diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..dca46c2 100644 --- a/your-code/1.-Data-Cleaning.ipynb +++ b/your-code/1.-Data-Cleaning.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -47,11 +47,283 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF18371.021.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F18573.021.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG17069.023.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F18584.024.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING17578.025.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
count142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000
mean184.61267678.97887323.09121427.11267624.429577500.10563474.401408168.70422543.10281714.83098643.69718324.97816939.53521149.42253575.82887322.06338061.59154983.65493044.51408517.7253529.78169032.288732203.1690141.1408450.007042
std8.69812810.9961102.0736913.6671807.075477289.37339355.980754117.1658099.85519917.37282946.15530218.45907536.74305344.24469718.53615121.51964849.66985468.20058541.49079013.41331212.53766921.447141153.0325592.9090020.083918
min165.00000055.00000018.39067521.0000002.00000012.0000001.0000003.00000016.7000000.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000002.0000000.0000000.0000000.0000002.0000002.0000000.0000000.000000
25%175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
75%191.00000086.00000024.18071530.00000029.000000752.500000105.000000244.75000048.62500022.00000065.50000036.17500053.25000066.50000085.92500031.00000084.000000116.50000066.75000027.50000012.00000048.000000277.7500001.0000000.000000
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
count142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000
mean184.61267678.97887323.09121427.11267624.429577500.10563474.401408168.70422543.10281714.83098643.69718324.97816939.53521149.42253575.82887322.06338061.59154983.65493044.51408517.7253529.78169032.288732203.1690141.1408450.007042
std8.69812810.9961102.0736913.6671807.075477289.37339355.980754117.1658099.85519917.37282946.15530218.45907536.74305344.24469718.53615121.51964849.66985468.20058541.49079013.41331212.53766921.447141153.0325592.9090020.083918
min165.00000055.00000018.39067521.0000002.00000012.0000001.0000003.00000016.7000000.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000002.0000000.0000000.0000000.0000002.0000002.0000000.0000000.000000
25%175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
75%191.00000086.00000024.18071530.00000029.000000752.500000105.000000244.75000048.62500022.00000065.50000036.17500053.25000066.50000085.92500031.00000084.000000116.50000066.75000027.50000012.00000048.000000277.7500001.0000000.000000
max206.000000113.00000031.55588036.00000032.0000001018.000000227.000000509.000000100.00000088.000000225.000000100.000000168.000000186.000000100.000000113.000000226.000000334.000000206.00000063.00000064.00000087.000000584.00000017.0000001.000000
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"75% 752.500000 105.000000 244.750000 48.625000 22.000000 \n", + "max 1018.000000 227.000000 509.000000 100.000000 88.000000 \n", + "\n", + " 3PA 3P% FTM FTA FT% OREB \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 43.697183 24.978169 39.535211 49.422535 75.828873 22.063380 \n", + "std 46.155302 18.459075 36.743053 44.244697 18.536151 21.519648 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 3.000000 0.000000 13.000000 17.250000 71.575000 7.000000 \n", + "50% 32.000000 30.550000 29.000000 35.500000 80.000000 13.000000 \n", + "75% 65.500000 36.175000 53.250000 66.500000 85.925000 31.000000 \n", + "max 225.000000 100.000000 168.000000 186.000000 100.000000 113.000000 \n", + "\n", + " DREB REB AST STL BLK TO \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 61.591549 83.654930 44.514085 17.725352 9.781690 32.288732 \n", + "std 49.669854 68.200585 41.490790 13.413312 12.537669 21.447141 \n", + "min 2.000000 2.000000 0.000000 0.000000 0.000000 2.000000 \n", + "25% 26.000000 34.250000 11.250000 7.000000 2.000000 14.000000 \n", + "50% 50.000000 62.500000 34.000000 15.000000 5.000000 28.000000 \n", + "75% 84.000000 116.500000 66.750000 27.500000 12.000000 48.000000 \n", + "max 226.000000 334.000000 206.000000 63.000000 64.000000 87.000000 \n", + "\n", + " PTS DD2 TD3 \n", + "count 142.000000 142.000000 142.000000 \n", + "mean 203.169014 1.140845 0.007042 \n", + "std 153.032559 2.909002 0.083918 \n", + "min 2.000000 0.000000 0.000000 \n", + "25% 77.250000 0.000000 0.000000 \n", + "50% 181.000000 0.000000 0.000000 \n", + "75% 277.750000 1.000000 0.000000 \n", + "max 584.000000 17.000000 1.000000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba.describe()\n" ] }, { @@ -74,7 +681,7 @@ "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n" ] }, { @@ -91,9 +698,40 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "plot_options, ((plot_1, plot_2), (plot_3, plot_4)) = plt.subplots(nrows = 2, ncols = 2)\n", + "\n", + "plot_1.hist(wnba[\"Height\"], color = \"red\")\n", + "plot_1.set_xlabel(\"Height\")\n", + "plot_1.set_ylabel(\"Count\")\n", + "\n", + "plot_2.hist(wnba[\"Weight\"], color = \"green\")\n", + "plot_2.set_xlabel(\"Weight\")\n", + "plot_2.set_ylabel(\"Count\")\n", + "\n", + "plot_3.hist(wnba[\"Age\"], color = \"blue\")\n", + "plot_3.set_xlabel(\"Age\")\n", + "plot_3.set_ylabel(\"Count\")\n", + "\n", + "plot_4.hist(wnba[\"BMI\"], color = \"orange\")\n", + "plot_4.set_xlabel(\"BMI\")\n", + "plot_4.set_ylabel(\"Count\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -109,7 +747,16 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "\"\"\"\n", + "1. The height and weight distributions seem to have different shapes, \n", + "suggesting that there might not be a strong linear relationship between height and weight among WNBA players.\n", + "\n", + "2. There could be some outliers in the weight and BMI distributions, possibly representing players with extreme values.\n", + "\n", + "3. The age distribution indicates a fairly even distribution of players across different age groups, \n", + "but further analysis might be needed to explore age-related trends or patterns.\n", + "\"\"\"" ] }, { @@ -134,11 +781,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "fig, ((plot_1, plot_2, plot_3), (plot_4, plot_5, _)) = plt.subplots(nrows = 2, ncols = 3, figsize = (15, 8))\n", + "\n", + "plot_1.hist(wnba[\"REB\"], color = \"brown\")\n", + "plot_1.set_xlabel(\"REB\")\n", + "plot_1.set_ylabel(\"Countr\")\n", + "\n", + "plot_2.hist(wnba[\"AST\"], color = \"grey\")\n", + "plot_2.set_xlabel(\"AST\")\n", + "plot_2.set_ylabel(\"Countr\")\n", + "\n", + "plot_3.hist(wnba[\"STL\"], color = \"skyblue\")\n", + "plot_3.set_xlabel(\"STL\")\n", + "plot_3.set_ylabel(\"Countr\")\n", + "\n", + "plot_4.hist(wnba[\"PTS\"], color = \"yellow\")\n", + "plot_4.set_xlabel(\"PTS\")\n", + "plot_4.set_ylabel(\"Countr\")\n", + "\n", + "plot_5.hist(wnba[\"BLK\"], color = \"pink\")\n", + "plot_5.set_xlabel(\"BLK\")\n", + "plot_5.set_ylabel(\"Countr\")\n", + "\n", + "# Hide the last subplot\n", + "_.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -154,7 +839,7 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n" ] }, { @@ -173,11 +858,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "fig, ((plot_1, plot_2, plot_3), (plot_4, plot_5, _)) = plt.subplots(nrows = 2, ncols = 3, figsize = (15, 8))\n", + "\n", + "plot_1.hist(wnba[\"REB\"] / wnba[\"MIN\"], color = \"brown\")\n", + "plot_1.set_xlabel(\"REB\")\n", + "plot_1.set_ylabel(\"Countr\")\n", + "\n", + "plot_2.hist(wnba[\"AST\"] / wnba[\"MIN\"], color = \"grey\")\n", + "plot_2.set_xlabel(\"AST\")\n", + "plot_2.set_ylabel(\"Countr\")\n", + "\n", + "plot_3.hist(wnba[\"STL\"] / wnba[\"MIN\"], color = \"skyblue\")\n", + "plot_3.set_xlabel(\"STL\")\n", + "plot_3.set_ylabel(\"Countr\")\n", + "\n", + "plot_4.hist(wnba[\"PTS\"] / wnba[\"MIN\"], color = \"yellow\")\n", + "plot_4.set_xlabel(\"PTS\")\n", + "plot_4.set_ylabel(\"Countr\")\n", + "\n", + "plot_5.hist(wnba[\"BLK\"] / wnba[\"MIN\"], color = \"pink\")\n", + "plot_5.set_xlabel(\"BLK\")\n", + "plot_5.set_ylabel(\"Countr\")\n", + "\n", + "# Hide the last subplot\n", + "_.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -193,7 +916,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "\"\"\"Its now a distribution of normalized statistics for each category.\"\"\"" ] }, { @@ -222,13 +946,14 @@ "metadata": {}, "outputs": [], "source": [ - "#your comments here" + "#your comments here\n", + "\"\"\"You may need to gather more relevant data or information from reliable sources\"\"\"" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +967,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 366765b..db89359 100644 --- a/your-code/3.-Inferential-Analysis.ipynb +++ b/your-code/3.-Inferential-Analysis.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -46,11 +46,283 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
\n", + "
" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "0 Aerial Powers DAL F 183 71 21.200991 US \n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "2 Alex Bentley CON G 170 69 23.875433 US \n", + "3 Alex Montgomery SAN G/F 185 84 24.543462 US \n", + "4 Alexis Jones MIN G 175 78 25.469388 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "0 January 17, 1994 23 Michigan State 2 8 173 30 \n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "2 October 27, 1990 26 Penn State 4 26 617 82 \n", + "3 December 11, 1988 28 Georgia Tech 6 31 721 75 \n", + "4 August 5, 1994 23 Baylor R 24 137 16 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \\\n", + "0 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 3 6 \n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 13 \n", + "2 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 22 3 \n", + "3 195 38.5 21 68 30.9 17 21 81.0 35 134 169 65 20 10 \n", + "4 50 32.0 7 20 35.0 11 12 91.7 3 9 12 12 7 0 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 \n", + "2 24 218 0 0 \n", + "3 38 188 2 0 \n", + "4 14 50 0 0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "wnba = pd.read_csv(\"wnba_clean.csv\")\n", + "wnba.head()" ] }, { @@ -70,11 +342,36 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nIn order to infer the average weight of professional wnba players, \\nwe need to consider the requirements and assumptions for using the sample to make an inference. \\nHere are some important considerations:\\n\\nRandom Sample: The sample of players from the wnba dataset should ideally be a random sample of all professional \\nfemale basketball players. If the sample is not representative, the inference might not be accurate.\\n\\nSample Size: The sample size should be large enough to satisfy the assumptions of the Central Limit Theorem. \\nA larger sample size tends to result in a more normal distribution of sample means.\\n\\nIndependence: The weights of the players in the sample should be independent of each other. \\nThis assumption is usually satisfied if the sample is drawn randomly and without replacement.\\n'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here\n", + "\"\"\"\n", + "In order to infer the average weight of professional wnba players, \n", + "we need to consider the requirements and assumptions for using the sample to make an inference. \n", + "Here are some important considerations:\n", + "\n", + "Random Sample: The sample of players from the wnba dataset should ideally be a random sample of all professional \n", + "female basketball players. If the sample is not representative, the inference might not be accurate.\n", + "\n", + "Sample Size: The sample size should be large enough to satisfy the assumptions of the Central Limit Theorem. \n", + "A larger sample size tends to result in a more normal distribution of sample means.\n", + "\n", + "Independence: The weights of the players in the sample should be independent of each other. \n", + "This assumption is usually satisfied if the sample is drawn randomly and without replacement.\n", + "\"\"\"" ] }, { @@ -86,11 +383,26 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "data": { + "text/plain": [ + "(77.17027122332428, 80.78747525554897)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_mean = wnba[\"Weight\"].mean()\n", + "s_std = wnba[\"Weight\"].std(ddof=1)\n", + "n = len(wnba[\"Weight\"])\n", + "\n", + "stats.norm.interval(0.95, loc=s_mean, scale=s_std/np.sqrt(n))" ] }, { @@ -106,7 +418,11 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\"\"\"\n", + "Grandmother might be right. As the confidence intervalls left end is still 10kg higer than my sisters weight.\n", + "However it is not impossible, but really difficult.\n", + "\"\"\"" ] }, { @@ -118,11 +434,26 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "outputs": [ + { + "data": { + "text/plain": [ + "'\\ni am afraid our grandma is right..\\n\\n'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your-answer-here\n", + "\"\"\"\n", + "i am afraid our grandma is right..\n", + "\n", + "\"\"\"" ] }, { @@ -154,11 +485,24 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24.171126760563382\n", + "1.0\n" + ] + } + ], "source": [ - "# your answer here" + "# your answer here\n", + "s = 100-wnba[\"FT%\"]\n", + "print(s.mean())\n", + "p_value = ttest_1samp(s,60,alternative=\"greater\")[1]\n", + "print(p_value)" ] }, { @@ -170,11 +514,27 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21.122365154174023, 27.21988836695274)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "s_mean = s.mean()\n", + "s_std = s.std(ddof=1)\n", + "n = len(s)\n", + "\n", + "stats.norm.interval(0.95, loc=s_mean, scale=s_std/np.sqrt(n))" ] }, { @@ -190,7 +550,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\"\"\"actually wnba players only miss between 21 - 27% of their freethrows\"\"\"" ] }, { @@ -227,11 +588,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "s = wnba[\"AST\"]" ] }, { @@ -243,20 +605,30 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" - ] - }, - { - "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "we can reject null hypothesis\n" + ] + } + ], + "source": [ + "#your code here\n", + "#H0: Is not higher\n", + "#H1: is higer\n", + "\n", + "\n", + "p_value = ttest_1samp(s,52,alternative=\"two-sided\")[1]\n", + "alpha = 0.05\n", + "\n", + "if p_value