Descriptive Statistics Part-3 | 38/100 Days of Python Algo Trading

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Day 38: Descriptive Statistics Part- 3

1. What is the primary purpose of Exploratory Data Analysis (EDA) in algorithmic trading?

A) To summarize the main characteristics of the data

B) To visualize data trends and patterns

C) To clean and preprocess the data

D) To validate the data against external sources

2. Which of the following best describes univariate analysis?

A) Analysis of a single variable

B) Analysis of two variables

C) Analysis of three or more variables

D) Analysis of the relationship between multiple variables

3. Why is EDA important before building a trading algorithm?

A) It helps to identify patterns and anomalies

B) It helps to optimize algorithm performance

C) It helps to reduce data redundancy

D) It helps to improve data quality

4. What is the first step in the EDA process?

A) Data cleaning and preparation

B) Data exploration and visualization

C) Data visualization

D) Feature engineering

5. How can you visualize the distribution of a single variable in a trading dataset?

A) Using histograms or box plots

B) Using scatter plots

C) Using pie charts

D) Using line charts

6. What is the purpose of bivariate analysis in EDA?

A) To analyze the relationship between two variables

B) To visualize the distribution of a single variable

C) To standardize the data format

D) To identify outliers in the data

7. How does feature engineering contribute to the performance of trading algorithms?

A) By creating new features that capture relevant information

B) By normalizing the data

C) By removing irrelevant features

D) By reducing the dimensionality of the data

8. Which pandas function is used to compute summary statistics for numerical columns?

A) df.describe()

B) df.info()

C) df.summary()

D) df.stats()

9. What is the role of data cleaning in the EDA process?

A) To remove inaccuracies and inconsistencies

B) To explore the data visually

C) To transform the data into a suitable format

D) To enhance data quality

10. Which visualization technique is most appropriate for showing the relationship between two continuous variables?

A) Scatter plot

B) Bar chart

C) Histogram

D) Line plot

11. What does a box plot reveal about a trading dataset?

A) It shows the distribution and potential outliers

B) It shows the central tendency

C) It reveals data trends over time

D) It highlights missing values

12. How can you detect outliers in a trading dataset during EDA?

A) By using statistical methods like Z-score

B) By visual inspection

C) By using machine learning models

D) By using regression analysis

13. Why is it important to understand the correlation between variables in a trading dataset?

A) To identify multicollinearity issues

B) To understand the impact of one variable on another

C) To enhance data visualization

D) To facilitate data transformation

14. What is the purpose of using histograms in univariate analysis?

A) To visualize the frequency distribution

B) To compare different variables

C) To show the proportion of categories

D) To create a time series plot

15. How can feature engineering help in improving model accuracy?

A) By transforming raw data into useful features

B) By reducing noise in the data

C) By combining multiple features into one

D) By normalizing the data

16. What is the significance of using scatter plots in bivariate analysis?

A) They help in identifying relationships between variables

B) They show the distribution of a single variable

C) They help in identifying outliers

D) They show data distribution over time

17. How do you handle missing data during the EDA process?

A) By using imputation techniques

B) By removing rows with missing values

C) By visualizing the missing data

D) By visualizing the missing data

18. What is the impact of scaling features on the performance of a trading algorithm?

A) It ensures that features are on the same scale

B) It makes the data easier to understand

C) It improves algorithm accuracy

D) It enhances data integrity

19. Which technique is used to reduce the dimensionality of a dataset in feature engineering?

A) Principal Component Analysis (PCA)

B) Linear Discriminant Analysis (LDA)

C) Independent Component Analysis (ICA)

D) Random Projection

20. What is the importance of understanding data distribution in EDA?

A) It helps to identify the shape and spread of the data

B) It helps to identify the central tendency

C) It helps to identify correlations

D) It helps to identify missing values

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