Quiz architecture 3

What must be done to variables of a dataset before applying principal component analysis and why?

• You must scale the variables so that only outliers are considered as principal components

• You must scale the variables so that principal components are not dominated by variables of much larger scale

• You must make all variables negative to work with values of the same sign

• You must take the square root of all data values to reduce the overall magnitudes of the dataset

Which of the following can be an appropriate way to deal with missing values? (Select all that apply.)

• Removing the columns or rows with missing values

• Imputing a value with averages of all other records

• Imputing a value from 'similar' data points

• Making 'missing' its own category

Your organization asks you to analyze a dataset that shows the number of FreeFly ALTA drones sold in 2016. You noticed that only 2 drones were sold the day after Black Friday, while the average number of drones sold in 2016 is around 100 a day. What is the most probable explanation for this small data value?

• It's a missing value that someone filled in with a guess

• There was a glitch in the system, and the data value was corrupted

• It's a censored value that was inputted incorrectly

• It's a censored value; drone inventory probably ran out

What are the risks of replacing a missing value with a guess? (Select all that apply.)

• None, the database is capable of correcting input mistakes

• Introducing biases

• Distorting the dataset

• Falsifying results

Why is removing all data records with missing values often not a good way to deal with missing values? (Select all that apply.)

• Some modeling tools require a data value for each row/column

• A dataset is incomplete if there are missing values

• We may end up with too little data to conduct meaningful analysis

• The pattern of missing values can have high predictive power

What are the characteristics of an outlier? (Select all that apply.)

• It is the data point most proximal to the mean

• It is the pivot point for the overall pattern that the data follows

• It falls far outside the overall data pattern

• It is above or below 3 standard deviations of the mean

A data point is not considered an outlier unless it deviates dramatically on either the x-axis or the y-axis.

• True

• False

Why do outliers exist? (Select all that apply.)

• Data recording errors

• Legitimate but odd observations

• Entropy of a system

• Distortion of time

Which statistical measure is more resistant to outliers?

• Mean

• Median

• Standard deviation

• Range

To say a variable is degenerate means which of the following? (Select all that apply.)

• The variable is immoral and corrupt

• The variable can only take on a single value

• When plotted, the variable is modeled with an exponential decay

• The variable is a zero variance variable