It’s time to put what you’ve learned to the test, get 5 questions right to pass this unit.
Q1.
Which of the following best describes the process of predictive modeling?
Choose the correct answer.
A
Creating a static report on past data
B
Building a statistical model to make future predictions based on historical data
C
Gathering and storing data without analysis
D
Visualizing data trends without further analysis
Q2.
Why is linear regression useful in predictive modeling? (Select all that apply)
There are 2 correct answers.
A
It helps to classify data into distinct categories
B
It helps to estimate the relationship between dependent and independent variables
C
It helps to forecast future events based on historical data
D
It helps to identify outliers in the dataset
Q3.
Which of the following scenarios would benefit most from classification techniques in predictive modeling?
Choose the correct answer.
A
Predicting stock prices based on historical data
B
Forecasting future sales based on past revenue data
C
Estimating the average temperature for the upcoming week
D
Identifying fraudulent transactions in a banking system
Q4.
You are a Data Scientist working for a healthcare provider. The organization wants to predict patient re-admissions to improve patient care and reduce costs. You have access to a dataset that includes patient demographics, medical history, treatments received, and previous hospital re-admissions. Your objective is to build a predictive model that can forecast which patients are at high risk of readmission within 30 days of discharge. Given the scenario provided, which steps would you take to define the concept of data analysis and predictive modeling in your project? Select all that apply.
There are 2 correct answers.
A
Analyze historical patient data to identify patterns and risk factors associated with re-admissions
B
Use the dataset to randomly assign re-admission probabilities to patients
C
Build a predictive model to forecast future re-admissions based on historical data
D
Ignore medical history and focus only on patient demographics
Q5.
You are a Data Analyst at a logistics company that has been experiencing significant delays in delivery times. Your manager asks you to investigate the root causes of these delays and to propose data-driven strategies to improve the efficiency of the delivery process. You have access to a wealth of data, including delivery times, routes, vehicle maintenance records, driver performance metrics, and customer feedback. Given the scenario provided, how can data help you in the decision-making process to improve delivery times? Select all that apply.
There are 2 correct answers.
A
Making decisions based on intuition and guesswork
B
Identifying patterns and trends in delivery times and routes
C
Ignoring customer feedback and focusing solely on internal metrics
D
Analyzing vehicle maintenance records to determine if there is a correlation with delays
Q6.
In which scenario would time series analysis techniques be most appropriate for predictive modeling?
Choose the correct answer.
A
Forecasting future demand for a product based on time series data
B
Predicting customer churn in a telecommunications company
C
Estimating the probability of default for a loan applicant