WorksheetsIntroduction to Problem Solving with Advanced Analytics
Total questions: 20
Worksheet time: 21mins
What is CRISP-DM?
Data modeling approach
Business analysis framework
Problem solving framework
Project management framework
What does CRISP-DM abbreviation mean?
Cross-Industry Standard Procedure for Data Management
Cross-Industry Standard Process for Data Mining
Cross-Industry Standard Platform for Digital Mindset
Cross-Industry Standard Patterns for Data Manipulation
Which of the following are valid steps in CRISP-DM?
Data preparation
Data consolidation
Data wrangling
Data understanding
Business issue understanding
Which of the following are valid steps in CRISP-DM?
Validation
Cross-validation
Presentation/visualization
Model training
Analysis/modeling
What is the correct sequence of steps in CRISP-DM?
Data understanding, business issue understanding, data preparation, analysis/modeling, validation, presentation/visualization
Data understanding, business issue understanding, data preparation, validation, analysis/modeling, presentation/visualization
Business issue understanding, data preparation, data understanding, analysis/modeling, validation, presentation/visualization
Business issue understanding, data understanding, data preparation, analysis/modeling, validation, presentation/visualization
Please select the questions that belong to business issue understanding CRISP-DM step
What data is available?
What decision needs to be made?
What data is needed?
What information is needed to inform the decision?
What type of analysis will provide the information to inform the decision?
What are the common steps used for data preparation?
Cleansing
Gathering
Formatting
Blending
Storing
Please select approaches that, according to methodology map, are suitable for addressing a problem where we want to predict a variable with specific time-based values in a data rich business problem
ARIMA
Decision trees
Boosted model
ETS
Linear regression
Please select approaches that, according to methodology map, are suitable for addressing a problem where we want to determine if a subject belongs to one of three categories in a data rich business problem:
Logistic regression
Decision tree
Forest model
Boosted model
What is an optimal approach to address a data poor business problem where we want to predict the outcome of introducing a new feature?
Numeric prediction
A/B testing
Classification
Please select non-predictive methods of data analysis:
Descriptive
Classification
Segmentation
Aggregation
Linear regression
Manager of an online shop wants to assess average order value and number of orders by customer location and customer category. What type of data analysis methodology is best for this business problem?
Predictive
Geospatial
Segmentation
Aggregation
Descriptive
An e-learning platform wants to be able to adjust their customer support agent pool based on expected number of requests and frequency of one request, taking into account such factors as season, day of week and time of day. What type of data analysis methodology is best for this business problem?
Predictive
Geospatial
Segmentation
Aggregation
Descriptive
A company producing toys wants to update the web page for their main product, and needs to assess whether the new page will generate a better customer response and more orders. What kind of data analysis will be appropriate in this case?
Classification
Regression
A/B testing
To make more attractive offers, HR department of an IT company wants to offer either a higher salary or a number of non-financial perks to new hires based on existing employee preferences. What kind of data analysis will be appropriate in this case?
Continuous numeric
Time-based numeric
Binary classification
Non-binary classification
In a linear regression equation y = mx + b, what does m denote?
Target variable
Predictor variable
Line slope
Line intercept
We have constructed a linear regression equation for predicting house price based on house square footage. The regression equation is: House Price = -46,773 + 382 * House area. We want to determine the selling price of a neighbor’s house. We know that a similar house with area smaller by 400 square feet was sold for $650,000. What would be the expected selling price of our house?
$497,200
$535,400
$673,600
$802,800
$879,200
We train a multiple linear regression model and record the r-squared. After that, we add one more feature to the regression model and retrain the model. We find that r-squared has increased. Please select the correct statements.
The added variable is significant
The added variable is not significant
R-squared provides no information about variable importance
The model’s goodness of fit has improved
How is a dummy variable coefficient interpreted in multiple linear regression?
The difference between two means
The difference between two coefficients
The difference between two r-squared values
None of the above
What are the possible cases for a dummy variable analysis?
Dependent variable – continuous, independent variable - continuous
Dependent variable – categorical, independent variable – continuous
Dependent variable – continuous, independent variable – categorical
Dependent variable – continuous, independent variable - binary
