During EDA, a data scientist wants to look for patterns, such as linearity, in the dat a. Which of the following plots should the data scientist use?

• A. Box-and-whisker
• B. Violin
• C. Scatter
• D. Q-Q

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Under perfect conditions, E. coli bacteria would cover the entire earth in a matter of days. Which of the following types of models is the best for explaining this type of growth?

• A. Linear
• B. Logarithmic
• C. Polynomial
• D. Exponential

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SIMULATION
A data scientist needs to determine whether product sales are impacted by other contributing factors. The client has provided the data scientist with sales and other variables in the data set.
The data scientist decides to test potential models that include other information.
INSTRUCTIONS
Part 1
Use the information provided in the table to select the appropriate regression model.
Part 2
Review the summary output and variable table to determine which variable is statistically significant.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.

Part 1
Linear regression.
Of the four models, linear regression has the highest R² (0.8), indicating it explains the greatest proportion of variance in sales.

Part 2
Var 4 - Net operations cost.
Net operations cost has a p-value of essentially 0 (far below 0.05), indicating it is the only additional predictor statistically significant in explaining sales. Neither inventory cost (p≈0.90) nor initial investment (p≈0.23) reach significance.

A data scientist is designing a real-time machine-learning model that classifies a user based on initial behavior. The run times of these models are provided in the following table:

Which of the following models should the data scientist recommend for deployment?

• A. Decision trees
• B. XGBoost
• C. Artificial neural network
• D. Random forest

Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

SIMULATION
A client has gathered weather data on which regions have high temperatures. The client would like a visualization to gain a better understanding of the data.
INSTRUCTIONS
Part 1
Review the charts provided and use the drop-down menu to select the most appropriate way to standardize the data.
Part 2
Answer the questions to determine how to create one data set.
Part 3
Select the most appropriate visualization based on the data set that represents what the client is looking for.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.

Part 1
Select Table 2. Table 2 contains mixed temperature scales (°F and °C) that must be standardized before visualization.
Variable: Temperature/scale
Action: Correct
Value to correct: 50 °C

Part 2
Method: Data matching
Join variable: Zip code
You need to merge the two tables by aligning matching records, which is a data-matching (join) operation, and ZIP code is the shared, uniquely identifying field linking each region's weather reading to its city.

Part 3
Choose the choropleth map (the first option).
A choropleth map best shows geographic variation in temperature by coloring each state (or region) according to its recorded value. This lets the client immediately see where the highest and lowest temperatures occur across the U.S. without distracting elements like bubble size or combined chart axes.