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Robert Blair 4 anos atrás
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47 arquivos alterados com 2453 adições e 15161 exclusões
  1. 2 2
      05/1/.ipynb_checkpoints/Arrays-checkpoint.ipynb
  2. 2 2
      05/1/Arrays.ipynb
  3. 2 2
      05/3/.ipynb_checkpoints/More_on_Arrays-checkpoint.ipynb
  4. 2 2
      05/3/More_on_Arrays.ipynb
  5. 1 1
      05/Sequences.ipynb
  6. 214 5654
      06/3/.ipynb_checkpoints/Example_Trends_in_the_Population_of_the_United_States-checkpoint.ipynb
  7. 214 5654
      06/3/Example_Trends_in_the_Population_of_the_United_States.ipynb
  8. 162 564
      06/4/.ipynb_checkpoints/Example_Gender_Ratio_in_the_US_Population-checkpoint.ipynb
  9. 162 564
      06/4/Example_Gender_Ratio_in_the_US_Population.ipynb
  10. 4 4
      07/Visualization.ipynb
  11. 121 145
      08/3/.ipynb_checkpoints/Cross-Classifying_by_More_than_One_Variable-checkpoint.ipynb
  12. 121 145
      08/3/Cross-Classifying_by_More_than_One_Variable.ipynb
  13. 4 4
      08/5/.ipynb_checkpoints/Bike_Sharing_in_the_Bay_Area-checkpoint.ipynb
  14. 4 4
      08/5/Bike_Sharing_in_the_Bay_Area.ipynb
  15. 2 2
      08/Functions_and_Tables.ipynb
  16. 40 40
      09/4/.ipynb_checkpoints/Monty_Hall_Problem-checkpoint.ipynb
  17. 40 40
      09/4/Monty_Hall_Problem.ipynb
  18. 5 5
      11/2/.ipynb_checkpoints/Multiple_Categories-checkpoint.ipynb
  19. 5 5
      11/2/Multiple_Categories.ipynb
  20. 96 96
      11/3/.ipynb_checkpoints/Decisions_and_Uncertainty-checkpoint.ipynb
  21. 96 96
      11/3/Decisions_and_Uncertainty.ipynb
  22. 60 74
      12/1/.ipynb_checkpoints/AB_Testing-checkpoint.ipynb
  23. 60 74
      12/1/AB_Testing.ipynb
  24. 62 369
      12/3/.ipynb_checkpoints/Causality-checkpoint.ipynb
  25. 62 369
      12/3/Causality.ipynb
  26. 29 38
      13/1/.ipynb_checkpoints/Percentiles-checkpoint.ipynb
  27. 29 38
      13/1/Percentiles.ipynb
  28. 164 195
      13/2/.ipynb_checkpoints/Bootstrap-checkpoint.ipynb
  29. 164 195
      13/2/Bootstrap.ipynb
  30. 1 1
      13/3/.ipynb_checkpoints/Confidence_Intervals-checkpoint.ipynb
  31. 1 1
      13/3/Confidence_Intervals.ipynb
  32. 0 0
      14/5/.ipynb_checkpoints/Variability_of_the_Sample_Mean-checkpoint.ipynb
  33. 0 0
      14/5/Variability_of_the_Sample_Mean.ipynb
  34. 0 0
      15/1/.ipynb_checkpoints/Correlation-checkpoint.ipynb
  35. 0 0
      15/1/Correlation.ipynb
  36. 12 120
      15/4/.ipynb_checkpoints/Least_Squares_Regression-checkpoint.ipynb
  37. 12 120
      15/4/Least_Squares_Regression.ipynb
  38. 0 0
      15/5/.ipynb_checkpoints/Visual_Diagnostics-checkpoint.ipynb
  39. 0 0
      15/5/Visual_Diagnostics.ipynb
  40. 243 262
      17/5/.ipynb_checkpoints/Accuracy_of_the_Classifier-checkpoint.ipynb
  41. 243 262
      17/5/Accuracy_of_the_Classifier.ipynb
  42. 2 2
      18/1/.ipynb_checkpoints/More_Likely_than_Not_Binary_Classifier-checkpoint.ipynb
  43. 2 2
      18/1/More_Likely_than_Not_Binary_Classifier.ipynb
  44. 4 4
      18/2/.ipynb_checkpoints/Making_Decisions-checkpoint.ipynb
  45. 4 4
      18/2/Making_Decisions.ipynb
  46. BIN
      images/BrittanyWagner.jpg
  47. BIN
      images/Dugong_dugon.jpg

+ 2 - 2
05/1/.ipynb_checkpoints/Arrays-checkpoint.ipynb

@@ -112,7 +112,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"array_arithmetic.png\" />"
+    "![Array Arithmetic](../../images/array_arithmetic.png)"
    ]
   },
   {
@@ -296,7 +296,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

+ 2 - 2
05/1/Arrays.ipynb

@@ -112,7 +112,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"array_arithmetic.png\" />"
+    "![Array Arithmetic](../../images/array_arithmetic.png)"
    ]
   },
   {
@@ -296,7 +296,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

+ 2 - 2
05/3/.ipynb_checkpoints/More_on_Arrays-checkpoint.ipynb

@@ -143,7 +143,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"array_subtraction.png\" />"
+    "![Array Subtraction](../../images/array_subtraction.png)"
    ]
   },
   {
@@ -270,7 +270,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

+ 2 - 2
05/3/More_on_Arrays.ipynb

@@ -143,7 +143,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"array_subtraction.png\" />"
+    "![Array Subtraction](../../images/array_subtraction.png)"
    ]
   },
   {
@@ -270,7 +270,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

+ 1 - 1
05/Sequences.ipynb

@@ -126,7 +126,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

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+ 214 - 5654
06/3/.ipynb_checkpoints/Example_Trends_in_the_Population_of_the_United_States-checkpoint.ipynb


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+ 214 - 5654
06/3/Example_Trends_in_the_Population_of_the_United_States.ipynb


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+ 162 - 564
06/4/.ipynb_checkpoints/Example_Gender_Ratio_in_the_US_Population-checkpoint.ipynb


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+ 162 - 564
06/4/Example_Gender_Ratio_in_the_US_Population.ipynb


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+ 4 - 4
07/Visualization.ipynb


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+ 121 - 145
08/3/.ipynb_checkpoints/Cross-Classifying_by_More_than_One_Variable-checkpoint.ipynb


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+ 121 - 145
08/3/Cross-Classifying_by_More_than_One_Variable.ipynb


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+ 4 - 4
08/5/.ipynb_checkpoints/Bike_Sharing_in_the_Bay_Area-checkpoint.ipynb


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+ 4 - 4
08/5/Bike_Sharing_in_the_Bay_Area.ipynb


+ 2 - 2
08/Functions_and_Tables.ipynb

@@ -354,7 +354,7 @@
    "source": [
     "Here is what happens when we run that cell:\n",
     "\n",
-    "![Function_Execution](function_execution.jpg)\n",
+    "![Function_Execution](../../images/function_execution.jpg)\n",
     "\n"
    ]
   },
@@ -459,7 +459,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

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+ 40 - 40
09/4/.ipynb_checkpoints/Monty_Hall_Problem-checkpoint.ipynb


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+ 40 - 40
09/4/Monty_Hall_Problem.ipynb


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11/2/.ipynb_checkpoints/Multiple_Categories-checkpoint.ipynb


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+ 5 - 5
11/2/Multiple_Categories.ipynb


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+ 96 - 96
11/3/.ipynb_checkpoints/Decisions_and_Uncertainty-checkpoint.ipynb


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+ 96 - 96
11/3/Decisions_and_Uncertainty.ipynb


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+ 60 - 74
12/1/.ipynb_checkpoints/AB_Testing-checkpoint.ipynb


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+ 60 - 74
12/1/AB_Testing.ipynb


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+ 62 - 369
12/3/.ipynb_checkpoints/Causality-checkpoint.ipynb


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+ 62 - 369
12/3/Causality.ipynb


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+ 29 - 38
13/1/.ipynb_checkpoints/Percentiles-checkpoint.ipynb


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+ 29 - 38
13/1/Percentiles.ipynb


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+ 164 - 195
13/2/.ipynb_checkpoints/Bootstrap-checkpoint.ipynb


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+ 164 - 195
13/2/Bootstrap.ipynb


+ 1 - 1
13/3/.ipynb_checkpoints/Confidence_Intervals-checkpoint.ipynb

@@ -1216,7 +1216,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

+ 1 - 1
13/3/Confidence_Intervals.ipynb

@@ -1216,7 +1216,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

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+ 0 - 0
14/5/.ipynb_checkpoints/Variability_of_the_Sample_Mean-checkpoint.ipynb


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14/5/Variability_of_the_Sample_Mean.ipynb


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15/1/.ipynb_checkpoints/Correlation-checkpoint.ipynb


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15/1/Correlation.ipynb


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+ 12 - 120
15/4/.ipynb_checkpoints/Least_Squares_Regression-checkpoint.ipynb


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+ 12 - 120
15/4/Least_Squares_Regression.ipynb


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15/5/.ipynb_checkpoints/Visual_Diagnostics-checkpoint.ipynb


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+ 0 - 0
15/5/Visual_Diagnostics.ipynb


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+ 243 - 262
17/5/.ipynb_checkpoints/Accuracy_of_the_Classifier-checkpoint.ipynb


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+ 243 - 262
17/5/Accuracy_of_the_Classifier.ipynb


+ 2 - 2
18/1/.ipynb_checkpoints/More_Likely_than_Not_Binary_Classifier-checkpoint.ipynb

@@ -338,7 +338,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![Students Tree Diagram](tree_students.png)"
+    "![Students Tree Diagram](../../images/tree_students.png)"
    ]
   },
   {
@@ -479,7 +479,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

+ 2 - 2
18/1/More_Likely_than_Not_Binary_Classifier.ipynb

@@ -338,7 +338,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![Students Tree Diagram](tree_students.png)"
+    "![Students Tree Diagram](../../images/tree_students.png)"
    ]
   },
   {
@@ -479,7 +479,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

+ 4 - 4
18/2/.ipynb_checkpoints/Making_Decisions-checkpoint.ipynb

@@ -81,7 +81,7 @@
     "## A Test for a Rare Disease\n",
     "Suppose there is a large population and a disease that strikes a tiny proportion of the population. The tree diagram below summarizes information about such a disease and about a medical test for it.\n",
     "\n",
-    "![Tree Rare Disease](tree_disease_rare.png)"
+    "![Tree Rare Disease](../../images/tree_disease_rare.png)"
    ]
   },
   {
@@ -248,7 +248,7 @@
    "source": [
     "The reason is that a huge fraction of the population doesn't have the disease in the first place. The tiny fraction of those that falsely test Positive are still greater in number than the people who correctly test Positive. This is easier to visualize in the tree diagram:\n",
     "\n",
-    "![Tree Rare Disease](tree_disease_rare.png)\n",
+    "![Tree Rare Disease](../../images/tree_disease_rare.png)\n",
     "\n",
     "- The proportion of true Positives is a large fraction (0.99) of a tiny fraction (0.004) of the population.\n",
     "- The proportion of false Positives is a tiny fraction (0.005) of a large fraction (0.996) of the population.\n",
@@ -467,7 +467,7 @@
     {
      "data": {
       "text/plain": [
-       "0.9171075837742504"
+       "0.9290909090909091"
       ]
      },
      "execution_count": 10,
@@ -506,7 +506,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

+ 4 - 4
18/2/Making_Decisions.ipynb

@@ -81,7 +81,7 @@
     "## A Test for a Rare Disease\n",
     "Suppose there is a large population and a disease that strikes a tiny proportion of the population. The tree diagram below summarizes information about such a disease and about a medical test for it.\n",
     "\n",
-    "![Tree Rare Disease](tree_disease_rare.png)"
+    "![Tree Rare Disease](../../images/tree_disease_rare.png)"
    ]
   },
   {
@@ -248,7 +248,7 @@
    "source": [
     "The reason is that a huge fraction of the population doesn't have the disease in the first place. The tiny fraction of those that falsely test Positive are still greater in number than the people who correctly test Positive. This is easier to visualize in the tree diagram:\n",
     "\n",
-    "![Tree Rare Disease](tree_disease_rare.png)\n",
+    "![Tree Rare Disease](../../images/tree_disease_rare.png)\n",
     "\n",
     "- The proportion of true Positives is a large fraction (0.99) of a tiny fraction (0.004) of the population.\n",
     "- The proportion of false Positives is a tiny fraction (0.005) of a large fraction (0.996) of the population.\n",
@@ -467,7 +467,7 @@
     {
      "data": {
       "text/plain": [
-       "0.9171075837742504"
+       "0.9290909090909091"
       ]
      },
      "execution_count": 10,
@@ -506,7 +506,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,

BIN
images/BrittanyWagner.jpg


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images/Dugong_dugon.jpg


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