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Which of the following describes the appropriate use case for PCA?
Principal Component Analysis transforms correlated features into a smaller set of uncorrelated components that capture most of the variance, making it ideal for reducing dimensionality before modeling or visualization.
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 R (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 (p0.90) nor initial investment (p0.23) reach significance.
Which of the following is best solved with graph theory?
The traveling-salesman problem is a prototypical graph theory challenge, finding the shortest tour through a graph's nodes, whereas the other tasks rely on different domains (OCR on image processing, fraud detection often on statistical/anomaly methods, bandit problems on sequential decision theory).
A movie production company would like to find the actors appearing in its top movies using data from the tables below. The resulting data must show all movies in Table 1, enriched with actors listed in Table 2.
Which of the following query operations achieves the desired data set?
A LEFT JOIN returns every row from Table 1 (all top movies) and brings in matching actors from Table 2 where the Movie = Acted_In, leaving NULLs for movies without listed actors.
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?
Scatter plots display pairs of numeric values on two axes, letting you visually assess relationships and patterns, such as linear trends, between variables.
