SlideShare una empresa de Scribd logo
1 de 23
Quantitative
Methods
for
Lawyers Class #13
Students “t” Distribution
@ computational
computationallegalstudies.com
professor daniel martin katz danielmartinkatz.com
lexpredict.com slideshare.net/DanielKatz
Students “T”
Distribution
Students “T” Distribution
v. Normal Distribution
is then distributed Standard Normal
Let X1, X2,..., Xn be drawn from N ( μ,σ )
We have learned that
But typically - we do not actually know σ
If we know σ than we can use Z Scores
Student “T” Distribution is preferred statistic for dealing with
continuous data
Students “T”
Distribution
Sample sizes are sometimes small, and often we do not know
the standard deviation of the population.
When either of these problems occur, statisticians rely on “t”
distribution
The t distributions were discovered by William S. Gosset
in 1908.
Students “T”
Distribution
Goal for Gosset: Determine the Likelihood that any
particular sample represented the true quality of the
entire product
Comparing the Mean of Population and
Mean of a Given Sample
Gosset was a statistician employed by the Guinness
brewing company which had stipulated that he not
publish under his own name.
He therefore wrote under the pen name “Student.”
Students “T”
Distribution
The t distribution should NOT be used with small
samples from populations that are NOT approximately
normal
Students “T”
Distribution
The particular form of the t distribution is determined
by its degrees of freedom
Students “T”
Distribution
NOTE: T-Distribution Converges to the Normal Distribution
A Student's t distribution converges to a normal distribution
when the number of degrees of freedom N becomes large
(converges to infinity).
http://www.nku.edu/~longa/stats/taryk/TDist.html
Students “T”
Distribution
A Student's t distribution when the N is small
Otherwise, use Normal and “Z Scores”
If the sample is small, n < 30, we use t and if
the sample is large, n ≥ 30, we use z.
What is “Small” in this context?
Students “T”
Distribution
http://www.nku.edu/~longa/stats/
taryk/TDist.html
Students “T”
Distribution
Different Forms
Comparing the Means
of Two Samples
Single Sample T Test Problem
Students “T”
Distribution
Acme Corporation manufactures light bulbs. The CEO
claims that an average Acme light bulb lasts 300 days. A
researcher randomly selects 15 bulbs for testing. The
sampled bulbs last an average of 290 days, with a
standard deviation of 50 days.
If the CEO’s claim were true, what is the probability that
15 randomly selected bulbs would have an average life
of no more than 290 days?
Students “T”
Distribution
Acme Corporation manufactures light bulbs. The CEO
claims that an average Acme light bulb lasts 300 days. A
researcher randomly selects 15 bulbs for testing. The
sampled bulbs last an average of 290 days, with a
standard deviation of 50 days.
If the CEO’s claim were true, what is the probability that
15 randomly selected bulbs would have an average life
of no more than 290 days?
This is Single Sample T Test Problem
Students “T”
Distribution
Students “T”
Distribution
P Value
Students “T”
Distribution
http://stattrek.com/Tables/T.aspx
Example From Our Book
Involving Damage Awards
235,000
175,000
750,000
230,000
450,000
150,000
1,000,060
910,000
150,000
220,000
130,000
170,000
234,000
450,000
890,000
101,000
120,000
560,000
321,000
456,000
102,000
30,000
793,000
250,900
862,000
673,000
463,000
54,000
39,000
687,000
260,800
682,000
3,514,000
67,000
356,000
13,000
42,000
4,000
402,000
943,000
961,600
630,000
398,800
52,000
976,500
540,000
Awards in Rest of State Awards in Bloom County
N = 21
N = 25
235,000
175,000
750,000
230,000
450,000
150,000
1,000,060
910,000
150,000
220,000
130,000
170,000
234,000
450,000
890,000
101,000
120,000
560,000
321,000
456,000
102,000
30,000
793,000
250,900
862,000
673,000
463,000
54,000
39,000
687,000
260,800
682,000
3,514,000
67,000
356,000
13,000
42,000
4,000
402,000
943,000
961,600
630,000
398,800
52,000
976,500
540,000
Awards in Rest of State Awards in Bloom County
N = 21
N = 25
Are Damage Awards in Bloom
County Excessive?
H0: There is No Difference Between the Mean Damage Award
in Bloom County and the Mean Damage Award in the Rest of
the State
This is
a
Two
Sample
Problem
H0: There is No Difference Between the Mean Damage
Award in Bloom County and the Mean Damage Award in
the Rest of the State
Num of Obs. Mean Std. Dev.
GROUP 1
Rest of State
21 $371,621 $289,823
GROUP 2
Bloom County
25 $547,784 $703,314
Here is the Data Set With 2 Variables:
Award = Award Amount in Dollars
Bloom = Indicator Variable
( where 1 = award in Bloom County )
( where 0 = award in rest of the State)
There are Various Approaches
You Might Take
You can then load this into
On the Left I Manually Entered
the Data is in Excel
Then you can calculate the two mean test
Use an online t-test calculator
http://www.graphpad.com/quickcalcs/ttest1.cfm
Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@

Más contenido relacionado

La actualidad más candente

Normal or skewed distributions (inferential)
Normal or skewed distributions (inferential)Normal or skewed distributions (inferential)
Normal or skewed distributions (inferential)Ken Plummer
 
Normal or skewed distributions (descriptive both2)
Normal or skewed distributions (descriptive both2)Normal or skewed distributions (descriptive both2)
Normal or skewed distributions (descriptive both2)Ken Plummer
 
Math533 finalexamreviewapr13
Math533 finalexamreviewapr13Math533 finalexamreviewapr13
Math533 finalexamreviewapr13Brent Heard
 
Infernetial vs desctiptive (jejit + indepth)
Infernetial vs desctiptive (jejit + indepth)Infernetial vs desctiptive (jejit + indepth)
Infernetial vs desctiptive (jejit + indepth)Ken Plummer
 
Confidence intervals
Confidence intervalsConfidence intervals
Confidence intervalsTanay Tandon
 
Psych stats Probability and Probability Distribution
Psych stats Probability and Probability DistributionPsych stats Probability and Probability Distribution
Psych stats Probability and Probability DistributionMartin Vince Cruz, RPm
 
Chapter2 slides-part 2-harish complete
Chapter2 slides-part 2-harish completeChapter2 slides-part 2-harish complete
Chapter2 slides-part 2-harish completeEasyStudy3
 
Pengenalan Ekonometrika
Pengenalan EkonometrikaPengenalan Ekonometrika
Pengenalan EkonometrikaXYZ Williams
 
Probability distribution in R
Probability distribution in RProbability distribution in R
Probability distribution in RAlichy Sowmya
 
Lecture 5: Interval Estimation
Lecture 5: Interval Estimation Lecture 5: Interval Estimation
Lecture 5: Interval Estimation Marina Santini
 
law of large number and central limit theorem
 law of large number and central limit theorem law of large number and central limit theorem
law of large number and central limit theoremlovemucheca
 
Nature of the data practice
Nature of the data   practiceNature of the data   practice
Nature of the data practiceKen Plummer
 
Probability Distribution
Probability DistributionProbability Distribution
Probability DistributionSarabjeet Kaur
 
Math533 finalexamreviewfeb13
Math533 finalexamreviewfeb13Math533 finalexamreviewfeb13
Math533 finalexamreviewfeb13Brent Heard
 

La actualidad más candente (20)

Normal or skewed distributions (inferential)
Normal or skewed distributions (inferential)Normal or skewed distributions (inferential)
Normal or skewed distributions (inferential)
 
Normal or skewed distributions (descriptive both2)
Normal or skewed distributions (descriptive both2)Normal or skewed distributions (descriptive both2)
Normal or skewed distributions (descriptive both2)
 
Math533 finalexamreviewapr13
Math533 finalexamreviewapr13Math533 finalexamreviewapr13
Math533 finalexamreviewapr13
 
Infernetial vs desctiptive (jejit + indepth)
Infernetial vs desctiptive (jejit + indepth)Infernetial vs desctiptive (jejit + indepth)
Infernetial vs desctiptive (jejit + indepth)
 
Confidence intervals
Confidence intervalsConfidence intervals
Confidence intervals
 
Psych stats Probability and Probability Distribution
Psych stats Probability and Probability DistributionPsych stats Probability and Probability Distribution
Psych stats Probability and Probability Distribution
 
Estimation
EstimationEstimation
Estimation
 
Chapter 3
Chapter 3Chapter 3
Chapter 3
 
Chapter2 slides-part 2-harish complete
Chapter2 slides-part 2-harish completeChapter2 slides-part 2-harish complete
Chapter2 slides-part 2-harish complete
 
Pengenalan Ekonometrika
Pengenalan EkonometrikaPengenalan Ekonometrika
Pengenalan Ekonometrika
 
Stats chapter 12
Stats chapter 12Stats chapter 12
Stats chapter 12
 
Probability distribution in R
Probability distribution in RProbability distribution in R
Probability distribution in R
 
Lecture 5: Interval Estimation
Lecture 5: Interval Estimation Lecture 5: Interval Estimation
Lecture 5: Interval Estimation
 
Testing a Claim About a Mean
Testing a Claim About a MeanTesting a Claim About a Mean
Testing a Claim About a Mean
 
Confidence interval
Confidence intervalConfidence interval
Confidence interval
 
Law of large numbers
Law of large numbersLaw of large numbers
Law of large numbers
 
law of large number and central limit theorem
 law of large number and central limit theorem law of large number and central limit theorem
law of large number and central limit theorem
 
Nature of the data practice
Nature of the data   practiceNature of the data   practice
Nature of the data practice
 
Probability Distribution
Probability DistributionProbability Distribution
Probability Distribution
 
Math533 finalexamreviewfeb13
Math533 finalexamreviewfeb13Math533 finalexamreviewfeb13
Math533 finalexamreviewfeb13
 

Similar a Quantitative Methods for Lawyers - Class #13 - Students "t" Distribution - Professor Daniel Martin Katz

Two Means, Two Dependent Samples, Matched Pairs
Two Means, Two Dependent Samples, Matched PairsTwo Means, Two Dependent Samples, Matched Pairs
Two Means, Two Dependent Samples, Matched PairsLong Beach City College
 
The t Test for Related.docx
The t Test for Related.docxThe t Test for Related.docx
The t Test for Related.docxchristalgrieg
 
Student's T test distributions & its Applications
Student's T test distributions & its Applications Student's T test distributions & its Applications
Student's T test distributions & its Applications vidit jain
 
DataHandlingStatistics.ppt
DataHandlingStatistics.pptDataHandlingStatistics.ppt
DataHandlingStatistics.pptssuser7f3860
 
Estimation and confidence interval
Estimation and confidence intervalEstimation and confidence interval
Estimation and confidence intervalHomework Guru
 
The Problem Statement By Dr. Marilyn Simon Find this a.docx
The Problem Statement By Dr. Marilyn Simon Find this a.docxThe Problem Statement By Dr. Marilyn Simon Find this a.docx
The Problem Statement By Dr. Marilyn Simon Find this a.docxoscars29
 
Converting-a-Normal-Random-Variable-to-a-Standard.pptx
Converting-a-Normal-Random-Variable-to-a-Standard.pptxConverting-a-Normal-Random-Variable-to-a-Standard.pptx
Converting-a-Normal-Random-Variable-to-a-Standard.pptxborielroy279
 
statistical inference.pptx
statistical inference.pptxstatistical inference.pptx
statistical inference.pptxSoujanyaLk1
 
Nargis present new 1
Nargis present new 1Nargis present new 1
Nargis present new 1umer6717
 
INTRODUCTION TO HYPOTHESIS TESTING Chapters 9 and 11 D.docx
INTRODUCTION TO HYPOTHESIS TESTING Chapters 9 and 11 D.docxINTRODUCTION TO HYPOTHESIS TESTING Chapters 9 and 11 D.docx
INTRODUCTION TO HYPOTHESIS TESTING Chapters 9 and 11 D.docxmariuse18nolet
 
Statistics assignment on statistical inference
Statistics assignment on statistical inferenceStatistics assignment on statistical inference
Statistics assignment on statistical inferencesadiakarim8
 
T- Distribution Report
T- Distribution ReportT- Distribution Report
T- Distribution ReportBahzad5
 
F ProjHOSPITAL INPATIENT P & L20162017Variance Variance Per DC 20.docx
F ProjHOSPITAL INPATIENT P & L20162017Variance Variance Per DC 20.docxF ProjHOSPITAL INPATIENT P & L20162017Variance Variance Per DC 20.docx
F ProjHOSPITAL INPATIENT P & L20162017Variance Variance Per DC 20.docxmecklenburgstrelitzh
 
jhghgjhgjhgjhfhcgjfjhvjhjgjkggjhgjhgjhfjgjgfgfhgfhg
jhghgjhgjhgjhfhcgjfjhvjhjgjkggjhgjhgjhfjgjgfgfhgfhgjhghgjhgjhgjhfhcgjfjhvjhjgjkggjhgjhgjhfjgjgfgfhgfhg
jhghgjhgjhgjhfhcgjfjhvjhjgjkggjhgjhgjhfjgjgfgfhgfhgUMAIRASHFAQ20
 

Similar a Quantitative Methods for Lawyers - Class #13 - Students "t" Distribution - Professor Daniel Martin Katz (20)

02a one sample_t-test
02a one sample_t-test02a one sample_t-test
02a one sample_t-test
 
lecture8.ppt
lecture8.pptlecture8.ppt
lecture8.ppt
 
Lecture8
Lecture8Lecture8
Lecture8
 
Two Means, Two Dependent Samples, Matched Pairs
Two Means, Two Dependent Samples, Matched PairsTwo Means, Two Dependent Samples, Matched Pairs
Two Means, Two Dependent Samples, Matched Pairs
 
The t Test for Related.docx
The t Test for Related.docxThe t Test for Related.docx
The t Test for Related.docx
 
Student's T test distributions & its Applications
Student's T test distributions & its Applications Student's T test distributions & its Applications
Student's T test distributions & its Applications
 
DataHandlingStatistics.ppt
DataHandlingStatistics.pptDataHandlingStatistics.ppt
DataHandlingStatistics.ppt
 
What is a t test
What is a t testWhat is a t test
What is a t test
 
Estimation and confidence interval
Estimation and confidence intervalEstimation and confidence interval
Estimation and confidence interval
 
The Problem Statement By Dr. Marilyn Simon Find this a.docx
The Problem Statement By Dr. Marilyn Simon Find this a.docxThe Problem Statement By Dr. Marilyn Simon Find this a.docx
The Problem Statement By Dr. Marilyn Simon Find this a.docx
 
Converting-a-Normal-Random-Variable-to-a-Standard.pptx
Converting-a-Normal-Random-Variable-to-a-Standard.pptxConverting-a-Normal-Random-Variable-to-a-Standard.pptx
Converting-a-Normal-Random-Variable-to-a-Standard.pptx
 
statistical inference.pptx
statistical inference.pptxstatistical inference.pptx
statistical inference.pptx
 
Nargis present new 1
Nargis present new 1Nargis present new 1
Nargis present new 1
 
05inference_2011.ppt
05inference_2011.ppt05inference_2011.ppt
05inference_2011.ppt
 
INTRODUCTION TO HYPOTHESIS TESTING Chapters 9 and 11 D.docx
INTRODUCTION TO HYPOTHESIS TESTING Chapters 9 and 11 D.docxINTRODUCTION TO HYPOTHESIS TESTING Chapters 9 and 11 D.docx
INTRODUCTION TO HYPOTHESIS TESTING Chapters 9 and 11 D.docx
 
Statistics assignment on statistical inference
Statistics assignment on statistical inferenceStatistics assignment on statistical inference
Statistics assignment on statistical inference
 
T- Distribution Report
T- Distribution ReportT- Distribution Report
T- Distribution Report
 
F ProjHOSPITAL INPATIENT P & L20162017Variance Variance Per DC 20.docx
F ProjHOSPITAL INPATIENT P & L20162017Variance Variance Per DC 20.docxF ProjHOSPITAL INPATIENT P & L20162017Variance Variance Per DC 20.docx
F ProjHOSPITAL INPATIENT P & L20162017Variance Variance Per DC 20.docx
 
jhghgjhgjhgjhfhcgjfjhvjhjgjkggjhgjhgjhfjgjgfgfhgfhg
jhghgjhgjhgjhfhcgjfjhvjhjgjkggjhgjhgjhfjgjgfgfhgfhgjhghgjhgjhgjhfhcgjfjhvjhjgjkggjhgjhgjhfjgjgfgfhgfhg
jhghgjhgjhgjhfhcgjfjhvjhjgjkggjhgjhgjhfjgjgfgfhgfhg
 
Poster template
Poster templatePoster template
Poster template
 

Más de Daniel Katz

Legal Analytics versus Empirical Legal Studies - or - Causal Inference vs Pre...
Legal Analytics versus Empirical Legal Studies - or - Causal Inference vs Pre...Legal Analytics versus Empirical Legal Studies - or - Causal Inference vs Pre...
Legal Analytics versus Empirical Legal Studies - or - Causal Inference vs Pre...Daniel Katz
 
Can Law Librarians Help Law Become More Data Driven ? An Open Question in Ne...
Can Law Librarians Help Law Become More Data Driven ?  An Open Question in Ne...Can Law Librarians Help Law Become More Data Driven ?  An Open Question in Ne...
Can Law Librarians Help Law Become More Data Driven ? An Open Question in Ne...Daniel Katz
 
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...Daniel Katz
 
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...Daniel Katz
 
Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...
Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...
Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...Daniel Katz
 
Law + Complexity & Prediction: Toward a Characterization of Legal Systems as ...
Law + Complexity & Prediction: Toward a Characterization of Legal Systems as ...Law + Complexity & Prediction: Toward a Characterization of Legal Systems as ...
Law + Complexity & Prediction: Toward a Characterization of Legal Systems as ...Daniel Katz
 
Building Your Personal (Legal) Brand - Some Thoughts for Law Students and Oth...
Building Your Personal (Legal) Brand - Some Thoughts for Law Students and Oth...Building Your Personal (Legal) Brand - Some Thoughts for Law Students and Oth...
Building Your Personal (Legal) Brand - Some Thoughts for Law Students and Oth...Daniel Katz
 
Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Tog...
Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Tog...Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Tog...
Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Tog...Daniel Katz
 
Artificial Intelligence and Law - 
A Primer
Artificial Intelligence and Law - 
A Primer Artificial Intelligence and Law - 
A Primer
Artificial Intelligence and Law - 
A Primer Daniel Katz
 
Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) ...
Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) ...Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) ...
Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) ...Daniel Katz
 
Technology, Data and Computation Session @ The World Bank - Law, Justice, and...
Technology, Data and Computation Session @ The World Bank - Law, Justice, and...Technology, Data and Computation Session @ The World Bank - Law, Justice, and...
Technology, Data and Computation Session @ The World Bank - Law, Justice, and...Daniel Katz
 
LexPredict - Empowering the Future of Legal Decision Making
LexPredict - Empowering the Future of Legal Decision MakingLexPredict - Empowering the Future of Legal Decision Making
LexPredict - Empowering the Future of Legal Decision MakingDaniel Katz
 
{Law, Tech, Design, Delivery} Observations Regarding Innovation in the Legal ...
{Law, Tech, Design, Delivery} Observations Regarding Innovation in the Legal ...{Law, Tech, Design, Delivery} Observations Regarding Innovation in the Legal ...
{Law, Tech, Design, Delivery} Observations Regarding Innovation in the Legal ...Daniel Katz
 
Legal Analytics Course - Class 11 - Network Analysis and Law - Professors Dan...
Legal Analytics Course - Class 11 - Network Analysis and Law - Professors Dan...Legal Analytics Course - Class 11 - Network Analysis and Law - Professors Dan...
Legal Analytics Course - Class 11 - Network Analysis and Law - Professors Dan...Daniel Katz
 
Legal Analytics Course - Class 12 - Data Preprocessing using dPlyR - Professo...
Legal Analytics Course - Class 12 - Data Preprocessing using dPlyR - Professo...Legal Analytics Course - Class 12 - Data Preprocessing using dPlyR - Professo...
Legal Analytics Course - Class 12 - Data Preprocessing using dPlyR - Professo...Daniel Katz
 
Legal Analytics Course - Class 10 - Information Visualization + DataViz in R ...
Legal Analytics Course - Class 10 - Information Visualization + DataViz in R ...Legal Analytics Course - Class 10 - Information Visualization + DataViz in R ...
Legal Analytics Course - Class 10 - Information Visualization + DataViz in R ...Daniel Katz
 
Legal Analytics Course - Class #4 - Github and RMarkdown Tutorial - Professor...
Legal Analytics Course - Class #4 - Github and RMarkdown Tutorial - Professor...Legal Analytics Course - Class #4 - Github and RMarkdown Tutorial - Professor...
Legal Analytics Course - Class #4 - Github and RMarkdown Tutorial - Professor...Daniel Katz
 
Legal Analytics Course - Class 9 - Clustering Algorithms (K-Means & Hierarch...
Legal Analytics Course - Class 9 -  Clustering Algorithms (K-Means & Hierarch...Legal Analytics Course - Class 9 -  Clustering Algorithms (K-Means & Hierarch...
Legal Analytics Course - Class 9 - Clustering Algorithms (K-Means & Hierarch...Daniel Katz
 
Legal Analytics Course - Class 8 - Introduction to Random Forests and Ensembl...
Legal Analytics Course - Class 8 - Introduction to Random Forests and Ensembl...Legal Analytics Course - Class 8 - Introduction to Random Forests and Ensembl...
Legal Analytics Course - Class 8 - Introduction to Random Forests and Ensembl...Daniel Katz
 
Legal Analytics Course - Class 7 - Binary Classification with Decision Tree L...
Legal Analytics Course - Class 7 - Binary Classification with Decision Tree L...Legal Analytics Course - Class 7 - Binary Classification with Decision Tree L...
Legal Analytics Course - Class 7 - Binary Classification with Decision Tree L...Daniel Katz
 

Más de Daniel Katz (20)

Legal Analytics versus Empirical Legal Studies - or - Causal Inference vs Pre...
Legal Analytics versus Empirical Legal Studies - or - Causal Inference vs Pre...Legal Analytics versus Empirical Legal Studies - or - Causal Inference vs Pre...
Legal Analytics versus Empirical Legal Studies - or - Causal Inference vs Pre...
 
Can Law Librarians Help Law Become More Data Driven ? An Open Question in Ne...
Can Law Librarians Help Law Become More Data Driven ?  An Open Question in Ne...Can Law Librarians Help Law Become More Data Driven ?  An Open Question in Ne...
Can Law Librarians Help Law Become More Data Driven ? An Open Question in Ne...
 
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...
 
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...
 
Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...
Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...
Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...
 
Law + Complexity & Prediction: Toward a Characterization of Legal Systems as ...
Law + Complexity & Prediction: Toward a Characterization of Legal Systems as ...Law + Complexity & Prediction: Toward a Characterization of Legal Systems as ...
Law + Complexity & Prediction: Toward a Characterization of Legal Systems as ...
 
Building Your Personal (Legal) Brand - Some Thoughts for Law Students and Oth...
Building Your Personal (Legal) Brand - Some Thoughts for Law Students and Oth...Building Your Personal (Legal) Brand - Some Thoughts for Law Students and Oth...
Building Your Personal (Legal) Brand - Some Thoughts for Law Students and Oth...
 
Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Tog...
Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Tog...Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Tog...
Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Tog...
 
Artificial Intelligence and Law - 
A Primer
Artificial Intelligence and Law - 
A Primer Artificial Intelligence and Law - 
A Primer
Artificial Intelligence and Law - 
A Primer
 
Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) ...
Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) ...Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) ...
Machine Learning as a Service: #MLaaS, Open Source and the Future of (Legal) ...
 
Technology, Data and Computation Session @ The World Bank - Law, Justice, and...
Technology, Data and Computation Session @ The World Bank - Law, Justice, and...Technology, Data and Computation Session @ The World Bank - Law, Justice, and...
Technology, Data and Computation Session @ The World Bank - Law, Justice, and...
 
LexPredict - Empowering the Future of Legal Decision Making
LexPredict - Empowering the Future of Legal Decision MakingLexPredict - Empowering the Future of Legal Decision Making
LexPredict - Empowering the Future of Legal Decision Making
 
{Law, Tech, Design, Delivery} Observations Regarding Innovation in the Legal ...
{Law, Tech, Design, Delivery} Observations Regarding Innovation in the Legal ...{Law, Tech, Design, Delivery} Observations Regarding Innovation in the Legal ...
{Law, Tech, Design, Delivery} Observations Regarding Innovation in the Legal ...
 
Legal Analytics Course - Class 11 - Network Analysis and Law - Professors Dan...
Legal Analytics Course - Class 11 - Network Analysis and Law - Professors Dan...Legal Analytics Course - Class 11 - Network Analysis and Law - Professors Dan...
Legal Analytics Course - Class 11 - Network Analysis and Law - Professors Dan...
 
Legal Analytics Course - Class 12 - Data Preprocessing using dPlyR - Professo...
Legal Analytics Course - Class 12 - Data Preprocessing using dPlyR - Professo...Legal Analytics Course - Class 12 - Data Preprocessing using dPlyR - Professo...
Legal Analytics Course - Class 12 - Data Preprocessing using dPlyR - Professo...
 
Legal Analytics Course - Class 10 - Information Visualization + DataViz in R ...
Legal Analytics Course - Class 10 - Information Visualization + DataViz in R ...Legal Analytics Course - Class 10 - Information Visualization + DataViz in R ...
Legal Analytics Course - Class 10 - Information Visualization + DataViz in R ...
 
Legal Analytics Course - Class #4 - Github and RMarkdown Tutorial - Professor...
Legal Analytics Course - Class #4 - Github and RMarkdown Tutorial - Professor...Legal Analytics Course - Class #4 - Github and RMarkdown Tutorial - Professor...
Legal Analytics Course - Class #4 - Github and RMarkdown Tutorial - Professor...
 
Legal Analytics Course - Class 9 - Clustering Algorithms (K-Means & Hierarch...
Legal Analytics Course - Class 9 -  Clustering Algorithms (K-Means & Hierarch...Legal Analytics Course - Class 9 -  Clustering Algorithms (K-Means & Hierarch...
Legal Analytics Course - Class 9 - Clustering Algorithms (K-Means & Hierarch...
 
Legal Analytics Course - Class 8 - Introduction to Random Forests and Ensembl...
Legal Analytics Course - Class 8 - Introduction to Random Forests and Ensembl...Legal Analytics Course - Class 8 - Introduction to Random Forests and Ensembl...
Legal Analytics Course - Class 8 - Introduction to Random Forests and Ensembl...
 
Legal Analytics Course - Class 7 - Binary Classification with Decision Tree L...
Legal Analytics Course - Class 7 - Binary Classification with Decision Tree L...Legal Analytics Course - Class 7 - Binary Classification with Decision Tree L...
Legal Analytics Course - Class 7 - Binary Classification with Decision Tree L...
 

Último

Objectives n learning outcoms - MD 20240404.pptx
Objectives n learning outcoms - MD 20240404.pptxObjectives n learning outcoms - MD 20240404.pptx
Objectives n learning outcoms - MD 20240404.pptxMadhavi Dharankar
 
6 ways Samsung’s Interactive Display powered by Android changes the classroom
6 ways Samsung’s Interactive Display powered by Android changes the classroom6 ways Samsung’s Interactive Display powered by Android changes the classroom
6 ways Samsung’s Interactive Display powered by Android changes the classroomSamsung Business USA
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management SystemChristalin Nelson
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDhatriParmar
 
How to Uninstall a Module in Odoo 17 Using Command Line
How to Uninstall a Module in Odoo 17 Using Command LineHow to Uninstall a Module in Odoo 17 Using Command Line
How to Uninstall a Module in Odoo 17 Using Command LineCeline George
 
An Overview of the Calendar App in Odoo 17 ERP
An Overview of the Calendar App in Odoo 17 ERPAn Overview of the Calendar App in Odoo 17 ERP
An Overview of the Calendar App in Odoo 17 ERPCeline George
 
Indexing Structures in Database Management system.pdf
Indexing Structures in Database Management system.pdfIndexing Structures in Database Management system.pdf
Indexing Structures in Database Management system.pdfChristalin Nelson
 
Employablity presentation and Future Career Plan.pptx
Employablity presentation and Future Career Plan.pptxEmployablity presentation and Future Career Plan.pptx
Employablity presentation and Future Career Plan.pptxryandux83rd
 
CLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptxCLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptxAnupam32727
 
Mythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWMythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWQuiz Club NITW
 
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptxBIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptxSayali Powar
 
MS4 level being good citizen -imperative- (1) (1).pdf
MS4 level   being good citizen -imperative- (1) (1).pdfMS4 level   being good citizen -imperative- (1) (1).pdf
MS4 level being good citizen -imperative- (1) (1).pdfMr Bounab Samir
 
Narcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdfNarcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdfPrerana Jadhav
 
ClimART Action | eTwinning Project
ClimART Action    |    eTwinning ProjectClimART Action    |    eTwinning Project
ClimART Action | eTwinning Projectjordimapav
 
Q-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITWQ-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITWQuiz Club NITW
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...Nguyen Thanh Tu Collection
 
ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6Vanessa Camilleri
 

Último (20)

Objectives n learning outcoms - MD 20240404.pptx
Objectives n learning outcoms - MD 20240404.pptxObjectives n learning outcoms - MD 20240404.pptx
Objectives n learning outcoms - MD 20240404.pptx
 
6 ways Samsung’s Interactive Display powered by Android changes the classroom
6 ways Samsung’s Interactive Display powered by Android changes the classroom6 ways Samsung’s Interactive Display powered by Android changes the classroom
6 ways Samsung’s Interactive Display powered by Android changes the classroom
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management System
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
 
How to Uninstall a Module in Odoo 17 Using Command Line
How to Uninstall a Module in Odoo 17 Using Command LineHow to Uninstall a Module in Odoo 17 Using Command Line
How to Uninstall a Module in Odoo 17 Using Command Line
 
An Overview of the Calendar App in Odoo 17 ERP
An Overview of the Calendar App in Odoo 17 ERPAn Overview of the Calendar App in Odoo 17 ERP
An Overview of the Calendar App in Odoo 17 ERP
 
Indexing Structures in Database Management system.pdf
Indexing Structures in Database Management system.pdfIndexing Structures in Database Management system.pdf
Indexing Structures in Database Management system.pdf
 
Employablity presentation and Future Career Plan.pptx
Employablity presentation and Future Career Plan.pptxEmployablity presentation and Future Career Plan.pptx
Employablity presentation and Future Career Plan.pptx
 
CLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptxCLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptx
 
Mythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWMythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITW
 
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptxBIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
 
MS4 level being good citizen -imperative- (1) (1).pdf
MS4 level   being good citizen -imperative- (1) (1).pdfMS4 level   being good citizen -imperative- (1) (1).pdf
MS4 level being good citizen -imperative- (1) (1).pdf
 
Faculty Profile prashantha K EEE dept Sri Sairam college of Engineering
Faculty Profile prashantha K EEE dept Sri Sairam college of EngineeringFaculty Profile prashantha K EEE dept Sri Sairam college of Engineering
Faculty Profile prashantha K EEE dept Sri Sairam college of Engineering
 
Mattingly "AI & Prompt Design: Large Language Models"
Mattingly "AI & Prompt Design: Large Language Models"Mattingly "AI & Prompt Design: Large Language Models"
Mattingly "AI & Prompt Design: Large Language Models"
 
Narcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdfNarcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdf
 
ClimART Action | eTwinning Project
ClimART Action    |    eTwinning ProjectClimART Action    |    eTwinning Project
ClimART Action | eTwinning Project
 
Q-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITWQ-Factor General Quiz-7th April 2024, Quiz Club NITW
Q-Factor General Quiz-7th April 2024, Quiz Club NITW
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
 
Mattingly "AI & Prompt Design" - Introduction to Machine Learning"
Mattingly "AI & Prompt Design" - Introduction to Machine Learning"Mattingly "AI & Prompt Design" - Introduction to Machine Learning"
Mattingly "AI & Prompt Design" - Introduction to Machine Learning"
 
ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6
 

Quantitative Methods for Lawyers - Class #13 - Students "t" Distribution - Professor Daniel Martin Katz