This document describes a new approach of Net Promoter Scoring ranking which uses Issue Resolution Rate to determine true ranking over whole population using Binomial Statistical Distribution instead of relying on actual scoring values based on a sample of Customers. Issue Resolution is factual and owned by the Company while actual NPS is based on Customer Empathy.
A new NPS benchmarking process with 2016 US Election interpretationMai Dang
This document describes a new approach of Net Promoter Scoring ranking which uses Issue Resolution Rate to determine true ranking over whole population using Binomial Statistical Distribution instead of relying on actual scoring values based on a sample of Customers. Issue Resolution is factual and owned by the Company while actual NPS is based on Customer Empathy.
The document describes a study that tested Miller's hypothesis about short-term memory capacity and chunking. It presented subjects with digit sets of varying lengths (3, 5, 7, or 9 digits) in either serial or sequential order. Recall accuracy declined significantly as digit set length increased but order had no effect, failing to support chunking. The study was limited by its small sample size of two. It did not provide clear evidence for Miller's "magic number of 7" or demonstrate chunking, but hinted capacity may be closer to Cowan's proposed 4 digits. Further research with varied designs and larger samples is needed to better understand short-term memory limits.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
This document provides notes for online students about quantitative data analysis and SPSS. It discusses that the lecture series will cover basic ideas in quantitative data analysis. It notes that many different statistical software programs are available but that the course will use SPSS because it is easy to use and popular for statistical analysis.
Intro to SVM with its maths and examples. Types of SVM and its parameters. Concept of vector algebra. Concepts of text analytics and Natural Language Processing along with its applications.
This document is a project report describing the use of a genetic algorithm to solve a traveling salesman problem. It details how the genetic algorithm was implemented to find the optimal route for a honeymoon couple visiting multiple European countries, given the starting and ending countries. Key steps included image processing to obtain country coordinates, initializing a population of routes, evaluating routes using a fitness function, and applying genetic operators like selection, crossover and mutation over multiple iterations to converge on the shortest route. The genetic algorithm approach was found to be well-suited for the traveling salesman problem by providing good solutions efficiently.
Intro and maths behind Bayes theorem. Bayes theorem as a classifier. NB algorithm and examples of bayes. Intro to knn algorithm, lazy learning, cosine similarity. Basics of recommendation and filtering methods.
Have you ever created a machine learning model that is perfect for the training samples but gives very bad predictions with unseen samples! Did you ever think why this happens? This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting.
A new NPS benchmarking process with 2016 US Election interpretationMai Dang
This document describes a new approach of Net Promoter Scoring ranking which uses Issue Resolution Rate to determine true ranking over whole population using Binomial Statistical Distribution instead of relying on actual scoring values based on a sample of Customers. Issue Resolution is factual and owned by the Company while actual NPS is based on Customer Empathy.
The document describes a study that tested Miller's hypothesis about short-term memory capacity and chunking. It presented subjects with digit sets of varying lengths (3, 5, 7, or 9 digits) in either serial or sequential order. Recall accuracy declined significantly as digit set length increased but order had no effect, failing to support chunking. The study was limited by its small sample size of two. It did not provide clear evidence for Miller's "magic number of 7" or demonstrate chunking, but hinted capacity may be closer to Cowan's proposed 4 digits. Further research with varied designs and larger samples is needed to better understand short-term memory limits.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
This document provides notes for online students about quantitative data analysis and SPSS. It discusses that the lecture series will cover basic ideas in quantitative data analysis. It notes that many different statistical software programs are available but that the course will use SPSS because it is easy to use and popular for statistical analysis.
Intro to SVM with its maths and examples. Types of SVM and its parameters. Concept of vector algebra. Concepts of text analytics and Natural Language Processing along with its applications.
This document is a project report describing the use of a genetic algorithm to solve a traveling salesman problem. It details how the genetic algorithm was implemented to find the optimal route for a honeymoon couple visiting multiple European countries, given the starting and ending countries. Key steps included image processing to obtain country coordinates, initializing a population of routes, evaluating routes using a fitness function, and applying genetic operators like selection, crossover and mutation over multiple iterations to converge on the shortest route. The genetic algorithm approach was found to be well-suited for the traveling salesman problem by providing good solutions efficiently.
Intro and maths behind Bayes theorem. Bayes theorem as a classifier. NB algorithm and examples of bayes. Intro to knn algorithm, lazy learning, cosine similarity. Basics of recommendation and filtering methods.
Have you ever created a machine learning model that is perfect for the training samples but gives very bad predictions with unseen samples! Did you ever think why this happens? This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting.
This document discusses estimating population parameters from sample statistics. It defines a point estimate of the population mean as the mean of sample means. The document provides an example where a consumer group took random samples of bottle capacities to estimate the true population mean capacity claimed by a company. It demonstrates computing the mean of each sample and the point estimate of the population mean. Finally, it provides formulas for computing variance and standard deviation as other measures of the population from sample statistics.
A Time Series Analysis for Predicting Basketball StatisticsJoseph DeLay
This document summarizes a time series analysis of points scored by NBA player Derrick Rose. The analysis found that an IMA(1,1) model best fit the data. When used to forecast future points, the model predictions narrowed to Rose's average points per game due to the limited data points. Adding more seasons of data would improve the model's accuracy for long-term predictions.
mat 540,str mat 540,mat 540 entire course new,mat 540 week all homework,mat 540 all discussion question,mat 540 midterm exam,mat 540 final exam,str mat 540 week 1,str mat 540 week 2,str mat 540 week 3,str mat 540 week 4,str mat 540 week 5,str mat 540 week 6,str mat 540 week 7,str mat 540 week 8,str mat 540 week 9,str mat 540 week 10,str mat 540 week 11,str mat 540 tutorials,str mat 540 assignments,mat 540 help
This document contains 40 multiple choice questions from a MAT 540 midterm exam covering topics like probability, statistics, forecasting, and simulation. It provides the questions only, without answers. The questions cover determining probability, using forecasting techniques like exponential smoothing and moving averages, interpreting simulation results, and applying decision analysis concepts. The document aims to help students prepare for the midterm by practicing different types of quantitative analysis questions.
mat 540,str mat 540,mat 540 entire course new,mat 540 week all homework,mat 540 all discussion question,mat 540 midterm exam,mat 540 final exam,str mat 540 week 1,str mat 540 week 2,str mat 540 week 3,str mat 540 week 4,str mat 540 week 5,str mat 540 week 6,str mat 540 week 7,str mat 540 week 8,str mat 540 week 9,str mat 540 week 10,str mat 540 week 11,str mat 540 tutorials,str mat 540 assignments,mat 540 help
The document discusses exploratory data analysis techniques used to analyze a telecommunications customer churn dataset containing 3,333 records and 20 variables.
Key techniques included:
1. Examining relationships between categorical variables like international plan and voice mail plan to churn, finding customers in international plans churned at higher rates.
2. Exploring distributions and correlations of numeric variables like account length, day minutes, and customer service calls with churn. Higher values in calls and minutes were linked to higher churn.
3. Using histograms, scatter plots, and other graphs to identify multivariate relationships, like finding customers with many calls but low minutes churned more.
The analysis helped identify variables likely to predict churn for modeling without pre
The document analyzes data from 150 drivers in a WorkAlert program to determine if there is a relationship between age and texting response rates. A chi-square test of independence finds no significant relationship, suggesting that age does not affect whether a driver will text. It is recommended to implement an aggressive texting campaign to contact all new drivers initially and help bridge communication, rather than investing more resources based on age assumptions.
This document discusses various numerical descriptive techniques used for summarizing and describing quantitative data, including:
- Measures of central location (mean, median, mode) and how to calculate them
- Measures of variability (range, variance, standard deviation) and how they are used to quantify the dispersion of data around the mean
- Other concepts like percentiles, the empirical rule, Chebyshev's theorem, and box plots. Examples are provided to illustrate how to apply these techniques to sample data sets.
1. The document discusses hypothesis testing of claims about population parameters such as proportions, means, standard deviations, and variances from one or two samples.
2. Key concepts include hypothesis tests using z-tests, t-tests, and chi-square tests. Confidence intervals are also constructed for parameters.
3. Two examples are provided to demonstrate hypothesis testing of claims about two population proportions using z-tests. The null hypothesis is rejected in one example but not the other.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This document provides a portfolio of projects from the architecture firm Pranav Shauche. It includes 15 projects spanning multiple types including healthcare, residential, institutional, and civic buildings. The projects showcase the firm's design approach and include case studies highlighting details like site plans, floor plans, sections, and renderings. They demonstrate a focus on sustainability, flexibility, and integration with the natural environment.
Burgos carlos david act 8. tallerpractico10luz sinisterra
Este documento describe un taller práctico sobre 10 claves para la implementación de tendencias y enfoques innovadores en la educación. El taller busca que los docentes identifiquen los cambios necesarios para incorporar las TIC al aula y currículo escolar. El taller se centra en las nuevas habilidades del siglo 21, políticas de acceso a la tecnología y los desafíos de adaptar la educación a la sociedad moderna. Incluye ejercicios para analizar tendencias pedagógicas emergentes y conceptualizar 10 claves esenciales
Robert Hoffpauir has over 40 years of experience in oil and gas completions and workovers, primarily in Louisiana, Texas, Arkansas, Utah, Colorado, Wyoming and North Dakota. He has experience with highly deviated wells, pressures up to 13,000 PSI, and H2S and CO2 environments. His qualifications include hydraulic fracturing, wireline operations, and experience with snubbing units, coiled tubing and standalone units. He is certified in various safety programs and his most recent roles have included consultant work in North Dakota cleaning out frac plugs and perforating wells.
El documento explica qué es un blog, sus características principales y los pasos para crear uno. Un blog es un sitio que recopila artículos de forma cronológica de uno o más autores. Permite incluir texto, imágenes, videos y etiquetas. Existen servidores gratuitos para crear blogs de forma sencilla.
This document discusses estimating population parameters from sample statistics. It defines a point estimate of the population mean as the mean of sample means. The document provides an example where a consumer group took random samples of bottle capacities to estimate the true population mean capacity claimed by a company. It demonstrates computing the mean of each sample and the point estimate of the population mean. Finally, it provides formulas for computing variance and standard deviation as other measures of the population from sample statistics.
A Time Series Analysis for Predicting Basketball StatisticsJoseph DeLay
This document summarizes a time series analysis of points scored by NBA player Derrick Rose. The analysis found that an IMA(1,1) model best fit the data. When used to forecast future points, the model predictions narrowed to Rose's average points per game due to the limited data points. Adding more seasons of data would improve the model's accuracy for long-term predictions.
mat 540,str mat 540,mat 540 entire course new,mat 540 week all homework,mat 540 all discussion question,mat 540 midterm exam,mat 540 final exam,str mat 540 week 1,str mat 540 week 2,str mat 540 week 3,str mat 540 week 4,str mat 540 week 5,str mat 540 week 6,str mat 540 week 7,str mat 540 week 8,str mat 540 week 9,str mat 540 week 10,str mat 540 week 11,str mat 540 tutorials,str mat 540 assignments,mat 540 help
This document contains 40 multiple choice questions from a MAT 540 midterm exam covering topics like probability, statistics, forecasting, and simulation. It provides the questions only, without answers. The questions cover determining probability, using forecasting techniques like exponential smoothing and moving averages, interpreting simulation results, and applying decision analysis concepts. The document aims to help students prepare for the midterm by practicing different types of quantitative analysis questions.
mat 540,str mat 540,mat 540 entire course new,mat 540 week all homework,mat 540 all discussion question,mat 540 midterm exam,mat 540 final exam,str mat 540 week 1,str mat 540 week 2,str mat 540 week 3,str mat 540 week 4,str mat 540 week 5,str mat 540 week 6,str mat 540 week 7,str mat 540 week 8,str mat 540 week 9,str mat 540 week 10,str mat 540 week 11,str mat 540 tutorials,str mat 540 assignments,mat 540 help
The document discusses exploratory data analysis techniques used to analyze a telecommunications customer churn dataset containing 3,333 records and 20 variables.
Key techniques included:
1. Examining relationships between categorical variables like international plan and voice mail plan to churn, finding customers in international plans churned at higher rates.
2. Exploring distributions and correlations of numeric variables like account length, day minutes, and customer service calls with churn. Higher values in calls and minutes were linked to higher churn.
3. Using histograms, scatter plots, and other graphs to identify multivariate relationships, like finding customers with many calls but low minutes churned more.
The analysis helped identify variables likely to predict churn for modeling without pre
The document analyzes data from 150 drivers in a WorkAlert program to determine if there is a relationship between age and texting response rates. A chi-square test of independence finds no significant relationship, suggesting that age does not affect whether a driver will text. It is recommended to implement an aggressive texting campaign to contact all new drivers initially and help bridge communication, rather than investing more resources based on age assumptions.
This document discusses various numerical descriptive techniques used for summarizing and describing quantitative data, including:
- Measures of central location (mean, median, mode) and how to calculate them
- Measures of variability (range, variance, standard deviation) and how they are used to quantify the dispersion of data around the mean
- Other concepts like percentiles, the empirical rule, Chebyshev's theorem, and box plots. Examples are provided to illustrate how to apply these techniques to sample data sets.
1. The document discusses hypothesis testing of claims about population parameters such as proportions, means, standard deviations, and variances from one or two samples.
2. Key concepts include hypothesis tests using z-tests, t-tests, and chi-square tests. Confidence intervals are also constructed for parameters.
3. Two examples are provided to demonstrate hypothesis testing of claims about two population proportions using z-tests. The null hypothesis is rejected in one example but not the other.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This document provides a portfolio of projects from the architecture firm Pranav Shauche. It includes 15 projects spanning multiple types including healthcare, residential, institutional, and civic buildings. The projects showcase the firm's design approach and include case studies highlighting details like site plans, floor plans, sections, and renderings. They demonstrate a focus on sustainability, flexibility, and integration with the natural environment.
Burgos carlos david act 8. tallerpractico10luz sinisterra
Este documento describe un taller práctico sobre 10 claves para la implementación de tendencias y enfoques innovadores en la educación. El taller busca que los docentes identifiquen los cambios necesarios para incorporar las TIC al aula y currículo escolar. El taller se centra en las nuevas habilidades del siglo 21, políticas de acceso a la tecnología y los desafíos de adaptar la educación a la sociedad moderna. Incluye ejercicios para analizar tendencias pedagógicas emergentes y conceptualizar 10 claves esenciales
Robert Hoffpauir has over 40 years of experience in oil and gas completions and workovers, primarily in Louisiana, Texas, Arkansas, Utah, Colorado, Wyoming and North Dakota. He has experience with highly deviated wells, pressures up to 13,000 PSI, and H2S and CO2 environments. His qualifications include hydraulic fracturing, wireline operations, and experience with snubbing units, coiled tubing and standalone units. He is certified in various safety programs and his most recent roles have included consultant work in North Dakota cleaning out frac plugs and perforating wells.
El documento explica qué es un blog, sus características principales y los pasos para crear uno. Un blog es un sitio que recopila artículos de forma cronológica de uno o más autores. Permite incluir texto, imágenes, videos y etiquetas. Existen servidores gratuitos para crear blogs de forma sencilla.
El documento habla sobre los contenidos multimedia, que son aquellos que utilizan múltiples medios como texto, imágenes, sonido y video para presentar información de manera interactiva. Los contenidos multimedia se distribuyen en línea a través de descargas o streaming y tienen ventajas como menores costos de producción y distribución al eliminar los medios físicos.
The City Council meeting discussed abandoning two public utility easements totaling 3,522 square feet located at 410 Stasney Street and 415 Nagle Street. Maps were presented showing the vicinity, location, proposed site plan of the easements. Questions were taken regarding the easement abandonments.
La Unión Europea ha acordado un embargo petrolero contra Rusia en respuesta a la invasión de Ucrania. El embargo prohibirá las importaciones marítimas de petróleo ruso a la UE y pondrá fin a las entregas a través de oleoductos dentro de seis meses. Esta medida forma parte de un sexto paquete de sanciones de la UE destinadas a aumentar la presión económica sobre Moscú y privar al Kremlin de fondos para financiar su guerra.
The document provides a summary for John Brangaccio including his contact information, professional summary, work experience with the US Army and US Marine Corps, education and training, personal awards, and references. He has over 20 years of experience as a non-commissioned officer in the Army and a gunnery sergeant in the Marines, with leadership roles such as platoon sergeant, company gunnery sergeant, and master gunner. He has received numerous awards and commendations for his service and leadership.
Este documento proporciona información técnica sobre una variedad de elementos de sujeción como pernos, tuercas y arandelas de diferentes grados, materiales y especificaciones. Incluye tablas con detalles como marcas, dimensiones, materiales y acabados de más de 50 artículos diferentes. El documento parece ser un catálogo o lista de productos de una empresa dedicada a la fabricación y venta de elementos de sujeción industrial.
Este documento presenta una línea de tiempo sobre la evolución de la criminología desde la antigüedad hasta el siglo XX. Los primeros pensadores como Esopo, Sócrates y Platón consideraban que la pobreza era un factor que influía en la criminalidad y abogaban por el castigo para prevenir futuros delitos. Más adelante, pensadores como Tomás Moro y Charles de Secondat propusieron enfocarse más en la prevención que en el castigo y abordar factores como la pobreza y la educación. Finalmente, autores como Emile Durk
Cucumber is a popular tool that is commonly used for writing and running functional tests that can drive the BDD (Behavior Driven Development) process on a project development team. Just as commonly, what started as a nice little garden of cukes can become overgrown and difficult to manage as a project’s life advances. This talk will cover several useful tools that can help you keep your Cucumber suites in shape.
See 2020 update: https://derwen.ai/s/h88s
SF Python Meetup, 2017-02-08
https://www.meetup.com/sfpython/events/237153246/
PyTextRank is a pure Python open source implementation of *TextRank*, based on the [Mihalcea 2004 paper](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf) -- a graph algorithm which produces ranked keyphrases from texts. Keyphrases generally more useful than simple keyword extraction. PyTextRank integrates use of `TextBlob` and `SpaCy` for NLP analysis of texts, including full parse, named entity extraction, etc. It also produces auto-summarization of texts, making use of an approximation algorithm, `MinHash`, for better performance at scale. Overall, the package is intended to complement machine learning approaches -- specifically deep learning used for custom search and recommendations -- by developing better feature vectors from raw texts. This package is in production use at O'Reilly Media for text analytics.
Lecture 4 Applied Econometrics and Economic Modelingstone55
The document discusses different methods for selecting random samples from a population, including simple random sampling, stratified sampling, cluster sampling, and systematic sampling. It provides examples of how to generate random samples in Excel and calculate summary statistics. The central limit theorem is also introduced, showing how the distribution of sample means approaches a normal distribution as sample size increases.
Question1The Tri-City School District has instituted a zero-tol.docxmakdul
Question1:
The Tri-City School District has instituted a zero-tolerance policy for students carrying any objects that could be used as weapons. The following data give the number of students suspended during each of the past 12 weeks for violating this school policy.
Find the mean, median, and mode.
Round your answers to two decimal places, where appropriate.
Mean = Median = Mode =
Question 2:
Recall the following from section 3.1 of the text. Mean : The mean for ungrouped data is obtained by dividing the sum of all values by the number of values in the data set. Median: The median is the value of the middle term in a data set that has been ranked in increasing order. If there is an even number of data, find the average of the two middle data values. Mode: The mode is the value that occurs with the highest frequency in a data set. If there are more than one data values with the highest frequency in a data set, we will have multiple modes. If all data values have the same frequency of occurrences, then the data set has no mode.
26,32,27,23,34,33,29,43,23,28
(a) Arrange the data in increasing order:
(b) Calculate the mean. The mean =
Question 3:
The following data represent the 2011 guaranteed salaries (in thousands of dollars) of the head coaches of the final eight teams in the 2011 NCAA Men's Basketball Championship. The data represent the 2011 salaries of basketball coaches of the following universities, entered in that order: Arizona, Butler, Connecticut, Florida, Kansas, Kentucky, North Carolina, and Virginia Commonwealth. (Source: www.usatoday.com)
1950,434,2300,3575,3376,3800,1655,418
Compute the range, variance and standard deviation for these data.
Round your answers to the nearest integer, where appropriate.
Range = $
Variance =
Standard deviation = $
Question 4:
The 2011 gross sales of all firms in a large city have a mean of $3.6 million and a standard deviation of $0.7 million. Using Chebyshev′s theorem, find a lower bound on the percentage of firms in this city that had 2011 gross sales between $0.8 and $6.4 million.
Round the answer to the nearest percent.
The lower bound on the percentage is at least %
Questiono 5:
The 2011 gross sales of all firms in a large city have a mean of $2.4 million and a standard deviation of $ 0.6 million. Using Chebyshev's theorem, find at least what percentage of firms in this city had 2011 gross sales of $1.0 to $3.8 million. Round your answer to the nearest whole number.
%
Question 6:
The following data give the weights (in pounds) lost by 15 members of a health club at the end of two months after joining the club.
5 10 8 7 24 12 5 13 11 10 21 9 8 11 18
(a) Calculate the approximate value of the 82nd percentile, denoted P82.
P82 =
(b) Find the percentile rank of 11.
Give the answer rounded to the nearest percent.
The percentile rank of 11 =
Question 7:
In a group of households, the national news is watched on one of the following networks – ABC, CBS ...
As mentioned earlier, the mid-term will have conceptual and quanti.docxfredharris32
As mentioned earlier, the mid-term will have conceptual and quantitative multiple-choice questions. You need to read all 4 chapters and you need to be able to solve problems in all 4 chapters in order to do well in this test.
The following are for review and learning purposes only. I am not indicating that identical or similar problems will be in the test. As I have indicated in the class syllabus, all the exams in this course will have multiple-choice questions and problems.
Suggestion: treat this review set as you would an actual test. Sit down with your one page of notes and your calculator, and give it a try. That way you will know what areas you still need to study.
ADMN 210
Answers to Review for Midterm #1
1) Classify each of the following as nominal, ordinal, interval, or ratio data.
a. The time required to produce each tire on an assembly line – ratio since it is numeric with a valid 0 point meaning “lack of”
b. The number of quarts of milk a family drinks in a month - ratio since it is numeric with a valid 0 point meaning “lack of”
c. The ranking of four machines in your plant after they have been designated as excellent, good, satisfactory, and poor – ordinal since it is ranking data only
d. The telephone area code of clients in the United States – nominal since it is a label
e. The age of each of your employees - ratio since it is numeric with a valid 0 point meaning “lack of”
f. The dollar sales at the local pizza house each month - ratio since it is numeric with a valid 0 point meaning “lack of”
g. An employee’s identification number – nominal since it is a label
h. The response time of an emergency unit - ratio since it is numeric with a valid 0 point meaning “lack of”
2) True or False: The highest level of data measurement is the ratio-level measurement.
True (you can do the most powerful analysis with this kind of data)
3) True or False: Interval- and ratio-level data are also referred to as categorical data.
False (Interval and ratio level data are numeric and therefore quantitative, NOT qualitative….Nominal is qualitative)
4) A small portion or a subset of the population on which data is collected for conducting statistical analysis is called __________.
A sample! A population is the total group, a census IS the population, and a data set can be either a sample or a population.
5) One of the advantages for taking a sample instead of conducting a census is this:
a sample is more accurate than census
a sample is difficult to take
a sample cannot be trusted
a sample can save money when data collection process is destructive
6) Selection of the winning numbers is a lottery is an example of __________.
convenience sampling
random sampling
nonrandom sampling
regulatory sampling
7) A type of random sampling in which the population is divided into non-overlapping subpopulations is called __________.
stratified random sampling
cluster sampling
systematic random sampling
regulatory sampling
8) A ...
The document discusses the chi-square test of independence and provides examples of applying it. It first defines the chi-square test and explains that it determines if there is a significant relationship between two categorical variables. Then, it analyzes four problems using a chi-square test to assess relationships between variables like product purchased and customer gender, region and computer ownership, current and preferred smartphone brand, and alcohol drinking and smoking habits. The document demonstrates how to set up and interpret the results of chi-square tests, including observing frequencies, calculating test statistics, and determining if relationships are statistically significant.
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docxSANSKAR20
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)SERvice – Years of serviceGender: 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
Chi square analysis-for_attribute_data_(01-14-06)Daniel Augustine
This document provides an overview of chi-square analysis, including what a chi-square test is, the different types of chi-square tests, the basics of when and how to apply a chi-square test, and how to use Minitab to conduct a chi-square test. It describes chi-square tests as a way to determine if there are statistically significant differences in the proportions between groups. The document outlines the steps for entering data into Minitab and interpreting the results, and provides tips, examples, and supplemental information on chi-square tests.
Accurate Campaign Targeting Using Classification AlgorithmsJieming Wei
This paper aims to build a binary classification model to help non-profit organizations efficiently target likely donors for direct mail campaigns. The authors use a dataset of over 1 million records containing demographic and campaign attributes to select relevant features and split the data into training and test sets. Several classification algorithms are tested on the data, with a neural network found to have the lowest false positive error rate, which is important to minimize costs. The authors further tune the neural network structure and regularization to optimize performance, and select a classification threshold that balances errors to maximize estimated net returns.
(Gaurav sawant & dhaval sawlani)bia 678 final project reportGaurav Sawant
PROJECT REPORT
• Performed memory-based collaborative filtering techniques like Cosine similarities, Pearson’s r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
• Studied the scalability of these methods on local machines & on Hadoop clusters
Stat-weight Improving the Estimator of Interleaved Methods Outcomes with Stat...Sease
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assume that a class mate has a preference different from yours, Challenge that position, Defend your position. Be detailed in your defense. Treat this as an email exchange with a project co-worker as you examine the two methodologies in preparation for submitting a decision paper to company executives on a model choice.
· APA format
· 1.5 pages, cover page and reference not counted
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You must include/use Russell, Operations Management, 8e
LEARNING OUTCOMES
Know what descriptive statistics are and why they are used
Create and interpret tabulation tables
Use cross-tabulations to display relationships
Perform basic data transformations
Understand the basics of testing hypotheses using inferential statistics
Z test
14–*
*
The Nature of Descriptive AnalysisDescriptive Analysis
The elementary transformation of raw data in a way that describes the basic characteristics such as central tendency, distribution, and variability.Histogram
A graphical way of showing a frequency distribution in which the height of a bar corresponds to the observed frequency of the category.
14–*
*
EXHIBIT 14.1 Levels of Scale Measurement and Suggested Descriptive Statistics
14–*
*
Cross-TabulationCross-Tabulation
Addresses research questions involving relationships among multiple less-than interval variables.
Results in a combined frequency table displaying one variable in rows and another variable in columns.Contingency Table
A data matrix that displays the frequency of some combination of responses to multiple variables.Marginals
Row and column totals in a contingency table, which are shown in its margins.
20–*
*
Cross-Tabulation Table
Did you watch the movie Into The Woods? Yes No
What’s your gender? Male Female
(Observed distribution)
*NoYesTotalMale14317Female151732Total292049
Cross-tab: Project Assignment Thirty respondents were asked if they have the access to the 4G network and if they have used mobile banking services. The results showed that 11 people do not have the access to 4G and have not used mobile banking, 4 people have the access to 4G but have not used mobile banking, 12 people have the access to 4G and have used mobile banking, and 3 people do not have the access to 4G but have used mobile banking (using friends’ smartphone).
Present the results in a cross-tabulation table in Project Assignment.
14–*
*
Cross-Tabulation TableConvert frequency table to percentage table.
Statistical base – the number of respondents or observations (in a row or column) used as a basis for computing percentages.
What was the percentage of males who watched the movie?
What was the percentage of moviegoers who were male?
*
*
Cros ...
Complete Parts A & BPart ASome questions in Part A r.docxluellaj
Complete
Parts A & B
Part A
Some questions in Part A require that you access data from
Statistics for People Who (Think
T
hey) Hate Statistics
.
This data is available on the student website under the Student Text Resources link.
Using the data in the file named Ch. 11 Data Set 2, test the research hypothesis at the .05 level of significance that boys raise their hands in class more often than girls. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? Remember to first decide whether this is a one- or two-tailed test.
Using the same data set (Ch. 11 Data Set 2), test the research hypothesis at the .01 level of significance that there is a difference between boys and girls in the number of times they raise their hands in class. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? You used the same data for this problem as for Question 1, but you have a different hypothesis (one is directional and the other is nondirectional). How do the results differ and why?
Practice the following problems by hand just to see if you can get the numbers right. Using the following information, calculate the
t
test statistic.
Using the results you got from Question 3 and a level of significance at .05, what are the two-tailed critical values associated with each? Would the null hypothesis be rejected?
Using the data in the file named Ch. 11 Data Set 3, test the null hypothesis that urban and rural residents both have the same attitude toward gun control. Use IBM
®
SPSS
®
software to complete the analysis for this problem.
A public health researcher tested the hypothesis that providing new car buyers with child safety seats will also act as an incentive for parents to take other measures to protect their children (such as driving more safely, child-proofing the home, and so on). Dr. L counted all the occurrences of safe behaviors in the cars and homes of the parents who accepted the seats versus those who did not. The findings: a significant difference at the .013 level. Another researcher did exactly the same study; everything was the same—same type of sample, same outcome measures, same car seats, and so on. Dr. R’s results were marginally significant (recall Ch. 9) at the .051 level. Which result do you trust more and why?
In the following examples, indicate whether you would perform a
t
test of independent means or dependent means.
Two groups were exposed to different treatment levels for ankle sprains. Which treatment was most effective?
A researcher in nursing wanted to know if the recovery of patients was quicker when some received additional in-home care whereas when others received the standard amount.
A group of adolescent boys was offered interpersonal skills counseling and then tested in September and May to see if there was any impact on family harmony.
One group of adult men was given instructio.
Part ASome questions in Part A require that you access data .docxbridgelandying
Part A
Some questions in Part A require that you access data from
Statistics for People Who (Think
T
hey) Hate Statistics
.
This data is available on the student website under the Student Text Resources link.
Using the data in the file named Ch. 11 Data Set 2, test the research hypothesis at the .05 level of significance that boys raise their hands in class more often than girls. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? Remember to first decide whether this is a one- or two-tailed test.
Using the same data set (Ch. 11 Data Set 2), test the research hypothesis at the .01 level of significance that there is a difference between boys and girls in the number of times they raise their hands in class. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? You used the same data for this problem as for Question 1, but you have a different hypothesis (one is directional and the other is nondirectional). How do the results differ and why?
Practice the following problems by hand just to see if you can get the numbers right. Using the following information, calculate the
t
test statistic.
Using the results you got from Question 3 and a level of significance at .05, what are the two-tailed critical values associated with each? Would the null hypothesis be rejected?
Using the data in the file named Ch. 11 Data Set 3, test the null hypothesis that urban and rural residents both have the same attitude toward gun control. Use IBM
®
SPSS
®
software to complete the analysis for this problem.
A public health researcher tested the hypothesis that providing new car buyers with child safety seats will also act as an incentive for parents to take other measures to protect their children (such as driving more safely, child-proofing the home, and so on). Dr. L counted all the occurrences of safe behaviors in the cars and homes of the parents who accepted the seats versus those who did not. The findings: a significant difference at the .013 level. Another researcher did exactly the same study; everything was the same—same type of sample, same outcome measures, same car seats, and so on. Dr. R’s results were marginally significant (recall Ch. 9) at the .051 level. Which result do you trust more and why?
In the following examples, indicate whether you would perform a
t
test of independent means or dependent means.
Two groups were exposed to different treatment levels for ankle sprains. Which treatment was most effective?
A researcher in nursing wanted to know if the recovery of patients was quicker when some received additional in-home care whereas when others received the standard amount.
A group of adolescent boys was offered interpersonal skills counseling and then tested in September and May to see if there was any impact on family harmony.
One group of adult men was given instructions in reducing their hi.
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A new nps benchmarking process with 2016 us election interpretation
1. Mai Dang, Telstra, November 11th
2016 1 of 19
A New NPS Benchmarking Process with
2016 US Election Interpretation
Mai Dang
Telstra Corporation Limited
11th
November 2016
Net Promoter Scoring (NPS) process consists of two sides:
1. Company’s effort to maximize its Products and Services Performance measured by “Issue
Resolution” metric. A “Resolution Yes” is when Customer answering “Yes” to the question
“Has your issue been resolved?”
Resolution Rate “Yes” (RR) over the survey volume is the most often used. Other answers
like “No” or “Too early to tell” are grouped as 1-RR. RR values are between 0 and 1.
2. Customer’s perception of this effort is the NPS scoring. NPS is narrowed down to 3
Categories according to scores between 0 and 10:
o Advocate (score 9-10) are loyal promoters who will keep buying and refer others.
o Passive (score 7-8) are satisfied but are vulnerable to churn due to different reasons.
o Detractor (score 0-6) are unhappy customers who can damage the brand through
negative word-of-mouth.
NPS is calculated based on difference between Advocate and Detractor in percentages of survey
volume. NPS values are between -100 and +100.
Current NPS benchmarking process [5] is based solely on NPS value. However Customer experience
is more than just NPS as the general Customer feeling is based on issue that was resolved or not.
Issue Resolution rate is a factual and objective metric as it reflects the Company Advocacy’s effort.
Issue Resolution rate determines the true NPS using Binomial Distribution with the whole population
over the reporting period whilst the actual NPS feedback only valid for a sample population.
The tips given in addition to the bill amount in a Restaurant is a good analogy with NPS. If the
services are received as either “very bad” or “very good” then the tips are consistent and variations
are small between payments. The tip amount is the NPS score and represents Customer Empathy
towards the Restaurant Service which is Resolution Rate.
Analogy with Restaurant stops here as NPS context is more intricate than restaurant payment as the
tips and bill amounts in NPS are collapsed into one figure which is NPS. To work out Customer
Empathy, Resolution Rate needs to be included in the process with the use of Binomial Distribution
where NPS values are calculated from the Resolution Rates.
This current exercise proposed a new NPS benchmarking process with a simultaneous ranking on
both NPS and Resolution Rate. If NPS ranking is consistent with Resolution Rate’s then both existing
and new NPS benchmark report the same outcome. If the rankings between NPS and Resolution
Rate are inconsistent or overlapping then the new benchmark outcome will be contradicting the
existing ranking.
2. Mai Dang, Telstra, November 11th
2016 2 of 19
The results of 2016 US Election [7] with Poll and Vote data demonstrate the use of the new
benchmarking process with Empathy weigh leans toward 1 for the Vote data. Poll data as a
snapshot, has Empathy weights of 0.95 and 1.05 for Republicans and Democrats respectively.
The 2014 NPS Benchmark between Apple and Samsung for Smartphones products [5] is another
example that shows an equivalence with US Election Poll data as both are just NPS snapshots.
To have a robust NPS Benchmarking system, Empathy weight equal to 1 must be used as base for
NPS ranking across the board.
There are three parts of the discussion:
Part I: Provide a method to work out a NPS score for a given Resolution “Yes” with a neutral
Empathy (weight=1), either by:
a. Using Binomial Distribution algorithm with RR as an input to the model.
b. Using “Bean machine” process in conjunction with 11 row calculation of NPS from
Pascal’s triangle algorithm to perform NPS calculation from RR as an input.
Method (a) can be implemented easily within a spreadsheet. For this exercise, the
results are obtained using approach (b) with R as programming language. A simulation
prototype written Java demonstrates the approach (b) is also discussed using a random
number between 0 and 1 to simulate a Resolution “Yes” or not equal “Yes”.
Part II: NPS Life Cycle revealed the Strategy to improve the Customer Experience.
Part III: Introduction of Customer’s Empathy to the NPS actual data. The results are consistent
between Human led and Technology based channels or between Residential and Business
segments and even the timing of NPS process (online and episode) also had a driving effect on
how the score was issued.
Empathy according to [1] is defined as “generally includes responding to positive affects as well as
negative ones without, however, necessarily requiring doing anything about it”.
3. Mai Dang, Telstra, November 11th
2016 3 of 19
I.NPS machine
1. Bean machine
The easy way to explain analytically the NPS process is to use a board game called “Bean machine”
with a demo from you-tube [2].
From a middle and narrow top entrance of the “Bean machine”, the beans were introduced and
fallen into the bottom slots (see Figure 1) through the gaps between the wooden ticks. Let’s assume
there was a block of 11 x 11 wooden sticks on the board. Each bean will hit these ticks on the way
down to the bottom slots. These ticks constitute the core of Customer interaction with the company.
The 7 leftmost bottom slots are dedicated for Detractors. The 2 rightmost slots are for Advocates.
The 2 slots in the middle are for Passive Customers.
If there are no intervention to the way the wooden ticks are setup, the chance the beans fallen to
right or to the left of the tick is 50% each. With NPS, more the beans fallen to the right better the
NPS is, hence all wooden ticks will have to be “tampered” which reflects the true essence of
Company’s effort in ethical terms which is measured as the Resolution Rate.
Figure 1: “Bean machine” from you-tube [2] https://www.youtube.com/watch?v=3m4bxse2JEQ
2. Pascal’s triangle
Pascal’s triangle calculation [3] was used to work out how many beans will be collected at 11 bins at
the bottom of the board as below. Pascal’s triangle principle is explained as figure 2.
Figure 2: Pascal’s triangle calculation. See Reference [3]
4. Mai Dang, Telstra, November 11th
2016 4 of 19
3. Bean machine simulation through Pascal’s triangle and Binomial Distribution
Table 1: Pascal’s triangle applied to NPS scoring with values at row 10 as Binomial Coefficients
Binomial Distribution is the statistical expression of the chance for two outcomes (like “Head” and
“Tail” of coin tossing) through a sequence of consecutive events which are linked to Pascal’s triangle
with also two outcomes (“left” and “right” direction) and number of events called Rows from Tables
1 and 2.
The “Bean machine” provides an idea and Pascal’s triangle, Binomial Distribution [4] provides a
framework for building of what is called a “NPS machine” with different adjustments of the wooden
ticks which are “Resolution Yes” in NPS terminology.
The results at the Rows 10 applied to NPS from the Table 2 are used to calculate the chance of each
NPS score using Binomial algorithm as below
1. Detractor score 0: (1-RR)^10
2. Detractor score 1: 10*(1-RR)^9 *(RR)^1
3. Detractor score 2: 45*(1-RR)^8 *( RR)^2
4. Detractor score 3: 120*(1-RR)^7 *(RR)^3
5. Detractor score 4: 210*(1-RR)^6 *( RR)^4
6. Detractor score 5: 252*(1-RR)^5 *( RR)^5
7. Detractor score 6: 210*(1-RR)^4 *( RR)^6
8. Passive score 7: 120*(1-RR)^3 *( RR)^7
9. Passive score 8: 45*(1-RR)^2 *( RR)^8
10. Advocate score 9: 10*(1-RR)^1 *( RR)^9
11. Advocate score 10: 1*(RR)^10
5. Mai Dang, Telstra, November 11th
2016 5 of 19
Table 2: Normalised NPS scoring with total of each row equal to 1 at a Resolution “Yes” 50%
The simulated scores at the bottom row of table 2 can be implemented within an excel spreadsheet.
Another method to calculate NPS from RR is to use a programming language without explicitly using
Binomial expressions discussed at next section 4.
4. Bean machine simulation through random number programming using Java.
There is another approach from calculating NPS from RR instead of using Binomial Distribution, is to
use a random number between 0 and 1 is generated each time the bean from the “Bean machine”
hits the wooden tick. If this random number is less than the value of Resolution “Yes” (RR) then the
bean will fall to the right, towards “Advocacy” end. If the random number is between value of RR
and 1 then the bean will fall to the left, towards the “Detractor” end.
This process is encapsulated in a java written application. The results are shown from the Figures 2
and 3 using RR as 50% and NPS is -81 after 10,730 surveys taken. This NPS -81 is confirmed by
Binomial algorithm approach from Table 2 with a value -82 which is good enough to be used.
6. Mai Dang, Telstra, November 11th
2016 6 of 19
Figure 2 : Logical “Bean Machine” NPS simulation using Java with Resolution “Yes” at 50
Figure 3 : NPS Histogram with Resolution Yes 50% after 10,730 surveys
At RR 50%, the highest outcome according to Figure 3 is Detractor with scores 5, 4 and 6. Passive
score 7 is fourth. Advocate score 9 and 10 occupied the last ranking.
7. Mai Dang, Telstra, November 11th
2016 7 of 19
5. NPS generation from Resolution “Yes” as an input, using R
Figure 3: Implementation of Pascal’s triangle algorithm using R codes
With Empathy index 1, NPS values are generated over 100 values of RR “Yes” between 0 and 1 are
shown from the figure 5 using the Pascal’s triangle algorithm with R codes as per Figure 4.
R coding:
Figure 4: R codes to generate NPS profile versus Resolution “Yes”
8. Mai Dang, Telstra, November 11th
2016 8 of 19
Figure 5: NPS profile versus Resolution Rate “Yes”
II. NPS Life Cycle
Figure 7 shows the NPS life cycle from the value -100 going through the median at 0 and
terminating at value 100. The birth of NPS at -100 is with 100% Detractor.
As NPS climbed up to the median value 0, Detractor population is converted to Passive at
same time Passive itself is converted to Advocate. At the left hand side of the median from
Figure 7, the production of Passive from Detractor is much faster than its consumption to
produce Advocate. The profile of Passive rising up to a maximum of 50% at NPS 0. This
dynamic interpretation is proven by the rising profile of Passive because if the consumption
of Passive toward Advocate is fast and instant then Passive profile would be closed to 0%
and flat.
From NPS values going from 0 towards 100, the speed of consumption of Passive to produce
Advocate overtakes its production from Detractor, hence its profile is dropping slowly
toward 0 and so Detractor when NPS reaches its value +100.
These observations from the Figure 7 implies the fact that Advocate doesn’t come from
Detractor but Passive. Passive itself comes from both Detractor and Advocate in a reversible
passage.
9. Mai Dang, Telstra, November 11th
2016 9 of 19
Strategy for NPS improvement depends on current location of NPS. To increase NPS, either
Detractor would have to diminish or Advocate to increase.
From Figure 7, at NPS equal - 37 both Detractor and Passive are equal in population 45%
with Advocate only 10%. An improvement of NPS will have to be from the consumption of
Detractor to produce Passive.
At NPS 0, both Detractor and Advocate are at 23.5% and Passive at 53%. A conversion of
Passive to Advocate is starting to be more effective than Detractor to Passive.
At NPS +37 where Passive and Advocate are equal at 45% and Detractor is only 10% then
Passive are the one Companies must listen to as Passive conversion to Advocacy is more
efficient.
R coding:
Figure 6: R codes to display NPS Life Cycle from Figure 7
Figure 7: NPS Life Cycle of Detractor, Passive and Advocacy
Conversion of Passive to Advocacy is
faster than Detractor to Passive
Conversion of Detractor to Passive
is faster than Passive to Advocacy
10. Mai Dang, Telstra, November 11th
2016 10 of 19
III. NPS with Ethical Empathy
1. Design of Customer Empathy Template
To include Empathy weight, NPS is recalculated with RR multiplied by the Empathy weight and
the results with different weights from 0.8 to 1.35 are shown in Figures 8 and 9. “Empathy” zone
with green lines where weight is greater than 1. “Less Empathy” zone with red lines and weights
are less than 1. The bold green line in the middle is with weight 1 where the correct NPS is
calculated for a given RR. This is the bill amount for the restaurant analogy discussed in section II
earlier.
1.1 Use case with constant Resolution Rate Yes
“Empathy” from Figure 8 implies the fact for a given RR says 80%, NPS calculated is +26 with
Empathy weight of 1 and when the weight goes up to 1.2, NPS is rising to +94. NPS drops to -44
when Empathy weight fell to 0.8. Empathy in this example involves an increase of NPS for a
constant RR 80%.
For Empathy weight 1.2, a typical Customer thinking is “I likes what you are doing even though
your resolution has not been improved compared to the past”.
For Empathy weight 0.8, a typical Customer thinking is “As my issue has not been resolved today
even though your service level is the same compared to the past, I can’t give you the same
score”.
Figure 8: Customer Empathy for a constant Resolution “Yes”
11. Mai Dang, Telstra, November 11th
2016 11 of 19
1.2 Use case with constant NPS
From figure 9, with a constant NPS at 0, value a RR equal to 75% with Empathy weight of 1. If
the weight goes up to 1.25, RR drops back to 60% or if the weight goes down to 0.8, RR
increases to 94%. “Empathy” here involves a drop of RR for same NPS (“I likes what you are
doing and I give you same NPS score even though your resolution has not been as good as
other channel”).
For Empathy weight 1.25, a typical Customer thinking is “I likes what you are doing even
though your resolution (60%) has not been as good as other channels. Instead of giving you a
negative NPS, l give a better NPS”
For Empathy weight 0.80, a typical Customer thinking is “Even though your overall service
level has increased to 94%, I am not comfortable with your resolution process hence give the
same NPS score”.
If the mass of positive thinking customers are more than the negative one, the Empathy at
the end of the day would define the channel Customer Service.
Figure 9: Customer Empathy for a constant NPS
2. Implementation of Empathy
The R codes from Figure 3 are now redesigned by including Empathy weights
between 0.8 and 1.35 are as per Figure 10 below. The final data frame now contains
one extra column Empathy weight and NPS is recalculated each time Empathy
weight changes.
For the method that used Binomial expressions, the equations are now including Empathy
weight with RR ranging from 0 to either 1/Empathy or 1 depends which one reaching 1 first:
12. Mai Dang, Telstra, November 11th
2016 12 of 19
1. Detractor score 0: (1-RR*Empathy)^10 with RR ∈ {0, maximum(1/Empathy, 1)}
2. Detractor score 1: 10*(1-RR*Empathy)^9 *(RR*Empathy)^1
3. Detractor score 2: 45*(1-RR*Empathy)^8 *( RR*Empathy)^2
4. Detractor score 3: 120*(1-RR*Empathy)^7 *(RR*Empathy)^3
5. Detractor score 4: 210*(1-RR*Empathy)^6 *( RR*Empathy)^4
6. Detractor score 5: 252*(1-RR*Empathy)^5 *( RR*Empathy)^5
7. Detractor score 6: 210*(1-RR*Empathy)^4 *( RR*Empathy)^6
8. Passive score 7: 120*(1-RR*Empathy)^3 *( RR*Empathy)^7
9. Passive score 8: 45*(1-RR*Empathy)^2 *( RR*Empathy)^8
10. Advocate score 9: 10*(1-RR*Empathy)^1 *( RR*Empathy)^9
11. Advocate score 10: 1*(RR*Empathy)^10
Figure 10: Empathy Implementation within NPS model using R codes
3. Where NPS actual data fit within Empathy template
Weekly aggregates of NPS and Resolution Rate Yes results are compiled against the
calculated NPS profiles of Resolution Rate Yes. Due to Commercial Sensitivity, the channel
names have been withdrawn.
The approach to measure actual Empathy is to calculate the minimum distance between
actual NPS and Resolution Rate Yes coordinator point to one of the Empathy weights
between 0.8 and 1.35. The closest weight will have a minimum of distance. Empathy weight
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variation is quite small (0.05) hence a maximum of error would be 5% on the Empathy
weight measured.
Residential and Business Customer segments from both Human Dialog and Technology
based channels are measured from table 2.
Table 2: Empathy Index measurement.
Figures 12 and 13 compare between Human Dialog and Technology based channels for
Residential and Business segments. The Empathy negative in Technology channel is only
observed in Retail and not in Business segment. This reversed effect in Empathy could be
due to many factors as the objectives of these Technology channels between Residential and
Business segments are quite different to each other.
With another Residential Customer Product from its Online and Episode channel, Empathy is
found more favourable when NPS was issued by Customer immediately after the transaction
than what was provided at some time later with Episode channel (see figure 14).
Figure 12: NPS versus Resolution Rate Yes for Residential Channels
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Figure 13: NPS versus Resolution Rate Yes for Business Channels
Figure 14: NPS versus Resolution Rate Yes for a Residential Online and Episode Channel
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The observed facts from figures 12, 13 and 14 above led to 3 concluding remarks
a. Human led channel received more Empathy than a technology based channel from
Residential Customers.
b. Business Customers tend to be more generous in Empathy than its Residential
counterparts.
c. Customers when asked to provide NPS online, have tendency to have more Empathy
than same question was asked some time later.
As a general guideline, if Customer Service is either improved or deteriorated then there
must be a follow up of NPS score accordingly for a neutral NPS scoring process (Empathy 1).
Reality is Customers don’t know what correct NPS for current experience level or cannot
remember what the previous experience was like to make a decision. Empathy plays an
important effect each time there is a variation in Customer Service expressed under
Resolution Rate Yes. If an instant NPS score calculated for Service Level for the day with an
Empathy equates 1, is displayed online, then there would be more chance that all channels
would converge to a neutral Empathy NPS.
IV. 2014 NPS Benchmark
Figure 15: A new NPS benchmark with overlapping between NPS and Resolution Rate
rankings
Empathy has a profound and changing effect on NPS benchmark with a hypothetical
situation where Company A has a NPS +67 higher than its competitor at NPS +54. From
traditional NPS benchmark, A’s Customer Experience is ranked first. However if Resolution
Rate and Empathy calculation are included in the benchmark as figure 15 shown and NPS is
taken at the projection of Empathy weight 1 then A’s NPS is now at around +25 which is
+25
+75Company A
(e.g. Apple NPS 2014 +67)
A’s Competitor
(e.g. Samsung NPS 2014 +54 )
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lower than its competitor with a corrected NPS at around +75 as shown from figure 15. With
a neutral Customer Empathy, A’s competitor is ranked first and A is in second.
A’s Customer Empathy at 1.1 higher than its competitor’s at 0.95 even though A’s Resolution
Rate 0.8 is lower than its competitor at 0.9. This underlined the fact that A’s Customer
Service is better than its competitor even though A’s issue resolution rate is lower.
A’s competitor resolves more issues than A and yet Customers expressed more Empathy to
A because of something A’s competitor doesn’t have. A’s competitor must look into its
Customer Service process to see where it failed in Customer Empathy.
This hypothetical scenario could have been the real situation for 2014 NPS Benchmark
results in Smartphones section between Apple (Company A) and Samsung with NPS at +67
and +54 respectively [5].
Figure 16: A new NPS benchmark with consistent NPS and Resolution Rate rankings
If Apple Resolution Rate is higher than Samsung’s with either the red line is now relocated to
the left of the Apple green line (see figure 16) or Apple’s green line is relocated to the right
of Samsung’s red line (see figure 17) then existing and new benchmark report the same
outcome with the new NPS projection at a neutral Empathy (weight=1).
Outcomes from figures 16 and 17 are now Apple and Samsung NPS are either +25 and +5 or
+95 and +75 respectively, which are consistent with current NPS ranking with Apple first and
Samsung second (Apple +67 and Samsung +54).
Company A
(e.g. Apple NPS 2014 +67)
A’s Competitor
(e.g. Samsung NPS 2014 +54 )
+25
+5
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Figure 17: A new NPS benchmark with consistent NPS and Resolution Rate rankings
V. 2016 US Election Benchmark
NPS is calculated between 2 parties Democrat and Republican with results taken from
National Polling Average [6] and Official 2016 US election results taken on the day after the
vote as below:
% Voters at the Poll NPS
Democrat = 45.5 % (45.5 – 42.2) / 100 = 3.3
Republican = 42.2 % (42.2 – 45.5) / 100 = -3.3
Electoral Votes at the Vote NPS
Democrat = 228 Electoral Votes (228 – 290) / (228+290) = + 12
Republican = 290 Electoral Votes (290 – 228) / (228+290) = + 12
Resolution Rate on x-axis is replaced by “% Issues promised to be resolved”.
Company A
(e.g. Apple NPS 2014 +67)
A’s Competitor
(e.g. Samsung NPS 2014 +54 )
+75
+95
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Figure 18: US 2016 Election results with NPS Benchmarking
Democrat as incumbent party by definition is not in a strong position to have “% Issues
promised to be resolved” higher than the challenger party, as a result Republican appears on
the right of the template similarly to the situation described between Apple and Samsung
from figure 15.
“% Issues promised to be resolved” is allocated at 0.72 for Democrat and 0.77 for Republican
according to figure 18.
The polling data is only a snapshot of voters with Empathy weights between 0.95 and 1.05.
Republicans have a higher “Issue promised to be resolved” (i.e. resolution rate) but poorer in
Empathy weight and % Voters (e.g. NPS) than Democrats before the vote.
Election data has a much larger population with Empathy weight leans towards 1.
Republican with its “% Issues promised to be resolved” higher won the election. Similar
interpretation for Samsung from the figure 15.
This adaptation exercise reinforces the use of Issue Resolution Rate in conjunction with NPS
at a neutral Empathy (weight=1) for NPS Benchmarking process.
NPS actual data is only a snapshot with Empathy weights fluctuate over a broad range of
Empathy weights hence the existing benchmarking process lacks of foundation for a correct
benchmarking. The only way to align all actual NPS snapshot results is to add Resolution
Rate to the NPS template and vertically project the NPS to the Empathy weight 1 curve.
The value allocation of “% Issues promised to resolve” is a key step to align the NPS at the
Poll and at the Vote on a vertical line where the Vote NPS will be located at the Empathy
weight equal to 1. If “% Issues promised to resolve” is well formulated as per Allan
Lichtman’s 13 keys [8] then this approach could be used for prediction of % Voters or NPS
on the election day.
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Reference
[1] http://www.iep.utm.edu/emp-symp/,
[2] https://www.youtube.com/watch?v=3m4bxse2JEQ
[3] http://www.mathsisfun.com/pascals-triangle.html
[4] http://en.wikipedia.org/wiki/Binomial_coefficient#Binomial_coefficient_with_n.3D1.2F2
[5] https://customergauge.com/news/2014-net-promoter-benchmarks/
[6] http://www.usatoday.com/pages/interactives/2016/election/poll-tracker/
[7] https://pollyvote.com/en/components/index-models/keys-to-the-white-house/
[8] “Predicting the Next President: The Keys to the White House 2016 ed. Edition”
by Allan Lichtman ISBN-13: 978-1442269200 ISBN-10: 1442269200