![]() ![]() set_title ( 'Distribución de bootstrapping de altura' ) ax. confidence_interval, color = "y", ls = "-" ) ax. ![]() bootstrap_distribution, bins = len ( binwidth ), density = True ) ax. set_title ( 'Distribución de bootstrapping de peso' ) ax. confidence_interval ) print ( "distribuicion de peso=", meanfw, "\u00B1", stdfw ) print ( "distribuicion de altura=", meanfh, "\u00B1", stdfh ) ax. subplots ( nrows = 1, ncols = 3, figsize = ( 18, 6 ) ) print ( bootstrap_cifw. set_title ( 'Masa y altura hombres' ) ax. plot ( X, Y_prediccionm, color = "r", label = 'regresion lineal' ) ax. regplot ( x = weightm, y = heightm, ci = 95, n_boot = 9999, color = 'y', scatter = False, ax = ax, label = 'confidence interval at 95%' ) #ax.plot(X,Y_prediccionm+ sigma, color = "y", ls = "-") #ax.plot(X,Y_prediccionm- sigma, color = "y",label='confidence interval at 95%', ls = "-") ax. ![]() confidence_interval ) print ( "distribuicion de peso=", meanmw, "\u00B1", stdmw ) print ( "distribuicion de altura=", meanmh, "\u00B1", stdmh ) ax. subplots ( nrows = 1, ncols = 3, figsize = ( 18, 6 ) ) print ( bootstrap_cimw. set_title ( 'Distribución de altura mujeres' ) ax. hist ( heightf, bins = 60, density = True ) ax. set_title ( 'Distribución de peso mujeres' ) ax. hist ( weightf, bins = 60, density = True ) ax. set_title ( 'Distribución de altura hombres' ) ax. hist ( heightm, bins = 60, density = True ) ax. set_title ( 'Distribución de peso hombres' ) ax. hist ( weightm, bins = 60, density = True ) ax. subplots_adjust ( hspace = 0.5, wspace = 0.3 ) ax. subplots ( nrows = 2, ncols = 2, figsize = ( 18, 7 ) ) fig. loc = 'Female' ] weightf = 0.453592 * female heightf = 2.54 * female / 100 fig, ax = plt. linspace ( min ( weightm ), max ( weightm ), len ( weightm ) ) female = data. loc = 'Male' ] weightm = 0.453592 * male #kilograms heightm = 2.54 * male / 100 #metros IMCm = weightm / heightm * * 2 x = np. read_csv ( "weight-height.csv" ) male = data. ![]()
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