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Chapter 3 Analyzing Normal Quantitative Data Introduction: In chapters 1 and 2, we focused on analyzing categorical data and exploring relationships between categorical data sets. We will now be doing

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How to fill out 7 exploratory data analysisr

01

Start by importing the necessary libraries such as numpy, pandas, and matplotlib.

02

Load your dataset into a pandas DataFrame using the read_csv() function.

03

Explore the basic statistics of your dataset using the describe() function.

04

Identify and handle missing values in your dataset using functions like isnull(), dropna(), or fillna().

05

Visualize the distribution of your data using histograms, box plots, or scatter plots.

06

Examine the correlation between variables using the correlation() function or by creating a correlation matrix.

07

Identify and handle outliers in your dataset using functions like z-score or interquartile range (IQR).

Who needs 7 exploratory data analysisr?

01

Data analysts, data scientists, and researchers often need to perform exploratory data analysis to gain insights into a dataset before further analysis or modeling.

02

Business analysts and decision-makers also benefit from exploratory data analysis to understand patterns, trends, and relationships in their data.

03

Anyone working with data, regardless of their field or role, can benefit from exploratory data analysis to make informed decisions based on data-driven insights.

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What is 7 exploratory data analysisr?

7 exploratory data analysisr is a process of analyzing data sets to summarize their main characteristics, often using visual methods.

Who is required to file 7 exploratory data analysisr?

Data analysts, data scientists, researchers, and anyone working with data may be required to perform exploratory data analysis.

How to fill out 7 exploratory data analysisr?

To fill out exploratory data analysis, one must first gather the data, clean it, and then use various statistical and visualization techniques to understand the data.

What is the purpose of 7 exploratory data analysisr?

The purpose of exploratory data analysis is to gain insights, detect patterns, spot outliers, and test assumptions in the data.

What information must be reported on 7 exploratory data analysisr?

The report must include summary statistics, visualizations, data distributions, correlations, and any interesting findings from the analysis.

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