Page 143 - 2019 - Leaders in Legal Business (n)
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data. Furthermore, software can depict those features of the data in graphics, such as histograms,
density plots, scatterplots, and bar charts.
Correlations between variables in the data.
It may be useful for legal managers to see how one variable (an element of data tracked for
every associate, client, lawyer, or whatever) moves up or down in relation to the average when
another variable changes. Thus, for instance, the software can show the correlation between the
number of matters worked on by a lawyer and billable hours reported. A correlation tells you
whether there is an association between two variables, how strong it is, and in what direction the
variables move. It is a positive correlation if both numbers move in the same direction (such as
higher billable hours and higher bonuses); it is a negative correlation if the numbers move in the
opposite direction (such as higher billable hours and lower psychological well-being).
Comparisons of averages and differences.
Several statistical tools can detect whether the difference between two or more numbers
has significance mathematically. So, for example, there are many techniques to tell whether the
average billable hours in a year between two offices of a law firm vary enough for managers to
consider intervening and taking some action. These tools, such as ANOVA and the Student’s T-
Test, help to determine whether variations are important enough to deserve discussion.
Measures of inequality.
Managers of lawyers may want to assess the quality of a set of numbers, such as bonus
distributions. Along with the well-known Gini coefficient, several other measures allow software
to put a number on inequality and even pinpoint where in the set of numbers the actual data
diverges from theoretical equality. These analytics help managers explain their decisions and make
better decisions in the first place, if equality is sought.
Understand influence of variables and make predictions.
A whole family of regression tools goes beyond correlations. If, for example, a firm wants
to predict the estimated amount of fees to be paid to it during the coming year, it could run a linear
regression. The software would then point out which of the variables was more influential in
predicting total fees paid and how much of the total fee paid is accounted for by the variables. One
of the best-known techniques is multiple linear regression. It makes some assumptions about the
relationships between whatever value is being predicted and the variables that are associated with
it (e.g., level of the person retaining the firm, presence of a law department, range of practice
groups involved, and years as a client).
The regression algorithms generate a “model.” Once you have a model, you can extract
information from it. A model often takes in data and makes predictions regarding new cases,
clients, or matters. Think of a model as the software learning on a “training set” of data that has
been labeled, such as settled for less than $10,000 or not) and applying that learning to predict
something (maybe total fees) for a new case or example. With multiple regression, naïve Bayes
algorithm, or neural nets prediction is a common output. For example, given a few dozen instances
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density plots, scatterplots, and bar charts.
Correlations between variables in the data.
It may be useful for legal managers to see how one variable (an element of data tracked for
every associate, client, lawyer, or whatever) moves up or down in relation to the average when
another variable changes. Thus, for instance, the software can show the correlation between the
number of matters worked on by a lawyer and billable hours reported. A correlation tells you
whether there is an association between two variables, how strong it is, and in what direction the
variables move. It is a positive correlation if both numbers move in the same direction (such as
higher billable hours and higher bonuses); it is a negative correlation if the numbers move in the
opposite direction (such as higher billable hours and lower psychological well-being).
Comparisons of averages and differences.
Several statistical tools can detect whether the difference between two or more numbers
has significance mathematically. So, for example, there are many techniques to tell whether the
average billable hours in a year between two offices of a law firm vary enough for managers to
consider intervening and taking some action. These tools, such as ANOVA and the Student’s T-
Test, help to determine whether variations are important enough to deserve discussion.
Measures of inequality.
Managers of lawyers may want to assess the quality of a set of numbers, such as bonus
distributions. Along with the well-known Gini coefficient, several other measures allow software
to put a number on inequality and even pinpoint where in the set of numbers the actual data
diverges from theoretical equality. These analytics help managers explain their decisions and make
better decisions in the first place, if equality is sought.
Understand influence of variables and make predictions.
A whole family of regression tools goes beyond correlations. If, for example, a firm wants
to predict the estimated amount of fees to be paid to it during the coming year, it could run a linear
regression. The software would then point out which of the variables was more influential in
predicting total fees paid and how much of the total fee paid is accounted for by the variables. One
of the best-known techniques is multiple linear regression. It makes some assumptions about the
relationships between whatever value is being predicted and the variables that are associated with
it (e.g., level of the person retaining the firm, presence of a law department, range of practice
groups involved, and years as a client).
The regression algorithms generate a “model.” Once you have a model, you can extract
information from it. A model often takes in data and makes predictions regarding new cases,
clients, or matters. Think of a model as the software learning on a “training set” of data that has
been labeled, such as settled for less than $10,000 or not) and applying that learning to predict
something (maybe total fees) for a new case or example. With multiple regression, naïve Bayes
algorithm, or neural nets prediction is a common output. For example, given a few dozen instances
128
