Review the plotted data distribution for these 1000 means. Does this confirm the first part of the central limit theorem? Why or why not

+ V A Run Code Step 6: Creating a histogram plot of the sample means from Step 5 Now you will plot the data distribution of the 1,000 means from Step 5. View the plot to confirm that it approximates a Normal distribution (bell-shaped curve) Note that the original data distribution in Step 2 was skewed. However, the distribution of sample means, calculated by repeatedly drawing large samples, is approximately Normally distributed. Click the block of code below and hit the Run button above NOTE: If the graph is not created, click the code section and hit the Run button again. In [9]: # create a figure for the plot. fig, ax = plt. subplots( ) # create a histogram plot with 50 bins of 1, 000 means. plt. hist(means_df [ 'means' ], bins=50) # set a title for the plot, x-axis and y-axis. pit. title( 'Distribution of 1000 sample means', fontsize=20) # title ax. set_xlabel( 'Means' ) ax. set_ylabel( ' Frequency' ) # show the plot. pit. show( ) Distribution of 1000 sample means 60 50 40 Frequency 30 20 350 400 450 500 550 600 650 Means