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H. Identify Potential Independent Variable

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

Question

to facilitate an accurate and meaningful look at an organization’s status, dashboard elements can be displayed in any order, but grouping related or (a) elements in (b) or with (c) colors.

Answer

grouping related or correlated elements in close proximity or with similar colors.

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

The text on the dashboard should be (a) to read, in a (b) style and size that presents the particular data well.

Answer

The text on the dashboard should be easy to read, in a font style and size that presents the particular data well.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

Dashboards can be distributed in various ways, including as a presentation, via email, or online.

status | not read | reprioritisations | ||
---|---|---|---|---|

last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

Question

Log graphs use (a) scales for both axes or for one axis, with the other axis using a conventional (b) scale.

Answer

Log graphs use logarithmic scales for both axes or for one axis, with the other axis using a conventional linear scale.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Log-log graphs have a logarithmic scale for the (a) and (b).

Answer

Log-log graphs have a logarithmic scale for the x-axis and y-axis.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A log-log graph draws out the area where the (a) values occur and, in cases where it is optimally used, shows meaningful (b).

Answer

A log-log graph draws out the area where the outlier values occur and, in cases where it is optimally used, shows meaningful comparisons.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Log-log graphs are used for displays when data ranges of both (a) and (b) values vary greatly.

Answer

Log-log graphs are used for displays when data ranges of both x and y values vary greatly.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Semi-log graphs use a (a) scale for one axis and a (b) one for the other.

Answer

Semi-log graphs use a linear scale for one axis and a logarithmic one for the other.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A sociogram, or (a) graph, is made up of (b) (vertices) and (c).

Answer

A sociogram, or network graph, is made up of nodes (vertices) and edges.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

When examining a sociogram, each (a) can be thought of as an actor and each (b) as a relationship.

Answer

When examining a sociogram, each node can be thought of as an actor and each edge as a relationship.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Some network graphs use (a) to distinguish that some connections are flowing in only one direction, and some use (b) edges (essentially, thicker or thinner lines) to show the strength or frequency of the (c).

Answer

Some network graphs use arrows to distinguish that some connections are flowing in only one direction, and some use weighted edges (essentially, thicker or thinner lines) to show the strength or frequency of the connections.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

in a network graph, a directed tie is represented by an (a).

Answer

in a network graph, a directed tie is represented by an arrow.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A sociogram of a large network can quickly become difficult to (a), and the best way to analyze a larger social network is through a (b).

Answer

A sociogram of a large network can quickly become difficult to follow, and the best way to analyze a larger social network is through a matrix.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Some experts note that people visually associate quantity with area rather than length. Therefore, it is best to set the circle’s diameter as the (a) of the quantity being mapped, so that each circle’s area is proportional to the actual quantity.

Answer

Some experts note that people visually associate quantity with area rather than length. Therefore, it is best to set the circle’s diameter as the square root of the quantity being mapped, so that each circle’s area is proportional to the actual quantity.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Data that is collected sequentially over time.

Answer

Time series data

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A statistic that identifies the center or middle value of a probability distribution.

Answer

Central tendency

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A measure of whether a probability distribution is symmetrical.

Answer

Skewness

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

One of various descriptions about the mean, where the first is zero, the second is the variance, and the third and fourth are used to define skewness and kurtosis

Answer

Moment

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Data that is without breaks, having an infinite number of possible values, in a selected range.

Answer

Continuous data

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A value that is much smaller or much greater than the other values in a dataset, or one that is outside the general pattern of the data.

Answer

Outlier

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A curve, or a type of conic section, that has an arch-like shape.

Answer

Parabola

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A nonlinear relationship in which the variables steadily increase or decrease, but not both, without abrupt reversals in fluctuation.

Answer

Monotonic

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A nonlinear relationship in which the variables oscillate inconsis- tently, making repeatable patterns difficult to observe.

Answer

Nonmonotonic

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A graph that shows the relationship between two variables or groups of paired variables.

Answer

Scatterplot

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A graphic representation of a social network

Answer

Sociogram

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

In network analytics, a point in a network.

Answer

Node (vertex)

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A link in a network.

Answer

Edge

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

An edge within a network graph that has direction

Answer

Directed tie

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

For nonlinear relationships, a linear correlation coefficient is not an appropriate indicator of (a).

Answer

For nonlinear relationships, a linear correlation coefficient is not an appropriate indicator of correlation.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

When pulling data from various sources, data scientists are often left with a very large dataset. It is a data scientist’s job to determine which data and which attributes are truly (a).

Answer

It is a data scientist’s job to determine which data and which attributes are truly valuable.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Data reduction can also be accomplished by removing variables from the modeling dataset that have no (a) value.

Answer

Data reduction can also be accomplished by removing variables from the modeling dataset that have no predictive value.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

When some of the potential predictors are correlated, it is possible to use (a) techniques to eliminate or reduce that correlation and to reduce the number of predictive variables (known as (b)).

Answer

When some of the potential predictors are correlated, it is possible to use unsupervised techniques to eliminate or reduce that correlation and to reduce the number of predictive variables (known as dimension reduction).

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Principal component analysis can be used to convert the original (a) variables into a set of variables that are (b) of each other.

Answer

Principal component analysis can be used to convert the original correlated variables into a set of variables that are independent of each other.

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

principal components are (a) combinations of the original variables that account for the (b) in the original data.

Answer

principal components are linear combinations of the original variables that account for the variance in the original data.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Principal components can be used as (a) variables instead of the (b) variables.

Answer

Principal components can be used as predictive variables instead of the original variables.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Variable clustering is another technique used for (a) reduction.

Answer

Variable clustering is another technique used for dimension reduction.

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Variable clustering is a technique that splits the variables into (a) in which the variables are more (b) to each other than with variables in (c).

Answer

Variable clustering is a technique that splits the variables into groups in which the variables are more correlated to each other than with variables in the other groups.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

To obtain an honest assessment of a model’s predictive power, it is best to test it on data that was not used to (a).

Answer

To obtain an honest assessment of a model’s predictive power, it is best to test it on data that was not used to train the model.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

sometimes alternative models are compared to each other using a test sample. When that is done, the test sample becomes (a).

Answer

sometimes alternative models are compared to each other using a test sample. When that is done, the test sample becomes contaminated.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Sometimes alternative models are compared to each other using a test sample. In these cases, it is best to reserve a (a) sample for evaluation of the (b).

Answer

Sometimes alternative models are compared to each other using a test sample. In these cases, it is best to reserve a holdout sample for evaluation of the final model.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Documenting the data preparation work is important for two reasons. First, if the model is implemented, the data used in (a) will need to be transformed and binned in the same way as the data used for (b). Second, it may be necessary to (c) the process to refresh the model in the future.

Answer

Documenting the data preparation work is important for two reasons. First, if the model is implemented, the data used in production will need to be transformed and binned in the same way as the data used for training the model. Second, it may be necessary to repeat the process to refresh the model in the future.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

status | not read | reprioritisations | ||
---|---|---|---|---|

last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

Question

The data scientist developing predictive models needs to understand both the data used to (a) the model and the data (b) that are often encountered using it.

Answer

The data scientist developing predictive models needs to understand both the data used to develop the model and the data problems that are often encountered using it.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A claims triage model predicts which claims will be more (a) to handle and may therefore require more-experienced or more-skilled (b) representatives.

Answer

A claims triage model predicts which claims will be more complex to handle and may therefore require more-experienced or more-skilled claims representatives.

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A primary concern involves the laws and regulations regarding data privacy and the related rights of the claimant. As with time-mismatch challenges, the data scientist must take into account not only what is available to build the model but also what will be available when a claims professional is actually using the model.

this describes a concern in this process

Answer

claims fraud modeling

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

identifying those that have previously been involved with fraudulent claims is a concern with this process.

Answer

claims fraud modeling

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

Often, the (a) in a claim (velocity) and the (b) of those changes (accelerataion) are as important to next-best-action models as the (c) of the claim at any given time (mass).

Answer

Often, the number of changes in a claim (velocity) and the rate of those changes (accelerataion) are as important to next-best-action models as the characteristics of the claim at any given time (mass).

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

To use data from claims notes, the data scientist must first convert text to (a) data, then ensure that the data quality is (b) for its intended use.

Answer

To use data from claims notes, the data scientist must first convert text to structured data, then ensure that the data quality is sufficient for its intended use.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

In model performance evaluation, a model’s correct predictions divided by its total predictions.

Answer

Accuracy

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

In model performance evaluation, a model’s correct positive predic- tions divided by its total positive predictions.

Answer

Precision

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

In model performance evaluation, a model’s correct positive pre- dictions divided by the sum of its correct positive predictions and incorrect negative predictions.

Answer

Recall

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

In statistics, the measure that combines precision and recall and is the harmonic mean of precision and recall.

Answer

F-score

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Question

A ratio that measures losses and loss adjustment expenses against earned premiums and that reflects the percentage of premiums being consumed by losses.

Answer

Loss ratio

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |