diff --git a/MLPP/LinAlg/LinAlg.cpp b/MLPP/LinAlg/LinAlg.cpp
index 9235aef..1b1fa1e 100644
--- a/MLPP/LinAlg/LinAlg.cpp
+++ b/MLPP/LinAlg/LinAlg.cpp
@@ -391,19 +391,11 @@ namespace MLPP{
     }
 
     double LinAlg::max(std::vector<std::vector<double>> A){
-        std::vector<double> max_elements;
-        for(int i = 0; i < A.size(); i++){
-            max_elements.push_back(max(A[i]));
-        }
-        return max(max_elements);
+        return max(flatten(A));
     }
 
     double LinAlg::min(std::vector<std::vector<double>> A){
-        std::vector<double> max_elements;
-        for(int i = 0; i < A.size(); i++){
-            max_elements.push_back(min(A[i]));
-        }
-        return min(max_elements);
+        return min(flatten(A));
     }
 
     std::vector<std::vector<double>> LinAlg::round(std::vector<std::vector<double>> A){
diff --git a/MLPP/Stat/Stat.cpp b/MLPP/Stat/Stat.cpp
index 211e2b1..82d92c7 100644
--- a/MLPP/Stat/Stat.cpp
+++ b/MLPP/Stat/Stat.cpp
@@ -6,6 +6,7 @@
 
 #include "Stat.hpp"
 #include "Activation/Activation.hpp"
+#include "LinAlg/LinAlg.hpp"
 #include "Data/Data.hpp"
 #include <algorithm>
 #include <map>
@@ -66,6 +67,27 @@ namespace MLPP{
         return modes;
     }
 
+    double Stat::range(std::vector<double> x){
+        LinAlg alg;
+        return alg.max(x) - alg.min(x);
+    }
+
+    double Stat::midrange(std::vector<double> x){
+        return range(x)/2;
+    }
+
+    double Stat::absAvgDeviation(std::vector<double> x){
+        double sum = 0;
+        for(int i = 0; i < x.size(); i++){
+            sum += std::abs(x[i] - mean(x));
+        }
+        return sum / x.size();
+    }
+
+    double Stat::standardDeviation(std::vector<double> x){
+        return std::sqrt(variance(x));
+    }
+
     double Stat::variance(std::vector<double> x){
         double sum = 0;
         for(int i = 0; i < x.size(); i++){
@@ -90,6 +112,11 @@ namespace MLPP{
         return correlation(x, y) * correlation(x, y);
     }
 
+    double Stat::chebyshevIneq(double k){
+        // X may or may not belong to a Gaussian Distribution
+        return 1 - 1 / (k * k);
+    }
+
     double Stat::weightedMean(std::vector<double> x, std::vector<double> weights){
         double sum = 0;
         double weights_sum = 0; 
@@ -189,21 +216,4 @@ namespace MLPP{
         }
         return (y - x) / (log(y) - log(x)); 
     }
-
-    double Stat::standardDeviation(std::vector<double> x){
-        return std::sqrt(variance(x));
-    }
-
-    double Stat::absAvgDeviation(std::vector<double> x){
-        double sum = 0;
-        for(int i = 0; i < x.size(); i++){
-            sum += std::abs(x[i] - mean(x));
-        }
-        return sum / x.size();
-    }
-
-    double Stat::chebyshevIneq(double k){
-        //Pr(|X - mu| >= k * sigma) <= 1/k^2, X may or may not belong to a Gaussian Distribution
-        return 1 - 1 / (k * k);
-    }
 }
\ No newline at end of file
diff --git a/MLPP/Stat/Stat.hpp b/MLPP/Stat/Stat.hpp
index b1c01f8..58e5fc4 100644
--- a/MLPP/Stat/Stat.hpp
+++ b/MLPP/Stat/Stat.hpp
@@ -20,10 +20,16 @@ namespace MLPP{
             double mean(std::vector <double> x);
             double median(std::vector<double> x);
             std::vector<double> mode(std::vector<double> x);
+            double range(std::vector<double> x);
+            double midrange(std::vector<double> x);
+            double absAvgDeviation(std::vector <double> x);
+            double standardDeviation(std::vector <double> x);
             double variance(std::vector <double> x);
             double covariance(std::vector <double> x, std::vector <double> y);
             double correlation(std::vector <double> x, std::vector<double> y);
             double R2(std::vector <double> x, std::vector<double> y);
+            double chebyshevIneq(double k);
+        
 
             // Extras
             double weightedMean(std::vector<double> x, std::vector<double> weights);
@@ -40,9 +46,6 @@ namespace MLPP{
             double stolarskyMean(double x, double y, double p);
             double identricMean(double x, double y);
             double logMean(double x, double y);
-            double standardDeviation(std::vector <double> x);
-            double absAvgDeviation(std::vector <double> x);
-            double chebyshevIneq(double k);
 
         private:
 
diff --git a/a.out b/a.out
new file mode 100755
index 0000000..36884c3
Binary files /dev/null and b/a.out differ
diff --git a/main.cpp b/main.cpp
index 6f73cf6..6ee9112 100644
--- a/main.cpp
+++ b/main.cpp
@@ -78,17 +78,23 @@ int main() {
     // std::vector<double> outputSet = {0,1,1,0};
 
     // // STATISTICS
-    // std::vector<double> x = {1,2,3,4,5,6,7,8,9,1};
+    // std::vector<double> x = {1,2,3,4,5,6,7,8,9,10};
     // std::vector<double> y = {10,9,8,7,6,5,4,3,2,1};
     // std::vector<double> w = {0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1};
 
     // std::cout << "Arithmetic Mean: " << stat.mean(x) << std::endl;
     // std::cout << "Median: " << stat.median(x) << std::endl;
     // alg.printVector(stat.mode(x));
+    // std::cout << "Range: " << stat.range(x) << std::endl;
+    // std::cout << "Midrange: " << stat.midrange(x) << std::endl;
+    // std::cout << "Absolute Average Deviation: " << stat.absAvgDeviation(x) << std::endl;
+    // std::cout << "Standard Deviation: " << stat.standardDeviation(x) << std::endl;
     // std::cout << "Variance: " << stat.variance(x) << std::endl;
     // std::cout << "Covariance: " << stat.covariance(x, y) << std::endl;
     // std::cout << "Correlation: " << stat.correlation(x, y) << std::endl;
     // std::cout << "R^2: " << stat.R2(x, y) << std::endl;
+    // // Returns 1 - (1/k^2)
+    // std::cout << "Chebyshev Inequality: " << stat.chebyshevIneq(2) << std::endl;
     // std::cout << "Weighted Mean: " << stat.weightedMean(x, w) << std::endl;
     // std::cout << "Geometric Mean: " << stat.geometricMean(x) << std::endl;
     // std::cout << "Harmonic Mean: " << stat.harmonicMean(x) << std::endl;
@@ -103,10 +109,7 @@ int main() {
     // std::cout << "Stolarsky Mean (p = 5): " << stat.stolarskyMean(1, 10, 5) << std::endl;
     // std::cout << "Identric Mean: " << stat.identricMean(1, 10) << std::endl;
     // std::cout << "Logarithmic Mean: " << stat.logMean(1, 10) << std::endl;
-    // std::cout << "Standard Deviation: " << stat.standardDeviation(x) << std::endl;
     // std::cout << "Absolute Average Deviation: " << stat.absAvgDeviation(x) << std::endl;
-    // // Returns 1 - (1/k^2)
-    // std::cout << "Chebyshev Inequality: " << stat.chebyshevIneq(2) << std::endl;
 
     // // LINEAR ALGEBRA
     // std::vector<std::vector<double>> A = {
@@ -144,12 +147,12 @@ int main() {
     // alg.printVector(model.modelSetTest((alg.transpose(inputSet))));
     // std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
 
-    // LOGISTIC REGRESSION
+    // // LOGISTIC REGRESSION
     // std::vector<std::vector<double>> inputSet; 
     // std::vector<double> outputSet; 
     // data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet);
     // LogReg model(inputSet, outputSet); 
-    // model.gradientDescent(0.0001, 100000, 0);
+    // model.SGD(0.001, 100000, 0);
     // model.MLE(0.1, 10000, 0);
     // alg.printVector(model.modelSetTest(inputSet));
     // std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
@@ -188,7 +191,6 @@ int main() {
     // std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
 
     // // SOFTMAX REGRESSION
-    // time_t start = time(0);
     // std::vector<std::vector<double>> inputSet; 
     // std::vector<double> tempOutputSet; 
     // data.setData(4, "/Users/marcmelikyan/Desktop/Data/Iris.csv", inputSet, tempOutputSet);
@@ -374,6 +376,8 @@ int main() {
     // std::vector<double> vectorOfCubes = {1,2,64,27};
     // alg.printMatrix(alg.cbrt(matrixOfCubes));
     // alg.printVector(alg.cbrt(vectorOfCubes));
+    // std::cout << alg.max({{1,2,3,4,5}, {6,5,3,4,1}, {9,9,9,9,9}}) << std::endl;
+    // std::cout << alg.min({{1,2,3,4,5}, {6,5,3,4,1}, {9,9,9,9,9}}) << std::endl;
 
     return 0;
 }
\ No newline at end of file