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Statistics for Data Scientists

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Proficiency Level
Beginner-Expert
Experience
0-8 years
Duration
45 mins
WeCP Verified
WeCP
Subject Matter Expert
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Use Case

  • Assesses key statistical concepts like skewness and central tendency.
  • Evaluates proficiency in probability theory and hypothesis testing.
  • Measures understanding of regression analysis and multicollinearity.
  • Identifies candidates adept in data analysis and handling outliers.

Skills Covered

Data Analysis
Descriptive Statistics
Probability Theory
Statistical Analysis
Regression Analysis
Inferential Statistics
Hypothesis Testing
+8 more

About

Statistics for Data Scientists

No test description provided
Target Audience
No targetAudience provided
Prerequisites
No prerequisites provided
Test Overview
Duration
45 mins
Questions
15
Passing Score
70%

Questions

The degree of skewness
Data Analysis
Data Analysis
L2
Skew
Statistics
What this question evaluates
This question evaluates the candidate's understanding of skewness in a distribution. It requires knowledge of statistical concepts and the ability to interpret the level of skewness based on the given options.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Understanding Measures of Central Tendency
Descriptive Statistics
Descriptive Statistics
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Easy
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Probability in Fair Coin Toss
Probability Theory
Probability Theory
Statistics
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Easy
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Measures of Central Tendency in Skewed Distributions
statistical analysis
statistical analysis
data science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Assessing Linearity and Homoscedasticity in Linear Regression
Linear Regression
Linear Regression
Data Science
Statistics
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Determining Statistical Significance Between Two Means
Inferential Statistics
Inferential Statistics
Statistics
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Hypothesis Testing Overview
Hypothesis Testing
Hypothesis Testing
Statistics
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding Central Limit Theorem
Inferential Statistics
Inferential Statistics
Central Limit Theorem
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Identifying Multicollinearity Issues in Regression Analysis
Data Science
Data Science
Regression Analysis
Statistics
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Hypothesis Testing for New Drug Efficacy
Statistics
Statistics
Hypothesis Testing
Data Science
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Naive Bayes Theorem Interpretation
MCQ
MCQ
Naive Bayes Theorem
Natural Language Processing
Probability and Statistics
Verified
What this question evaluates
This question evaluates the candidate's understanding of the Naive Bayes Theorem and probability concepts. It specifically tests their knowledge of the meaning of one variable in the equation.
Type:
Programming
Difficulty:
Easy
Time:
0.75 mins
Attempts:
100+
Success Rate:
70.01%
Correlation in Company Performance
Correlation Coefficients
Correlation Coefficients
Descriptive Statistics
File Input/Output
Pandas library
Python Programming
What this question evaluates
This question assesses the candidate's skill in practicing data analysis techniques using Python's pandas library, understanding of correlation coefficients, and handling of data input/output.
Type:
Programming
Difficulty:
Medium
Time:
15 mins
Attempts:
100+
Success Rate:
70.01%
Variable with clear ordering
Data Analysis
Data Analysis
L2
Ordinals
Statistics
What this question evaluates
This question evaluates the candidate's knowledge of different measurement scales used in statistics. It requires understanding of the terms nominal, ordinal, interval, and ratio, and how they describe the characteristics of variables.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
A standard normal distribution
Data Analysis
Data Analysis
L1
Normal Distribution
Statistics
What this question evaluates
This question evaluates the candidate's understanding of the mode of a standard normal distribution or curve. It requires knowledge of statistical distributions and their properties, specifically the mode of a standard normal distribution. The candidate must recognize that the mode of a standard normal distribution is always zero.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
Representing data
Data Analysis
Data Analysis
L1
Mean
Statistics
What this question evaluates
This question evaluates the candidate's understanding of statistical measures and their sensitivity to outliers. It assesses the ability to identify the impact of outliers on different measures, specifically mean, mode, and median.
Type:
Programming
Difficulty:
Medium
Time:
1 mins
Attempts:
100+
Success Rate:
70.01%
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Dashboard mockup
Diego Morales
Candidate
Passed
85%
AI Summary
Skills Performance
Score
Data Analysis
87%
Descriptive Statistics
80%
Probability Theory
85%
Statistical Analysis
82%
Areas of Improvement
Review
Statistical Analysis
Practice
Descriptive Statistics
Skill Assessment
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Retention-Focused Features
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