Which of the following is NOT a type of data analysis used in Marketing research

Data Analysis MCQ Questions And Answers

This section focuses on "Data Analysis" in Data Science. These Data Science Multiple Choice Questions (MCQ) should be practiced to improve the skills required for various interviews (campus interview, walk-in interview, company interview), placements, entrance exams and other competitive examinations.

1. Data Analysis is a process of?

A. inspecting data B. cleaning data C. transforming data D. All of the above View Answer Ans : D

Explanation: Data Analysis is a process of inspecting, cleaning, transforming and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making.

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

A. Data Mining B. Predictive Intelligence C. Business Intelligence D. Text Analytics View Answer Ans : B

Explanation: Predictive Analytics is major data analysis approaches not Predictive Intelligence.

1. How many main statistical methodologies are used in data analysis?

A. 2 B. 3

C. 4

D. 5

View Answer Ans : A

Explanation: In data analysis, two main statistical methodologies are used Descriptive statistics and Inferential statistics.

1. In descriptive statistics, data from the entire population or a sample is summarized with?

A. integer descriptors B. floating descriptors C. numerical descriptors D. decimal descriptors View Answer Ans : C

Explanation: In descriptive statistics, data from the entire population or a sample is summarized with numerical descriptors.

1. Data Analysis is defined by the statistician?

A. William S. B. Hans Peter Luhn C. Gregory Piatetsky-Shapiro D. John Tukey View Answer Ans : D

Explanation: Data Analysis is defined by the statistician John Tukey in 1961 as "Procedures for analyzing data.

1. Which of the following is true about hypothesis testing?

A. answering yes/no questions about the data B. estimating numerical characteristics of the data

A. answering yes/no questions about the data B. estimating numerical characteristics of the data C. modeling relationships within the data D. describing associations within the data View Answer Ans : C

Explanation: modeling relationships within the data (E. regression analysis).

1. Text Analytics, also referred to as Text Mining?

A. TRUE B. FALSE C. Can be true or false D. Can not say View Answer Ans : A

Explanation: Text Data Mining is the process of deriving high-quality information from text.

1. Data Analytics uses ___ to get insights from data.

A. Statistical figures B. Numerical aspects C. Statistical methods D. None of the mentioned above

Answer: C) Statistical methods

Explanation:

To gain insights from data, Data Analytics use statistical approaches. Organizations can use data analytics to uncover trends and develop insights by analyzing all of their data (real-time, historical, unstructured, structured, and qualitative).

2. Amongst which of the following is / are the branch of statistics which deals with the development of statistical methods is classified as ___.

A. Industry statistics B. Economic statistics C. Applied statistics D. None of the mentioned above

Answer: C) Applied statistics

Explanation:

The discipline of statistics that works with the development of statistical procedures is known as applied statistics. Planning for data collecting, maintaining data, analyzing, interpreting, and drawing conclusions from data, and finding issues, solutions, and opportunities utilizing analysis are all part of applied statistics. In data analysis and empirical research, these major fosters critical thinking and problem-solving skills.

3. Linear Regression is the supervised machine learning model in which the model finds the best fit ___ between the independent and dependent variable.

A. Linear line B. Nonlinear line C. Curved line D. All of the mentioned above

Answer: A) Linear line

Explanation:

Linear Regression is a supervised Machine Learning model that identifies the best fit linear line between the independent and dependent variables, i., the linear connection between the dependent and independent variables.

A. True B. False

Answer: A) True

Explanation:

Linear regression analysis predicts the value of one variable depending on the value of another. The variable we wish to forecast is referred to as the dependent variable. The variable we are utilizing to predict the value of the other variable is referred to as the independent variable.

7. A Linear Regression model's main aim is to find the best fit linear line and the ___ of intercept and coefficients such that the error is minimized.

A. Optimal values B. Linear line C. Linear polynomial D. None of the mentioned above

Answer: A) Optimal values

Explanation:

The basic goal of a Linear Regression model is to determine the best fit linear line and the ideal intercept and coefficient values such that the error is minimized. A linear regression model describes the relationship between one or more independent variables, X, and a dependent variable, y. A multiple linear regression model is a type of regression model that has numerous lines of regression. A multiple linear regression model is yi = β 0+ β 1 Xi 1+ β 2 Xi 2+⋯+ βpXip + εi , i =1,⋯, n

8. Error is the difference between the actual value and Predicted value and the goal is to reduce this difference.

A. True B. False

Answer: A) True

Explanation:

In statistics, the actual value is the value derived from observation or measurement of the available data. It is also known as the observed value. The expected value is the predicted value of the variable based on the regression analysis. Linear regression is most commonly used to calculate model error using mean-square error (MSE). MSE is derived by measuring the distance between the observed and anticipated y-values at each value of x and then computing the mean of the squared distances.

9. The process of quantifying data is referred to as ___.

A. Decoding B. Structure C. Enumeration D. Coding

Answer: C) Enumeration

Explanation:

Enumeration is the term for the process of quantifying data. Any quantifiable information that can be used for mathematical calculations or statistical analysis is referred to as quantitative data. This type of information aids in the development of real-world decisions based on mathematical derivations. To answer inquiries like how many, quantitative data is used. How often do you do it? How much is it? This information can be confirmed and validated.

10. Text Analytics, also referred to as Text Mining?

A. True B. False

Answer: A) True

Explanation:

charts are another name for them. In statistics, bar graphs are one of the data management methods.

13. Data Analysis is a process of,

A. Inspecting data B. Data Cleaning C. Transforming of data D. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

The process of reviewing, cleansing, and manipulating data with the objective of identifying usable information, informing conclusions, and assisting decision- making is known as data analysis. Data analysis is important in today's business environment since it helps businesses make more scientific decisions and run more efficiently.

14. Least Square Method uses ___.

A. Linear polynomial B. Linear regression C. Linear sequence D. None of the mentioned above

Answer: B) Linear regression

Explanation:

Linear regression employs the Least Square Method. The least-squares approach is a type of mathematical regression analysis that determines the best fit line for a collection of data, displaying the relationship between the points visually. The relationship between a known independent variable and an unknown dependent variable is represented by each piece of data.

15. What is a hypothesis?

A. A statement that the researcher wants to test through the data collected in a study B. A research question the results will answer C. A theory that underpins the study D. A statistical method for calculating the extent to which the results could have happened by chance

Answer: A) A statement that the researcher wants to test through the data collected in a studyp

Explanation:

A hypothesis is a proposition that a researcher wishes to evaluate using data from a study. A hypothesis is a conclusion reached after considering evidence. This is the first step in any investigation, where the research questions are translated into a prediction. Variables, population, and the relationship between the variables are all included. A research hypothesis is a hypothesis that is tested to see if two or more variables have a relationship.

16. Linear-regression models are relatively simple and provide an easy-to- interpret mathematical formula that can generate ___.

A. Predictions B. Interpretation C. Conclusion D. None of the mentioned above

Answer: A) Predictions

Explanation:

Linear-regression models are straightforward and provide a basic mathematical method for generating predictions. Linear regression can be used in a variety of corporate and academic study.

17. Amongst which of the following is / are the applications of Linear Regression,

Answer: A) Independent variable

Explanation:

The dependent variable's distribution must be normal for each value of the independent variable. For all values of the independent variable, the variance of the dependent variable's distribution should be constant. The dependent variable should have a linear relationship with each independent variable, and all observations should be independent.

20. Residual plot helps in analyzing the model using the values of residues.

A. True B. False

Answer: A) True

Explanation:

The residue plot aids in the analysis of the model by displaying the values of the residues. It's shown as a line between the projected values and the residual. Their values are all the same. The point's distance from 0 indicates how inaccurate the prediction was for that number. If the value is positive, the probability of success is minimal. If the value is negative, the probability of success is high. A number of 0 implies that the forecast is perfect. The model can be improved by detecting residual patterns.

21. Amongst which of the following is / are not a major data analysis approach?

A. Predictive Intelligence B. Business Intelligence C. Text Analytics D. Data Mining

Answer: A) Predictive Intelligence

Explanation:

The practice of collecting data about consumers' and potential consumers' behaviors/actions from a number of sources and perhaps integrating it with profile data about their qualities is known as predictive intelligence.

22. By 2025, the volume of data will increase to,

A. TB B. YB C. ZB D. EB

Answer: C) ZB

Explanation:

It is projected that 2 quintillion bytes of data are created every day, with the volume of digital data expected to reach Zeta Byte by 2025.

23. Alternative Hypothesis is also called as?

A. Null Hypothesis B. Research Hypothesis C. Simple Hypothesis D. None of the mentioned above

Answer: B) Research Hypothesis

Explanation:

The alternative hypothesis is the assertion that is being tested against the null hypothesis. Ha or H1 are common abbreviations for alternative hypotheses. The alternative hypothesis is the hypothesis that is inferred from a null hypothesis that has been rejected. It is best stated as an explanation for why the null hypothesis was rejected. It is also known as the research hypothesis. Unlike the null hypothesis, the researcher is usually most interested in the alternative hypothesis.

Answer: A) True

Explanation:

The rate at which data is generated, distributed, and gathered is referred to as data velocity. High data velocity is created at such a rapid rate that it necessitates the use of specialized processing techniques. The faster data can be captured and processed, the more valuable the data collected will be and the longer it will hold its worth.

27. ___ refers to the ability to turn your data useful for business.

A. Value B. Variety C. Velocity D. None of the mentioned above

Answer: A) Value

Explanation:

The ability to turn our data into business value is referred to as value. The usefulness of obtained data for our business is referred to as data value. Data, regardless of its magnitude, is rarely useful on its own; to be useful, it must be transformed into insights or knowledge, which is where data processing comes in.

28. Correlation is the relationship between two variables -

A. One B. Two C. Zero D. All of the mentioned above

Answer: B) Two

Explanation:

Correlation is the strength of a relationship between two variables, and the Pearson's correlation coefficient measures how strong that relationship is. The correlation of two variables is the statistical link between them. A positive correlation means that both variables move in the same direction, while a negative correlation means that when one variable's value rises, the other variable's value falls.

29. The Mean Squared Error is a measure of the average of the squares of the residuals.

A. True B. False

Answer: A) True

Explanation:

The degree of inaccuracy in statistical models is measured by the mean squared error (MSE). The average squared difference between observed and expected values is calculated. The MSE equals zero when a model has no errors. Its value rises as the model inaccuracy rises. The mean squared deviation is another name for the mean squared deviation (MSD). The average squared residual is represented by the mean squared error in regression.

30. Logistic regression is used to find the probability of event = Success and event = ____.

A. Failure B. Success C. Both A and B D. None of the mentioned above

Answer: A) Failure

Explanation:

The likelihood of event=Success and event=Failure is calculated using logistic regression. When the dependent variable is in nature, we should utilize logistic

33. Predictive analytics involves taking historical data -

A. True B. False

Answer: A) True

Explanation:

The approach or practice of utilizing data to generate projections about the possibility of certain future events in your organization is known as predictive analytics, which is a form of advanced analytics. Predictive analytics models unknown future occurrences by combining historical and current data with advanced statistics and machine learning approaches. It is commonly characterized as utilizing data science and machine learning to learn from an organization's previous collective experience in order to make better decisions in the future.

34. With reference to Predictive analytics, it allows organizations to predict customer behavior -

A. True B. False

Answer: A) True

Explanation:

Predictive analytics enables businesses to forecast consumer behavior and business results by combining historical and real-time data. Furthermore, predictive modeling is a subset of this activity that entails constructing and maintaining models, testing and iterating with existing data, and embedding models into applications.

35. Customer analytics refers -

A. Customer Relationship Management: churn analysis and prevention B. Marketing: cross-sell, up-sell C. Pricing: leakage monitoring, promotional effects tracking, competitive price responses D. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

Customer analytics includes churn analysis and prevention, marketing: cross-sell and up-sell, and pricing: leakage monitoring, promotional effects tracking, and competitive price reactions.

36. ___ is the cyclical process of collecting and analyzing data during a research study.

A. Extremis Analysis B. Constant analysis C. Interim Analysis D. All of the mentioned above

Answer: C) Interim Analysis

Explanation:

The cyclical process of gathering and assessing data throughout a research Endeavour is known as interim analysis.

37. An advantage of using computer programs for qualitative data is that they ___.

A. Can reduce time required to analyze data B. Help in storing and organizing data C. Make many procedures available that are rarely done by hand due to time constraints D. All of the mentioned above

What is data analysis in marketing research?

What are types of data analysis?

Which of following is not research problem of marketing?

Which of the following is not one of the steps of online marketing research?