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# Statistics in Data Science Multiple-Choice Questions (MCQs)

## What is statistics in data science?

Statistics is a set of mathematical approaches and tools that allow us to solve key data-related issues. It is broken into two sections:

1. Descriptive Statistics - this provides strategies for summarizing data by converting raw observations into meaningful information that is simple to analyze and distribute.
2. Inferential Statistics - this provides tools for studying experiments on small samples of data and drawing conclusions for the full population.

The processing of information is the most crucial part of any Data Science strategy. When we talk about gaining insights from data, we are essentially mining for possibilities. Statistical Analysis is the term used to describe these possibilities in Data Science.

Statistical analysis answers the questions like -

• What are the most crucial features?
• How should the experiment be designed in order to build our product strategy?
• What performance indicators should we track?
• What is the most usual and anticipated result?
• How can we tell the difference between noise and legitimate data?

## What is the importance of statistics for data science?

1. The majority of Data Scientists always invest more on data pre-processing. This necessitates a solid grasp of statistics. To process any data, there are a few common processes that must always be taken.
2. Determine the significance of characteristics using various statistical tests.
3. Identifying the link between characteristics in order to rule out the potential of duplicate features.
4. Converting the features to the necessary format.
5. Data normalization and scaling this stage also includes determining the distribution of data and the type of data.
6. Taking the data for further processing by making the necessary changes to the data.
7. After reviewing the data, choose the best mathematical approach/model. Once the data are acquired, they are validated using the various accuracy measuring scales.

Statistics are required at every stage of data processing, from the beginning to the finish of the entire cycle. That is why a skilled statistician can also be a good Data Scientist.

## Statistics in Data Science MCQs with Answers

Statistics in Data Science MCQs: This section contains Statistics in Data Science Multiple-Choice Questions with Answers. These MCQs are written for beginners as well as advanced, practice these MCQs to enhance and test the knowledge of Statistics in Data Science.

## List of Statistics in Data Science MCQs

1. Who is better at statistics than any programmer?

1. Data Scientist
2. Machine Learning
3. Data Science
4. None of the mentioned above

Explanation:

A data scientist is someone who is more skilled at statistics than any programmer and more skilled at programming than any statistician. Math and Statistics are vital for Data Science since they form the foundation of all Machine Learning Algorithms. Indeed, mathematics is everything around us, from forms, patterns, and colors to the number of petals in a flower. Mathematics is present in every part of our existence.

2. Machine Learning algorithms are having data-driven approach to become a Data Scientist.

1. True
2. False

Explanation:

Machine Learning techniques and a data-driven approach are required to become a Data Scientist; however, Data Science is not limited to these domains. Math and statistics are important in Data Science because they may be utilized to develop Machine Learning models.

3. What are the basic building blocks of Machine learning algorithms?

1. Math
2. Statistics
3. Both A and B
4. None of the mentioned above

Answer: C) Both A and B

Explanation:

Machine learning algorithms are built on the foundations of mathematics and statistics. To be a successful Data Scientist, we must first understand the fundamentals. Math and statistics are the foundations of Machine Learning algorithms. It is critical to understand the techniques underlying various Machine Learning algorithms in order to determine when and how to use them.

4. Statistics is a Mathematical Science pertaining to ____.

1. Data collection
2. Analysis
3. Interpretation and presentation
4. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

Statistics is a Mathematical Science that deals with the gathering, analysis, interpretation, and presentation of data. Statistics is utilized in the real world to analyze complicated issues so that Data Scientists and Analysts may seek for relevant trends and changes in data. Statistics, in a nutshell, may be used to gain useful insights from data by doing mathematical computations on it.

5. How many types of analysis in any event can be done in ____ ways.

1. One
2. Two
3. Three
4. None of the mentioned above

Explanation:

Any event may be analyzed using one of two methods: quantitative analysis or qualitative analysis.

6. How many categories are in Statistics?

1. 2
2. 4
3. 6
4. 8

Explanation:

Statistics is divided into two categories: descriptive statistics and inferential statistics. Descriptive statistics makes use of data to describe the population, either through numerical computations, graphs, or tables. Inferential Statistics draws conclusions and makes predictions about a population based on a sample of data drawn from that group.

7. ____ generalizes a large data set and applies probability to arrive at a conclusion.

1. Inferential statistics
2. Descriptive Statistics
3. Data science
4. None of the mentioned above

Explanation:

Inferential statistics generalizes a broad amount of data and employs probability to get a conclusion. It enables us to infer population parameters from sample statistics and develop models based on them. We may use inferential statistics to construct predictions ("inferences") based on the data. We use inferential statistics to generate generalizations about a population based on data from samples.

8. ____ helps organize data and focuses on the characteristics of data providing parameters.

1. Inferential Statistics
2. Descriptive Statistics
3. Both A and B
4. None of the mentioned above

Explanation:

Descriptive statistics aids in data organization and focuses on data qualities by giving parameters. Descriptive statistics are short descriptive coefficients that describe a particular data set, which might be a representation of the complete population or a sample of the population. Measures of central tendency and measures of variability are two types of descriptive statistics (spread). The mean, median, and mode are examples of measurements of central tendency, whereas standard deviation, variance, minimum and maximum variables, kurtosis, and skewness are examples of measures of variability.

9. Which are the measures of the center?

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

Answer: D) All of the mentioned above

Explanation:

The measurements of the centre are the mean, median, and mode. The average number obtained by summing all data points and dividing the total number of data points by the total number of data points. The middle number is obtained by sorting all of the data points and selecting the one in the centre. The most common number—that is, the number that appears the most frequently.

10. Central tendency measures like, mean, median, or measures of the spread, etc are used for statistical analysis.

1. True
2. False

Explanation:

Data is represented graphically in the form of graphs such as histograms, line plots, and so on. The data is represented using some sort of central tendency. For statistical analysis, central tendency measurements such as mean, median, or measures of the spread, among others, are utilized.

11. Several Statistical functions, principles and algorithms are implemented to ____.

1. Data interpretation
2. Analyze raw data
3. Data Mining
4. None of the mentioned above

Explanation:

Several statistical functions, concepts, and algorithms are used to examine raw data, construct a statistical model, and infer or forecast the outcome. Statistics has an impact on all aspects of life, including the stock market, life sciences, weather, retail, insurance, and education, to mention a few.

12. Quantitative Analysis refers to ____.

1. Data collection
2. Data interpretation
3. Identify patterns and trends
4. All of the mentioned above

Answer: B) All of the mentioned above

Explanation:

The discipline of gathering and analyzing data with numbers and graphs to detect patterns and trends is known as quantitative analysis or statistical analysis.Quantitative analysis is a strategy for understanding behavior that employs mathematical and statistical modeling, measurement, and investigation. A particular reality is represented numerically by quantitative analysts. Quantitative analysis is used to measure, evaluate, and value financial instruments, as well as anticipate real-world occurrences.

13. Qualitative or Non-Statistical Analysis gives generic information and uses text, sound, other forms of media.

1. True
2. False

Explanation:

Non-Statistical or Qualitative Analysis provides general information and employs text, sound, and other types of media. Subjective judgment is used in this case, based on "soft" or non-quantifiable evidence. Qualitative analysis deals with intangible and imprecise data that might be difficult to gather and quantify. Machines struggle with qualitative analysis because intangibles cannot be described numerically.

14. Variance describes how much a random variable differs from its expected value. It entails computing squares of deviations.

1. True
2. False

Explanation:

The variance of a random variable defines how much it deviates from its predicted value. It requires calculating deviation squares. The difference between each element and the mean is referred to as the deviation. The population variance is the sum of the squared deviations. The sample variance is the average of the squared deviations from the mean.

15. The ____ is simply the square root of the variance and measures the extent to which data varies from its mean.

1. Standard deviation
2. Mean
3. Mode
4. None of the mentioned above

Explanation:

The standard deviation is just the square root of the variance, and it represents how much data deviates from its mean. The standard deviation is frequently favored over the variance because it has the same unit as the data points, making it easier to read. Sigma's standard deviation can be stated as follows: