia/issues/22733
-@testset "optimizing sparse $elty covariance" for elty in (Float64, Complex{Float64})
-    n = 10
-    p = 5
-    np2 = div(n*p, 2)
-    nzvals, x_sparse = guardsrand(1) do
-        if elty <: Real
-            nzvals = randn(np2)
-        else
-            nzvals = complex.(randn(np2), randn(np2))
-        end
-        nzvals, sparse(rand(1:n, np2), rand(1:p, np2), nzvals, n, p)
-    end
-    x_dense  = convert(Matrix{elty}, x_sparse)
-    @testset "Test with no Infs and NaNs, vardim=$vardim, corrected=$corrected" for vardim in (1, 2),
-                                                                                 corrected in (true, false)
-        @test cov(x_sparse, dims=vardim, corrected=corrected) ≈
-              cov(x_dense , dims=vardim, corrected=corrected)
-    end
-
-    @testset "Test with $x11, vardim=$vardim, corrected=$corrected" for x11 in (NaN, Inf),
-                                                                     vardim in (1, 2),
-                                                                  corrected in (true, false)
-        x_sparse[1,1] = x11
-        x_dense[1 ,1] = x11
-
-        cov_sparse = cov(x_sparse, dims=vardim, corrected=corrected)
-        cov_dense  = cov(x_dense , dims=vardim, corrected=corrected)
-        @test cov_sparse[2:end, 2:end] ≈ cov_dense[2:end, 2:end]
-        @test isfinite.(cov_sparse) == isfinite.(cov_dense)
-        @test isfinite.(cov_sparse) == isfinite.(cov_dense)
-    end
-
-    @testset "Test with NaN and Inf, vardim=$vardim, corrected=$corrected" for vardim in (1, 2),
-                                                                            corrected in (true, false)
-        x_sparse[1,1] = Inf
-        x_dense[1 ,1] = Inf
-        x_sparse[2,1] = NaN
-        x_dense[2 ,1] = NaN
-
-        cov_sparse = cov(x_sparse, dims=vardim, corrected=corrected)
-        cov_dense  = cov(x_dense , dims=vardim, corrected=corrected)
-        @test cov_sparse[(1 + vardim):end, (1 + vardim):end] ≈
-              cov_dense[ (1 + vardim):end, (1 + vardim):end]
-        @test isfinite.(cov_sparse) == isfinite.(cov_dense)
-        @test isfinite.(cov_sparse) == isfinite.(cov_dense)
-    end
-end
-
 @testset "similar should not alias the input sparse array" begin
     a = sparse(rand(3,3) .+ 0.1)
     b = similar(a, Float32, Int32)
diff --git a/test/dimensionful.jl b/test/dimensionful.jl
index 21c9747806d54..deebb69c0643a 100644
--- a/test/dimensionful.jl
+++ b/test/dimensionful.jl
@@ -38,13 +38,13 @@ sylvester(a::Furlong,b::Furlong,c::Furlong) = -c / (a + b)
 for f in (:isfinite, :isnan, :isreal)
     @eval Base.$f(x::Furlong) = $f(x.val)
 end
-for f in (:real,:imag,:complex,:middle,:+,:-)
+for f in (:real,:imag,:complex,:+,:-)
     @eval Base.$f(x::Furlong{p}) where {p} = Furlong{p}($f(x.val))
 end
 
 import Base: +, -, ==, !=, <, <=, isless, isequal, *, /, //, div, rem, mod, ^,
-             middle, hypot
-for op in (:+, :-, :middle, :hypot)
+             hypot
+for op in (:+, :-, :hypot)
     @eval function $op(x::Furlong{p}, y::Furlong{p}) where {p}
         v = $op(x.val, y.val)
         Furlong{p}(v)
diff --git a/test/docs.jl b/test/docs.jl
index cf8bb7127f9e4..5ee2e00aacdfd 100644
--- a/test/docs.jl
+++ b/test/docs.jl
@@ -669,7 +669,6 @@ end
 @test (@repl :@r_str) !== nothing
 
 # Simple tests for apropos:
-@test occursin("cor", sprint(apropos, "pearson"))
 @test occursin("eachindex", sprint(apropos, r"ind(exes|ices)"))
 using Profile
 @test occursin("Profile.print", sprint(apropos, "print"))
diff --git a/test/offsetarray.jl b/test/offsetarray.jl
index 991f366b02c75..3d84e07ba26ae 100644
--- a/test/offsetarray.jl
+++ b/test/offsetarray.jl
@@ -370,13 +370,10 @@ I = findall(!iszero, z)
 @test findall(x->x>0, h) == [-1,1]
 @test findall(x->x<0, h) == [-2,0]
 @test findall(x->x==0, h) == [2]
-@test mean(A_3_3) == median(A_3_3) == 5
+@test mean(A_3_3) == 5
 @test mean(x->2x, A_3_3) == 10
-@test mean(A_3_3, dims=1) == median(A_3_3, dims=1) == OffsetArray([2 5 8], (0,A_3_3.offsets[2]))
-@test mean(A_3_3, dims=2) == median(A_3_3, dims=2) == OffsetArray(reshape([4,5,6],(3,1)), (A_3_3.offsets[1],0))
-@test var(A_3_3) == 7.5
-@test std(A_3_3, dims=1) == OffsetArray([1 1 1], (0,A_3_3.offsets[2]))
-@test std(A_3_3, dims=2) == OffsetArray(reshape([3,3,3], (3,1)), (A_3_3.offsets[1],0))
+@test mean(A_3_3, dims=1) == OffsetArray([2 5 8], (0,A_3_3.offsets[2]))
+@test mean(A_3_3, dims=2) == OffsetArray(reshape([4,5,6],(3,1)), (A_3_3.offsets[1],0))
 @test sum(OffsetArray(fill(1,3000), -1000)) == 3000
 
 @test vecnorm(v) ≈ vecnorm(parent(v))
diff --git a/test/ranges.jl b/test/ranges.jl
index c29f348ff1524..2414a01017e32 100644
--- a/test/ranges.jl
+++ b/test/ranges.jl
@@ -806,12 +806,10 @@ end
     @test length(map(identity, UInt64(1):UInt64(5))) == 5
     @test length(map(identity, UInt128(1):UInt128(5))) == 5
 end
-@testset "mean/median" begin
-    for f in (mean, median)
-        for n = 2:5
-            @test f(2:n) == f([2:n;])
-            @test f(2:0.1:n) ≈ f([2:0.1:n;])
-        end
+@testset "mean" begin
+    for n = 2:5
+        @test mean(2:n) == mean([2:n;])
+        @test mean(2:0.1:n) ≈ mean([2:0.1:n;])
     end
 end
 @testset "issue #8531" begin
diff --git a/test/reducedim.jl b/test/reducedim.jl
index 2d5e5ae4e5160..e2527e3df4fcc 100644
--- a/test/reducedim.jl
+++ b/test/reducedim.jl
@@ -127,7 +127,6 @@ end
     @test prod(A) === 1
     @test_throws ArgumentError minimum(A)
     @test_throws ArgumentError maximum(A)
-    @test var(A) === NaN
 
     @test isequal(sum(A, dims=1), zeros(Int, 1, 1))
     @test isequal(sum(A, dims=2), zeros(Int, 0, 1))
@@ -137,10 +136,6 @@ end
     @test isequal(prod(A, dims=2), fill(1, 0, 1))
     @test isequal(prod(A, dims=(1, 2)), fill(1, 1, 1))
     @test isequal(prod(A, dims=3), fill(1, 0, 1))
-    @test isequal(var(A, dims=1), fill(NaN, 1, 1))
-    @test isequal(var(A, dims=2), fill(NaN, 0, 1))
-    @test isequal(var(A, dims=(1, 2)), fill(NaN, 1, 1))
-    @test isequal(var(A, dims=3), fill(NaN, 0, 1))
 
     for f in (minimum, maximum)
         @test_throws ArgumentError f(A, dims=1)
@@ -320,7 +315,6 @@ end
 
 # issue #6672
 @test sum(Real[1 2 3; 4 5.3 7.1], dims=2) == reshape([6, 16.4], 2, 1)
-@test std(AbstractFloat[1,2,3], dims=1) == [1.0]
 @test sum(Any[1 2;3 4], dims=1) == [4 6]
 @test sum(Vector{Int}[[1,2],[4,3]], dims=1)[1] == [5,5]
 
diff --git a/test/statistics.jl b/test/statistics.jl
index 3d7f2c846c8f5..d871f753055b8 100644
--- a/test/statistics.jl
+++ b/test/statistics.jl
@@ -2,52 +2,6 @@
 
 using Test, Random, LinearAlgebra
 
-@testset "middle" begin
-    @test middle(3) === 3.0
-    @test middle(2, 3) === 2.5
-    let x = ((realmax(1.0)/4)*3)
-        @test middle(x, x) === x
-    end
-    @test middle(1:8) === 4.5
-    @test middle([1:8;]) === 4.5
-
-    # ensure type-correctness
-    for T in [Bool,Int8,Int16,Int32,Int64,Int128,UInt8,UInt16,UInt32,UInt64,UInt128,Float16,Float32,Float64]
-        @test middle(one(T)) === middle(one(T), one(T))
-    end
-end
-
-@testset "median" begin
-    @test median([1.]) === 1.
-    @test median([1.,3]) === 2.
-    @test median([1.,3,2]) === 2.
-
-    @test median([1,3,2]) === 2.0
-    @test median([1,3,2,4]) === 2.5
-
-    @test median([0.0,Inf]) == Inf
-    @test median([0.0,-Inf]) == -Inf
-    @test median([0.,Inf,-Inf]) == 0.0
-    @test median([1.,-1.,Inf,-Inf]) == 0.0
-    @test isnan(median([-Inf,Inf]))
-
-    X = [2 3 1 -1; 7 4 5 -4]
-    @test all(median(X, dims=2) .== [1.5, 4.5])
-    @test all(median(X, dims=1) .== [4.5 3.5 3.0 -2.5])
-    @test X == [2 3 1 -1; 7 4 5 -4] # issue #17153
-
-    @test_throws ArgumentError median([])
-    @test isnan(median([NaN]))
-    @test isnan(median([0.0,NaN]))
-    @test isnan(median([NaN,0.0]))
-    @test isequal(median([NaN 0.0; 1.2 4.5], dims=2), reshape([NaN; 2.85], 2, 1))
-
-    @test median!([1 2 3 4]) == 2.5
-    @test median!([1 2; 3 4]) == 2.5
-
-    @test invoke(median, Tuple{AbstractVector}, 1:10) == median(1:10) == 5.5
-end
-
 @testset "mean" begin
     @test_throws ArgumentError mean(())
     @test mean((1,2,3)) === 2.
@@ -80,363 +34,13 @@ end
     end
 end
 
-@testset "var & std" begin
-    # edge case: empty vector
-    # iterable; this has to throw for type stability
-    @test_throws ArgumentError var(())
-    @test_throws ArgumentError var((); corrected=false)
-    @test_throws ArgumentError var((); mean=2)
-    @test_throws ArgumentError var((); mean=2, corrected=false)
-    # reduction
-    @test isnan(var(Int[]))
-    @test isnan(var(Int[]; corrected=false))
-    @test isnan(var(Int[]; mean=2))
-    @test isnan(var(Int[]; mean=2, corrected=false))
-    # reduction across dimensions
-    @test isequal(var(Int[], dims=1), [NaN])
-    @test isequal(var(Int[], dims=1; corrected=false), [NaN])
-    @test isequal(var(Int[], dims=1; mean=[2]), [NaN])
-    @test isequal(var(Int[], dims=1; mean=[2], corrected=false), [NaN])
-
-    # edge case: one-element vector
-    # iterable
-    @test isnan(@inferred(var((1,))))
-    @test var((1,); corrected=false) === 0.0
-    @test var((1,); mean=2) === Inf
-    @test var((1,); mean=2, corrected=false) === 1.0
-    # reduction
-    @test isnan(@inferred(var([1])))
-    @test var([1]; corrected=false) === 0.0
-    @test var([1]; mean=2) === Inf
-    @test var([1]; mean=2, corrected=false) === 1.0
-    # reduction across dimensions
-    @test isequal(@inferred(var([1], dims=1)), [NaN])
-    @test var([1], dims=1; corrected=false) ≈ [0.0]
-    @test var([1], dims=1; mean=[2]) ≈ [Inf]
-    @test var([1], dims=1; mean=[2], corrected=false) ≈ [1.0]
-
-    @test var(1:8) == 6.
-    @test varm(1:8,1) == varm(Vector(1:8),1)
-    @test isnan(varm(1:1,1))
-    @test isnan(var(1:1))
-    @test isnan(var(1:-1))
-
-    @test @inferred(var(1.0:8.0)) == 6.
-    @test varm(1.0:8.0,1.0) == varm(Vector(1.0:8.0),1)
-    @test isnan(varm(1.0:1.0,1.0))
-    @test isnan(var(1.0:1.0))
-    @test isnan(var(1.0:-1.0))
-
-    @test @inferred(var(1.0f0:8.0f0)) === 6.f0
-    @test varm(1.0f0:8.0f0,1.0f0) == varm(Vector(1.0f0:8.0f0),1)
-    @test isnan(varm(1.0f0:1.0f0,1.0f0))
-    @test isnan(var(1.0f0:1.0f0))
-    @test isnan(var(1.0f0:-1.0f0))
-
-    @test varm([1,2,3], 2) ≈ 1.
-    @test var([1,2,3]) ≈ 1.
-    @test var([1,2,3]; corrected=false) ≈ 2.0/3
-    @test var([1,2,3]; mean=0) ≈ 7.
-    @test var([1,2,3]; mean=0, corrected=false) ≈ 14.0/3
-
-    @test varm((1,2,3), 2) ≈ 1.
-    @test var((1,2,3)) ≈ 1.
-    @test var((1,2,3); corrected=false) ≈ 2.0/3
-    @test var((1,2,3); mean=0) ≈ 7.
-    @test var((1,2,3); mean=0, corrected=false) ≈ 14.0/3
-    @test_throws ArgumentError var((1,2,3); mean=())
-
-    @test var([1 2 3 4 5; 6 7 8 9 10], dims=2) ≈ [2.5 2.5]'
-    @test var([1 2 3 4 5; 6 7 8 9 10], dims=2; corrected=false) ≈ [2.0 2.0]'
-
-    @test stdm([1,2,3], 2) ≈ 1.
-    @test std([1,2,3]) ≈ 1.
-    @test std([1,2,3]; corrected=false) ≈ sqrt(2.0/3)
-    @test std([1,2,3]; mean=0) ≈ sqrt(7.0)
-    @test std([1,2,3]; mean=0, corrected=false) ≈ sqrt(14.0/3)
-
-    @test stdm([1.0,2,3], 2) ≈ 1.
-    @test std([1.0,2,3]) ≈ 1.
-    @test std([1.0,2,3]; corrected=false) ≈ sqrt(2.0/3)
-    @test std([1.0,2,3]; mean=0) ≈ sqrt(7.0)
-    @test std([1.0,2,3]; mean=0, corrected=false) ≈ sqrt(14.0/3)
-
-    @test std([1.0,2,3]; dims=1)[] ≈ 1.
-    @test std([1.0,2,3]; dims=1, corrected=false)[] ≈ sqrt(2.0/3)
-    @test std([1.0,2,3]; dims=1, mean=[0])[] ≈ sqrt(7.0)
-    @test std([1.0,2,3]; dims=1, mean=[0], corrected=false)[] ≈ sqrt(14.0/3)
-
-    @test stdm((1,2,3), 2) ≈ 1.
-    @test std((1,2,3)) ≈ 1.
-    @test std((1,2,3); corrected=false) ≈ sqrt(2.0/3)
-    @test std((1,2,3); mean=0) ≈ sqrt(7.0)
-    @test std((1,2,3); mean=0, corrected=false) ≈ sqrt(14.0/3)
-
-    @test std([1 2 3 4 5; 6 7 8 9 10], dims=2) ≈ sqrt.([2.5 2.5]')
-    @test std([1 2 3 4 5; 6 7 8 9 10], dims=2; corrected=false) ≈ sqrt.([2.0 2.0]')
-
-    let A = ComplexF64[exp(i*im) for i in 1:10^4]
-        @test varm(A, 0.) ≈ sum(map(abs2, A)) / (length(A) - 1)
-        @test varm(A, mean(A)) ≈ var(A)
-    end
-
-    @test var([1//1, 2//1]) isa Rational{Int}
-    @test var([1//1, 2//1], dims=1) isa Vector{Rational{Int}}
-
-    @test std([1//1, 2//1]) isa Float64
-    @test std([1//1, 2//1], dims=1) isa Vector{Float64}
-end
-
-function safe_cov(x, y, zm::Bool, cr::Bool)
-    n = length(x)
-    if !zm
-        x = x .- mean(x)
-        y = y .- mean(y)
-    end
-    dot(vec(x), vec(y)) / (n - Int(cr))
-end
-X = [1.0  5.0;
-     2.0  4.0;
-     3.0  6.0;
-     4.0  2.0;
-     5.0  1.0]
-Y = [6.0  2.0;
-     1.0  7.0;
-     5.0  8.0;
-     3.0  4.0;
-     2.0  3.0]
-
-@testset "covariance" begin
-    for vd in [1, 2], zm in [true, false], cr in [true, false]
-        # println("vd = $vd: zm = $zm, cr = $cr")
-        if vd == 1
-            k = size(X, 2)
-            Cxx = zeros(k, k)
-            Cxy = zeros(k, k)
-            for i = 1:k, j = 1:k
-                Cxx[i,j] = safe_cov(X[:,i], X[:,j], zm, cr)
-                Cxy[i,j] = safe_cov(X[:,i], Y[:,j], zm, cr)
-            end
-            x1 = vec(X[:,1])
-            y1 = vec(Y[:,1])
-        else
-            k = size(X, 1)
-            Cxx = zeros(k, k)
-            Cxy = zeros(k, k)
-            for i = 1:k, j = 1:k
-                Cxx[i,j] = safe_cov(X[i,:], X[j,:], zm, cr)
-                Cxy[i,j] = safe_cov(X[i,:], Y[j,:], zm, cr)
-            end
-            x1 = vec(X[1,:])
-            y1 = vec(Y[1,:])
-        end
-
-        c = zm ? Base.covm(x1, 0, corrected=cr) :
-                 cov(x1, corrected=cr)
-        @test isa(c, Float64)
-        @test c ≈ Cxx[1,1]
-        @inferred cov(x1, corrected=cr)
-
-        @test cov(X) == Base.covm(X, mean(X, dims=1))
-        C = zm ? Base.covm(X, 0, vd, corrected=cr) :
-                 cov(X, dims=vd, corrected=cr)
-        @test size(C) == (k, k)
-        @test C ≈ Cxx
-        @inferred cov(X, dims=vd, corrected=cr)
-
-        @test cov(x1, y1) == Base.covm(x1, mean(x1), y1, mean(y1))
-        c = zm ? Base.covm(x1, 0, y1, 0, corrected=cr) :
-                 cov(x1, y1, corrected=cr)
-        @test isa(c, Float64)
-        @test c ≈ Cxy[1,1]
-        @inferred cov(x1, y1, corrected=cr)
-
-        if vd == 1
-            @test cov(x1, Y) == Base.covm(x1, mean(x1), Y, mean(Y, dims=1))
-        end
-        C = zm ? Base.covm(x1, 0, Y, 0, vd, corrected=cr) :
-                 cov(x1, Y, dims=vd, corrected=cr)
-        @test size(C) == (1, k)
-        @test vec(C) ≈ Cxy[1,:]
-        @inferred cov(x1, Y, dims=vd, corrected=cr)
-
-        if vd == 1
-            @test cov(X, y1) == Base.covm(X, mean(X, dims=1), y1, mean(y1))
-        end
-        C = zm ? Base.covm(X, 0, y1, 0, vd, corrected=cr) :
-                 cov(X, y1, dims=vd, corrected=cr)
-        @test size(C) == (k, 1)
-        @test vec(C) ≈ Cxy[:,1]
-        @inferred cov(X, y1, dims=vd, corrected=cr)
-
-        @test cov(X, Y) == Base.covm(X, mean(X, dims=1), Y, mean(Y, dims=1))
-        C = zm ? Base.covm(X, 0, Y, 0, vd, corrected=cr) :
-                 cov(X, Y, dims=vd, corrected=cr)
-        @test size(C) == (k, k)
-        @test C ≈ Cxy
-        @inferred cov(X, Y, dims=vd, corrected=cr)
-    end
-end
-
-function safe_cor(x, y, zm::Bool)
-    if !zm
-        x = x .- mean(x)
-        y = y .- mean(y)
-    end
-    x = vec(x)
-    y = vec(y)
-    dot(x, y) / (sqrt(dot(x, x)) * sqrt(dot(y, y)))
-end
-@testset "correlation" begin
-    for vd in [1, 2], zm in [true, false]
-        # println("vd = $vd: zm = $zm")
-        if vd == 1
-            k = size(X, 2)
-            Cxx = zeros(k, k)
-            Cxy = zeros(k, k)
-            for i = 1:k, j = 1:k
-                Cxx[i,j] = safe_cor(X[:,i], X[:,j], zm)
-                Cxy[i,j] = safe_cor(X[:,i], Y[:,j], zm)
-            end
-            x1 = vec(X[:,1])
-            y1 = vec(Y[:,1])
-        else
-            k = size(X, 1)
-            Cxx = zeros(k, k)
-            Cxy = zeros(k, k)
-            for i = 1:k, j = 1:k
-                Cxx[i,j] = safe_cor(X[i,:], X[j,:], zm)
-                Cxy[i,j] = safe_cor(X[i,:], Y[j,:], zm)
-            end
-            x1 = vec(X[1,:])
-            y1 = vec(Y[1,:])
-        end
-
-        c = zm ? Base.corm(x1, 0) : cor(x1)
-        @test isa(c, Float64)
-        @test c ≈ Cxx[1,1]
-        @inferred cor(x1)
-
-        @test cor(X) == Base.corm(X, mean(X, dims=1))
-        C = zm ? Base.corm(X, 0, vd) : cor(X, dims=vd)
-        @test size(C) == (k, k)
-        @test C ≈ Cxx
-        @inferred cor(X, dims=vd)
-
-        @test cor(x1, y1) == Base.corm(x1, mean(x1), y1, mean(y1))
-        c = zm ? Base.corm(x1, 0, y1, 0) : cor(x1, y1)
-        @test isa(c, Float64)
-        @test c ≈ Cxy[1,1]
-        @inferred cor(x1, y1)
-
-        if vd == 1
-            @test cor(x1, Y) == Base.corm(x1, mean(x1), Y, mean(Y, dims=1))
-        end
-        C = zm ? Base.corm(x1, 0, Y, 0, vd) : cor(x1, Y, dims=vd)
-        @test size(C) == (1, k)
-        @test vec(C) ≈ Cxy[1,:]
-        @inferred cor(x1, Y, dims=vd)
-
-        if vd == 1
-            @test cor(X, y1) == Base.corm(X, mean(X, dims=1), y1, mean(y1))
-        end
-        C = zm ? Base.corm(X, 0, y1, 0, vd) : cor(X, y1, dims=vd)
-        @test size(C) == (k, 1)
-        @test vec(C) ≈ Cxy[:,1]
-        @inferred cor(X, y1, dims=vd)
-
-        @test cor(X, Y) == Base.corm(X, mean(X, dims=1), Y, mean(Y, dims=1))
-        C = zm ? Base.corm(X, 0, Y, 0, vd) : cor(X, Y, dims=vd)
-        @test size(C) == (k, k)
-        @test C ≈ Cxy
-        @inferred cor(X, Y, dims=vd)
-    end
-
-    @test cor(repeat(1:17, 1, 17))[2] <= 1.0
-    @test cor(1:17, 1:17) <= 1.0
-    @test cor(1:17, 18:34) <= 1.0
-    let tmp = range(1, stop=85, length=100)
-        tmp2 = Vector(tmp)
-        @test cor(tmp, tmp) <= 1.0
-        @test cor(tmp, tmp2) <= 1.0
-    end
-end
-
-@testset "quantile" begin
-    @test quantile([1,2,3,4],0.5) == 2.5
-    @test quantile([1,2,3,4],[0.5]) == [2.5]
-    @test quantile([1., 3],[.25,.5,.75])[2] == median([1., 3])
-    @test quantile(100.0:-1.0:0.0, 0.0:0.1:1.0) == 0.0:10.0:100.0
-    @test quantile(0.0:100.0, 0.0:0.1:1.0, sorted=true) == 0.0:10.0:100.0
-    @test quantile(100f0:-1f0:0.0, 0.0:0.1:1.0) == 0f0:10f0:100f0
-    @test quantile([Inf,Inf],0.5) == Inf
-    @test quantile([-Inf,1],0.5) == -Inf
-    @test quantile([0,1],1e-18) == 1e-18
-    @test quantile([1, 2, 3, 4],[]) == []
-    @test quantile([1, 2, 3, 4], (0.5,)) == (2.5,)
-    @test quantile([4, 9, 1, 5, 7, 8, 2, 3, 5, 17, 11], (0.1, 0.2, 0.4, 0.9)) == (2.0, 3.0, 5.0, 11.0)
-    @test quantile([1, 2, 3, 4], ()) == ()
-end
-
-# StatsBase issue 164
-let y = [0.40003674665581906, 0.4085630862624367, 0.41662034698690303, 0.41662034698690303, 0.42189053966652057, 0.42189053966652057, 0.42553514344518345, 0.43985732442991354]
-    @test issorted(quantile(y, range(0.01, stop=0.99, length=17)))
-end
-
-@testset "variance of complex arrays (#13309)" begin
-    z = rand(ComplexF64, 10)
-    @test var(z) ≈ invoke(var, Tuple{Any}, z) ≈ cov(z) ≈ var(z,dims=1)[1] ≈ sum(abs2, z .- mean(z))/9
-    @test isa(var(z), Float64)
-    @test isa(invoke(var, Tuple{Any}, z), Float64)
-    @test isa(cov(z), Float64)
-    @test isa(var(z,dims=1), Vector{Float64})
-    @test varm(z, 0.0) ≈ invoke(varm, Tuple{Any,Float64}, z, 0.0) ≈ sum(abs2, z)/9
-    @test isa(varm(z, 0.0), Float64)
-    @test isa(invoke(varm, Tuple{Any,Float64}, z, 0.0), Float64)
-    @test cor(z) === 1.0
-    v = varm([1.0+2.0im], 0; corrected = false)
-    @test v ≈ 5
-    @test isa(v, Float64)
-end
-
-@testset "cov and cor of complex arrays (issue #21093)" begin
-    x = [2.7 - 3.3im, 0.9 + 5.4im, 0.1 + 0.2im, -1.7 - 5.8im, 1.1 + 1.9im]
-    y = [-1.7 - 1.6im, -0.2 + 6.5im, 0.8 - 10.0im, 9.1 - 3.4im, 2.7 - 5.5im]
-    @test cov(x, y) ≈ 4.8365 - 12.119im
-    @test cov(y, x) ≈ 4.8365 + 12.119im
-    @test cov(x, reshape(y, :, 1)) ≈ reshape([4.8365 - 12.119im], 1, 1)
-    @test cov(reshape(x, :, 1), y) ≈ reshape([4.8365 - 12.119im], 1, 1)
-    @test cov(reshape(x, :, 1), reshape(y, :, 1)) ≈ reshape([4.8365 - 12.119im], 1, 1)
-    @test cov([x y]) ≈ [21.779 4.8365-12.119im;
-                        4.8365+12.119im 54.548]
-    @test cor(x, y) ≈ 0.14032104449218274 - 0.35160772008699703im
-    @test cor(y, x) ≈ 0.14032104449218274 + 0.35160772008699703im
-    @test cor(x, reshape(y, :, 1)) ≈ reshape([0.14032104449218274 - 0.35160772008699703im], 1, 1)
-    @test cor(reshape(x, :, 1), y) ≈ reshape([0.14032104449218274 - 0.35160772008699703im], 1, 1)
-    @test cor(reshape(x, :, 1), reshape(y, :, 1)) ≈ reshape([0.14032104449218274 - 0.35160772008699703im], 1, 1)
-    @test cor([x y]) ≈ [1.0                                          0.14032104449218274-0.35160772008699703im
-                        0.14032104449218274+0.35160772008699703im  1.0]
-end
-
 @testset "Issue #17153 and PR #17154" begin
     a = rand(10,10)
     b = copy(a)
-    x = median(a, dims=1)
-    @test b == a
-    x = median(a, dims=2)
-    @test b == a
     x = mean(a, dims=1)
     @test b == a
     x = mean(a, dims=2)
     @test b == a
-    x = var(a, dims=1)
-    @test b == a
-    x = var(a, dims=2)
-    @test b == a
-    x = std(a, dims=1)
-    @test b == a
-    x = std(a, dims=2)
-    @test b == a
 end
 
 # dimensional correctness
@@ -447,33 +51,11 @@ using .Main.TestHelpers: Furlong
     a = Vector(r)
     @test sum(r) == sum(a) == Furlong(3)
     @test cumsum(r) == Furlong.([1,3])
-    @test mean(r) == mean(a) == median(a) == median(r) == Furlong(1.5)
-    @test var(r) == var(a) == Furlong{2}(0.5)
-    @test std(r) == std(a) == Furlong{1}(sqrt(0.5))
+    @test mean(r) == mean(a) == Furlong(1.5)
 
     # Issue #21786
     A = [Furlong{1}(rand(-5:5)) for i in 1:2, j in 1:2]
     @test mean(mean(A, dims=1), dims=2)[1] === mean(A)
-    @test var(A, dims=1)[1] === var(A[:, 1])
-    @test std(A, dims=1)[1] === std(A[:, 1])
-end
-
-# Issue #22901
-@testset "var and quantile of Any arrays" begin
-    x = Any[1, 2, 4, 10]
-    y = Any[1, 2, 4, 10//1]
-    @test var(x) === 16.25
-    @test var(y) === 65//4
-    @test std(x) === sqrt(16.25)
-    @test quantile(x, 0.5)  === 3.0
-    @test quantile(x, 1//2) === 3//1
-end
-
-@testset "Promotion in covzm. Issue #8080" begin
-    A = [1 -1 -1; -1 1 1; -1 1 -1; 1 -1 -1; 1 -1 1]
-    @test Base.covzm(A) - mean(A, dims=1)'*mean(A, dims=1)*size(A, 1)/(size(A, 1) - 1) ≈ cov(A)
-    A = [1//1 -1 -1; -1 1 1; -1 1 -1; 1 -1 -1; 1 -1 1]
-    @test (A'A - size(A, 1)*Base.mean(A, dims=1)'*Base.mean(A, dims=1))/4 == cov(A)
 end
 
 @testset "Mean along dimension of empty array" begin
@@ -486,10 +68,3 @@ end
     @test isequal(mean(a01, dims=1)     , fill(NaN, 1, 1))
     @test isequal(mean(a10, dims=2)     , fill(NaN, 1, 1))
 end
-
-@testset "cov/var/std of Vector{Vector}" begin
-    x = [[2,4,6],[4,6,8]]
-    @test var(x) ≈ vec(var([x[1] x[2]], dims=2))
-    @test std(x) ≈ vec(std([x[1] x[2]], dims=2))
-    @test cov(x) ≈ cov([x[1] x[2]], dims=2)
-end