diff --git a/findings/data_plot.py b/findings/data_plot.py
new file mode 100644
index 0000000..af9fb25
--- /dev/null
+++ b/findings/data_plot.py
@@ -0,0 +1,13 @@
+from matplotlib import pyplot as plt
+import pandas as pd
+
+
+def plot_xy(x, y):
+    plt.scatter(x, y)
+    plt.xlabel(x.name)
+    plt.ylabel(y.name)
+    plt.show()
+
+
+wellness = pd.read_csv('../data_preparation/data/wellness.csv')
+plot_xy(wellness['Fatigue'], wellness['SleepHours'])
diff --git a/hypotheses_modeling/hypotheses.txt b/hypotheses_modeling/hypotheses.txt
new file mode 100644
index 0000000..ed54461
--- /dev/null
+++ b/hypotheses_modeling/hypotheses.txt
@@ -0,0 +1,22 @@
+Team:
+    1.
+    x - normFatigue, TimeSince
+    y - normSoreness
+    r2 - 0.16830803694995466
+
+    2.
+    x - normFatigue, TimeSince
+    y - normDesire
+    r2 - 0.1722466278587138
+
+    3.
+    x - normFatigue, TimeSince
+    y - normIrritability
+    r2 - 0.09563459728506452
+
+    4.
+    x - normSleepHours, TimeSince
+    y - normSleepQualiyt
+    r2 - 0.09717095676693188
+
+    5.
\ No newline at end of file
diff --git a/hypotheses_modeling/team_regressions.py b/hypotheses_modeling/team_regressions.py
index 5753708..c7fced1 100644
--- a/hypotheses_modeling/team_regressions.py
+++ b/hypotheses_modeling/team_regressions.py
@@ -33,10 +33,28 @@ def k_days_into_future_regression(X, y, k, n0):
   return regr.intercept_, regr.coef_, r2, mse
 
 
+def standard_lr(x, y):
+  regr = linear_model.LinearRegression()
+  regr.fit(x, y)
+  predictions = regr.predict(x)
+  mse = mean_squared_error(y, predictions) / (len(y) - 2)
+  r2 = r2_score(y, predictions)
+  return regr.intercept_, regr.coef_, r2, mse
+
+
 def main():
-  fatigueSums = pd.read_csv("fatigue_total_sum.csv")
-  performance = pd.read_csv("../data_preparation/cleaned/expSmoothWorkAndFatigueData.csv", index_col=0).drop(columns=["totalWork", "averageWorkLoad", "smoothedFatigueData"])
-  print(k_days_into_future_regression(fatigueSums, performance, 0, 1))
+  # fatigueSums = pd.read_csv("fatigue_total_sum.csv")
+  # workMovingAverage21 = pd.read_csv("21DaySlidingWorkAverage.csv", index_col=0)
+  # print(k_days_into_future_regression(workMovingAverage21, fatigueSums, 0, 21))
+
+
+  wellness = pd.read_csv("../data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv")
+
+  wellness = wellness.fillna(0)
+  x = wellness[['normSoreness', 'TimeSinceAugFirst']]
+  y = wellness['normFatigue']
+  print(wellness.isnull().sum())
+  print(standard_lr(x, y))
 
 
 if __name__ == "__main__":