# Data Analysis MCQs

## Multiple Choice Questions on Data Analysis

1. Which of the following is not a form of data analysis?

1. Descriptive analysis
2. Inferential analysis
3. Predictive analysis
4. Observational analysis

2. What comes first in the process of data analysis?

1. Collecting data
2. Cleaning data
3. Analyzing data
4. Visualizing data

3. Which of the following is not a method for displaying data?

1. Bar chart
2. Scatter plot
3. Box plot
4. Random forest

4. What is the purpose of descriptive analysis?

1. To make predictions about future data
2. To describe and summarise data
3. To evaluate data-related hypotheses
4. To identify outliers in data

Answer: B) To describe and summarise data

5. What does inferential analysis aim to accomplish?

1. To summarize and describe data
2. To make predictions about future data
3. To test hypotheses about data
4. To identify outliers in data

6. What distinguishes correlation from causation?

1. In contrast to causation, which is a direct connection between two variables, correlation is a statistical relationship between two variables.
2. In contrast to causation, which is a statistical link between two variables, correlation is a direct relationship between two variables.
3. While causation does not entail correlation, correlation indicates causation.
4. The terms "correlation" and "cause" are interchangeable.

Answer: A) In contrast to causation, which is a direct connection between two variables, correlation is a statistical relationship between two variables.

7. Which of the following is not a sampling method?

1. Random sampling
2. Stratified sampling
3. Convenience sampling
4. Correlation sampling

8. What is the purpose of predictive analysis?

1. To summarize and describe data
2. To forecast data for the future
3. To test hypotheses about data
4. To identify outliers in data

Answer: B) To forecast data for the future

9. Which of the following is an algorithm for supervised learning?

1. K-means clustering
2. Principal component analysis
3. Decision tree
4. Apriori algorithm

10. Which of the following describes a dispersion measure?

1. Mode
2. Median
3. Variance
4. Mean

11. What is the purpose of exploratory data analysis?

1. To make predictions about future data
2. To summarize and describe data
3. To evaluate data-related hypotheses
4. To spot trends and connections in data

Answer: D) To spot trends and connections in data

12. What kind of analysis does not fall within the category of regression?

1. Straight-line regression
2. A number of linear regressions
3. Logistic regression
4. Hierarchical regression

13. What distinguishes a parameter from a statistic?

1. A statistic is a characteristic of a sample, whereas a parameter is a feature of a population.
2. A statistic is a characteristic of the population, whereas a parameter is a characteristic of a sample.
3. A statistic is a measure of dispersion, whereas a parameter measures central tendency.
4. A parameter is the same as a statistic.

Answer: A) A statistic is a characteristic of a sample, whereas a parameter is a feature of a population.

14. What accomplishes cluster analysis?

1. To forecast data for the future
2. To summarize and describe data
3. To group similar observations together
4. To identify relationships between variables

Answer: C) To group similar observations together

15. Which of the following is the kind of algorithm for unsupervised learning?

1. Linear regression
2. Naive Bayes
3. K-means clustering
4. Support vector machines

16. What is the purpose of time series analysis?

1. To make predictions about future data
2. To summarize and describe data
3. To evaluate data-related hypotheses
4. Finding connections between different factors

17. Which of the following is not a kind of data transformation?

1. Standardization
2. Normalization
3. Aggregation
4. Correlation

18. What is the purpose of data mining?

1. To make predictions about future data
2. To summarize and describe data
3. To identify patterns and relationships in data
4. To test hypotheses about data

Answer: C) To identify patterns and relationships in data

19. Which of the following is a measure of association between two categorical variables?

1. Pearson's coefficient of correlation
2. Coefficient of correlation with Spearman's rank
3. Chi-squared test
4. T-test

20. What is the purpose of factor analysis?

1. To compile related observations
2. To spot trends and connections in data
3. Minimising the amount of data in a dataset that has variables
4. To make predictions about future data

Answer: C) Minimising the amount of data in a dataset that has variables

21. Which one of the following describes a data bias?

1. Sampling bias
2. Descriptive bias
3. Predictive bias
4. Inferential bias

22. What distinguishes a population from a sample?

1. A population is a subset of a sample, whereas a sample is a subset of a population.
2. A population is a collection of all possible observations, whereas a sample is a subset of the population.
3. A population is a collection of all possible samples, whereas a sample is a collection of all possible observations.
4. A population and a sample are the same thing.

Answer: B) A population is a collection of all possible observations, whereas a sample is a subset of the population.

23. Which of the following is data imputation method?

1. Mean imputation
2. Median imputation
3. Mode imputation
4. All of the above

Answer: D) All of the above

24. What does cross-validation provide as a means of?

1. To gauge model effectiveness using fresh data
2. To identify outliers in data
3. To group similar observations together
4. To visualize relationships between variables

Answer: A) To gauge model effectiveness using fresh data

25. What kind of data distribution is not one of the following?

1. Skewed distribution
2. Bimodal distribution
3. Multimodal distribution
4. Hierarchical distribution

26. What is the purpose of data cleaning?

1. To make predictions about future data
2. To summarize and describe data
3. To test hypotheses about data
4. To correct errors and inconsistencies in data

Answer: D) To correct errors and inconsistencies in data

27. Which of the following is a type of sampling error?

1. Measurement error
2. Non-response error
3. Sampling bias
4. Sampling variance

28. What is the purpose of text mining?

1. To compile related observations
2. To find correlations and patterns in text data
3. To reduce the number of variables in a text dataset
4. To make predictions about future text data

Answer: B) To find correlations and patterns in text data

29. Which of the following is a measure of central tendency?

1. Variance
2. Standard deviation
3. Mean
4. Skewness

30. Which of the following is a kind of data visualisation?

1. Scatter plot
2. Box plot
3. Bar chart
4. All of the above

Answer: D) All of the above

31. What is the purpose of hypothesis testing?

1. To make predictions about future data
2. To summarize and describe data
3. To test whether a sample statistic differs significantly from a population parameter
4. To spot trends and connections in data

Answer: C) To test whether a sample statistic differs significantly from a population parameter

32. Which of the following is a type of time series analysis?

1. Autoregressive integrated moving average (ARIMA)
2. Principal component analysis (PCA)
3. Decision tree analysis
4. K-nearest neighbors (KNN)

Answer: A) Autoregressive integrated moving average (ARIMA)

33. What is the purpose of data normalization?

1. Minimising the amount of data in a dataset that has variables
2. Converting data to a common scale
3. Combining comparable observations to find patterns and
4. Connections in data

Answer: B) Converting data to a common scale

34. Which of the following options does not fall within the category of data mining?

1. Association rule mining
2. Clustering
3. Neural networks
4. Linear regression

35. What is the purpose of data wrangling?

1. To make predictions about future data
2. To summarize and describe data
3. Data transformation and cleaning
4. To spot trends and connections in data

Answer: C) Data transformation and cleaning

36. Which of the following is a type of data feature selection?

1. Recursive feature elimination
2. Principal component analysis
3. Lasso regression
4. All of the above

Answer: D) All of the above

37. What is the purpose of correlation analysis?

1. To spot trends and connections in data
2. To group similar observations together
3. To test hypotheses about data
4. To make predictions about future data

Answer: A) To spot trends and connections in data

38. Which of the following is a kind of data interpolation?

1. Linear interpolation
3. Cubic interpolation
4. All of the above

Answer: D) All of the above

39. What does data sampling accomplish?

1. Minimising the amount of data in a dataset that has variables
2. To transform and clean data
3. To select a subset of observations from a population
4. To spot trends and connections in data

Answer: C) To select a subset of observations from a population

40. Which of the following is a type of data clustering?

1. K-means clustering
2. Decision tree analysis
3. Neural networks
4. Linear regression

41. What does data preparation serve?

1. To make predictions about future data
2. To summarize and describe data
3. Data transformation and cleaning
4. To spot trends and connections in data

Answer: C) Data transformation and cleaning

42. Which of the following is a type of data outlier detection?

1. Z-score method
2. Interquartile range (IQR) method
3. Cook's distance method
4. All of the above

Answer: D) All of the above

43. What function does data visualisation serve?

1. To summarize and describe data
2. To test hypotheses about data
3. To identify patterns and relationships in data
4. To transform and clean data

Answer: C) To identify patterns and relationships in data

44. What kind of reasoning is based on statistics?

1. Regression analysis
2. Hypothesis testing
3. Cluster analysis
4. Time series analysis

45. What is the purpose of data imputation?

1. Minimising the amount of data in a dataset that has variables
2. Data transformation and cleaning
3. Calculating the missing values in a dataset
4. To spot trends and connections in data

Answer: C) Calculating the missing values in a dataset

46. Which of the following is a type of dimensionality reduction technique?

1. Principal component analysis
2. Linear regression
3. Decision tree analysis
4. K-nearest neighbours

47. What is the purpose of data feature extraction?

1. To forecast data for the future
2. To spot trends and connections in data
3. Data transformation and cleaning
4. Minimising the amount of data in a dataset that has variables

Answer: D) Minimising the amount of data in a dataset that has variables

48. What is the purpose of data rescaling?

1. To group similar observations together
2. To transform data to a standard scale
3. To select a subset of observations from a population
4. To spot trends and connections in data

Answer: B) To transform data to a standard scale

49. Which of the following is a type of data transformation?

1. Log transformation
2. Exponential transformation
3. Square root transformation
4. All of the above

Answer: D) All of the above

50. What is the purpose of data standardization?

1. Minimising the amount of data in a dataset that has variables
2. To transform and clean data
3. To transform data to a standard scale
4. To spot trends and connections in data

Answer: C) To transform data to a standard scale

51. Which of the following is a type of data validation?

1. Hypothesis testing
2. Cross-validation
3. Principal component analysis
4. K-means clustering

52. What is the purpose of data augmentation?

1. To make predictions about future data
2. To summarize and describe data
3. In order to expand a dataset
4. To spot trends and connections in data

Answer: C) In order to expand a dataset

53. What kind of data fusion is which of the following?

1. Feature extraction
2. Feature selection
3. Data integration
4. Data cleaning

54. What is the purpose of data privacy?

1. To guarantee the data is correct
2. To safeguard private data in a dataset
3. To identify patterns and relationships in data
4. To transform and clean data

Answer: B) To safeguard private data in a dataset

55. Which of the following is a type of data smoothing?

1. Interpolation
2. Extrapolation
3. Moving average
4. Outlier detection

56. What is the purpose of data enrichment?

1. Minimising the amount of data in a dataset that has variables
2. To transform and clean data
3. To enhance a dataset with additional information
4. To spot trends and connections in data

57. Which of the following is a type of data governance?

1. Data privacy
2. Data analysis
3. Data modelling
4. Data quality management

58. What is the purpose of data profiling?

1. To describe and summarise data
2. To spot trends and connections in data
3. To transform and clean data
4. To test hypotheses about data

Answer: A) To summarize and describe data

59. Which of the following is a type of data reduction?

1. Principal component analysis
2. Linear regression
3. Decision tree analysis
4. K-nearest neighbours

60. What is the purpose of data aggregation?

1. Data transformation and cleaning
2. To spot trends and connections in data
3. To group similar observations together
4. To make predictions about future data

Answer: C) To group similar observations together

61. Which of the following is a type of data linkage?

1. Data validation
2. Data integration
3. Data cleaning
4. Data transformation

62. What is the purpose of data partitioning?

1. Data transformation and cleaning
2. To spot trends and connections in data
3. The act of dividing a dataset into training and test sets
4. To reduce the number of variables in a dataset

Answer: C) The act of dividing a dataset into training and test sets

63. What is the purpose of data normalization?

1. Data transformation and cleaning
2. Minimising the amount of data in a dataset that has variables
3. Converting data to a common scale
4. To spot trends and connections in data

Answer: C) Converting data to a common scale

64. What is the purpose of data classification?

1. To spot trends and connections in data
2. To make predictions about future data
3. To transform and clean data
4. Minimising the amount of data in a dataset that has variables