managerial statistics term paper

7/30/2019 Managerial Statistics Term Paper

1/103

A STUDY ON THE PERFORMANCE OF THOMSON REUTER MANILAS

CASES-GENERALIST TEAM


2/103

Table of Contents

Abbreviations and Acronyms ..................................................................................................................... i

Executive Summary ................................................................................................................................... ii

INTRODUCTION ......................................................................................................................................... 1

Nature and Scope of the Study ............................................................................................................. 1

PROBLEM DEFINITION............................................................................................................................... 3

Purpose of the Study ............................................................................................................................. 3

Significance of the Study ....................................................................................................................... 3

Current Knowledge about the Problem ................................................................................................ 4

Preliminary Hypotheses ........................................................................................................................ 4

METHODOLOGY ........................................................................................................................................ 5

Analytical Procedure ............................................................................................................................. 8

DATA PRESENTATION .............................................................................................................................. 12

Descriptive Statistics of Key Variables ................................................................................................ 12

STATISTICAL ANALYSIS ............................................................................................................................ 19

Analysis of Performance based on Output ......................................................................................... 19

Analysis of Performance based on Errors ........................................................................................... 26

Analysis of Performance based on Output vs. Benchmark ................................................................. 40

Analysis of Performance based on Independence between Error Types ........................................... 43

Analysis of Performance based on Output of Multiple Sample Means .............................................. 46

Analysis of Factors Affecting the Performance based on Output and Error....................................... 47

Conclusion ........................................................................................................................................... 49

RECOMMENDATIONS ............................................................................................................................. 51

APPENDICES ............................................................................................................................................ 52

Audit Criteria ....................................................................................................................................... 52

PH-Stat Results .................................................................................................................................... 53

BIBLIOGRAPHY ........................................................................................................................................ 96


3/103

Tables and Figures

List of Tables

Table 1 Team Performance by Output ........................................................................................................ 16

Table 2 Number of Errors Committed by Team .......................................................................................... 17Table 3 Number of Errors Committed by Gender ....................................................................................... 17

Table 4 Number of Errors Committed by PS Level...................................................................................... 17

Table 5 Number of Errors Committed by Age............................................................................................. 18

Table 6 Number of Errors Committed by Tenure ....................................................................................... 18

Table 7 Number of Sub-Type Errors Committed by Team .......................................................................... 18

Table 8 Number of Sub-Type Errors Committed by Gender ....................................................................... 18

Table 9 Number of Sub-Type Errors Committed by PS Level ...................................................................... 18

Table 10 Number of Sub-Type Errors Committed by Age........................................................................... 19

Table 11 Number of Sub-Type Errors Committed by Tenure ..................................................................... 19

List of Figures

Figure 1 Frequency Distribution by Age ...................................................................................................... 12

Figure 2 Frequency Distribution by Gender ................................................................................................ 13

Figure 3 Frequency Distribution by Income ................................................................................................ 13

Figure 4 Frequency Distribution by Tenure ................................................................................................ 14

Figure 5 Error Categories and Sub-Categories ............................................................................................ 17


4/103

i

Abbreviations, Acronyms, and Definitions

Workflow: A progression of functions that comprise a work process, involve a group of people, and

create or add value to the organizations activities.

Team: A group of people working on a common workflow. In the study, it is composed of Teams H and

M.

Name: A personal identification of the member of the team.

Wave: A type of hiring process where a company hires in groups. In this case, wave is indicated by

numbers which identifies when the member was hired. Wave 1 is the very first wave hired and wave 8 is

the last. All the next hires after wave 8 do not come in groups anymore.

Function: A step or task in order to complete a work process. The team being observed in this study has

three functions:

a) Statute Verification (SV): The process of verifying and styling laws cited in a case.

b) Opinion Verification (OV): The process of verifying case laws cited in a case.

c) Copy Preparation (CP): The process of copy editing and preparing the case for publication.

PS Level: It stands for Publishing Specialist Level, which refers to the position label or hierarchy of teammembers in this study.

Total cases: Total number of cases processed by a team member.

Total number of pages: Sum of the pages of cases processed.

Production hours: Number of hours spent in doing the function.

Average Pages per Hour: Computed as total number of pages divided by production hours.

Transition Phase Benchmark: Daily performance metrics; the higher the position, the higher the

benchmark is set.


5/103

ii

Executive Summary

This research study report is submitted by Group 2 in partial fulfillment of the course

Management Statistics 501M under Ms. Maria Angeli Reyes. The subject of the study is the

performance of the Manila Cases-Generalist Team of Thomson Reuters, a leading and

worldwide intelligent information provider. The purpose of the study was to evaluate the

performance of two sub-teams vis--vis performance benchmarks and other variables of

interest.

The specific objectives of the study were: 1) to assess and compare the performance of

two sub-teams of the Cases-Generalist workflow on statute verification, opinion verification,

and copy preparation functions; 2) to determine the process area where higher numbers of

errors have been committed to recommend ways for improvement of the workflow process; 3)

to identify factors which significantly affect the number of errors committed by team members

in each of the workflow process; and 4) to provide feedback to the Team and Management on

the result of the performance assessment for the purpose of further improving effectiveness

and efficiency. The research study is observational and retrospective and employed descriptive

statistics to summarize data, Chi-Square statistics to test the independence of variables, means

hypotheses to compare performance against benchmarks, between teams, gender, age ranges,

seniority levels, analyses of variance to compare performance among wave levels, and linear

regression to determine linear relationships between production and age, income, and tenure.

Data was provided by Thomson Reuter Manilas Cases-Generalist team managers and was

processed using Excel and its add-in PHStat.

Two-tailed hypothesis tests indicated that there is no difference in the means of total

production hours and average production per hour between sub-teams, members with age

ranges 24 and >24, and members with tenureof


6/103

iii

With regards to PS level, we found no differences in the means of all errors by PS 1 and

2, and no differences in the means of copy preparing, statute verification, opinion verification,

content, and major content errors. There is a significant difference in the means of stylistic

errors, with PS 2 having committed more error on average. With regards to age, we found no

differences in the means of all errors by members with ages 24 and >24, and no differences in

the means of copy preparing, statute verification, opinion verification, stylistic, content, and

major content errors. With regards to tenure, we found no differences in the means of all errors

by members with tenure of


7/103

iv

Analysis of variance showed that there is no significant difference in the performance of

among the six Waves that comprised the Teams in terms of total cases and average page per

hour production. On the other hand, regression analysis found no linear relationship between

average per hour production and members age, income, and tenure. It also found no linear

relationship between members total errors and their age, income, and tenure.

Overall, members of the Cases-Generalist Team seem to be performing well. However,

based on research results, it is recommended that review should be conducted for Team H on

statute verification and for Team M on opinion verification and copy preparing. It is also

suggested that review on stylistics should be conducted for PS level 2 and for members with

tenure of more than 2.5 years. Finally, more intensive training should be conducted for new

hires on opinion verification as this seems to be a weakness that should be addressed.


8/103

1

INTRODUCTION

Nature and Scope of the Study

Thomson Reuters is an intelligent information provider. It publishes credible information

both in printed and online publications covering financial, governance, risk, and compliance,

intellectual property, legal, media, science, and tax and accounting products and services. These

products and services have their respective departments that work closely on the information to

be provided to its customers. Its values Business is Global, Customers are the Heart of

Everything, People Make the Difference, and Performance Matters all aim to uphold quality work

from its employees in order to satisfy its customers.

The Legal Department of Thomson Reuters has strategically moved some of its

operations in Manila last June 2009. With a huge amount of work being transitioned from

employees in the US who have been working for several years to Manila employees who have

just learned how to process information, the company aims to keep its work at its best quality.

This case study focuses on one of the workflow in the Legal Department in the Manila

branch, which is the Cases-Generalist workflow. Due to a big number of members, the Cases-

Generalist workflow is divided into two (2) sub-teams with a total of 35 members. The team

works on live Federal and state cases in all US jurisdictions. The case being reviewed goes

through three (3) processes before it is submitted and published online at Westlaw.com:

1) Statute verification: Team members check the correct citation of state and Federal

laws;

2) Opinion verification: Team members check the correct citations to previous court

cases; and

3) Copy Preparation: Team members check the spacing, indention, spelling errors,

more on how the document looks like.

Each member of the team is tasked to achieve a certain metrics or quota within a day.

The metrics depend on the workflow he or she is assigned to. Since statute verification is the


9/103

2

fastest to do as not all cases cite a lot of laws, statute verifiers are tasked with a larger metrics to

follow. The longest process is the copy preparation as copy preparers do not look for available

citations to verify, but rather verify the whole case for any typographical errors. Metrics also

depend on the position of the team members in the company. The higher the hierarchy, the

higher metrics he or she must achieve within the day. To ensure the quality of work against the

quantity of work, a quarterly audit is held by their mentors in the US. These mentors follow a

certain audit criteria (See Appendices) and send feedback on the errors of the team members.

Team members are always expected to have very minimal errors to no errors at all. Both

quantity and quality of work are set in each of the team members annual objectives affecting

their annual bonus and appraisal. The study seeks to evaluate the performance of team

members against benchmarks and other variables such as age and tenure, and to assess factors

that may affect how errors occur in the workflow spectrum.

This study is observational in nature as no treatments will be applied to the elements of

the study, i.e. members of the cases-generalists teams. It is also a retrospective study as data

have been collected about the elements (or sample) with regards to performance outcomes that

have already taken place. With regards to scope, the sample comprises of 28 cases-generalist

team members (14 members each from the two sub-teams) whose performances for two

quarters of the year will be assessed and compared. It should be noted that complete audit

data is available only for the 2nd and 3rd quarter of 2012 as no audit was conducted during the 1st

quarter and the 4th

quarter is still ongoing.


10/103

3

PROBLEM DEFINITION

Purpose of the Study

The general objective of this study was to evaluate the performance of the two sub-

teams of the Cases Generalist Team vis--vis performance benchmarks and other independent

variables such as gender and tenure. Specifically, the study aimed to:

1) To assess and compare the performance of two sub-teams of the Cases-Generalist

Team on statute verification, opinion verification, and copy preparation workflows;

2) To determine the process area where higher numbers of errors have been

committed to recommend ways for improvement of the workflow process;

3) To identify factors which significantly affect the number of errors committed by

team members in each of the workflow process; and

4) To provide feedback to the Team and Management on the result of the

performance assessment for the purpose of further improving effectiveness and

efficiency;

Significance of the Study

While Thomson Reuters use regular metrics to assess individual performance against

performance benchmarks, there have been no existing studies or written analysis on the

performance of the Cases Generalist Team of Thomson Reuters that compares the performance

of sub-teams, and that examines the relationship between performance, workflow errors and

variables such as gender and tenure. The primary beneficiary of this study is the Legal

Department of the company who will learn from the implications of the results of this study.

These will serve as input on what they can do to maintain or improve, if needed, the workflow

performance of the Cases Generalist Team.

Each team member provides raw data to their managers on how many cases and how

many pages they processed and how long they processed them. The managers are tasked to

summarize all of the members production statements and present it to higher management to


11/103

4

see whether the team is hitting targets or having backlogs into the cases being sent from the US.

No other study with regards as to why they perform as such had been conducted in the past.

This study will also give team members a statistical analysis of their production outputs

and workflow errors. With the results that will be analyzed in this study, team members will gaininsights on how they perform as a team.

This study will also provide the readers a general idea of the workflow of the Cases

Generalist Team. This will make them appreciate what it is like to be in a team of young,

competitive and enthusiastic professionals, who work on tedious U.S. cases. US cases or cases in

general are very delicate products as the information they contain serve as basis for the

credibility of attorneys, basis of court decisions, and ultimately, basis of justice. Studying

performance on a workflow that involves this delicate information is relevant as implications aresignificant.

Current Knowledge about the Problem

Basically, each member knows about his or her accomplishment, since at the end of the

week they submit a summary performance to their manager. It also known to them how the

other team is performing based on performance metrics. But by connecting one variable to

another, the study will give them a sense of how their performance can be further evaluated.

Because the study is observational in nature, it will answer questions that provide statistical

inferences about performance and relationships among variables of interest.

Preliminary Hypotheses

A number of assumptions have been considered in formulating the preliminary

hypotheses. First, the training provided by Thomson Reuters to members of the cases generalist

term was deemed to be standardized and to be effective in providing requisite skills and

knowledge. Second, weekly performance metrics provided to all team members is succeeding in

driving peak performance and compliance with benchmarks among all team members.


12/103

5

Therefore, the following are the preliminary hypotheses of the study:

1) There is no difference in the performance of two sub-teams of the Cases-Generalist

Team on statute verification, opinion verification, and copy preparation workflows with

regards to transition phase benchmarks;2) There is no difference in the workflow errors of the two Cases-Generalist sub-teams;

and

3) There is no difference in the performance and workflow errors vis--vis variables such as

age, gender, income and tenure.

METHODOLOGY

This study made use of secondary data in determining the performance of the Cases-

Generalist workflow. Weekly, each team member sends his or her production statements

composed of the number of cases and pages made across all the jurisdictions he or she handles

and how long it took him or her to finish everything. From these raw data, the managers of each

team consolidate all information per team into one Microsoft Excel spreadsheet. The

researchers were able to ask the secondary data from the managers. There was a limitation on

getting the raw data from each of the team members as not all of them were able to keep their

data from the time period that the researchers wanted to explore. Also, it was very time

consuming to gather data from each of the 35 members of the team. The secondary data of the

managers truly reflects the raw data. It is complete and comprehensive, thus the researchers

opted to use it. The complete data was filtered down to two (2) quarter, the secondand third

quarters of 2012, as data is complete for these. Due to the busy work schedule of the US

counterparts of the teams during the first quarter, they were not able to audit cases.

Meanwhile, the fourth quarter is still on-going. The secondary data were provided by the Cases-

Generalist workflow team managers who sent the data through electronic mail.

The Cases Generalist Team of Thomson Reuters was purposively chosen since one of the

members of our group (Group 2 of Managerial Statistics class) belongs to it. In addition to the

statistical exercise which results can be utilized by Thomson Reuters, we also want to have an


13/103

6

appreciation of what it is like to work in a multinational company, which handles foreign

accounts, specifically federal and state cases.

The Cases-Generalist Team is composed of 35 members. It is divided into two sub-

teams: Team H and Team M. From each team, we purposely selected 14 members who havecomplete records comprising of audit and performance information. The following shows the

list of variables, the way these will be measured, and the analysis that will be performed. Each

heading (i.e., A, B, C) corresponds to an Excel file that was provided by Thomson Reuters Cases-

Generalist Team Manager.

A.Individual Production Summaries

Variable Class Level of Measurement

1. Team Qualitative Nominal

2. Name Qualitative Nominal

3. Wave Qualitative Ordinal

4. Function Qualitative Nominal

5. PS Level Qualitative Ordinal

6. Total Cases Quantitative (discrete) Ratio

7. Total Number of Pages Quantitative (discrete) Ratio

8. Production Hours Quantitative (continuous) Ratio

9. Average Pages per Hour Quantitative (continuous) Ratio

10.Transition Phase Benchmark Quantitative (discrete) Ratio

Definition

1. Team- Composed of Teams H and M

2. Name- Name of the Team Member

3. Wave- Indicates when the member was hired (wave 1 being the earliest, wave 7 being

the last. The company hires until wave 9.

4. Function- Each team has three functions:

a. SV - statute verification - process of verifying and styling statutes/laws cited in a

case

b. OV - opinion verification - process of verifying case laws cited in a case

c. CP - copy preparing - process of copy editing and preparing the case for publication


14/103

7

5. PS Level- Publishing Specialist - position label or hierarchy.6. Total Cases- Total number of cases processed

7. Total Number of Pages- Number of pages per case

8. Production Hours- Number of hours spent doing the function

9. Average Pages per Hour- computed as total number of pages divided by production

hours

10.Transition Phase Benchmark daily performance metrics; the higher the position, the

higher the benchmark is set.

Measurement

1. Mean of total cases, total number of pages, production hours, per team

2. Mean of total cases, total number of pages, production hours, per wave

3. Mean of total cases, total number of pages, production hours, per function

4. Mean of total cases , total number of pages, production hours, per PS level

B.Audits/Analysis of Performance Errors




3. Wave Qualitative Ordinal

4. Function Qualitative Nominal5. PS Level Qualitative Ordinal

6. Copy Preparing Errors Quantitative (discrete) Ratio

7. Opinion Verification Errors Quantitative (discrete) Ratio

8. Stylistic Errors Quantitative (discrete) Ratio

9. Content Errors Quantitative (discrete) Ratio

10.Major Content Errors Quantitative (discrete) Ratio

11.Overall Quality Rating Quantitative (continuous) Interval

Measurement

1. Means and standard deviation of all types of errors

2. Means and standard deviation of each type of error (Copy Preparing, Statute

Verification, Opinion Verification)


15/103

8

3. Means and standard deviation of each subtypes of error (Stylistics, Content, Major

Content)

4. Means of each type and subtype of error per team, per gender, per PS level, per age

range, per tenure duration

C. Member Information


1. ID Qualitative Nominal


3. Role Qualitative Nominal

4. Wave Qualitative Nominal


6. Age Qualitative Ordinal7. Tenure Quantitative Interval

8. Income Quantitative Interval

Measurement

1. Count of members (by name)

2. Count of roles (copy preparing, statute verification, opinion verification)

3. Count of wave

4. Count of Team membership

5. Mean, median, frequency distribution, etc. of age

6. Mean, median, frequency distribution, etc. of tenure

7. Mean, median, frequency distribution, etc. of income

Analytical Procedure

Descriptive statistics was used in analyzing the performance basis of each team.

Frequency distributions and proportions based on the frequency tables were computed. The

average output of each team (based on total cases, number of pages, production hours, average

pages per hour and number of errors) were also calculated.

To present a comparison on the performance of the two teams based on output and

errors, two-sample hypothesis testing will be used. Two-sample hypothesis testing is statistical


16/103

9

analysis designed to test if there is a difference between two means from two different

populations. In this case, a two-tailed test regarding the differences will be applied.

Since the samples of size is less than 30 and are taken from normally-distributed

populations, a t-test was used to test the difference between the population means1and2:

1. Comparison of the performance of two teams by:

a) Total Cases Submitted

b) Total Number of Pages per case

c) Production Hours

d) Average Pages/Hour

2. Comparison of the performance of members Using Total Cases SubmittedAverage Pages per Hour and Total Number of Errors per Type of Error by:

a) Gender

b) Age (24 Years Old and Below, Above 24 Years Old)

c) Tenure (below 2.5 years, 2.5 years and more)

d) PS Level

A two-sample z-test was used to test the difference between two sample means and

when a large sample (at least 30) is randomly selected from each population and the

samples are independent. When the samples are less than 30, t-test was used to test

the difference between the sample means.

3. Total Number of Errors Committed by:

a) Team

b) Gender

c) PS Level

d) Age (24 Years Old and Below, Above 24 Years Old)

e) Tenure (below 2.5 years, 2.5 years and more)


17/103

10

To assess the performance (using the average pages per hour) of each team against the

Transition Phase Benchmark (target), one-sample hypothesis testing was used. A one sample

test is a hypothesis test for answering questions about the mean where the data are a random

sample of independent observations from an underlying normal distribution N. In this case, an

upper-tailed test regarding the differences was applied.

The t-test for the mean is a statistical test for a population mean. The t-test can be used

when the population is normal or nearly normal, is unknown, and n < 30.

1. Members of Statute Verification vs. Transition Phase Benchmark

2. Members of Copy Preparing vs. Transition Phase Benchmark

3. Members of Opinion Verification vs. Transition Phase Benchmark

To check if there is a difference in total cases processed and average pages per hour of

waves 3,5,6,7 and 8.5, analysis of variance (ANOVA) was used. ANOVA is used to compare

means between three or more groups, in this case, the period when the employee was hired

(wave). The ANOVA F-Test is a comparison of the average variability between groups to the

average variability within groups. The variability within each group is a measure of the spread of

the data within each of the groups. The variability between groups is a measure of the spread of

the group means around the overall mean for all groups. In this case, a single-factor ANOVA

analysis was used.

The Chi-Square statistic compares the counts of categorical responses between

independent groups. In this study, The Chi-square distribution was used to test the

independence between types as well as subtypes of errors committed vis--vis team

membership, gender, PS level, age, and tenure. It should be noted that in comparing the counts

of categorical responses between PS levels, PS level 3 (the highest level) was not included since

there was only one person in this category who performed just one function. Further, the test

was not used to compare categorical responses according to Wave since not all Waves perform

all functions (i.e. copy preparing, statute verification, opinion verification).


18/103

11

Regression analysis was done to estimate or predict the value of the dependent variable

(average pages per hour and total number of errors committed) on the basis of known

independent variables (age, tenure, salary, gender). The equation that was used is given below:

Y= f(X1, X2. Xn ),

Where:

Y=Average Pages per Hour

Number of Errors Committed

X1=Gender

X2=Age

X3=Income

X4=Tenure

All the data processing and analysis were done with the help of MS Excel and its add-in

PH-Stat.

In all the statistical tests performed in this study, a 95% CI is used, which indicates that

we are 95% certain where the true unknown parameter lies.


19/103

12

DATA PRESENTATION

Descriptive Statistics of Key Variables

This part of the study is a graphical presentation of the different frequency distributions

of the factors examined in the case. Mainly, the researchers used the Excel Chart Wizard to

produce the figures below.

In Figure 1, the number of people aged 24 years and below is faintly higher in Team H

than Team M. However, those aged above 24 years are higher in Team M than Team H. Also, the

bar graph depicts that there are more 24 years old and below employees than above 24 years

old employees.

Figure 1. Frequency Distribution by Age

Figure 2 portrays that there are more females than males in the Cases-Generalist

workflow. Females are slightly higher in number in Team H than Team M, while there are slightly

more males in Team M than Team H.

0

1

2

3

45

6

7

8

9

10

Team H Team M

Age

24 years old and

belowAbove 24 years old


20/103

13

Figure 2. Frequency Distribution by Gender

Figure 3 shows that income ranging from 19K-25K and above 25K are equally distributed

in both Team H and Team M. There is no difference in the frequency distribution of the given

range of incomes.

Figure 3. Frequency Distribution by Income

0

2

4

6

8

10

12

Team H Team M

Gender

Female

Male

0

1

2

3

4

5

6

7

8

Team H Team M

Income

19K-25K

Above 25K


21/103

14

In Figure 4, Team H employees with tenure 2.5 years and above outweighs those with

tenure below 2.5 years; however, distributions of below 2.5 years and 2.5 years and above are

equivalent in Team M. Team H has lower number of employees who have worked below 2.5

years in the company than Team M, and it has higher number of employees who have worked

2.5 years and above in the company than Team M.

Figure 4. Frequency Distribution by Tenure

Figure 5 demonstrates a pie chart of the distribution of the three functions in the Cases-

Generalist workflow. From the graph, copy preparers are significantly higher in count than the

other two functions. Between the other two, opinion verifiers are greater in number than

statute verifiers.

0

2

4

6

8

10

12

Team H Team M

Tenure

Below 2.5 years

2.5 years and

above


22/103

15

Figure 5. Frequency Distribution by Function

Figure 6 presents the distribution by wave. The team does not have a member from

wave 4 as being absent in the pie chart. The largest wave is wave 3 while the least member in a

wave is wave 1 having only one sample. Waves 2 and 5 are gently comparable in proportion.

There is a wave identified as 8.5 because members from this wave were hired in between waves

8 and 9. The name 8.5 is just an identifier.

Function

Statute Verifiers

Opinion Verifiers

Copy Preparers

1

2

3

5

6

7

8.5

Wave

wave 1

wave 2

wave 3

wave 4

wave 5

wave 6

wave 7


23/103

16

Figure 7 reveals that half of the Cases-Generalist team ranks PS 2. The number of PS 1

team members is not very far from that of PS 2. PS 3 level, which requires more tenure and

more experience, has a minute number of members.

Figure 7. Frequency Distribution by PS Level

Table 1 below shows the output computed by the mean number of cases, pages,

production hours, and the average pages per hour as performed by Teams H and M. The

researchers used excel function =AVERAGE(data_array) for computing the different means.

Table 1. Team Performance by Output

Team H Team M

Mean number of cases 1,473.7857 1,777.5714

Mean number of pages 13,316.5714 12,842.6429

Mean production hours 379.3571 367.5536

Average pages per hour 38.0291 35.3055

There were a total of 101 errors encountered in 168 instances whereby a team member

can commit any number of errors or not commit any error at all. In this study therefore, zero (0)

is absolute, indicating that a team member did not commit any error during performance on a

case. The main types of errors are Copy Preparing errors which are errors committed in the

process of copy editing and preparing the case for publication, Opinion Verification errors which

PS Level

PS 1

PS 2

PS 3


24/103

17

are errors committed in the process of verifying case laws cited in a case, and Statute

Verification errors which are errors committed in the process of verifying and styling statutes

and federal and state laws (as compared to case laws which result from promulgation of cases).

The errors are further sub-categorized into stylistic, content, and major content errors. In

tabular form, this is illustrated as follows:

Figure 8. Error Types and Sub-Types

Copy Preparing Statute Verification Opinion Verification

Stylistic ContentMajor

ContentStylistic Content

MajorContent

Stylistic ContentMajor

Content

Stylistic errors are errors with regards to writing style, for example in copy preparing, a

stylistic error would be a wrong mnemonic or a heading not styled correctly. A content error isa minor error regarding content, for example paragraphs split or merged incorrectly. A major

content error is a significant error with regards to content, for example missing text. Tables

below show the numbers of errors and sub-type errors committed by team, gender, PS level,

age and tenure.

Table 2. Number of Errors Committed by Team

Team Copy Prep Statute Ver OpinionVer

Total

H 20 12 8 40

M 44 2 15 61

Total 64 14 23 101

Table 3. Number of Errors Committed by Gender

Gender Copy Prep Statute Ver OpinionVer

Total

Female 49 3 13 65

Male 15 11 10 36

Total 64 14 23 101

Table 4. Number of Errors Committed by PS Level

PS Level Copy Prep Statute Ver OpinionVer

Total

1 15 3 19 37

2 49 11 4 64

Total 64 14 23 101


25/103

18

Table 5. Number of Errors Committed by Age

Error 24 and Below Above 24 Total

Copy Prep 29 35 64

Statute Ver 5 9 14

Opinion Ver 19 4 23

Total 53 48 101

Table 6. Number of Errors Committed by Tenure

Error Below 2.5 Yrs. 2.5 Yrs and Above Total

Copy Prep 6 58 64

Statute Ver 3 11 14

OpinionVer

16 7 23

Total 25 76 101

Table 7. Number of Sub-Type Errors Committed by Team

Team Stylistic Content MajorContent

Total

H 26 13 1 40

M 40 18 3 61

Total 66 31 4 101

Table 8. Number of Sub-Type Errors Committed by Gender

Team Stylistic Content MajorContent

Total

Female 46 16 3 65

Male 20 15 1 36

Total 66 31 4 101

Table 9. Number of Sub-Type Errors Committed by PS Level

PS Level Stylistic Content MajorContent

Total

1 19 17 1 37

2 47 14 3 64

Total 66 31 4 101


26/103

19

Table 10. Number of Sub-Type Errors Committed by Age

Error 24 andBelow

Above 24 Total

Stylistics 33 33 66

Content 19 12 31

Major Content 1 3 4Total 53 48 101

Table 11. Number of Sub-Type Errors Committed by Tenure

Error Below 2.5 Yrs. 2.5 Yrs and Above Total

Stylistics 11 55 66

Content 14 17 31

Major Content 0 4 4

Total 25 76 101

STATISTICAL ANALYSIS

Analysis of Performance based on Output

This analysis compared the number of errors between teams H and I, females and

males, PS levels 1 and 2, members with ages 24 and >24 (median age is 23.5 years), and

members with tenures of


27/103

20

Ha: 1-2 0

Decision Rule: Reject Ho if t>2.05 or t 2.05 or t 2.05 or t


28/103

21

Two-tailed Hypothesis Testing: Performance Comparison by Gender

With regards to gender, results show that there is no significant difference

between the total cases processed, total production hours and average pages per

hour processed by males and females as follows:

o Total Cases Processed

Ho: 1-2 = 0(There is no significant difference in the total cases processed between

males and females)

Ha: 1-2 0

Decision Rule: Reject Ho if t>2.05 or t 2.05 or t


29/103

22



30/103

23

Interpretation: The mean production hours of members 24 years old and below

and members above 24 years old has no significant difference.

o Average Pages per Hour

Ho : 1-2 = 0 (There is no significant difference in the average pages per hour

between members 24 years old and below and members above 24 years old)

Ha: 1-2 0



31/103

24

o Total Production Hours

Ho : 1-2 = 0 (There is no significant difference in the total production hours

between members working for 2.5 years and below and members working above

2.5 years)

Ha: 1-2 0



32/103

25


Ho : 1-2 = 0 (There is no significant difference in the total cases processed

between PS Level 1 and PS Level 2)

Ha: 1-2 0



33/103

26

significant difference.

Analysis of Performance based on Errors

This analysis compared the number of errors between teams H and M, females and

males, PS levels 1 and 2, members with ages 24 and >24 (median age is 23.5 years), and

members with tenures of 1.96 or z


34/103

27

o Copy Preparing Errors

Ho: 1-2 = 0 (There is no difference between the means of Copy Preparing errors of

Team H and M)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if z>1.96 or z2.05 or t2.01 or t


35/103

28

o Stylistic Errors

Ho: 1-2 = 0 (There is no difference between the means of Stylistic errors of Team H

and M)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if t>2.005 or t2.005 or t2.005 or t


36/103

29

Conclusion: We do not have sufficient evidence to Reject Ho

Interpretation: There is no difference between the means of Major Content errors

of Team H and M.

Two-tailed Hypothesis Testing: Gender Performance Comparison based on Error and

Sub-Type Errors by Gender

With regards to gender, we found no differences in the means of all errors by females

and males, and no differences in the means of copy preparing, statute verification, and

opinion verification errors between females and males. We also did not find any

differences in the means of stylistic, content, and major content errors between them.

o

All ErrorsHo: 1-2 = 0 (There is no difference between the means of errors of Females and

Males)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if z>1.96 or z1.98 or t


37/103

30

Interpretation: There is no difference between the means of Copy Preparing errors

of females and males. However, the p-value of .63 signifies a high probability that

there is a difference between the means of copy preparing errors of females and

males.

o Statute Verification Errors

Ho: 1-2 = 0 (There is no difference between the means of Statute Verification

errors of females and males)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if t>2.05 or t2.013 or t


38/103

31

Ha: 1-2 0

= 0.05



39/103

32

Interpretation: There is no difference between the means of Major Content errors

of females and males. However, the p-value of .75 points to a high probability that

the means of major content errors of females and males may be different.

Two-tailed Hypothesis Testing: Performance Comparison based on Error and Error

Sub-Type by PS Level

With regards to PS level, we found no differences in the means of all errors by PS 1 and

2, and no differences in the means of copy preparing, statute verification, and opinion

verification errors between PS 1 and 2. We also did not find any differences in the

means of content and major content errors. However, we found a significant

difference in the means of stylistic errors between the two.

o All Errors

Ho: 1-2 = 0 (There is no difference between the means of errors of PS 1 and 2)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if z>1.96 or z1.96 or z


40/103

33



errors of PS 1 and 2)

Ha: 1-2 0

= 0.05



41/103

34

Interpretation: There is a significant difference between the means of Stylistic

errors of PS 1 and 2. PS 2 commits more error of average than PS 1.

o Content Errors

Ho: 1-2 = 0 (There is no difference between the means of Content errors of PS 1

and 2)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if t>2.006 or t2.006 or t24, and no differences in the means of copy preparing, statute


42/103

35

verification, and opinion verification errors. We also did not find any differences in the

means of stylistic, content, and major content errors between them.

o All Errors

Ho: 1-2 = 0 (There is no difference between the means of errors of members with

ages 24 and >24)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if z>1.96 or z24.

o Copy Preparing Errors

Ho: 1-2 = 0 (There is no difference between the means of Copy Preparing errors of

members with ages 24 and >24)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if z>1.96 or z24.



errors of members with ages 24 and >24)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if t>2.05 or t


43/103

36


Interpretation: There is no difference between the means of Statute Verification

errors of members with ages 24 and >24.

o Opinion Verification Errors

Ho: 1-2 = 0 (There is no difference between the means of Opinion Verification

errors of members with ages 24 and >24)

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if t>2.01 or t24. A p-value of .54 may mean that there is

a difference in the opinion verification errors ofmembers with ages 24 and >24.

o Stylistic Errors

Ho: 1-2 = 0 (There is no difference between the means of Stylistic errors of


Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if t>2.005 or t24.

o Content Errors

Ho: 1-2 = 0 (There is no difference between the means of Content errors of


Ha: 1-2 0

= 0.05


44/103

37

Decision Rule: Reject Ho if t>2.005 or t24. However, a p-value of .96 points out that it is very

likely that there is a difference between the content errors of members with ages

24 and >24.

o Major Content Errors

Ho: 1-2 = 0 (There is no difference between the means of Major Content errors of


Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if t>2.005 or t24.

Two-tailed Hypothesis Testing: Performance Comparison based on Error and Sub-

Type Errors by Gender

With regards to tenure, we found no differences in the means of all errors by members

with tenure of


45/103

38

Ha: 1-2 0

= 0.05

Decision Rule: Reject Ho if z>1.96 or z


46/103

39

Ho: 1-2 = 0 (There is no difference between the means of Opinion Verification

errors of members with tenure of 2.05 or t


47/103

40


Interpretation: There is no difference between the means of Content errors of

members with tenure of


48/103

41

o Statute Verification

Ho: 1 50 pages per hour(Members of this function does not meet the transition

phase benchmark)

Ha: 1 50 pages per hour

Decision Rule: Reject Ho if t > 6.3137

t Test Statistic: 6.8421 with a p-value of 0.046195261

Conclusion: We have sufficient evidence to Reject Ho

Interpretation: Members of Statute Verification are able to comply with the

benchmark of 50 pages per hour.

o Copy Preparing

All Members of Copy Preparing Process were able to meet the required number of

pages per hour.


phase benchmark)



t Test Statistic: 7.608 with a p-value of 1.64992E-05


Interpretation: Members of Copy Preparing are able to comply with the



phase benchmark)





49/103

42


Interpretation: Members of Copy Preparing are able to comply with the


o Opinion Verification

Not all members of Opinion Verification Process were able to meet the required

number of pages per hour. New hires with a benchmark of 9 pages per hour, were

not able to comply.


phase benchmark)





Interpretation: Members of Opinion Verification are able to comply with the



phase benchmark)





Interpretation: Members of Opinion Verification are able to comply with the



phase benchmark)


50/103

43




Conclusion: We dont have sufficient evidence to Reject Ho

Interpretation: Members of Opinion Verification are not able to comply with the


Analysis of Performance based on Independence between Error Types

The Chi-square distribution test was utilized to determine the independence between

types as well as subtypes of errors and team membership, gender, PS level, age, and tenure. We

found out that type of error is related to team membership as follows:

Ho: Type of error and team are independent.

Ha: Type of error and team are dependent or related.

= 0.05

Decision Rule: Reject Ho if

Chi-Square Test Statistic: 14.53533309 with a p-value of 0.000697738


Interpretation: Type of error and team membership are related.

Comparing the observed and expected frequencies, it seems that Team H is prone to

committing statute verification errors while Team M is prone to committing copy preparing and

opinion verification errors. On the other hand, it was found out that there is no relation

between error subtypes (stylistics, etc.) and team membership. However, the p-value (.80) is

big enough to put the result into question, as follows.

Ho: Error subtype and team are independent.

Ha: Error subtype and team are dependent or related.

= 0.05



51/103

44

Chi-Square Test Statistic: 0.428329067with a p-value of 0.80721556


Interpretation: Error subtype and team membership are not related.

It was also found out that type of error is related to gender. By comparing observed frequencies

to expected frequencies, females tend to commit copy preparing errors while males tend to

commit status verification and opinion verification errors.

Ho: Type of error and gender are independent.

Ha: Type of error and gender are dependent or related.

= 0.05




Interpretation: Type of error and gender are related.

Again, it was found out that error subtypes and gender are not related.

Ho: Error subtypes and gender are independent.

Ha: Error subtypes and gender are dependent or related.

= 0.05




Interpretation: Error subtypes and gender are related.

With regards to PS level, we found out that type of error is related to a members PS level as

follows:Ho: Type of error and PS level are independent.

Ha: Type of error and PS level are dependent or related.

= 0.05


Chi-Square Test Statistic: 27.13810142 with a p-value of 1.27949E-06


52/103

45


Interpretation: Type of error and PS level are related.

Also, error subtypes and PS level were found to be dependent or related.

Ho: Error subtypes and PS level are independent.

Ha: Error subtypes and PS level are dependent or related.

= 0.05




Interpretation: Error subtypes and PS level are related.

Comparing observed and expected frequencies, PS level 2 members tend to commit copy

preparing errors while PS level 1 members tend to commit opinion verification errors. Further,

PS level 2 members tend to commit stylistic errors while PS level 1 members tend to commit

content errors.

Type of error and age was also found to be related or dependent. Members who are 24 years

old and below are prone to committing opinion verification errors while members who are more

than 24 years old are prone to committing copy preparation and statute verification errors.

Ho: Type of error and age are independent.

Ha: Type of error and age are dependent or related.

= 0.05




Interpretation: Type of error and age are related.

On the other hand, error subtypes and age were found to be independent.

Ho: Error subtypes and age are independent.

Ha: Error subtypes and age are dependent or related.


53/103

46

= 0.05




Interpretation: Error subtypes and age are not related.

Type of error and tenure was also found to be related or dependent. Comparing observed and

expected frequencies, members with tenure of below 2.5 years tend to commit opinion

verification errors while members who are with the company for 2.5 years or more tend to

commit copy preparing errors. The Chi-Square test result for error subtypes and tenure is not

presented because expected frequency assumptions were not met.

Ho: Type of error and tenure are independent.

Ha: Type of error and tenure are dependent or related.

= 0.05


Chi-Square Test Statistic: 33.00653339 with a p-value of 6.80334E-08


Interpretation: Type of error and tenure are related.

Analysis of Performance based on Output of Multiple Sample Means

A single-factor ANOVA Analysis was used to check if there is a significant difference in total cases

processed and average pages per hour of waves 3, 5, 6, 7 and 8.5.

Analysis of Variance: Performance by Wave

There is no significant difference in the performance among the 6 waves classified.


Ho: 1= 2= 3= 4= 5= 6 (Mean number of cases processed by all waves are the

same)

Ha: At least one 1 differs from the others


54/103

47

Decision Rule: Reject Ho if F 2.68

F Test Statistic: 0.904793


Interpretation: The mean total cases processed of Waves 2, 3, 5, 6, 7 and 8.5 are

the same.

Average Pages per Hour

Ho: 1= 2= 3= 4= 5= 6 (Mean pages per hour processed by all waves are the

same)

Ha: At least one 1 differs from the others

Decision Rule: Reject Ho if F 2.68F Test Statistic: 0.734984


Interpretation: The mean pages per hour processed of Waves 2, 3, 5, 6, 7 and 8.5

are the same.

Analysis of Factors Affecting the Performance based on Output and Error

In analyzing how the performance of the two teams are influenced by different factors. Factors

identified as Age, Gender, Income and Tenure were used.

Regression Analysis: Average Pages per Hour

Regression Statistics

R Square 0.168122876

P-valueIntercept 0.677799

Gender: (0-F), (1-M) 0.486851

Age 0.31748

Income 0.138449

Tenure (Years) 0.185843


55/103

48

Interpretation:

16.82% of the variation in the Average Pages per Hour processed is explained by the

independent variables: Gender, Age, Income and Tenure. This indicates that these

predictor variables explain very little variation in the response variable.

Gender: p-value=0.486851 is greater than =0.05, we fail to find significant statistical

evidence for a linear relationship between the Average pages per hour processed and

the members Gender.

Age: p-value=0.31748 is greater than =0.05, we fail to find significant statistical


the members Age.

Income: p-value=0.138449 is greater than =0.05, we fail to find significant statisticalevidence for a linear relationship between the Average pages per hour processed and

the members Income.

Tenure: p-value=0.185843 is greater than =0.05, we fail to find significant statistical


the members Tenure.

Regression Analysis: Total Number of Errors Committed

Regression Statistics

R Square 0.114443

P-value

Intercept 0.235803

Gender: (0-F), (1-M) 0.29054

Age 0.231104

Income 0.14784

Tenure (Years) 0.705145

Interpretation:

11.44% of the variation in the Total errors committed is explained by the independent

variables: Gender, Age, Income and Tenure. This indicates that these predictor variables

explain very little variation in the response variable.


56/103

49

Gender: p-value=0.29054 is greater than =0.05, we fail to find significant statistical

evidence for a linear relationship between the Total Errors Committed and the

members Gender.

Age: p-value=0.231104 is greater than =0.05, we fail to find significant statistical


members Age.

Income: p-value=0.14784 is greater than =0.05, we fail to find significant statistical


members Income.

Tenure: p-value=0.705145 is greater than =0.05, we fail to find significant statistical


members Tenure.

Conclusion

The researchers found out that the performance of the two teams by Age, Gender, Tenure, PS

Level, based on their outputs has no significant difference.

Being mostly fresh graduates upon entry, team members are afraid to commit mistakes and aim

to do good work to fit in the corporate world. Also, the company values highlight quality of work

among its employees, thus affect the performance to be at the same caliber.

Further, with the performance metrics comparison among teams and with the fresh working

attitude of these members who are mostly young professionals, members tend to be in a

competitive mode, thus they try to keep up with the performance of one another.

There is no high variation among the age, income and tenure of the members. Thus,

performance of the members based on output and errors are not affected much by these

factors.

The researchers merit these results to the delicateness of the product. Information is the

product of the company. Legal departments products are cases used by professionals in courts,

corporate counsels, governments, and schools. Upon arriving in the company, employees

undergo one-week on-boarding that discusses the companys history, products, and the


57/103

50

American legal system. After which, employees are distributed to their teams and go through a

thorough training in their specific functions for two straight months. For the Cases-Generalist

workflow, there are several reviews held per jurisdiction per employee until he or she passes

the review period. If he or she fails to pass the quality control, he or she remains on-review for

the specific jurisdiction he or she is assigned to. Ultimately, from the first week up to the walls

of the company, the tag line We help the legal system perform better. Every day. Worldwide.

and its values are continually instilled to the minds of the employees. Thus, there is no

significant difference on the outputs and with their performance vis--vis their level of hierarchy

in the company, income, tenure, or age. The team knows and understands the product of the

company and what the company wants from him or her.


58/103

51

RECOMMENDATIONS

It is recommended that review should be conducted for Team H on statute verification and for

Team M on opinion verification and copy preparing. It is also suggested that review on stylistics

should be conducted for PS level 2 and for members with tenure of more than 2.5 years.

Though stylistic errors may be characterized as human-natured errors because people tend to

see past the details when working quickly, the team must devise a strategy or train the

members to have better eye for details. Better yet, in the future, members may suggest to

construct a button in their work tool that would help minimize stylistic errors. Moreover, more

intensive training should be conducted for new hires on opinion verification as this seems to be

a weakness that should be addressed.

A higher sample size for the analysis could provide a variation among the age and income and

could give a sound analysis of the working group. A higher sample for the audited cases may

also be recommended to increase the true mean of errors.


59/103

52

APPENDICES

Audit Criteria

Stylistic Content Major Content

Missing Punctuation Cite has wrong serial no. Wrong judge/justice

Judges names not capped Missing case itself references Appendix missed

Wrong mnemonic used Missing lower court info. in syn. Dissent or Concur. Opinions

missing

Titles not marked as italic Missing prior reports Wrong statute used in headnotes

N.J.-book titles arent italic Missing attorneys Wrong court in synopsis

CT.- forgot to add at in

partial cites

Forgot to markup cites in syn. &

headnotes

Missing Text

DEL.- forgot to add both AP1

lines

Wrong title used for

Judge/Justice

Released a case when it should

have been heldSpacing errors Forgot to add cities to attorneys Missing a title

Cite markup in wrong place Paragraphs split or merged

incorrectly

Wrong volume or page

information in citations

Forgot to add Full names to

Judges/Justices that are on

our list

Missing fileline information Wrong Courtline

Statutes not in correct order

in headnotes

Statute should have had a year

in parenthesis

Missing Star-paging

Incorrect tagging Partial cite, missed markup

Headings not styled correctly

(Bold, Italic, etc.)

Popular name incorrect or

incomplete

Forgot to add Assigned,

etc. to synopsis

Statute style incorrect

Statute Template style invalid

Spell check errors/not corrected

Incorrect parallel added or not

deleted (ex. Ala & WestlawOnly

cites)

Ratings:

A rating of 1 to 5 is given with 5 being the highest.

0 Errors = 5

1-2 Stylistic Errors = 4

3 Stylistic and/or 1 Content Error = 3

>3 Stylistic and/or 2 Content Errors = 2

>2 Content and/or 1 Major Content Errors = 1

>1 Major Content Error = 0

Difficulty of Case = High, Medium or Low


60/103

53

PH-Stat Results

Two-tailed Hypothesis Testing Results

Performance Comparison by Team (Total Cases)

(assumes equal population variances)

Data

Hypothesized Difference 0

Level of Significance 0.05

Population 1 Sample

Sample Size 14

Sample Mean 1473.786

Sample Standard Deviation 1006.569

Population 2 Sample

Sample Size 14



Intermediate Calculations

Population 1 Sample Degrees of Freedom 13


Total Degrees of Freedom 26

Pooled Variance 1733910

Difference in Sample Means -303.785

tTest Statistic -0.61038

Two-Tail Test

Lower Critical Value -2.05553

Upper Critical Value 2.055529

p-Value 0.546907

Do not reject the null hypothesis

Performance Comparison by Team (Total Number of Pages)


Data



Population 1 Sample

Sample Size 14

Sample Mean 13316.57Sample Standard Deviation 9570.975

Population 2 Sample

Sample Size 14






61/103

54




Difference in Sample Means 473.928

tTest Statistic 0.125782

Two-Tail Test



p-Value 0.900872


Performance Comparison by Team(Total Production Hours)


Data



Population 1 SampleSample Size 14

Sample Mean 379.357


Population 2 Sample

Sample Size 14

Sample Mean 367.553






Pooled Variance 16712.33



Two-Tail Test



p-Value 0.811001


Performance Comparison by Team(Average Pages/Hour)(assumes equal population variances)

Data



Population 1 Sample

Sample Size 14

Sample Mean 38.029



62/103

55

Population 2 Sample

Sample Size 14

Sample Mean 35.305









Two-Tail Test



p-Value 0.816897


Performance Comparison by Gender(Total Cases)


Data



Population 1 Sample

Sample Size 19



Population 2 Sample

Sample Size 9

Sample Mean 1950







Difference in Sample Means -477.948tTest Statistic -0.90453

Two-Tail Test



p-Value 0.374018



63/103

56

Performance Comparison by Gender(Total Number of Pages)


Data



Population 1 Sample

Sample Size 19



Population 2 Sample

Sample Size 9





Population 2 Sample Degrees of Freedom 8Total Degrees of Freedom 26




Two-Tail Test



p-Value 0.12057


Performance Comparison by Gender(Total Production Hours)


Data



Population 1 Sample

Sample Size 19

Sample Mean 378.105


Population 2 Sample

Sample Size 9









64/103

57


Two-Tail Test



p-Value 0.784251


Performance Comparison by Gender(Average Pages/Hour)


Data



Population 1 Sample

Sample Size 19

Sample Mean 29.341



Sample Mean 47.496









Two-Tail Test



p-Value 0.144976


Performance Comparison by Age (Total Cases)


Data

Hypothesized Difference 0Level of Significance 0.05

Population 1 Sample

Sample Size 17



Population 2 Sample

Sample Size 11



65/103

58









Two-Tail Test



p-Value 0.849014


Performance Comparison by Age(Total Number of Pages)

(assumes equal population variances)Data



Population 1 Sample

Sample Size 17



Population 2 Sample

Sample Size 11










Two-Tail Test


Upper Critical Value 2.055529p-Value 0.602732


Performance Comparison by Age(Total Production Hours)


Data




66/103

59

Population 1 Sample

Sample Size 17

Sample Mean 355.794


Population 2 Sample

Sample Size 11

Sample Mean 400.75









Two-Tail TestLower Critical Value -2.05553


p-Value 0.370185


Performance Comparison by Age(Average Pages/Hour)


Data



Population 1 Sample

Sample Size 17

Sample Mean 34.529


Population 2 Sample

Sample Size 11

Sample Mean 35.702





Total Degrees of Freedom 26Pooled Variance 863.0418



Two-Tail Test




67/103

60

p-Value 0.918607


Performance Comparison by Tenure(Total Cases)


Data



Population 1 Sample

Sample Size 9



Population 2 Sample

Sample Size 19










Two-Tail Test



p-Value 0.627734


Performance Comparison by Tenure(Total Number of Pages)


Data



Population 1 Sample

Sample Size 9


Sample Standard Deviation 8170.03Population 2 Sample

Sample Size 19







68/103

61





Two-Tail Test



p-Value 0.923771


Performance Comparison by Tenure(Total Production Hours)


Data



Population 1 Sample

Sample Size 9Sample Mean 405


Population 2 Sample

Sample Size 19

Sample Mean 358.513









Two-Tail Test



p-Value 0.375637


Performance Comparison by Tenure(Average Pages/Hour)




Population 1 Sample

Sample Size 9

Sample Mean 32.948


Population 2 Sample


69/103

62

Sample Size 19

Sample Mean 36.133









Two-Tail Test



p-Value 0.800403


Performance Comparison by PS Level(Total Number of Cases)


Data



Population 1 Sample

Sample Size 12

Sample Mean 1681.75


Population 2 Sample

Sample Size 14










Two-Tail Test



p-Value 0.499893


Performance Comparison by PS Level(Total Number of Pages)



70/103

63

Data



Population 1 Sample

Sample Size 12

Sample Mean 13083.5


Population 2 Sample

Sample Size 14

Sample Mean 11386.5







Difference in Sample Means 1697tTest Statistic 0.478789

Two-Tail Test



p-Value 0.636422


Performance Comparison by Position(Total Production Hours)


Data



Population 1 Sample

Sample Size 12

Sample Mean 427.645


Population 2 Sample

Sample Size 14

Sample Mean 309.286


Intermediate CalculationsPopulation 1 Sample Degrees of Freedom 11







71/103

64

Two-Tail Test



p-Value 0.014351

Reject the null hypothesis

Performance Comparison by PS Level (Average Pages/Hour)


Data



Population 1 Sample

Sample Size 12

Sample Mean 30.594


Population 2 Sample

Sample Size 14









Two-Tail Test



p-Value 0.612232


Comparison of All Errors of Team H and M

Data





Population Standard Deviation 0.943102

Population 2 Sample

Sample Size 84




72/103

65


Difference in Sample Means -0.48596Standard Error of the Difference inMeans 0.158968

Z-Test Statistic -3.05695

Two-Tail TestLower Critical Value -1.95996


p-Value 0.002236

Reject the null hypothesis

CP Errors by Team

Data



Population 1 Sample

Sample Size 42



Population 2 Sample

Sample Size 48

Sample Mean 0.6875




Standard Error of the Difference inMeans 0.245065

Z-Test Statistic -1.05657

Two-Tail Test



p-Value 0.290707


Opinion Ver Errors by Team




Population 1 Sample

Sample Size 24




73/103

66

Population 2 Sample

Sample

managerial statistics term paper

Documents