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Please enable JavaScript in your browser settings or update your browser.Challenge 5: Correlation | Statistics
Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions
2. NumPy
Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions
3. Pandas
Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions
4. Matplotlib
Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions
5. Seaborn
Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions
6. Statistics
Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions
7. Scikit-learn
Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical Questions

bookChallenge 5: Correlation

Distinguishing between correlation and causation is a cornerstone concept in statistics. While correlation denotes a relationship between two variables, it doesn't imply that one variable causes the other. Causation, on the other hand, suggests a direct relationship where a change in one variable results in a change in another.

For example, consider an ice cream shop that notices sales increasing in the summer months and decreasing in the winter. While there's a correlation between temperature and ice cream sales, it doesn't mean higher temperatures cause an increase in sales. There could be confounding variables, such as people preferring cold treats in hot weather. People don't buy ice cream just because the temperature increased; they buy it because they find it refreshing in the heat.

So, while there's a clear correlation between temperature and ice cream sales, we cannot definitively say that higher temperatures cause an increase in sales without considering other factors. Making causal statements requires more rigorous examination and, ideally, controlled experiments to rule out or account for potential confounding variables.

Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.


              
1234567

            
import seaborn as sns

# Load the dataset
data = sns.load_dataset('tips')

# Sample of data
display(data.head())
copy
Task
test

Swipe to show code editor

Using Seaborn's tips dataset, perform the following tasks:

  1. Determine the Pearson correlation coefficient between the total_bill and tip columns, which gives a measure of the linear association between the two numerical variables.
  2. Visualize the relationship between total_bill (for X-axis) and tip (for Y-axis) with a linear regression plot, allowing you to observe how changes in the total_bill might predict changes in the tip.
  3. Create a matrix of correlations for the categorical variables in the dataset using Cramér's V, a measure based on the chi-squared statistic which quantifies the association between two categorical variables.

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Section 6. Chapter 5
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bookChallenge 5: Correlation

Distinguishing between correlation and causation is a cornerstone concept in statistics. While correlation denotes a relationship between two variables, it doesn't imply that one variable causes the other. Causation, on the other hand, suggests a direct relationship where a change in one variable results in a change in another.

For example, consider an ice cream shop that notices sales increasing in the summer months and decreasing in the winter. While there's a correlation between temperature and ice cream sales, it doesn't mean higher temperatures cause an increase in sales. There could be confounding variables, such as people preferring cold treats in hot weather. People don't buy ice cream just because the temperature increased; they buy it because they find it refreshing in the heat.

So, while there's a clear correlation between temperature and ice cream sales, we cannot definitively say that higher temperatures cause an increase in sales without considering other factors. Making causal statements requires more rigorous examination and, ideally, controlled experiments to rule out or account for potential confounding variables.

Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.


              
1234567

            
import seaborn as sns

# Load the dataset
data = sns.load_dataset('tips')

# Sample of data
display(data.head())
copy
Task
test

Swipe to show code editor

Using Seaborn's tips dataset, perform the following tasks:

  1. Determine the Pearson correlation coefficient between the total_bill and tip columns, which gives a measure of the linear association between the two numerical variables.
  2. Visualize the relationship between total_bill (for X-axis) and tip (for Y-axis) with a linear regression plot, allowing you to observe how changes in the total_bill might predict changes in the tip.
  3. Create a matrix of correlations for the categorical variables in the dataset using Cramér's V, a measure based on the chi-squared statistic which quantifies the association between two categorical variables.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 6. Chapter 5
toggle bottom row

bookChallenge 5: Correlation

Distinguishing between correlation and causation is a cornerstone concept in statistics. While correlation denotes a relationship between two variables, it doesn't imply that one variable causes the other. Causation, on the other hand, suggests a direct relationship where a change in one variable results in a change in another.

For example, consider an ice cream shop that notices sales increasing in the summer months and decreasing in the winter. While there's a correlation between temperature and ice cream sales, it doesn't mean higher temperatures cause an increase in sales. There could be confounding variables, such as people preferring cold treats in hot weather. People don't buy ice cream just because the temperature increased; they buy it because they find it refreshing in the heat.

So, while there's a clear correlation between temperature and ice cream sales, we cannot definitively say that higher temperatures cause an increase in sales without considering other factors. Making causal statements requires more rigorous examination and, ideally, controlled experiments to rule out or account for potential confounding variables.

Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.


              
1234567

            
import seaborn as sns

# Load the dataset
data = sns.load_dataset('tips')

# Sample of data
display(data.head())
copy
Task
test

Swipe to show code editor

Using Seaborn's tips dataset, perform the following tasks:

  1. Determine the Pearson correlation coefficient between the total_bill and tip columns, which gives a measure of the linear association between the two numerical variables.
  2. Visualize the relationship between total_bill (for X-axis) and tip (for Y-axis) with a linear regression plot, allowing you to observe how changes in the total_bill might predict changes in the tip.
  3. Create a matrix of correlations for the categorical variables in the dataset using Cramér's V, a measure based on the chi-squared statistic which quantifies the association between two categorical variables.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Distinguishing between correlation and causation is a cornerstone concept in statistics. While correlation denotes a relationship between two variables, it doesn't imply that one variable causes the other. Causation, on the other hand, suggests a direct relationship where a change in one variable results in a change in another.

For example, consider an ice cream shop that notices sales increasing in the summer months and decreasing in the winter. While there's a correlation between temperature and ice cream sales, it doesn't mean higher temperatures cause an increase in sales. There could be confounding variables, such as people preferring cold treats in hot weather. People don't buy ice cream just because the temperature increased; they buy it because they find it refreshing in the heat.

So, while there's a clear correlation between temperature and ice cream sales, we cannot definitively say that higher temperatures cause an increase in sales without considering other factors. Making causal statements requires more rigorous examination and, ideally, controlled experiments to rule out or account for potential confounding variables.

Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.


              
1234567

            
import seaborn as sns

# Load the dataset
data = sns.load_dataset('tips')

# Sample of data
display(data.head())
copy
Task
test

Swipe to show code editor

Using Seaborn's tips dataset, perform the following tasks:

  1. Determine the Pearson correlation coefficient between the total_bill and tip columns, which gives a measure of the linear association between the two numerical variables.
  2. Visualize the relationship between total_bill (for X-axis) and tip (for Y-axis) with a linear regression plot, allowing you to observe how changes in the total_bill might predict changes in the tip.
  3. Create a matrix of correlations for the categorical variables in the dataset using Cramér's V, a measure based on the chi-squared statistic which quantifies the association between two categorical variables.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 6. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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