Statistics using Python MCQs 16

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Test your skills with this 20-question quiz on Statistics using Python MCQs. Master key concepts like pandas describe(), data normalization, LinearRegression, and Pearson Correlation. Perfect for data science interviews and beginners for the preparation of Python Programming. Topics include handling missing values, get_dummies(), groupby(), correlation, and regression. Let us start with the Statistics using Python MCQs now.

Online Statistics using Python MCQs with Answers

Only Multiple Choice Questions about Data Analysis in Python Programming

1. Which of the following methods is used to calculate the average delivery time in a pandas DataFrame containing time delta columns?

 
 
 
 

2. Which function provides summary statistics, including mean and count, for a numeric column in a DataFrame?

 
 
 
 

3. What range of Pearson Coefficient ‘p’ is considered too high to support any certainty about the correlation of variables?

 
 
 
 

4. Which of the following is the primary purpose of the get_dummies() method?

 
 
 
 

5. What is the Pearson Correlation between two variables if the input variable is equal to the output variable?

 
 
 
 

6. Which of the following steps are generally involved in performing exploratory data analysis (EDA)?

 
 
 
 
 

7. What is the maximum value of $R^2$ that you can obtain?

 
 
 
 

8. What is the primary purpose of data normalization?

 
 
 
 

9. How do you generate descriptive statistics for all the columns of the data frame df?

 
 
 
 

10. How would you use the describe() method with a data frame df to get a statistical summary of all the columns in the data frame?

 
 
 
 

11. What is the function of the MinMaxScaler when applied to a column of data?

 
 
 
 

12. Which of the following methods should you use to replace a missing value of an attribute with continuous values?

 
 
 
 

13. Consider the following lines of code. What value does the variable out contain?
lm = LinearRegression()
X = df[['highway-mpg']]
Y = df['price']
lm.fit(X, Y)
out=lm.score(X,Y)

 
 
 
 

14. What steps are involved in preparing a dataset for exploratory analysis?

 
 
 
 

15. As the Pearson Correlation value nears zero, then

 
 
 
 

16. What method provides summary statistics of a data frame?

 
 
 
 

17. Which of the following are valid aggregation methods that can be applied to groups in a pandas DataFrame?

 
 
 
 
 

18. When compiling a regression model, which of the following elements must be defined? Select all that apply.

 
 
 
 
 

19. Consider the following data frame: df_test = df[['body-style,' 'price']]. The following operation is applied:
df_grp = df_test.groupby(['body-style'], as_index=False).mean()
What are the resulting values of: df_grp['price']?

 
 
 
 

20. What does the following line of code do? lm = LinearRegression()

 
 
 
 

Question 1 of 20

Only Statistics using Python MCQs with Answers

  • How would you use the describe() method with a data frame df to get a statistical summary of all the columns in the data frame?
  • How do you generate descriptive statistics for all the columns of the data frame df?
  • Which of the following methods should you use to replace a missing value of an attribute with continuous values?
  • What is the primary purpose of data normalization?
  • Which of the following is the primary purpose of the get_dummies() method?
  • What method provides summary statistics of a data frame?
  • As the Pearson Correlation value nears zero, then
  • Consider the following data frame: df_test = df[[‘body-style,’ ‘price’]]. The following operation is applied:
    df_grp = df_test.groupby([‘body-style’], as_index=False).mean()
    What are the resulting values of: df_grp[‘price’]?
  • Consider the following lines of code. What value does the variable out contain?
    lm = LinearRegression()
    X = df[[‘highway-mpg’]]
    Y = df[‘price’] lm.fit(X, Y)
    out=lm.score(X,Y)
  • What does the following line of code do? lm = LinearRegression()
  • Which of the following methods is used to calculate the average delivery time in a pandas DataFrame containing time delta columns?
  • Which of the following are valid aggregation methods that can be applied to groups in a pandas DataFrame?
  • Which function provides summary statistics, including mean and count, for a numeric column in a DataFrame?
  • What is the function of the MinMaxScaler when applied to a column of data?
  • When compiling a regression model, which of the following elements must be defined? Select all that apply.
  • What steps are involved in preparing a dataset for exploratory analysis?
  • Which of the following steps are generally involved in performing exploratory data analysis (EDA)?
  • What range of Pearson Coefficient ‘p’ is considered too high to support any certainty about the correlation of variables?
  • What is the Pearson Correlation between two variables if the input variable is equal to the output variable?
  • What is the maximum value of $R^2$ that you can obtain?

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