Traditional Forecasting Vs. Scenario Planning

This post compares and contrasts the concepts of scenario planning versus traditional forecasting. As well as, fully explain both concepts’ the differences, advantages, and disadvantages of each.


Traditional Forecasting

Traditional forecast is essentially extrapolating where you were and where are you are now into the future, and at the end of this extrapolated line this is “the most likely scenario” (Wade, 2012; Wade, 2014).  Mathematical formulations and extrapolations is a mechanical basis for traditional forecasting (Wade, 2012). At one point, these forecasts make ±5-10% in their projections and call it the “the best and worst case scenario” (Wade, 2012; Wade, 2014).  This ± difference is a range of possibilities out of an actual 360o solution spherical space (Wade, 2014). There are both mathematical forms of extrapolation and mental forms of extrapolation and both are quite dangerous because they assume that the world doesn’t change much (Wade, 2012).  However, disruptions like new political situations, new management ideas, new economic situations, new regulations, new technological developments, a new competitor, new customer behavior, new societal attitudes and new geopolitical tensions, could move this forecast in either direction, such that it is no longer accurate (Wade, 2014). We shouldn’t just forecast the future via extrapolation; we should start to anticipate it through scenario analysis (Wade, 2012).

Advantages (Wade, 2012; Wade, 2014):

+ Simple to personally understand, only three outcomes, with one that is “the most likely scenario.”

+ Simple for managements to understand and move forward on

Disadvantages (Wade, 2012; Wade, 2014):

– Considered persistence forecasting, which is the least accurate in the long term

– Fails to take into account disruptions that may impact the scenario that is being analyzed

– Leads to a false sense of security that could be fatal in some situations

– A rigid technique that doesn’t allow for flexibility.

Scenario Planning

Scenario planning could be done with 9-30 participants (Wade, 2012).  But, a key requirement of scenario planning is for everyone to understand that knowing the future is impossible and yet people want to know where the future could go (Wade, 2014).  However, it is important to note that scenarios are not predictions; scenarios only illuminate different ways the future may unfold (Wade, 2012)!

Therefore, this tool to come up with an approach that is creative, yet methodological, that would help spell out some of the future scenarios that could happen has ten steps (Wade, 2012; Wade, 2014):

  • Framing the challenge
  • Gathering information
  • Identifying driving forces
  • Defining the future’s critical “either/or” uncertainties
  • Generating the scenarios
  • Fleshing them out and creating story lines
  • Validating the scenarios and identifying future research needs
  • Assessing their implications and defining possible responses
  • Identifying signposts
  • Monitoring and updating the scenarios as times goes on

However, in a talk Wade (2014), distilled his 10 step process, to help cover the core steps in scenario planning:

  • Create a brainstorming session to identify as many of the driving force(s) or trend(s) that could have an impact on the problem at hand? Think of any trend or force (direct, indirect, or very indirect) that would have any effect in any way and any magnitude to the problem and they could fall under the following categories:
    • Political
    • Economical
    • Environmental
    • Societal
    • Technological
  • Next, the group must understand the critical uncertainties in the future, from the overwhelming list. There are three types of uncertainties:
    • Some forces have a very low impact but very in uncertainty called secondary elements.
    • Some forces have a very high impact but low uncertainty called predetermined elements.
    • Some forces have a very high impact and high uncertainty call critical uncertainties.
  • Subsequently, select the top two most critical uncertainties and model the most extreme cases of each outcome, it is “either … or …”. They must be contrasting landscapes from each other. Place one critical uncertainty’s either/or in one axis, and the other on the other axis.
  • Finally, the group should describe the different types of scenarios. What would be the key challenges and key issues would be faced in either of these four different scenarios? How should the responses look like?  What are the opportunities and the challenges will be faced? This helps the group to strategically plan and find a way to potentially innovate in this landscape, to outthink their competitors (Wade, 2014)?

Advantages (Frum, 2013; Wade, 2012; Wade, 2014):

+ Focuses on the top two most critical uncertainties to drive simplicity

+ Helps define the extremes in the four different Landscapes and their unique Challenges, Responses, and Opportunities to innovate to create a portfolio of future scenarios

+ An analytical planning method helping to discover the Strengths, Weaknesses, Opportunities, and Threats affecting each scenario

+ Helps you focus on the players in each landscape: competitors, customers, suppliers, employees, key stakeholders, etc.

Disadvantages (Wade, 2012; Wade, 2014):

– No one has a crystal ball

– More time consuming than traditional forecasting

– Only focuses on 2 of the most critical uncertainties, in the real world there are more critical uncertainties needed for analysis.


Play-Doh: An innovation that came from error or accidents

A discussion of a game-changing ideas that came from an error or accident. Something different from well-known accidental inventions such as sticky notes.

The mixture of flour, water, salt, boric acid and mineral oil was first originally used as a reusable soup product to help clean wallpaper as part of the Kutol company (Biddle, 2012; Hiskey, 2015; Wonderopolis, n.d.). Hiskey (2015), chronicles that in 1933 coal was used to heat a home in a chimney, but came at the cost of causing sooty wallpapers, which established the need for the product, and there was the added dimension of the problem that wallpaper couldn’t get wet.  Noah McVicker and Cleo McVicker were able to create a component to clean wallpaper without getting it wet and partnered with Kroger groceries to be their distributor (Hiskey, 2015).  When coal fireplaces were being replaced with oil and gas and a new type of wallpaper that can be cleaned with water and soap was introduced, sales plummeted (Hiskey, 2015).  However, the lack of toxic chemicals made it an ideal not only as a cleaning product but to become the toy it is today eventually (Hiskey, 2015; Wonderopolis, n.d.).  The transition occurred when teachers began to use this wallpaper cleaner in an innovative way, for a molding compound to make art for craft projects in school (Hiskey, 2015; The Strong, n.d.; Wonderopolis, n.d.).  When, the inventor’s nephew, Joe McVicker, eventually came into the Kutol Company and noticed this secondary use of their product, and though it would be good to rename the product “Play-Doh” and market it to schools (Biddle, 2012; The Strong, n.d.; Wonderopolis, n.d.). In 1956, the nephew devoted his time to creating Play-Doh as part of a company called Rainbow Crafts Company and sold to both Macy’s and Marshall Fields, and in one year made $3 million just by selling Play-Doh in the primary colors (Hiskey, 2015; The Strong, n.d.; Wonderopolis, n.d.).  In the 1980s, the color pallet was expanded to 8 colors, with future versions glowing in the dark, containing glitter, and smell like shaving cream (The Strong, n.d.) The recipe has been perfected over time and has remained a trade secret; Play-Doh is now part of the Hasbro Company (Wonderopolis, n.d.). Under the wallpaper utility of this product, it sold for 34 cents per can, but under the toy utility of this product the company was able to sell it at $1.50 per can (Hiskey, 2015).  In 2003, Play-Doh was added to the “Century of Toys List,” as it has hit 100 years of existence (Wonderopolis, n.d.) 700 million pounds of Play-Doh have been sold and played with (The Strong, n.d.).In 2016, a Play-Doh Super Color pack with 20 different colors goes for $14.99, and a Play-Doh Rainbow Starter Pack with eight colors goes for $4.99 (Hasbro, n.d.). However, the amount of Play-Doh per mini color tub is small compared to homemade versions.  There are many ways to make your version of Play-Doh.  One version of this non-toxic homemade version of Play-Doh, as stated by Nicko’s Kids DIY (2012): (1) mix 2 cups of flour, 2 cups of water, 1 cup of salt, 2 tbsp. of vegetable oil, and 1 tbsp. Of cream of tartar over low heat in a pan until it becomes a dough; (2) while it is still warm, knead the dough and don’t add any more flour to it; (3) finally poke a hole to the center of the dough and drop in a few drops of food coloring and work in the color.

Forces that supported it

  • Commercial: Besides selling it in one-gallon tubs to schools, sales skyrocketed when it got a national platform to the kids show Captain Kangaroo, who was promised to get 2% of the sales as long as the product was featured (Hiskey, 2011; Hiskey, 2015). Play-Doh, after leaving Kutol and joining Rainbow Crafts Company, was sold to General Mills, which sold it to Hasbro who still owns the right and intellectual property of Play-Doh (Hiskey, 2011).
  • Technological: It’s non-toxic everyday household product chemical mixture allowed it to be safely used by children (Biddle, 2012; Hiskey, 2015; The Strong, n.d.; Wonderopolis, n.d.). However, the formula was reinvented in 1955 to make it last longer and not dry out so quickly by chemist Dr. Tien Liu (Hiskey, 2011).
  • Financial: Under the wallpaper utility of this product, it sold for 34 cents per can, but under the toy utility of this product the company was able to sell it at $1.50 per can (Hiskey, 2015).


Think Tank Methods

A discussion on the concept of think tank methods, or methods that are deliberate and foster innovation.

Think tanks are a group of people that review the literature, discuss about the literature, think about ideas, do tons of research, write, provide ideas, legitimize ideas, advocate, lobby, and arguing just to address a problem(s) (Mendizabal, 2011; TBS, 2015; Whittenhauer, n.d.). In short, they are idea factories: creating, producing, and sharing (Whittenhauer, n.d.). The balance between research, consultancy, and advocacy and their source of their arguments/ideas: applied, empirical, synthesis, theoretical or academic research; help shape what type of think tank they are (Mendizabal, 2011). Finally, there are two types of think tank models, one roof model where everyone gathers in one physical place to meet face-to-face or the without walls model where members only communicate through technological means (Whittenhauer, n.d.).

McGann (2015) stated that the explosive growth of think tanks could be attributed to the growth in information and technology and a decline of government’s control of information, while there is a rise in the complexity and nature of the issues.  The U.S. houses 1989 think tanks, which is about 33% of the world’s total think tanks at 6,618 and housed in 182 countries around the world (McGann, 2015; TBS, 2015). Meanwhile, Europe houses 1822 think tanks (McGann, 2015).

Current trends in think tanks are: globalization; growth of international actors; democratization; demands for independent information and analysis; big data and super computers; increased complexity of policy issues; the information age and the rate of technological change; increasingly open debate about government decision making; global “hacktivist”, anarchist, and populist movements; global structural adjustment; economic crisis and political paralysis; policy tsunamis; increasing political polarization; and short-termism (McGann, 2015).

Think tanks within a company can be used to help Research and Development teams within the company (Penttila, 2007).  Think tanks in both capacities have the challenge to harness their knowledge, information, and energy to support progress (McGann, 2015). However, some companies cannot afford an innovation center or a think tank, even though it is a vital in today’s current market, due to competitive challenges, resource challenges, technological challenges, and policy challenges (McGann, 2015; Penttila, 2007).  Penttila (2007) gathered five strategies from think tanks that are a positive force for innovation: (1) combining ideas by looking for intersections between ideas and how they may work together; (2), think backwards by starting with the desired outcome in mind and working your way back; (3) rapidly prototype by putting ideas into action on a small yet realistic scale; (4) have funds set aside for encouraging people incubate and chasing after ideas; and (5) record ideas through an online environment.  For companies with little budget adopting a without walls, model thinks tank is more economical, and most overhead costs are not paid by the think tank, allowing for more money to be invested into research (Whittenhauer, n.d.).

Measuring the influence of a think tank composes of: the number of active scholars in it, publication record, scholarly achievements, how well they are attracting and holding visitor traffic from their web portals, average yearly revenue, number of categories they address, and how deep did their research affect the culture (TBS, 2015).  This is essentially assessing them by their intellectual depth, influence (politically or within the organization), marketability, value generating capabilities, etc. (McGann, 2015).

The top 10 most influential think tanks in the U.S. according to TBS (2015) are:

  • Belfer Center for Science and International Affairs (Politically Independent)
  • Earth Institute (Politically Centrist)
  • Heritage Foundation (Politically Conservative)
  • Human Rights Watch (Politically Liberal)
  • Kaiser Family Foundation (Politically Independent)
  • Council on Foreign Relations (Politically Independent)
  • Brookings Institute (Politically Progressive)
  • Cato Institute (Politically Libertarian)
  • Ludwig von Mises Institute (Politically Libertarian/Classical Liberal)
  • American Enterprise Institute (Politically Conservative)

Looking at the top two think tanks more closely

Belfer Center for Science and International Affairs: Based off of Harvard, this university-affiliated think tank deals with issues like nuclear power plants, nuclear security, international security and defense, cyber espionage, environment and climate change, energy, science and technology, international relations, conflict and conflict resolution, governance, economics and global affairs (Belfer Center, n.d., TBS, 2015). They have a monetary monthly traffic of $7.7M and have over 100 media references (TBS, 2015).

Earth Institute: Another university-affiliated think tank, founded by Columbia University, the primary focus of research for this think tank revolves around the climate, water, energy, agriculture, ecosystems, global health, urbanization, hazards and risk reduction, which are all foundational to the earth’s systems and life (Earth Institute, n.d.; TBS, 2015). They have a monetary monthly traffic of $5.2M and have over 100 media references (TBS, 2015).


An Innovative Topic discussed in TED

A review of one of many various TED videos on an innovative idea that is interesting and worth spreading.

In Winter’s (2016) TED talk, she expresses her thoughts on re-engineering the process flow of our by-products back into nature.  Similar to this idea is the use of greywater, which is gently used water from bathrooms, showers, etc., which appears to be dirty due to its contents but are great for irrigation systems of yards, parks, and green spaces (Greywater Action, n.d.).  Winter (2016) goes one step further; she wants us to the excrement and manure of our body, which is rich in bacteria and carbon to feed trees, yards, parks, and other green spaces.  She is suggesting that the use of manure never touches or comes to contact with people, but is buried under gravel and soil under areas to help foster a green space. This is considered as holistic (or closed-loop) waste/sanitation management because everything gets reused (Winter, 2016).

Rosen and Bierman (2005), suggested that manure is a valuable fertilizer, that is cost efficient, greener, readily available, and best for giving fruit and vegetable crops a nutrient source.  Charles (2013) agreed and stated that this is part of the natural cycle and manure from other animals have been used in organic farming.  Manure from animal and humans provide many nutrients and micronutrients, for plants and crops (Rosen & Bierman, 2005; Winters 2016). Nutrients from the food we and animals eat, don’t just disappear, but they reappear as manure and excrement, and the best thing to do is to bring it back to the source of the nutrients, plants (Charles, 2013; Winter, 2016). Other benefits to using manure include improvements in soil structure, soil water holding capacity, drainage, reduction of wind and water erosion, etc. (Rosen & Bierman, 2005).

The amount of manure use on plants can vary on a case by case basis.  A stingy application of this innovation can lead to nutrient deficiencies and low yields, while the excessive application can yield to excessive growth in some groups and lakes of certain chemicals, like nitrate, phosphorus, etc. (Rosen & Bierman, 2005).  The type of manure also matters.  Winter (2016) suggested using raw/fresh manure.  But Rosen and Bierman (2005) warn that raw/fresh it can have a high concentration of nitrogen, and in some cases pathogens.

Finally, impacts of this holistic approach to waste/sanitation management can be seen through the lens of climate change.  This innovative process can help provide carbon and many of the key nutrients and micronutrients needed to make trees grow, which not only reduces entry of carbon into the atmosphere from our waste product but with the new tree growth, these trees can remove more carbon dioxide from the air (Winter, 2016).  This is one of the many amazing feedback loops of reusing our waste that just keeps getting better.

Forces that impact the innovation

Legal – The Food and Drug Association finds manure a food safety risk, with harmful bacteria like e Coli. and Sal Manila (Charles, 2013). Winters (2016), said that some of the laws used to keep humans safe from getting sick of manure are outdated and were assumed that there was not going to be a reinvention to the way we should treat our waste.  Rosen and Bierman (2005), suggested that for farming it is best to apply this waste product 3 months before harvesting.  However, if we remove the farming aspect out of the picture, then there would be no need for the Food and Drug Administration to get upset about.  However, Winters (2016) stated that in some states there are laws on how we should deal with this particular type of waste, outside of just farming applications must be addressed to move forward with this innovative use of our waste. Laws must change, but treating this innovation as “better safe than sorry” without further research is not a solution (Charles, 2013).

Cultural –  People are uncomfortable about talking about their bodily waste products, which is what is slowing down how we innovate in waste management (Winter, 2016).  I agree with this thought; it is difficult to discuss it.  Out of all the different types of innovation that could have been discussed, I thought it would be best to bring this innovation into the light, through this post.  To help break down this cultural barrier to innovation in waste/sanitation management.


Using R and Spark for health care

Use of R with regard to healthcare field case study by Pereira and Noronha (2016):

R and RStudio have been used to look at patient health and diseases records located in Electronic Medical Records (EMR) for fraud detection.  Anomaly detection revolves around using a mapping code that filters data based on geo-locations.  Secondly, a reducer code which aggregates the data based on extreme values of cost claims per disease along with calculating the difference.  Finally, a code that analyzed the data that meets a 60% cost fraud threshold. It was found that as the geo-location resolution increased, the anomalies detected increased.

R and RStudio have been able to use big data analytics to predict diabetes from the Health Information System (HIS) which houses patient information, based on symptoms. For predicting diabetes, the authors used a classification algorithm (decision tree) with a 70%-30% training-test dataset split, to eventually plot the false positive rate v. True positive rate.  This plot showed skill in predicting diabetes.

Use of Spark about the healthcare field case study by Pita et al. (2015):

Data quality in healthcare data is poor and in particular that from the Brazilian Public Health System.  Spark was used to help in data processing to improve quality through deterministic and probabilistic record linking within multiple databases.  Record linking is a technique that uses common attributes across multiple databases and identifies a 1-to-1 match.  Spark workflows were created to help do record linking by (1) analyzing all data in each database and common attributes with high probabilities of linkage; (2) pre-processing data where data is transformed, anonymization, and cleaned to a single format so that all the attributes can be compared to each other for a 1-to-1 match; (3) record linking based on deterministic and probabilistic algorithms; and (4) statistical analysis to evaluate the accuracy. Over 397M comparisons were made in 12 hours.  They concluded that accuracy depends on the size of the data, where the bigger the data, the more accuracy in record linking.


  • Pereira, J. P., & Noronha, V. (2016). Anomalies Detection and Disease Prediction in Healthcare Systems using Big Data Analytics. Retrieved from
  • Pita, R., Pinto, C., Melo, P., Silva, M., Barreto, M., & Rasella, D. (2015). A Spark-based Workflow for Probabilistic Record Linkage of Healthcare Data. In EDBT/ICDT Workshops (pp. 17-26).

An Innovation that is possible 15-20 years from now

This post describes an innovation idea that is not possible today but could be available in the next 15–20 years in the United States.

Innovation idea that is not possible today but will be in the next 15-20 years

Mobile technology is everywhere today, and their use is prolific among all the diverse populations in the U.S., even to segments of the populations that do not own a computer own a smartphone (Kumar, 2015).  Electronic transactions carrying trillions of dollars, sensitive flight data, etc. take place all the time (Kumar, 2015; Safian, 2015).  Safian (2015) is calling that mobile voting will be one of the many things that will occur in the next 20 years.

Thirty-three states offer online voter registration and that allowed for 6.5% of the electorate to register for 2014 up from 1.7% in 2010 (Election Assistance Commission [EAC], 2015; Jayakumar, 2015). About 19.2% of ballots in 2014 were rejected due to improper registration (EAC, 2015).  Eighty cities and towns in Canada have experimented with mobile voting since 2003, and Sweden, Latvia, and Switzerland have tested the idea (Gross, 2011).  Since 2005, Estonia with a mobile voting period that last about seven days and is available for all citizens had about 1/4 to 1/3 votes cast were online (Vabariigi Valimiskomisjon, 2016).

Mobile voting, can help reduce the cost of elections, reduce the need for polling places, encourage and engage disenfranchised voters, reduce the time it takes to cast a vote, reduce the need to travel to a polling place, facilitate fast results, more convenient way of collecting huge data about the voting population and their turnout, while finally allowing for easier voter registration (Jayakumar, 2015; Kumar, 2015). However, to make mobile voting a key innovation in the next 15-20 years, the main goals of mobile voting must be addressed: security, accessibility, anonymity, conveniency, and verifiable (Gross, 2011; Jayakumar, 2015; Kumar, 2015 Safian, 2015).

Forces that define the innovation that may facilitate or reduce its likelihood of success

Technological: Paper ballots allow for and provide anonymity, free from manipulation (Jayakumar, 2015). Even though, some ballots could be switched. Mobile voting devices currently have issues with security and verifiability (Jayakumar, 2015).  However, other countries are working on providing democracy to all through allowing both paper and electronic ballots as previously discussed.  However, mobile voting is not like other typical transactional data from a bank, where a user can correct errors (Jayakumar, 2015).  Technology must take this into account.  Such that, voting data is unalterable in transit from the mobile device to the main destination (Jayakumar, 2015).  However, in 2014, Zimmerman and Kiniry were able to show how Alaska’s PDF Ballots are insecure, as proof that the technology is currently not as reliable to ensure a tamper free election.

Ethical: Mobile voting can allow for the lowest income workers afraid to take time off from work to vote, or single parents with no daycare options, or people without cars in a remote rural area, increase turnout during midterm and off-season elections, e.g. runoff elections (Jayakumar, 2015; Kumar, 2015). It is suggested that voter intimidation may also be resolved through mobile voting, as people can vote in the privacy of the person’s home (Kumar, 2015).

Financial: Huge cost savings could be realized because, in 2014, 732K poll workers were hired for 114K polling locations, which amounts to 6.4 people per polling location (Election Assistance Commission [EAC], 2015).


Adv Quant: Compelling Topics

A discussion on what were the most compelling topics learned in the subject of Advance Quantitative Analysis.

Compelling topics summary/definitions

  • Supervised machine learning algorithms: is a model that needs training and testing data set. However it does need to validate its model on some predetermined output value (Ahlemeyer-Stubbe & Coleman, 2014, Conolly & Begg, 2014).
  • Unsupervised machine learning algorithms: is a model that needs training and testing data set, but unlike supervised learning, it doesn’t need to validate its model on some predetermined output value (Ahlemeyer-Stubbe & Coleman, 2014, Conolly & Begg, 2014). Therefore, unsupervised learning tries to find the natural relationships in the input data (Ahlemeyer-Stubbe & Coleman, 2014).
  • General Least Squares Model (GLM): is the line of best fit, for linear regressions modeling along with its corresponding correlations (Smith, 2015). There are five assumptions to a linear regression model: additivity, linearity, independent errors, homoscedasticity, and normally distributed errors.
  • Overfitting: is stuffing a regression model with so many variables that have little contributional weight to help predict the dependent variable (Field, 2013; Vandekerckhove, Matzke, & Wagenmakers, 2014). Thus, to avoid the over-fitting problem, the use of parsimony is important in big data analytics.
  • Parsimony: is describing a dependent variable with the fewest independent variables as possible (Field, 2013; Huck, 2013; Smith, 2015). The concept of parsimony could be attributed to Occam’s Razor, which states “plurality out never be posited without necessity” (Duignan, 2015).  Vandekerckhove et al. (2014) describe parsimony as a way of removing the noise from the signal to create better predictive regression models.
  • Hierarchical Regression: When the researcher builds a multivariate regression model, they build it in stages, as they tend to add known independent variables first, and add newer independent variables in order to avoid overfitting in a technique called hierarchical regression (Austin, Goel & van Walraven, 2001; Field, 2013; Huck 2013).
  • Logistic Regression: multi-variable regression, where one or more independent variables are continuous or categorical which are used to predict a dichotomous/ binary/ categorical dependent variable (Ahlemeyer-Stubbe, & Coleman, 2014; Field, 2013; Gall, Gall, & Borg, 2006; Huck, 2011).
  • Nearest Neighbor Methods: K-nearest neighbor (i.e. K =5) is when a data point is clustered into a group, by having 5 of the nearest neighbors vote on that data point, and it is particularly useful if the data is a binary or categorical (Berson, Smith, & Thearling, 1999).
  • Classification Trees: aid in data abstraction and finding patterns in an intuitive way (Ahlemeyer-Stubbe & Coleman, 2014; Brookshear & Brylow, 2014; Conolly & Begg, 2014) and aid the decision-making process by mapping out all the paths, solutions, or options available to the decision maker to decide upon.
  • Bayesian Analysis: can be reduced to a conditional probability that aims to take into account prior knowledge, but updates itself when new data becomes available (Hubbard, 2010; Smith, 2015; Spiegelhalter & Rice, 2009; Yudkowsky, 2003).
  • Discriminate Analysis: how should data be best separated into several groups based on several independent variables that create the largest separation of the prediction (Ahlemeyer-Stubbe, & Coleman, 2014; Field, 2013).
  • Ensemble Models: can perform better than a single classifier, since they are created as a combination of classifiers that have a weight attached to them to properly classify new data points (Bauer & Kohavi, 1999; Dietterich, 2000), through techniques like Bagging and Boosting. Boosting procedures help reduce both bias and variance of the different methods, and bagging procedures reduce just the variance of the different methods (Bauer & Kohavi, 1999; Liaw & Wiener, 2002).



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