What Does Baseline Data Mean?

In order to study different subjects, researchers require a certain degree of previous information to establish the scope and reach of their investigation. This information is often called baseline data, since it serves as the starting point for the research project and it can help the researchers to develop hypotheses founded on results obtained previously by other investigators. For companies, baseline data is particularly important to keep research projects small enough so they can be actually funded. Consulting and research companies often provide these broad data sets where different conclusions, findings and hypothesis can be drawn to develop deeper research studies more associated with the company’s target market or segment.

A baseline data, in this case, can be the gender distribution of the country, state or city where the company functions. By using this data, companies can understand what’s the actual size of their market and then they can develop further studies to understand the behavior of the segment they are interested to reach. Baseline data also serves as comparison, since it can be weighed against new information obtained from other sources to judge the accuracy or relevance of this newly acquired set of data.

Example

Fami Toys Co. is a company that designs and manufactures educational toys for small children from 3 to 10 years old. They have been in business for 3 years and they currently have two very successful products already in the market. Nevertheless, the company has seen its sales drop recently since the design of both products appears to be unattractive for new generations. They decided to do a little research about the colors and images children prefer.

A consulting firm called Mosley and Sons Co. provided the baseline data required to start developing hypothesis for the research. This information included color preferences per age and gender and a research study that contained the 100 most appealing images for kids between 5 and 10 years old. This information was very useful for Fami Toys since they could employ the findings to develop prototypes that were later evaluated by using small focus groups.

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What Does Stochastic Mean in Business?

The word stochastic originates from the Greek stochastikos, which means, “able to guess”. It is often employed to describe different scenarios where concise results can’t be obtained since there is a random variable that will cause the outcome to vary each time the phenomenon is observed. In the field of statistics, a stochastic approach means to input different values to a given random variable in order to develop a probabilistic distribution where patterns can be identified.

Nevertheless, since the term refers to scenarios with unexpected results these probabilistic approaches have limited applicability. On the other hand, stochastic predictions can also be derived from previous observation of the situation. A company that already has many years selling its products on a stable market can create a reliable forecast of its sales, even if the event of buying a product itself is somehow random. Companies can predict district, city, state, region and national sales figures, by using forecasting models based on past data. A stochastic variable can, nevertheless, be incorporated in these models, to evaluate different possible scenarios.

Example

Liu & Co. is a financial services firm that conducts day trading operations for complex financial instruments in many financial markets around the world. The company is currently developing a forecasting model for a given category of securities to predict its prices. Since the financial market is in itself a stochastic environment, the founder of the company Mr. Liu, has identified several variables that have a fairly considerable influence in the price of securities.

He designed a model where 10 variables are evaluated to derive a price for the security. Some of these variables have steady values that have a predictable conduct but the other three are considered random. In order to calculate a range of possible prices, Mr. Liu inputs different random values to each, through a stochastic model, to achieve a probability distribution where the trader can identify the most probable price for the asset. This system have proven very profitable in the past and it is already being used by various other financial firms.

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What Does Exponential Growth Mean?

It is difficult to establish a rigid rule to state that something is growing exponentially. In order to identify this kind of growth a trend must be previously recognized. In order to do so, the numerical data being evaluated should have a proper period of time where it has been progressing. During this time, an exponential growth will normally be illustrated graphically as a “J”, which is an almost-vertical ascending curve along the horizontal axis.

On the other hand, exponential growth can also be defined according to the following equation: X_t = X₀ (1+r)^t, with r being the growth rate and t being the amount of time during which this growth will be experienced. In business, an exponential growth is a desirable phenomenon, particularly in sales figures, stock prices and earnings per share. Business and financial analysts understand exponential growth rates as temporary situations often created by new product developments or launch stages of the business.

Maintaining an exponential growth over time is a very difficult and improbable tasks for a regular business, since competitors start to take market share and customer’s utility levels are higher than they were at the beginning, meaning their purchase frequency might decrease.

Example

Seasonal Flowers is a store located in a crowded neighborhood of a mid-size U.S. city. The store was founded 3 years ago and it is a city’s favorite when it comes to flower delivery services and arrangements for special occasions. The business’ owners have a lot of experience in the flower industry and they know which are the crucial dates where sales increase exponentially.

During these days or seasons, the number of daily flower arrangements sold increases by 500%, compared to regular seasons. The business understands that in order to make the most out of these occasions they have to hire temporary workers and lease some delivery vehicles to cover for the rising demand.

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What Does Convenience Sampling Mean?

A convenience sampling process is one of the non-probability sampling methods employed to conduct a given statistical research. After identifying the target population the researcher must define what’s the proper method to gather data from a representative group (sample) of that population. Differently from probabilistic techniques such as random sampling or stratified sampling, convenience sampling is based on the degree of accessibility to data sources, this means, subjects are selected because they are easy to reach. This reduces the researcher’s effort but increases the chances of not representing the population’s overall picture with the chosen sample.

On the other hand, this is a convenient technique for startups and pilot testing projects, where resources are limited and the margins of error allowed are wider than more complex research assignments. This method is also frequently employed in work environment studies, where Human Resources Departments investigate the employee’s opinions about their workplace. Normally, these studies cover the most convenient locations and the findings are regarded as reliable but they are actually based on convenience and they don’t always cover all the facilities where the company functions.

Example

Mex-Gourmet Co. is a Mexican restaurant located in a highly transited shopping mall. The restaurant just opened and they want to see the public’s reaction to the new exotic flavors they are currently offering on the menu. In order to do so, they designed a sampling campaign that will include a survey to gather and analyze the people’s opinion.

This campaign will be implemented at the mall and they will have five different stands in different locations of the building. Since there is no way Mex-Gourmet can screen the sample properly (anyone can taste and comment), the opinions obtained through the research can’t be considered representative to the whole population but the convenience of locating the stands at the mall is the only option they have, for now, to research their audience. They hope to get enough surveys to get a clearer view about the public’s preferences, to adjust the menu accordingly.

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What Does Data Integrity Mean?

The integrity of stored data is essential in the digital environment as most of the records and information produced by companies and organizations is now stored digitally, the risks of losing data even partially must be avoided as much as possible. System have many different methods to ensure data integrity, the most popular among them being error checking, validation procedures and backups.

On the other hand, all data should be interconnected. This way, if data is partially or entirely lost somewhere, it can be traced back to another point, therefore avoiding a complete loss. Data integrity can be compromised by external intrusions, damaged hardware, malware and viruses, human error, update failures or errors experienced during a transfer or a replication.

On the other hand, access to raw data or certain levels of data should be restricted by using authorization structures that allow or deny certain individuals to make changes or to erase data without previous clearance. Databases should be equipped to react properly to any of these scenarios, to protect data integrity.

Example

Idaho State University is one of the biggest colleges within the state. The University is currently migrating from a previous student management system to a new one called CollegeHelper. The system has many new capabilities that will allow teacher, students and college authorities to communicate and relate to each other more effectively. The first problem encountered by the university when they wanted to install CollegeHelper was that the databases of each system were very different.

This raised concerns about data integrity and caused the University to almost back out of the system’s implementation. Nevertheless, the company that created the system ensured the Board that data will be backed up reliably in order to maintain its integrity, before the migration starts. They said that even if they find a big problem during the process, worst case scenario will be the impossibility to migrate, but data will remain safely backed and accessible through the old system.

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What Does Likert Scale Mean?

This method was created by Rensis Likert in 1932 and it is widely implemented in surveys to determine the degree of acceptance, opinion or likelihood of qualitative variables being measured. By employing numerical scales, mostly scores from 1 to 5 or 1 to 7, the researcher can analyze different metrics to understand patterns of behavior evidenced in the data, which would be impossible to do if the qualitative data wasn’t organized numerically.

The scales can be classified as agreement scales (strongly disagree to strongly agree), frequency scales (very often to never), quality scales (very good to very bad), likelihood scales (definitely to never) and importance scales (very important to unimportant). Likert scales are frequently employed in market research projects to gauge customer’s preferences, tastes, opinions and attitudes towards a given product, service or company. By using scales, richer information can be obtained from the customers, since the answers are more intuitive and allow the individual being questioned to give a more accurate opinion about what he actually feels.

Example

A company called Longer Life LLC. manufactures power banks for mobile phones and is currently working on a market research project to understand its customer’s actual needs. In order to do so, they need to measure many qualitative variables like battery quality, power source availability during the day and the phone’s degree of usage during the day. The researcher decided to employ likert scales to measure these qualitative data.

In the case of battery quality the question will be ¿How would you grade the quality of your battery? 1=Very low quality 2=Low Quality 3=Average Quality 4=High quality 5=Very high quality. By measuring the average quality of the customer’s batteries the company can understand how much a power bank is actually needed and how many charges a day the person might require.

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What Does Inferential Statistics Mean?

Inferential statistics is one of the two statistical methods employed to analyze data, along with descriptive statistics. The goal of this tool is to provide measurements that can describe the overall population of a research project by studying a smaller sample of it.

This way the researcher can make assumptions about key elements with a fair degree of confidence. Among these inferential tools we have regression models, normal distributions and R-squared analysis, among others. The study of the smaller sample can be used to explain the variable’s overall behavior from a whole-population perspective which opens the path for theories and new hypothesis. In business, inferential statistics are widely used.

One of the most common places we can find this method is at forecasting models. These statistical models study a small portion of data to predict the future behavior of the variables, making inferences based on historical data.

Example

A company called Pizza Palace Co. is currently performing a market research about their customer’s behavior when it comes to eating pizza. The company is trying to understand the favorite tastes of its customers in order to re-design the menu. They hired a marketing consulting company to gather a focus group to study the matter. The company gathered a group of 50 people of different ages and genders, all residents of neighborhoods adjacent to where the store is located.

After the focus group was finished the marketing company proceeded to apply inferential statistics to this sample, in order to understand what would be the most desired tastes of the total population. The results revealed that 75% of the sample loved pepperoni pizza; also, they discovered that 75% of women in the group liked their pizza with some pineapple on it.

By applying inferential statistic’s tools to the study, the company could understand with a high degree of confidence which were the favorite tastes of the population they currently serve.

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What Does the Lorenz Curve Mean?

This model was developed by Max Lorenz in the beginning of the 20^th century. The purpose of the curve is to show the degree of social equality of a country by displaying how much wealth a given percentage of the population possesses. The X axis measures the cumulative % of the population and the Y axis measures the cumulative percentage of income or wealth for each percentage. Perfect distribution is represented by a straight diagonal line between both axis that starts at the (0;0) point and ends at the (1;1) or the (100;100) point depending on the scale.

Then the curve is draw by using the data of income or wealth distribution. The X axis should be arranged to have an ascendant order from lowest to highest income or wealth proportion. This means that cumulative percentages of population should be arranged starting at the less wealthy bracket of people to the wealthiest bracket.

Here’s an example of how this curve will be constructed.

Example

Country BVN is currently in the process of measuring income equality among its population. The financial entities of the government decided to use the Lorenz Curve to analyze this. There was a general census to collect the required data to construct the model. The results were the following: the first 45% of the population earned 10% of the income; the next 25% earned 20% of the income; the next 20% earned 20% of the income and the next 10% earned 50%. How would a Lorenz Curve look like?

Here’s how a Lorenz Curve will look like for BVN. By using the data provided by the Government we can develop this graphic to show the country’s current wealth distribution.

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What Does Statistical Analysis Mean?

What is the definition of statistical analysis? This is done in many ways such as: regression formulas, means, r-squared calculations, and ratio analysis. The purpose of these methods is to take quantitative, or mathematical, data to determine the correlation between one or more variables or predict the possibility of a future event occurring again in similar situations.

Statistical analyzes are frequently used in business for company valuations and in many business decisions, such as stock holding and assessment. Stock volatility is one area where businesses frequently use this analysis to derive results. Businesses also analyze this data during risk management in order to assess the probability of a certain risk being incurred by a company and how substantial the risk may be. Companies also use regression formulas in order to test some of their more general hypotheses about the effect of a certain factor on their assets or market price of stock.

Let’s take a look at an example.

Example

Johnny owns a social media company, FriendKeeper. Since its inception in 2005, the social media company has grown to unprecedented heights and is now a global force. However, recently Johnny has noticed that general membership on the website has declined in addition to new sign-ups. Johnny believes that one of the primary causes of this decline in numbers is due to the prevalence of news coverage by popular news sources on the ills of social media coverage. He decides to test this hypothesis. Johnny will thus take the following steps.

1. Accumulate the quantitative data of the two variables in question.
2. Put the data in a software program that can run some automatic statistical analyses.
3. Run a linear regression.
4. Analyze the r-coefficient and other relevant results.

After the analysis of the Pearson’s coefficient, Johnny will be able to determine whether the adverse news coverage on social media has had a negative impact on his membership.

Summary Definition

Define Statistical Analysis: Statistical analysis means to evaluate models and formulas in an effort to find mathematical relationships between to variables.

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What Does Standard Deviation Mean?

What is the definition of standard deviation? Standard Deviation is a statistical tool that is used widely by statisticians, economists, financial investors, mathematicians, and government officials. It allows these experts to see how variable a collection of data is. Furthermore, SD is calculated as the square root of the variance of the data. Specifically in finance, investors can use SD to determine the volatility of a certain portfolio of investments.

Let’s look at an example.

Example

Matthew has just graduated, and has entered the financial industry as an investment analyst for a large firm. One of his first tasks is to determine the SD of a portfolio of three investments: AAC, BBA, and CCD. These investments have annualized returns of 8%, 15%, and 13%, respectively. First, Matthew knows he must calculate the mean, or average, of the data: (.08 + .15 + .13) x 100 / (3) = 12%.

Furthermore, he knows that the variance is simply the difference between each investment’s return, and the mean. Thus, the variance for AAC is (8% – 12%) = -4%, the variance for BBA is (15% – 12%) = 3%, and the variance for CCD is (13% – 12%) = 1%. Then, he squares these values to arrive at .16%, .09%, and .01%.

Lastly, he adds these values together, and arrives at 0.26%, which is divided by 2 to arrive at the total variance of 0.13%. Then, he takes the square root of the total variance, or .0013,. which equals a deviation of 3.6%. Although this formula can seem intimidating, it is actually an extremely useful tool for investors and non-investors to use to find patterns and information in a set of data.

Summary Definition

Standard Deviations: Standard deviation means a statistical measurement used to calculate the significance of items as they relate to averages, means, and outliers.

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