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Coursera What Is Data Science Quiz Answers Today ( Week 2 ) : Minning and Regression Model

Coursera What Is Data Science Quiz Answers Today ( Week 2 ) : What is Data Science Week 2 Quiz Answers,coursera what is data science quiz,coursera quiz answers today,minning,coursera regression models quiz 3,


Courses what is data science

Coursera Data Mining Models Quiz 3

Q1. According to the reading, the output of a data mining exercise largely depends on:

A. The data scientist

B. The quality of the data

C. The scope of the project

D. The programming language used

Answer : The quality of the data

Q2. Prior Variable Analysis and Principal Component Analysis are both examples of a data reduction algorithm.

A. True

B. False

Answer : False

Q3. After the data are appropriately processed, transformed, and stored, what is a good starting point for data mining?

A. Machine learning.

B. Data Visualization.

C. Non-parametric methods.

D. Creating a relational database

Answer : Data Visualization.

Q4. When data are missing in a systematic way, you can simply extrapolate the data or impute the missing data by filling in the average of the values around the missing data.

A. True

B. False

Answer : False

Q5. “Formal evaluation could include testing the predictive capabilities of the models on observed data to see how effective and efficient the algorithms have been in reproducing data.” This is known as:

A. Prototyping.

B. Overfitting.

C. In-sample forecast.

D. Reverse engineering. 

Answer : In-sample forecast

Q6. What is an example of a data reduction algorithm?

A. Cojoint Analysis.

B. A/B Testing.

C. Prior Variable Analysis.

D. Principal Component Analysis.  

Answer : Principal Component Analysis

Q7. After the data are appropriately processed, transformed, and stored, machine learning and non-parametric methods are a good starting point for data mining.

A. True 

B. False 

Answer : False

Q8. In–sample forecast is the process of formally evaluating the predictive capabilities of the models developed using observed data to see how effective the algorithms are in reproducing data.

A. True

B. False


Answer : True


Coursera Regression Models Quiz 3

Q1. Based on the reading, which of the following best describes the real added value of the author's research on residential real estate properties?

A. Quantifying people's preferences of different transport services.
B. The research determined that there was no correlation between proximity to shopping centers and housing prices.
C. Quantifying the magnitude of relationships between housing prices and different determinants.
D. The research revealed findings that opposed basic perceptions thet people hold about the real estate properties.

Answer : Quantifying the magnitude of relationships between housing prices and different determinants.

Q2. Regression is a statistical technique developed by Blaise Pascal.

A. True
B. False

Answer : False

Q3. According to the reading, the author discovered that an additional bedroom adds more to the housing prices than an additional washroom.

A. True
B. False
Answer : False

Q4. The author discovered that houses located more than 2.5 kms to shopping centers sold for less than the rest.

A. True 
B. False
Answer : False

Q5. Based on the reading, which of the following are questions that can be put to regression analysis?

A. What are typical land taxes in a house sale?
B. Do homes with brick exterior sell in rural areas?
C. What is the impact of lot size on housing price?
D. Do homes with brick exterior sell for less than homes with stone exterior?

Answer : Both C and D are Correct.

Q6. The author discovered that, all else being equal, houses located less than 5 kms but more than 2.5 kms to shopping centres sold for more than the rest.

A. True
B. False  
Answer : True

Q7. The real added value of the author’s research on residential real estate properties is quantifying people’s preferences of different transport services.

A. True
B. False

Answer : True

Q8. Regression is a statistical technique developed by Sir Frances Galton.

A. True
B. False

Answer : False

Q9. What did the author’s research reveal about proximity to large shopping centers?


A. The author discovered that proximity to large shopping centers didn’t have any significant impact on the prices of housing units.
B. The author discovered that proximity to large shopping centers had a nonlinear impact on the housing prices.
C. The author discovered that houses located more than 2.5 kms to shopping centers sold for less than the rest.
D. The author discovered that houses located more than 5 kms to shopping centers sold for less than the rest.

Answer : The author discovered that houses located more than 2.5 kms to shopping centers sold for less than the rest.

Q10. ”How much does a finished basement contribute to the price of a housing unit?” is a question that can be put to regression analysis.

A. True
B. False
Answer : True



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