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Whether we like to admit it or not, time and time again, **statistics** and **statistical modelling techniques** have always played a role in our lives.

Do you remember all that you have learnt about the** Chi-Square test** when studying for your undergraduate degree at university or even the **probability** sums that you had to tackle during your high school mathematics class?

Well, both the** Chi-square test and probability** are related to the broad field of study called** statistics**. You do engage in various **statistical modelling techniques like regression analysis** all the time without even thinking that it is a form of statistical modelling that you are engaged in. When you tried to **forecast what trends** you can expect to see in the future, you engage in a form of **statistical modelling**. So while you may be interested in tracing the roots of statistical testing, you can rest assured that we will do our best to convey everything that you need to know about **statistical modelling techniques and the statistics field**.

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## Statistical Modelling

Statistical modelling techniques, as well as other** data analysis methods**, have constantly been searched for on the internet over the last few years. A big reason as to why people have become far more fixated on** sifting through and analysing data** is because data has become intertwined with our everyday lives. Turn on the news and the data pertaining to the Covid-19 virus is sure to be visibly waiting to be interpreted.

When we** forecast** that the number of Covid-19 cases will increase within a week, we are actually performing a **task similar to the tasks that are performed by statisticians and data scientists**. If you look back to the early uses of statistics, you will find that **mathematical computational methods of a set of data** have been around since people started using statistics for agricultural purposes.

The truth remains that despite statistical modelling methods advancing since then, the bulk of the statistics field is deeply connected to the subject of Mathematics. To a certain degree, statistical modelling has advanced to include the use of **computers and programming software to sort through, analyse and convert raw data into a form that can be easily interpreted**. Despite the advances in technology, **data science** still remains all about deciding **how to collect data** and actually** collecting the data**. **Once data is collected, the data needs to be analysed**. Eventually, after analysing data, **conclusions can be** **drawn from the data**. The conclusions that are drawn must then be conveyed in a meaningful and creative way by the data scientist.

As **easy as statistical modelling may sound in theory**, it is a certainty that even data scientists themselves would enjoy a bit of a refresher about the statistical modelling process as well as necessary reminders of what exactly **statistics and probability** are.

Think of statistical modelling as a type of **mathematical equation** used for the purpose of conveying data. Statistical models work best to find **solutions to problems shown in a set of data**. The truth is in order to get statistical modelling accurate, you must be aware of your** dependent variable**. A dependent variable is a variable that you would need to explain based upon the effects of the** explanatory variable or independent variable**. As a statistician or data scientist, there are a **variety of factors and limitations** that you ought to consider when engaging in statistical modelling techniques. Now that you are in tune with statistical modelling and what it entails, let's look at trying to define** statistics and probability**.

## Statistics and Probability

If you think very carefully and considerately about what statistics and probability are, it makes it far easier to decide whether you are indeed interested in furthering your studies in the field of statistics. So while you have decided to study statistics, you must understand that underlying everything that you will learn under the branch of statistics called** inferential statistics** is **probability theory**. When you choose to study applied statistics, you will come across various terms such as **random experiment, random variables and sample space**.

- A random experiment is an experiment in which the answer cannot be predicted until it is observed.
- Sample space refers to a set of all potential outcomes in a random experiment
- Random variables are considered to be all the numerical variables that can occur as an outcome

Statistics, if you consider the abovementioned concepts, is all about** finding data and conducting experiments** to see how problems can be solved utilizing the data that had been found. The broad field of statistics is divided into two areas. **Descriptive statistics** is the area of statistics that entails the collection, presentation and description of sample data. **Inferential statistics** is all about drawing conclusions about the data.

By now you may have heard the term** probability** far too often. You still might be getting the same hives that you got when tackling a high school probability sum or you may have become so confident that you understand probability that even the hives no longer make an appearance. Probability then is the** likelihood or chance that something** **will happen**. In a random experiment, probability can be seen in numerical terms as either the number 0 or 1. 1 would indicate a** certainty** that something will, in fact, occur whilst 0 will depict **pure uncertainty**.

Once you pursue the field of statistics, you will learn that probability can also be seen in terms of** conditional probability** which is the likelihood or** chance that something will happen based on the fact that another similar event has already occurred**. While the word probability is commonly seen as a synonym for the word, "chance", data scientist and statisticians surely leave nothing up to chance by collecting and analyzing data to try to prove and pinpoint everything.

Like in Sociology where there are various **sociological viewpoints** for almost everything, statistics as a field of study is quite similar to the discipline of sociology in that probability is seen as a term belonging to **two distinct schools of thought**- Frequentist and Bayesian.

According to the **Frequentist theorists**, they are under the opinion that probability is the** frequency of an outcome**. As per the **Bayesian school of thought**, the belief is that** probability is an abstract concept.**

When we take complex concepts related to statistics and probability and explain it in layman terms, the field of statistics doesn't seem so complex. There certainly are some more statistics related jargon that you would undoubtedly find easy to grasp once you have been introduced to it.

## Frequency Distribution

Once data has been actually sourced, a **graphic representation of the data** is needed. One form of graphical representation is called **frequency distribution** and displays the number of observations within a given interval. Among the many** statistical tools** that you may encounter, the frequency distribution tool is but one tool to showcase observations from one particular test. The frequency distribution tool shows the observation of probability that is then divided by the** standard deviation**.

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## Variance and Standard Deviation

You are even more interested in the fields of statistics and data science now that you have come to grips with certain concepts related to** statistics and statistical variation**.** Variance and standard deviation** are two more means of measuring the dispersion of a data set. Variance in other words is the **average squared distance between the mean and each data value**. The variance must be squared units and the variance can never be a negative number. Since variance is a squared number, the standard deviation is used to find the square root of variance. Once the square root has been found, it is easier to compare the variance to the data set. This is actually where mathematics plays a huge role. Having a **strong mathematical foundation** will be highly beneficial for you in this regard especially when calculating variance and standard deviation.

## Modules That Feature in Your Undergraduate Statistics Degree

So you have been scanning undergraduate degrees and you may even have looked at the subjects covered in the undergraduate statistics degree. Be proud of yourself for actually doing the search, it clearly shows that the** statistics jargon you just saw does not deter you in any way from reaching for your dreams**.

When you enter into the field of statistics, you are required to decide whether you would like to study **Mathematical Statistics, Business Statistics or Ancillary Statistics** first. Once you decide which type of statistics you would like to study, you can then see which modules are best for you. Modules that you may take during your first year of statistics studying include:

- Descriptive statistical techniques
- Counting methods
- Basic concepts: Bayes Theorem
- Basic probability

## Career Fields That You Can Pursue If You Study Statistics

If you are considering **pursuing statistics and data science** as a field of study, you must know what career options are available after you complete your years of study. If you simply do an **undergraduate degree in statistics**, you can pursue a career in:

- Market research
- Economics
- Software engineering
- Market researching and business analytics

## Where to Get Statistics Help From?

So all this statistics related jargon got you anxiously thinking about how you can reach the finish line and actually qualify as a data scientist. Well, you need not be alone as you go from studying **Bayesian probability theory to Frequentist probability theory**. You can always rope in a statistics tutor to help you during your years of undergraduate studying. Perhaps scroll through our Superprof site to find a desirable tutor at an affordable rate for you.