Dell EMC Data Science and BigData Certification Questions and Answers
Question : Which of the following statement true with regards to Linear Regression Model?
A. Ordinary Least Square can be used to estimates the parameters in linear model
B. In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.
C. Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.
D. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.
1. A,B
2. B,C
3. C,D
4. A,D
5. B,D
Correct Answer : 4
Explanation: Linear regression model are represented using the below equation
Y=B(0) + B(1)X
Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and
B(1). And similarly it is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression minimizes the sum of the squared residuals. A model fits the data well if the
differences between the observed values and the model's predicted values are small and unbiased.
Question : Which of the following is correct definition of Residual?
1. Residual is a mean squared error
2. Residual is calculated as square of actual value minus predicted value
3. Residual is calculated as Actual Value minus Predicted Values
4. Residual is calculated as Actual Value plus Predicted Values
Correct Answer : 3
Explanation: Residuals are errors in Linear Regression model and can be calculated using the Actual value minus Predicted Value. It can be positive or negative.
Question : Which of the following statement is true with regards to R square?
A. It helps in finding that how close the data are to the fitted model.
B. R Square has value between 0 to 999
C. R square 0 means the model explains none of the variability of the response data around the mean.
D. R square 999 indicates that the model explains all the variability of the response data around mean.
E. Higher the squared, the better the model fits your data.
1. A,B
2. B,C
3. A,C
4. D,E
5. A,E
Correct Answer : 3
Explanation: R square is a statistical measure to find that how close the data are in the fitted regression line. This is known as coefficient of determination.
R Square is the percentage of the response variable variation that is explained by a linear model
Which you can say R square = Explained Variation/Total Variation
R square always have value between 0% and 100%
Where 0% represents that the model explains none of the variability of the response data around the mean. And 100% represents that model are able to explain all the variability around the mean. Hence, if your model
could explain the the 100% of the variance, the fitted values would always equal to the observed values and you can say that all the data points would fall on the fitted regression line.
Related Questions
Question : Data shown in the below image falls under which category?
1. Semi-structured data
2. Structured data
3. Quasi-structured data
4. Unstructured data
Question : Data shown in below image fall under which types of data category?
1. Semi-structured data
2. Structured data
3. Quasi-structured data
4. Unstructured data
Question : Which of the following data are quasi-structured data?
1. A
2. B
3. C
4. D
Question : What a data scientists can do with the clickstream data?
A. It can be used to discover the usage patterns.
B. It helps in finding the relationships
C. It uncovers the relationships among clicks and areas of interest on a group of websites.
1. A,B
2. B,C
3. A,C
4. A,B,C
Question : What all are the benefits of using the BigData Projects which were not available in the Non-BigData solutions?
1. It gives very quick results whatever is the data volume.
2. It helps in increasing the data security, which were not available previously.
3. It helps in complex data processing as well as helps in making quick decision even for the real-time high volume data.
4. It helps in your organization technologist requirement.
5. It helps in reducing the marking team size
Question : Which of the following question statement falls under data science category?
A. What happened in last six months?
B. How many products have been sold in a last month?
C. Where is a problem for sales?
D. Which is the optimal scenario for selling this product?
E. What happens, if these scenario continues?
1. A,B
2. B,C
3. C,D
4. D,E
5. A,E