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Let’s come straight to the point on this one – there are only 2 types of variables you see – Continuous and Discrete. Further, discrete variables can divided into Nominal (categorical) and Ordinal. We did a post on how to handle categorical variables last week, so you would expect a similar post on continuous variable. Yes, you are right – In this article, we will explain all possible ways for a beginner to handle continuous variables while doing machine learning or statistical modeling.

But, before we actually start, first things first.

What are Continuous Variables?

Simply put, if a variable can take any value between its minimum and maximum value, then it is called a continuous variable. By nature, a lot of things we deal with fall in this category: age, weight, height being some of them.

Just to make sure the difference is clear, let me ask you to classify whether a variable is continuous or categorical:

Gender of a person

Number of siblings of a Person

Time on which a laptop runs on battery

How to handle Continuous Variables?

While continuous variables are easy to relate to – that is how nature is in some ways. They are usually more difficult from predictive modeling point of view. Why do I say so? It is because the possible number of ways in which they can be handled.

For example, if I ask you to analyze sports penetration by gender, it is an easy exercise. You can look at percentage of males and females playing sports and see if there is any difference. Now, what if I ask you to analyze sports penetration by age? How many possible ways can you think to analyze this – by creating bins / intervals, plotting, transforming and the list goes on!

Hence, handling continuous variable in usually a more informed and difficult choice. Hence, this article should be extremely useful to beginners.

Methods to deal with Continuous Variables Binning The Variable:

This would help in minimal loss of information and produces better results. However, I’ve encountered cases where small bins doesn’t prove to be helpful. In such cases, you must decide for bin size according to your chúng tôi should consider distribution of data prior to deciding bin size.

For example: Let’s take up the inbuilt data set state.x77 in R to create bins:

#load data data <- data.frame(state.x77) #check data head(data) #plot Frost variable and check the data points are all over qplot(y = Frost, data = data, colour = 'blue') #use cut() to create bins of equal sizes bins <- cut(data$Frost, 3, include.lowest = TRUE) bins #add labels to bins bins <- cut(data$Frost, 3, include.lowest = TRUE, labels = c('Low','Medium','High')) bins Normalization:

In simpler words, it is a process of comparing variables at a ‘neutral’ or ‘standard’ scale. It helps to obtain same range of values. Normally distributed data is easy to read and interpret. As shown below, in a normally distributed data, 99.7% of the observations lie within 3 standard deviations from the mean. Also, the mean is zero and standard deviation is one. Normalization technique is commonly used in algorithms such as k-means, clustering etc.

A commonly used normalization method is z-scores. Z score of an observation is the number of standard deviations it falls above or below the mean. It’s formula is shown below.

x = observation, μ = mean (population), σ = standard deviation (population)

For example: Randy scored 76 in maths test. Katie score 86 in science test. Maths test has (mean = 70, sd = 2). Science test has (mean = 80, sd = 3). Who scored better? You can’t say Katie is better since her score is much higher than mean. Since, both values are at different scales, we’ll normalize these value at z scale and evaluate their performance.

z(Randy) = (76 – 70)/2 = 3

z(Katie) = (86 – 80)/3 = 2

Interpretation: Hence, we infer than Randy scored better than Katie. Because, his score is 3 standard deviations away from the class mean whereas Katie’s score is just 2 standard deviations away from mean.

Transformations for Skewed Distribution:

Transformation is required when we encounter highly skewed data. It is suggested not to work on skewed data in its raw form. Because, it reduces the impact of low frequency values which could be equally significant. At times, skewness is influenced by presence of outliers. Hence, we need to be careful while using this approach. The technique to deal with outliers is explained in next sections.

There are various types of transformation methods. Some are Log, sqrt, exp, Box-cox, power etc. The commonly used method is Log Transformation. Let’s understand this using an example.

For example: I’ve score of 22 students. I plot their scores and find out that distribution is left skewed. To reduce skewness, I take log transformation (shown below). As you can see after transformation, the data is no longer skewed and is ready for further treatment.

Use of Business Logic:

Business Logic adds precision to output of a model. Data alone can’t suggest you patterns which understanding its business can. Hence, in companies, data scientists often prefer to spend time with clients and understand their business and market. This not only helps them to make an informed decision. But, also enables them to think outside the data. Once you start thinking, you are no longer confined within data.

For example: You work on a data set from Airlines Industry. You must find out the trends, behavior and other parameters prior to data modeling.

New Features:

Once you have got the business logic, you are ready to make smart moves. Many a times, data scientists confine themselves within the data provided. They fail to think differently. They fail to analyze the hidden patterns in data and create new variables. But, you must practice this move. You wouldn’t be able to create new features, unless you’ve explored the data to depths. This method helps us to add more relevant information to our final model. Hence, we obtain increase in accuracy.

For example: I have a data set where you’ve following variables: Age, Sex, Height, Weight, Area, Blood Group, Date of Birth. Here we can make use of our domain knowledge. We know that (Height*Weight) can give us BMI Index. Hence, we’ll create HW = (Height*Weight) as a new variable. HW is nothing but BMI (Body Mass Index). Similarly, you can think of new variables in your data set.

Treating Outliers:

Data are prone to outliers. Outlier is an abnormal value which stands apart from rest of data points. It can happen due to various reasons. Most common reason include challenges arising in data collection methods. Sometime the respondents deliberately provide incorrect answers; or the values are actually real. Then, how do we decide? You can any of these methods:

Considering the scope of analysis, you can remove the top 1% and bottom 1% of values. However, this would result in loss of information. Hence, you must be check impact of these values on dependent variable.

Treating outliers is a tricky situation – one where you need to combine business understanding and understanding of data. For example, if you are dealing with age of people and you see a value age = 200 (in years), the error is most likely happening because the data was collected incorrectly, or the person has entered age in months. Depending on what you think is likely, you would either remove (in case one) or replace by 200/12 years.

Principal Component Analysis:

Sometime data set has too many variables. May be, 100, 200 variables or even more. In such cases, you can’t build a model on all variables. Reason being, 1) It would be time consuming.  2) It might have lots of noise 3) A lot of variables will tell similar information

Hence, to avoid such situation we use PCA a.k.a Principal Component Analysis. It is nothing but, finding out few ‘principal‘ variables which explain significant amount of variation in dependent variable. Using this technique, a large number of variables are reduced to few significant variables. This technique helps to reduce noise, redundancy and enables quick computations.

In PCA, components are represented by PC1 or Comp 1, PC2 or Comp 2.. and so on. Here, PC1 will have highest variance followed by PC2, PC3 and so on. Our motive should be to select components with eigen values greater than 1. Eigen values are represented by ‘Standard Deviation’. Let check this out in R below:

#set working directory >setwd('C:/Users/manish/desktop/Data') #load data from package >data(Boston, package = 'MASS') #descriptive statistics >summary(myData) #check correlation table and analyze which variables are highly correlated. >cor(myData) #Principal Component Analysis >pcaData <- princomp(myData, scores = TRUE, cor = TRUE) >summary(pcaData) #loadings - This represents the contribution of variables in each factor. Higher the #number higher is the contribution of a particular variable in a factor >loadings(pcaData) #screeplot of eigen values ( Value of standard deviation is considered as eigen values) >screeplot(pcaData, type = 'line', main = 'Screeplot') #Biplot of score variables >biplot(pcaData) #Scores of the components >pcaData$scores[1:10,] Factor Analysis:

Factor Analysis was invented by Charles Spearman (1904). This is a variable reduction technique. It is used to determine factor structure or model. It also explains the maximum amount of variance in the model. Let’s say some variables are highly correlated. These variables can be grouped by their correlations i.e. all variables in a particular group can be highly correlated among themselves but have low correlation with variables of other group(s). Here each group represents a single underlying construct or factor. Factor analysis is of two types:

EFA (Exploratory Factor Analysis) – It identifies and summarizes the underlying correlation structure in a data set

CFA (Confirmatory Factor Analysis) – It attempts to confirm hypothesis using the correlation structure and rate ‘goodness of fit’.

Let’s do exploratory analysis in R. As we run PCA previously, we inferred that Comp 1, Comp 2 and Comp 3.  We’ve now identified the components. Below is the code for EFA:

#Exploratory Factor Analysis #Using PCA we've determined 3 factors - Comp 1, Comp 2 and Comp 3 >pcaFac <- factanal(myData, factors = 3, rotation = 'varimax') >pcaFac #To find the scores of factors >pcaFac.scores <- factanal(myData, factors = 3, rotation = 'varimax', scores = 'regression' ) >pcaFac.scores >pcaFac.scores$scores[1:10,]

Note: VARIMAX rotation involves shift in coordinates which maximizes the sum of the variances of the squared loadings. It rotates the alignment of coordinates orthogonally.

Methods to work with Date & Time Variable

Presence of Data Time variable in a data set usually give lots of confidence. Seriously! It does. Because, in data-time variable, you get lots of scope to practice the techniques learnt above. You can create bins, you can create new features, convert its type etc. Date & Time is commonly found in this format:


Considering this format, let’s quickly glance through the techniques you can undertake while dealing with data-time variables:

Create New Variables:

Have a look at the date format above. I’m sure you can easily figure out the possible new variables. If you have still not figure out, no problem. Let me tell you. We can easily break the format in different variables namely:





Days of Month

Days of Week

Days of Year

I’ve listed down the possibilities. You aren’t required to create all the listed variables in every situation. Create only those variables which only sync with your hypothesis. Every variable would have an impact( high / low) on dependent variable. You can check it using correlation matrix.

Create Bins:

Once you have extracted new variables, you can now create bins. For example: You’ve ‘Months’ variable. You can easily create bins to obtain ‘quarter’, ‘half-yearly’ variables. In ‘Days’, you can create bins to obtain ‘weekdays’. Similarly, you’ll have to explore with these variables. Try and Repeat. Who knows, you might find a variable of highest importance.

Convert Date to Numbers:

You can also convert date to numbers and use them as numerical variables. This will allow you to analyze dates using various statistical techniques such as correlation. This would be difficult to undertake otherwise. On the basis of their response to dependent variable, you can then create their bins and capture another important trend in data.

Basics of Date Time in R

There are three good options for date-time data types: built-in POSIXt, chron package, lubridate package. POSIXt has two types, namely POSIXct and POSIXlt. “ct” can stand for calendar time and “lt” is local time.

# create a date as.Date("2024-12-1") # specify the format as.Date("11/30/2024", format = "%m/%d/%Y") # take a difference - Sys.Date() gives present day date Sys.Date() - as.Date("2014-12-01") #using POSIXlt - find current time as.POSIXlt(Sys.time()) #finds class of each data time component unclass(as.POSIXlt(Sys.time())) # create POSIXct variables as.POSIXct("080406 10:11", format = "%y%m%d %H:%M") # convert POSIXct variables to character strings format(as.POSIXct("080406 10:11", format = "%y%m%d %H:%M"), "%m/%d/%Y %I:%M %p") End Notes

You can’t explore data unless you are curious and patience. Some people are born with them. Some acquire them with experience. In anyway, the techniques listed above would help you to explore continuous variables at any level. I’ve tried to keep the explanation simple. I’ve also shared R codes. However, I haven’t shared their output. You can run these codes. Try to infer the findings.

In this article, I’ve shared 8 methods to deal with continuous variables. These include binning, variable creation, normalization, transformation, principal component analysis, factor analysis etc. Additionally, I’ve also shared the techniques to deal with date time variables.

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How To Deal With Nomophobia?

How To Deal With Nomophobia? Nomophobia is a phobia of being away from your smartphone.

It further consists of two variants:

Low Self Esteem – For individuals looking for reassurance use the mobile phone in inappropriate ways.

Extroverted Personalities – For individuals who spend most of their time socially to access.

How To Deal With Nomophobia?

To learn ways to deal with Nomophobia, it is very important to know what Nomophobia is, what are the associated symptoms, after effects, diagnosis, and treatment. Starting with the symptoms and ending at its treatment, this article includes almost everything that you as a Nomophobic or someone you know with Nomophobia might need. Read on further to know more about Nomophobia and its treatment in detail.

What is Nomophobia? Symptoms Of Nomophobia

Nomophobia is not yet a clinical diagnosis but some of these symptoms clearly state Nomophobia signs. Look at the infographic below to know the common Nomophobia symptoms.

Additional Symptoms

–Fact Check–

80% of people with Nomophobia were willing to answer a call while watching television.

40% of them would respond to a call while having a meal, and

18% with Nomophobia would be willing to answer the phone when they were in bed.

Nomophobia: Causes

How To Treat A Person With Nomophobia?

There are several ways to help a person with Nomophobia both medically and with the help of technology itself. Wondering how technology can help treating a person with Nomophobia? Well, technology makes its own path in every nook and corner of the world, it is able to bring solutions for all the prevailing problems and what not.

If you think that your mobile phone is digging problems in your life or you anytime experience the symptoms of Nomophobia, you should firstly talk to a mental health professional. However, there is no significant treatment for Nomophobia but talking to a few therapists, we could conclude these few recommendations. One is Exposure Therapy, Cognitive-Behavioral Therapy, or taking help of technology to cope up with Nomophobia. Here we recommend using the Social Fever app for Android devices to get rid of mobile phone addiction.

Install Social Fever for Android Now!

Treatments for Nomophobia: The Fear Of Being Away From Your Phone 1. Exposure Therapy

Exposure Therapy helps you to learn how to face your fears. This therapy helps you slowly and gradually deal with your addictions and get going without your phone. Take baby steps in the initial stage and progressively work on your way where you spend some time without your phone. Start from leaving it in another room to turning it off while working on something important and worth your time.

2. Cognitive-Behavioral Therapy

The cognitive behavioral therapy is quite useful for the people experiencing Nomophobia symptoms as it reinforces the autonomous behavior that is independent from such techno-addictions. It further addresses the negative and irrational thoughts that contribute maximum to maladaptive behavior. Seek a therapist and ask him to help you identify the thinking ways and replace these negative and irrational thoughts with those that are realistic and rational.

3. Use Social Fever App To Detach Yourself From Smartphone Usage

Another best way to cope with Nomophobia is using technology. You might be wondering how technology can help beat a digital phobia. Well, using Social Fever can help you fight against your smartphone addiction via its dedicated Phone Usage Tracker. This amazing app lets you use your phone for a confined time. All you have to do is set up time limits and this well-designed app will ensure notifying you immediately when the time limit is exceeded. It comes with an upgraded tracking engine that provides comprehensive and accurate daily reports to keep a check on your device/app usage. You can also check the number of times your phone has been unlocked.

Final Words:

If you find yourself using your smartphone excessively, or experience any of the emotional, cognitive or physical symptoms of Nomophobia, try these treatments explained above. Consult a therapist or try detaching yourself from social media or your mobile phone using Social Fever app for Android. Nomophobia is a commonly growing problem along with several other fears and behavioral addictions. Try not to be wholly dependent on your mobile devices and seek consultancy at the right time to avoid surrounding yourself with severe health issues. Be realistic and don’t force yourself to quit everything there and then. Take your time and slowly and steadily reach your goal.

Keep reading Tweak Library for more such informative articles.

Recommended Readings:

How To Check Screen Time on Android?

COVID-19 Tips: Social Distancing Rules, Dos and Don’ts To Stay Safe

How To Tackle The Ill Effects of Social Media?

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Akshita Gupta

How To Deal With Yelp Disasters

Customers aren’t always right, but their opinions almost always count. Glenn Reit discovered this the hard way when his New York dental practice got a single negative review from an anonymous user on Yelp. According to Reit’s legal filings, his business dropped off drastically, from 10 to 15 new appointment calls per day to a mere 4 or 5. By any standard, the result of that one bad review nestled among ten good ones was disproportionately devastating.

What Not to Do

As the old saying goes, you can kill more bees with kindness than vinegar. (Or something like that.) The time-honored adage goes double on customer-driven review sites like Yelp. If you suddenly find yourself staring slack-jawed at the most mind-bendingly hateful criticism you’ve ever seen for your business, take a deep breath and resist the temptation to do either of the following things.

Flame your critics: As a rule, fighting with people on the Internet is never a good move. Even if one person is unquestionably right, both participants usually end up looking stupid, mean, and uneducated in the eyes of all outside observers. (Case in point: The screenshot at right came from a friend’s Yelp account, after the owner of an Illinois limo service decided to let her know what he thought of her review. Needless to say, she has not changed her opinion of the company.) The last thing you need to do is drag your business into the mud by publicly antagonizing someone who has already put some thought into criticizing you.

Sue them: I’m not a lawyer, so I can’t say whether you’d even have a case against your most profane Yelp hater. What I can say is that lawsuits against Yelp and its users frequently make the papers, generally make the business owner look petty, cost a lot of money, and usually lose or settle without gaining anything for the business. From a PR perspective, suing a Yelp user over a bad review is a really effective, though frivolously expensive, way to raise widespread public awareness about the fact that people hate your company. Remember Glenn Reit, the dentist? He sued, went all the way to the New York Supreme Court, and lost.

A Better Approach

Assuming you’re running a good business that’s worthy of customer praise, the best way to manage or improve your Yelp rating is to make positive moves within the framework of Yelp’s toolset. That means understanding how Yelp works and applying sound customer-service practices to encourage, reinforce, and reward praise, and to find opportunities to win back your critics.

I like to think that most people are generally sensible, but the Internet has an uncanny knack for transforming rational adults into raving, infantile morons. Yelp, doubly so. Once you accept this basic tenet, you can begin to view your online critics as the reasonable minds they probably are, rather than the juvenile half-wits they appear to be.

The message here is that Yelp reviews often say as much about the critic as they do about the business being critiqued, and since the nature of the site can bring out the worst in people, there’s no point in taking the nastiness personally. Instead, use the tips and tools below to ratchet up your Yelp reputation.

Claim Your Business

Claiming your business’s page is easy. First, sign up for a business account. Business accounts are free, and they include features for managing your company’s profile, so they’re distinct from ordinary user accounts. Once you have an account, you can verify that you’re the owner of the business by letting Yelp’s automated system call the phone number listed for your business and entering a PIN code that Yelp provides.

Encourage Good Reviews

For potential customers who find your business through Yelp’s mobile app, the star rating is likely to be the biggest determinant in whether they’ll drop by. Sure, they may read the first few reviews that pop up, but if you don’t have a rating of four or five stars, most users probably won’t even tap on your listing. For this reason, your primary objective should be to overwhelm any mediocre or negative ratings with a healthy supply of more positive ones.

Although it would be unethical to gin up your Yelp rating with fake reviews, offering an incentive for genuine customers to leave positive reviews is good business. Advertising a small, one-time discount or freebie to anyone who brings in a printout of their review about your business can drive plenty of good reviews your way. You should probably refrain from insisting on positive reviews, for three reasons:

1. It makes you look desperate.

Court Your Critics

When you’re faced with an unhappy customer in the real world, you likely don’t just ignore them on the assumption that they represent a minority view. Instead, you try to win them over with apologies, discounts, and freebies, and you leave them walking out the door with a coupon for their next visit. The Internet should be no different.

You can send any user a note privately through the ‘Send Message’ link at the top of the review, which might be the best course of action for an initial contact. You might discover that, if the person wrote their review in a fit of anger that they’ve since recovered from, they’ll feel compelled to edit it a little once they see that their words have had an impact.

Alternatively, you can leave a public response directly on the review. Even if the customer doesn’t change their mind, this approach makes a public statement about the kind of business you’re running–that you take your customers seriously and care about making things right.

Have Fun With It

Realistically, you’re probably not going to win with every single customer. Some people are just ornery, and too many folks like to hold a grudge. If life hands you an oversupply of sour reviews, make lemonade the way San Francisco’s Pizzeria Delfina did. Rather than fret over their one-star ratings, they printed them on T-shirts worn by restaurant employees.

As with anything else in business, success on Yelp is about knowing your customers and responding appropriately, even if that response is printing their words on a T-shirt in open mockery of their opinion. This approach won’t work for everyone, but I have to credit Pizzeria Delfina’s creative problem solving.

8 Important Ways To Break Groupism In Organisations

Groupism in Organisations

In almost every company, whether big and famous or small and sophisticated, a culture of office politics finds a way to creep inside the walls of a foundation built with years of hard work. It is something that is inevitable and exists in every organization. Groups are created between employees according to their comfort level and how well they get along. It is somewhat like the groups in school, with dorks and nerds on one side and the cool ones on another. For competitive purposes, this might be helpful for an organization to some extent. Still, it soon gets ugly, too, with the addition of new employees who are de-motivated by these groups.

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Going by what some top management experts have to say about how they managed their company, the truth is that groups in companies need to be denounced, and the sooner your workers get rid of this, the better it is for your company’s growth.

Here are some of the ways how you can attempt to get rid of groupism among the employees of your office:

8 Important Ways to Get Rid of Groupism Between Employees 1. Encourage diversity

It is beneficial for employers to hire individuals who come from different backgrounds, has a different cultures, and hold different beliefs. It is not necessary to go by typical stereotypes and hire only those who come from a reputed college or belong to a particular community only because you, as a leader, belong to that community. It can backfire and lead to groups in your company since like-mindedness would be a factor in having a group with the majority belonging to a particular community.

Also, there is no point in hiring people with similar ideas and beliefs since there would be no active competition if that happened. Therefore, when you form a team or a department, make sure that people from different walks of society work together and collaborate to boost their team’s performance. This way, there would be no particular excellent group or a biased view held by a specific set of people or groups. Also, this would significantly improve the performance of your employees since competition is always a score booster.

2. When making teams, consider the age group of employees

Rather than going to their race, skin color, or friendship, it is better to divide the teams in your office depending on the age group of your employees. Put the younger and the same age group in one team and put the older and more experienced ones in another group. This will not seem prejudiced and is pretty logical too. The formation of groups based on age or experience is unlikely, thus eliminating the possibility of groupism. When new employees join in, divide them likewise into teams.

3. Do not allow regional languages to be spoken

One of the persistent reasons behind the formation of groupism in the office has been the division among the employees based on their mother tongues or the regional languages they speak. For instance, when three or four members in an office happen to be of Spanish origin, they take the liberty to talk among themselves in Spanish, which is very unsocial to do among many people in the office. They eventually form a group, and this starts the flame of groupism.

To ensure that such incidents do not occur in your workplace, seize every opportunity to communicate clearly to your employees that a single universal language should be spoken in the office, whether English or any other language that all employees understand. This keeps things from getting ugly, even if most people from a particular community won’t go ahead and talk in their mother tongue in the office. Such minor details sometimes sow the seeds of office politics, and it is better to stop it in the very beginning than to watch it dismantle your groupism in the workplace.

4. Choose team leaders or supervisors

Having managers or supervisors in teams makes the team work responsibly with someone above them to guide and assist them when needed. A supervisor can also work to keep the team united and encourage them to work better than the other team. It keeps the employees motivated, giving their best in their tasks or responsibilities. Furthermore, a leader knows the shortcomings and the high point of the employees he has in his team. Thus, when it comes to working as a group, he can divide the work so that everybody does what they are good at, ensuring the efficient working of the team.

5. As a boss, never encourage backbiting

In every office, one of those employees tries to stay in constant connection with the boss by bringing him gossip. They are not as competitive as the others, and becoming the favorite seems easier than working hard. It is essential to discourage such behavior if any employee tries to practice it. As a senior, it is your responsibility to keep the workers united, and not one of your pawns should be out of place.

6. Give equal opportunities to everyone

When four individuals work together in a team, and one gets praised and promoted for the combined effort of the entire team, it becomes unacceptable for the other three members since they did equal handwork for that particular project and deserve a fair share of opportunity too. Still, maybe because they are not as friendly or easy to like as the one who got selected, they lose a chance and have the right to feel unappreciated. Such conduct from the senior bosses is like a spark that fuels the fire of office politics.

To avoid such scenarios from happening, give each member a fair opportunity to compete for the promotion by assigning them separate tasks, and rather than handing over the promotion to one, who seemed the unanimous choice, go for the one whose performance impressed all the bosses equally. This will, at least, let those who are not promoted know why they were not chosen. Such small things can also fuel groups; in fact, anything done unmindfully on the boss’s front can be so the thing that can be a risk for the organization. Therefore, each step you take shall be unbiased, not partial, and acceptable to your seniors and employees.

7. Organised outings or dinners for the entire office

If there comes the point where you realize that the seeds of groupism have already started to bud in your company, you should take some instant steps to avoid it. If you have never taken your workers out for dinner or organized a get-together for the entire office, it may be a good idea to do so now.

Such get-togethers allow everyone to meet outside the workplace where there is no competition to mind, and they can all take freely to each other. Talk to all your employees when they sit around the table with you, and try to tell them how you loathe groupism in the workplace and expect them to work together as one united organization. If there are issues, try to make them talk it out to you and resolve them at your level. It will further ease the tension that might arise among the employees due to constant politics in the office.

8. Keep all official conversations on paper

It is essential to have disciplined and organized working in companies to keep any controversies from rising out of nothing. Usually, in workplaces, one not-so-ethical employee would make up something out of his brain and pass it on to another person until it takes the form of a well-spread rumor. Such cases usually happen during appraisal or promotions, and the office has to bear the wrath of politics and groupism.

To avoid such scenarios from occurring at your workplace, it is better to keep everything regarding work and business on paper so that all your employees know how things happen at their workplace and do not fall prey to such rumors spread out of sheer insecurity from an employee not competitive enough to deal with situations. Even if the story gets some acknowledgment, it is better to bring the one responsible to face questioning and the news source. Having everything on paper will help you prove your ground and explain the truth behind such nonsense to your employees.

Groupism and office politics can be a very common culture, and it becomes a biological need of the individuals working together. They tend to get together and talk about someone in the office they don’t appreciate, and sometimes it is even the boss against whom the entire group of employees holds a grudge. It is not a very big deal as long as it does not start affecting the performance of your employees. However, it is not something that should be promoted either. Gossip and random chatty attitude are acceptable, but when it comes to the point where an employee feels threatened by it, you need to take action.

The best way to get your groupism in the workplace rid of issues is to maintain a disciplined work environment and work as a team. You could also keep the entire group of workers working together as one big family by organizing random get-togethers and parties on special days that are not holidays. An office free from politics and gossip is a workplace that ensures 100%, efficient employees. The chances of meeting goals are much higher than in companies that have to witness a battle between their employees and employers.

Recommended Articles

Here are some articles that will help you to get more detail about Groupism in Organisations, so go through the link.

Types Of Variables In Statistics

Introduction to Types of Variables in Statistics

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Different Types of Variables in Statistics

In statistics, the variable is an algebraic term that denotes the unknown value that is not a fixed value which is in numerical format. Such types of variables are implemented for many types of research for easy computations. So there are many different types of variables available that can be applied in varied domains. Many other variables are discussed in minimally are listed are active variable which the researcher evaluates. A variable that occurs before the independent variable is called an antecedent variable.

1. Independent Variables

The independent variable is the one that is computed in research to view the impact of dependent variables. It is also called as resultant variables, predictor or experimental variables. For example, A manager asks 100 employees to complete a project. He should know the capacity of the individual employee. He wants to know the reason behind smart guys and failure guys. The first reason is that some will be working hard for day and night to complete the project within the estimated time, and the other one is that some guys are born intelligent and smarter than others. The variable which is similar to an independent variable is called a covariate variable but is impacted by the dependent variable but not as common as a variable of interest.

2. Dependent Variables 3. Categorical Variables

It is a wide category of variable which is infinite and has no numerical data. These variables are called as qualitative variables or attribute variable in terms of statistics software. Such variables are further divided into nominal variables, ordinal and dichotomous variables. Nominal variables don’t have any intrinsic order. For instance, a developer classifies his environment into different types of networks based on their structure, such as P2P, cloud computing, pervasive computing, IoT. So here, the type of network is a nominal variable comprised of four categories. The varied categories present in the nominal variable can be known as the nominal variable levels or groups.Dichotomous variables are also called binary values, which have only two categories.

For example, if we question a person that he owns a car, he would reply only with yes or no. such types of two distinct variables that are nominal are called as dichotomous. It just accounts for only two values, such as 0 or 1. It could be yes or no, short or long, etc.Ordinal variables are nominal variables that include two or multiple categories. If you see any hotel feedback form, it has five ratings such as excellent, good, better, poor and very poor. So we can rank the level with the help of ordinal variables that hold meaning to the research. It is unambiguous, and values can be considered for decision making.

4. Continuous Variables

It can account for only a certain set of values, such as several bikes in a parking area are discrete as the floor holds only a limited portion to park bikes. Ratio variables occur with intervals; it has an extra condition that zero on any measurement denotes that there is no value of that variable. In simple, the distance of four meters is twice the distance of two meters. It operates on the ratio of measurements. Apart from these mentioned variables, a dummy variable can be applied in regression analysis to establish a relationship to unlinked categorical variables. For instance, if the user had categories ”has pet” and ”owns a home” can assign as 1 to ”’has pet” and 0 to ”’owns a home”.

A factor that remains constant in an experiment is termed as a control variable. In an experiment, if the scientist wants to test the plant’s light for its growth, he should control the value of water and soil quality. The additional variable which has a hidden impact on the obtained experimental values are called confounding variables.

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This is a guide to Types of Variables in Statistics. Here we discuss the introduction and different types of variables in statistics. You may also have a look at the following articles to learn more –

Ensuring Continuous Remediation In Your Build Pipeline

Take a closer look at some additional components of the build pipeline ensuring continuous remediation 

The build pipeline is now being used by every organization. A build pipeline is a collection of automated procedures that assist users (developers or DevOps professionals) in compiling, building, and deploying their code to the production server in a reliable and efficient manner. 

The building of the automation, testing it, and finally deploying that automation is all critical components of the development pipeline. The next stages show us that automation is the foundation of the build pipeline, which automatically compiles code, tests it, and deploys it to a target environment. 

The continuous build is a fundamental requirement of end clients in continuous integration and continuous delivery (


), and it can only be achieved through automation. Let’s take a closer look at some additional components of the construction pipeline.

Why Continuous Remediation in CI/CD Is Needed

The CI/CD process eliminates delays in the development process and offers users agility by allowing them to address build faults in each phase or in gradual increments. As a majority of today’s software applications rely on a variety of open source code or dependencies, effectively managing those reliances is essential. Thus it is very important to refer to the list that contains all the dependencies being used in a software application. We refer to this list as a “software bill of materials.” 

It is vital to manage and verify the

software bill of materials

because the software might be a combination of open-source, a range of assets, and occasionally third-party software, which makes it difficult to manage and verify. As they may have a variety of dependencies, it is probable that some of them may have vulnerabilities. Therefore, maintaining a list of them makes it easier to identify and remediate those flaws.

These vulnerabilities can leave you open to attacks that can cause your organization to lose its important data and reputation. As changes in the build are continuous, there’s a need for continuous remediation. This

continuous remediation

process can reduce the chances of vulnerabilities. Continuous remediation processes provide early warning or vulnerable code to developers so that they can fix it at an early stage. The benefit of the whole process is that it makes the organization bug free and makes employees more productive. 

Ensuring Proper Remediation in Build Pipelines

On a daily basis, a product management organization releases thousands of lines of code for a variety of reasons, including the introduction of a new product or fixing a vulnerability in an existing application. Developers are usually good at what they do and take great care when writing code, but human error is always possible. There is a requirement to incorporate security into the CI/CD security pipeline so that when code is delivered, it can be checked to determine if it contains vulnerabilities before being uploaded to the production server.

There are different countermeasures organizations can take to build proper remediation and detection in the CI/CD pipeline. 

A lot of organizations use SAST tools to perform the scanning of the code. Static analysis tools are crucial because they can perform checks on the code of an application before it is deployed to ensure that it does not include any software vulnerabilities or coding errors. They are identified when the code is deployed. If there is any vulnerability, the code merging fails, and it needs to be fixed. 

Companies should require the use of

IDE plugins

and linters by the entire team when installing these utilities in order to standardize their efforts to incorporate security into their projects. Some open-source components on which these projects rely have not been updated in a long time, they may contain some of the known vulnerabilities. 

They should also use code quality tools that are specifically designed to analyze open-source components for known vulnerabilities. A lot of different tools are available which can connect with different building tools such as CircleCI, GHAS, and SonarQube. They detect the errors before the code is merged into the production.

Lots of organizations are now integrating peer review of code before it is merged into the productions, and this is becoming increasingly popular. As a result of this, organizations can improve the quality of their code through the use of rigorous inspection procedures. Having said that, they are unable to analyze every line of code, but they are able to examine some of the more fundamental aspects of the code, such as input validation. 

All development teams should adhere to the

OWASP security best practices

while auditing their code, such as scanning for input validation errors and looking for components that may contain known vulnerabilities. It is possible to do these checks manually while keeping typical vulnerabilities in mind throughout the development process as well.


The methods I have outlined in this post can be used by any business, regardless of its size or financial ability, to protect itself from vulnerabilities in the CI/CD process. A number of others may exist, but they will differ greatly from organization to organization. 

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