Descriptive and Inferential Stats

SciPy's scipy.stats module provides tools for both descriptive and inferential statistics, giving you a complete toolkit for analyzing and drawing conclusions from data.

Descriptive statistics summarize and describe the key features of your dataset.

Some useful functions in scipy.stats include:

• Mean, Median, Mode: Measures of central tendency.
• Variance and Standard Deviation: Describe how spread out the data is.
• Skewness and Kurtosis: Show the shape of the distribution.
• Percentiles and Quartiles: Indicate where values fall within the dataset.

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from scipy import stats

data = [2, 4, 6, 8, 10]
mean_value = stats.tmean(data)
variance = stats.tvar(data)
std_dev = stats.tstd(data)

print("Mean:", mean_value)
print("Variance:", variance)
print("Standard Deviation:", std_dev)

Inferential statistics allow you to make predictions or test hypotheses about a population based on a sample.

Common tools in SciPy include:

• T-tests: Compare means between two groups.
• Chi-Square Tests: Examine relationships between categorical variables.
• ANOVA: Compare means across multiple groups.
• Correlation Tests: Measure relationships between variables.

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group1 = [1, 2, 3, 4, 5]
group2 = [2, 3, 4, 5, 6]

t_stat, p_val = stats.ttest_ind(group1, group2)

print("t-statistic:", t_stat)
print("p-value:", p_val)

• Descriptive statistics help you understand your dataset.
• Inferential statistics help you draw conclusions and make decisions based on your data.

Together, they form the foundation of effective data analysis.