diff --git a/mlpp/stat/stat_old.cpp b/mlpp/stat/stat_old.cpp
index a493819..3450db6 100644
--- a/mlpp/stat/stat_old.cpp
+++ b/mlpp/stat/stat_old.cpp
@@ -22,13 +22,6 @@ real_t MLPPStatOld::b1Estimation(const std::vector<real_t> &x, const std::vector
 	return covariance(x, y) / variance(x);
 }
 
-real_t MLPPStatOld::b0_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y) {
-	return meanv(y) - b1_estimation(x, y) * meanv(x);
-}
-real_t MLPPStatOld::b1_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y) {
-	return covariancev(x, y) / variancev(x);
-}
-
 real_t MLPPStatOld::mean(const std::vector<real_t> &x) {
 	real_t sum = 0;
 	for (uint32_t i = 0; i < x.size(); i++) {
@@ -121,56 +114,6 @@ real_t MLPPStatOld::chebyshevIneq(const real_t k) {
 	return 1 - 1 / (k * k);
 }
 
-real_t MLPPStatOld::meanv(const Ref<MLPPVector> &x) {
-	int x_size = x->size();
-	const real_t *x_ptr = x->ptr();
-
-	real_t sum = 0;
-	for (int i = 0; i < x_size; ++i) {
-		sum += x_ptr[i];
-	}
-
-	return sum / x_size;
-}
-
-real_t MLPPStatOld::standard_deviationv(const Ref<MLPPVector> &x) {
-	return Math::sqrt(variancev(x));
-}
-
-real_t MLPPStatOld::variancev(const Ref<MLPPVector> &x) {
-	real_t x_mean = meanv(x);
-
-	int x_size = x->size();
-	const real_t *x_ptr = x->ptr();
-
-	real_t sum = 0;
-	for (int i = 0; i < x_size; ++i) {
-		real_t xi = x_ptr[i];
-
-		sum += (xi - x_mean) * (xi - x_mean);
-	}
-	return sum / (x_size - 1);
-}
-
-real_t MLPPStatOld::covariancev(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y) {
-	ERR_FAIL_COND_V(x->size() != y->size(), 0);
-
-	real_t x_mean = meanv(x);
-	real_t y_mean = meanv(y);
-
-	int x_size = x->size();
-	const real_t *x_ptr = x->ptr();
-	const real_t *y_ptr = y->ptr();
-
-	real_t sum = 0;
-
-	for (int i = 0; i < x_size; ++i) {
-		sum += (x_ptr[i] - x_mean) * (y_ptr[i] - y_mean);
-	}
-
-	return sum / (x_size - 1);
-}
-
 real_t MLPPStatOld::weightedMean(const std::vector<real_t> &x, const std::vector<real_t> &weights) {
 	real_t sum = 0;
 	real_t weights_sum = 0;
@@ -270,6 +213,3 @@ real_t MLPPStatOld::logMean(const real_t x, const real_t y) {
 	}
 	return (y - x) / (log(y) - std::log(x));
 }
-
-void MLPPStatOld::_bind_methods() {
-}
diff --git a/mlpp/stat/stat_old.h b/mlpp/stat/stat_old.h
index 14a795a..d477736 100644
--- a/mlpp/stat/stat_old.h
+++ b/mlpp/stat/stat_old.h
@@ -10,24 +10,14 @@
 
 #include "core/math/math_defs.h"
 
-#include "core/object/reference.h"
-
-#include "../lin_alg/mlpp_matrix.h"
-#include "../lin_alg/mlpp_vector.h"
-
 #include <vector>
 
-class MLPPStatOld : public Reference {
-	GDCLASS(MLPPStatOld, Reference);
-
+class MLPPStatOld {
 public:
 	// These functions are for univariate lin reg module- not for users.
 	real_t b0Estimation(const std::vector<real_t> &x, const std::vector<real_t> &y);
 	real_t b1Estimation(const std::vector<real_t> &x, const std::vector<real_t> &y);
 
-	real_t b0_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
-	real_t b1_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
-
 	// Statistical Functions
 	real_t mean(const std::vector<real_t> &x);
 	real_t median(std::vector<real_t> x);
@@ -42,11 +32,6 @@ public:
 	real_t R2(const std::vector<real_t> &x, const std::vector<real_t> &y);
 	real_t chebyshevIneq(const real_t k);
 
-	real_t meanv(const Ref<MLPPVector> &x);
-	real_t standard_deviationv(const Ref<MLPPVector> &x);
-	real_t variancev(const Ref<MLPPVector> &x);
-	real_t covariancev(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
-
 	// Extras
 	real_t weightedMean(const std::vector<real_t> &x, const std::vector<real_t> &weights);
 	real_t geometricMean(const std::vector<real_t> &x);
@@ -62,9 +47,6 @@ public:
 	real_t stolarskyMean(const real_t x, const real_t y, const real_t p);
 	real_t identricMean(const real_t x, const real_t y);
 	real_t logMean(const real_t x, const real_t y);
-
-protected:
-	static void _bind_methods();
 };
 
 #endif /* Stat_hpp */