Confidence Intervals with z*


                    # This will get executed each time the exercise gets initialized.
                    import numpy as np
                    import pandas as pd
                    import scipy.stats as stats
                    
                    x = np.array(['R','R','R','R','R','R']*10)
                    n = len(x)
                    N = 1000
                    counts = np.random.binomial(n,0.5,N)
                    phat = counts/n


                    # libraries
                    import numpy as np
                    import pandas as pd
                    import scipy.stats as stats
                    
                    # initialization code
                    phatdf = pd.DataFrame(phat, columns = ['phat'])
                    
                    # find mean and standard error
                    meandist = ...
                    SEdist = ...
                    
                    # 99% confidence interval
                    P = ...
                    zstar = stats.norm.interval(...)
                    zstar = np.array(zstar)
                    
                    CI = ...
                    CI


                    # libraries
                    import numpy as np
                    import pandas as pd
                    import scipy.stats as stats
                    
                    # initialization code
                    phatdf = pd.DataFrame(phat, columns = ['phat'])
                    
                    # find mean and standard error
                    meandist = phatdf['phat'].mean()
                    SEdist = phatdf['phat'].std()
                    
                    # 99% confidence interval
                    P = 99
                    zstar = stats.norm.interval(P/100)
                    zstar = np.array(zstar)
                    
                    CI = meandist + zstar*SEdist
                    CI