Try It Single Mean


                    import numpy as np

                    ## x contains the sample of Tesla Model S ranges, in miles
                    x = np.array([240, 241, 236, 240, 240]) 
                    
                    ## STEP 1: Calculate the sample statistic
                    
                    ## STEP 2: Randomization distribution
                    
                    ## STEP 3: Calculate p-value. Direction of inequality agrees with Ha above!!!


                    import numpy as np

                    ## x contains the sample of Tesla Model S ranges, in miles
                    x = np.array([240, 241, 236, 240, 240]) 
                    
                    ## STEP 1: Calculate the sample statistic
                    xbar = x.mean()
                    print("Sample mean = ", xbar)
                    
                    ## STEP 2: Randomization distribution
                    x_shift = x + (240 - xbar)   # shift the sample so that the mean is equal to the null hypothesis mean value
                    N = 1000   # number of bootstrap samples
                    n = len(x)  # sample size
                    randmean = np.empty(N)
                    for j in range(N):
                      randmean[j] = np.random.choice(x_shift, size = n, replace = True).mean()
                    
                    ## STEP 3: Calculate p-value. Direction of inequality agrees with Ha above!!!
                    print("p-value =", len(randmean[randmean <= xbar])/N)