Statistics Interview Questions | Useful And Most Asked

Introduction To Statistics Interview Questions And Answers

Statistics is a branch of mathematics, mainly concerns about collection, analysis, interpretation, and presentation of tons of numerical facts. Statistics is used in almost every field of research and it’s a backbone of data science. It helps us to understand the data.

Below is the most common feature of Statistics Interview Questions, that can give you a great foundation into the language.

1. Name and explain few methods/techniques used in Statistics for analyzing the data?

Answer:

Arithmetic Mean:
It is the important technique in statistics Arithmetic Mean can also be called an average. It is the number or the quantity obtained by summing two or more numbers/variables and then dividing the sum by the number of numbers/variables.

Median:
Median is also a way of finding the average of a group of data points. It’s the middle number of a set of numbers. There are two possibilities, the data points can be an odd number group or it can be en even number group.
If the group is odd, arrange the numbers in the group from smallest to largest. The median will be the one which is exactly sitting in the middle, with an equal number on either side of it. If the group is even, arrange the numbers in order and pick the two middle numbers and add them then divide by 2. It will be the median number of that set.

Mode:
The mode is also one of the types for finding the average. A mode is a number, which occurs most frequently in a group of numbers. Some series might not have any mode; some might have two modes which is called bimodal series.

In the study of statistics, the three most common ‘averages’ in statistics are Mean, Median and Mode.

Standard Deviation (Sigma):
Standard Deviation is a measure of how much your data is spread out in statistics.

Regression:
Regression is an analysis in statistical modeling. It’s a statistical process for measuring the relationships among the variables; it determines the strength of the relationship between one variable and a series of other changing independent variables.

2. Explain about statistics branches?

Answer:
The two main branches of statistics are descriptive statistics and inferential statistics.

Descriptive statistics: Descriptive statistics summarizes the data from a sample using indexes such as mean or standard deviation.

Descriptive Statistics, methods include displaying, organizing and describing the data.

Inferential Statistics: Inferential Statistics draws the conclusions from data that are subject to random variation such as observation errors and sample variation.

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3. List all the other models work with statistics to analyze the data?

Answer:
Statistics along with Data Analytics analyzes the data and help business to make good decisions. Predictive ‘Analytics’ and ‘Statistics’ are useful to analyze current data and historical data to make predictions about future events.

4. List the fields, where statistic can be used?

Answer:
Statistics can be used in many research fields. Below are the lists of files in which statistics can be used

• Science
• Technology
• Business
• Biology
• Computer Science
• Chemistry
• It aids in decision making
• Provides comparison
• Explains action that has taken place
• Predict the future outcome
• Estimate of unknown quantities.

5. What is a linear regression in statistics?

Answer:
Linear regression is one of the statistical techniques used in a predictive analysis, in this technique will identify the strength of the impact that the independent variables show on deepened variables.

6. What is a Sample in Statistics and list the sampling methods?

Answer:
In a Statistical study, a Sample is nothing but a set of or a portion of collected or processed data from a statistical population by a structured and defined procedure and the elements within the sample are known as a sample point.

Below are the 4 sampling methods:

• Cluster Sampling: IN cluster sampling method the population will be divided into groups or clusters.
• Simple Random: This sampling method simply follows the pure random division.
• Stratified: In stratified sampling, the data will be divided into groups or strata.
• Systematical: Systematical sampling method picks every kth member of the population.

7. What is P- value and explain it?

Answer:
When we execute a hypothesis test in statistics, a p-value helps us in determine the significance of our results. These Hypothesis tests are nothing but to test the validity of a claim that is made about a population. A null hypothesis is a situation when the hypothesis and the specified population is with no significant difference due to sampling or experimental error.

8. What is Data Science and what is the relationship between Data science and Statistics?

Answer:
Data Science is simply data-driven science, it involves the interdisciplinary field of automated scientific methods, algorithms, systems, and process to extracts the insights and knowledge from data in any form, either structured or unstructured. Data Science and Data mining have similarities, both abstracts the useful information from data.

Data Sciences include Mathematical Statistics along with Computer science and Applications. By combing aspects of statistics, visualization, applied mathematics, computer science Data Science is turning the vast amount of data into insights and knowledge.

Statistics is one of the main components of the Data Science. Statistics is a branch of mathematics commerce with the collection, analysis, interpretation, organization, and presentation of data.

9. What is correlation and covariance in statistics?

Answer:
Covariance and Correlation are two mathematical concepts; these two approaches are widely used in statistics. Both Correlation and Covariance establish the relationship and also measure the dependency between two random variables. Though the work is similar between these two in mathematical terms, they are different from each other.

Correlation: Correlation is considered or described as the best technique for measuring and also for estimating the quantitative relationship between two variables. Correlation measures how strongly two variables are related.

Covariance: In covariance two items vary together and it’s a measure that indicates the extent to which two random variables change in cycle. It is a statistical term; it explains the systematic relation between a pair of random variables, wherein changes in one variable reciprocal by a corresponding change in another variable.

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