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# Calculate the variance using statistics.variance()
                 variance_weight = statistics.variance(weights)


                 # Calculate the standard deviation using statistics.stdev()
                 std_dev_weight = statistics.stdev(weights)


                 # Print the variance, and standard deviation
                 print("Variance of weights:", variance_weight)
                 print("Standard deviation of weights:", std_dev_weight)
                 Output:

                 Variance of weights: 207.52666666666664
                 Standard deviation of weights: 14.405785874663923


                               Brainy Fact

                       In machine learning, statistical measurements such as mean, median,  and standard  deviation are
                       used to analyse data distribution and identify outliers. Data scientists investigate the outliers to see
                       if they are caused by data entry errors, measurement errors, or actual abnormalities, and then decide
                       whether to remove, correct, or maintain them based on their significance to the analysis.



                                Reboot


                      Fill in the blanks:
                      1.   Mean, median and mode provide the             value of the dataset while variance and standard
                         deviation provide information about the            of data around the centre.
                      2.  It is better to use the          in multimodal distributions.
                      3.  The mean in Python can be calculated using            function.
                      4.  Datasets with             variance have data grouped closely about the mean.

                      5.  The mean in statistics is also known as the        .






                        Representation of Data

                 Statistics is a branch of Mathematics that involves the collection, analysis, interpretation, presentation, and
                 organisation of data. It is used to make informed decisions and understand the world through data. To accomplish
                 this goal, statisticians summarise a significant amount of data in a compact format that yields relevant results. Without
                 displaying values for each observation (from populations), it is possible to portray the data in a concise manner while
                 retaining its significance using techniques known as ‘data representation’. It may also be defined as a technique for
                 presenting enormous amounts of data in a way that allows the user to quickly and easily interpret the most relevant
                 information.
                 There are two broad categories of data representation techniques:
                    • Non-Graphical  Technique:  Non-graphical techniques include tabular and case forms. This is an older data
                   representation format that is unsuitable for huge datasets. Non-graphical strategies are ineffective when we want
                   to make decisions based on a set of data.
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