Page 293 - AI_Ver_3.0_class_11
P. 293
# 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.
Data Literacy—Data Collection to Data Analysis 291

