Unit 31-LO2 Analyse and evaluate raw business data using a number of statistical methods-BTEC-HND-Level 4 & 5

Course: Pearson BTEC Levels 4 and 5 Higher Nationals in Business

Here are 5 of the most common statistical methods that you might find yourself using.

Point Estimate Analysis: Point estimations yield a single statistic for every data point, such as the mean or median. You can think of it as a one-dimensional estimation instead of looking at all dimensions at once.

Mean Median Analysis: Considered together with scatter plots and variable plot analysis, it yields an idea of what analytical methods work better or worse in certain situations.

Cohen’s Kappa Test: Used to compare two independent groups means for quantitative variables to see if they cover the same range or disparity in values.

Correlation Analysis: Analysis of the strength of the relationship between two variables.

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Cross-Tabulation and Chi-Square Test: A statistical tool that measures how closely categorical data (e.g., survey results) match up with one another as determined by a particular set of theoretical expectations, or a description given beforehand as to what the relationships are expected to be between different categories within data sets such Abbreviated.

Statistical methods that are used to analyze and evaluate data:

Differences between qualitative and quantitative raw data analysis

Qualitative data analysis involves cutting out unnecessary information or generalizing basic observations unrecognized by the original researcher.

Quantitative data analysis, on the other hand, relies more heavily on precise measurements. The difference is mostly in how each type of data is analyzed and what conclusions can be drawn from it.

To put it simply, qualitative data provides a quick take of what word-of-mouth buzz says about a product or service. Quantitative data provides specifics about what features are appealing to consumers and why they are appealing (along with other information). Qualitative research requires less time and money than quantitative research though both types give valuable insight into consumer opinions and behaviors.

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Descriptive statistics:

Measures of central tendency (e.g. mean, median)

Descriptive statistics can be generally defined as statistics that summarize sample data.

Mean: The mean (also called the arithmetic average or simply the average) is calculated by adding together all of the values in a dataset and then dividing by how many are there, which comes to 51.5 on our dataset. This means what we have here are medians with non-integer values, so it’s not possible to interpret this as an “average.” I’ll tell you more about this in a minute, but don’t worry for now if my mention of the mean seemed confusing.

Median: The median is different from other descriptive values because rather than being determined statistically, one must specify some method beforehand just like was done above with the mean. This is because it cannot be calculated with a formula.

In our case, we can choose to use either of the medians 52 or 53 (or any other value) as our median since they both have an equal number of values above and below them compared to the other two (50 and 54).

To understand why should don’t want to take a mean if we do not know what sort of data we have, think about how averages are used. One way that people use estimates such as average or mean often is for compensation purposes in job interviews by employers. The reason this is important is that if you were unknowingly given non-continuous data, then your “average” might be extremely high or low.

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Measures of variability (e.g. range, standard deviation)

The range is computed by finding the difference between the highest and lowest value.

The standard deviation is based on how many values you want to be included, with a smaller number meaningless confidence in that measurement.

Stats for both are typically published with graphs so you can put them in context visually.

Application to business data (e.g. finding average earnings, measuring variability in business processes such as queuing times and customer arrival rates).

Application to business data is any piece of software that allows companies to work on their data in an effective manner.

One powerful example is a news application, which aggregates various feeds from authoritative sources and displays them on a user’s screen using an easy-to-read format. Specifically, this type of software can sort articles by keywords or interest category as specified by the user. Other applications allow you to conduct research on public databases without having to sit through hours and hours of videos and lectures in order to get information.

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For those who are total news junkies like me, I have an amazing app that lets me watch all the latest stories from my favorite journalists around the world. Businesses utilize these tools not only because they save time but also because they allow the user to stay abreast of current events in addition to providing analysis and commentary.

Inferential statistics:

The difference between sample and population.

In statistics, the sample is a subset of the population. It has to be representative in terms of all the attributes (both visible and hidden) of that population—that’s what “sample” means.

If you know nothing about your subject matter and just picked things at random from around you, then this is a “random sample”.

Ideally, though, you’ll want to deliberately choose subjects who are representative – for this, we need a way to identify which members are most typical (and can therefore be chosen more often).

Now it becomes art because there are no rules: number uneducated guesses until something sticks or use some kind of process that will produce results that more accurately represent the whole population.

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Different sampling techniques and methods

The following are different sampling techniques:

– Convenience sampling, where only the population members closest to the researcher are selected.

– Probability sampling, which is a non-self-oriented process that ensures members of every group have an equal chance for selection.

 – Non-probability sampling, which is a self-oriented process that doesn’t attempt to include each individual with equal probability.

Measuring association:

Use of scatter plots, correlation and regression analysis, simple forecasting

Scatter plots are often used by analysts to visually identify patterns in data. It’s probably their most simple and arguably most useful application. Whenever there is a linear trend between two variables, it can be graphically illustrated by plotting one against the other on a scatter plot; when the data points lie along a line (in any general direction), this means that there’s an approximate one-to-one relationship between these two bits of data. X and Y axes are perpendicular to each other with zero as the origin.

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Correlation and regression analysis help forecast the values of future observations in response to current or past observations on both numerical scales, such as measurements from thermometers or stock trading prices ̶or qualitative scales, such as a customer’s response to an e-mail survey. In this way, they help people view and predict the correlations between variables in large populations of data.

Simple Forecasting is an easy and straightforward way to make projections in the short term. It can be used for a variety of project timelines, such as the next 6 months, 12 months, or 1 year.

There are many quick-to-use templates available on the internet that generalize how to match demand with supply for a given period of time in order to keep up with workloads at an organization.

They calculate total work hours based on historical data and planned vacations and holidays in order so that there is enough staffing allocated for all projects without overstaffing periods where there’s not much going on or understaffing during peak periods.

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Business applications such as the association between output and cost, advertising and sales

A firm’s output is a function of its inputs, as well as the prices for those inputs.

For example, in constructing a bridge it will take at least four years to finish construction given $10 million in funds and two worker-hours per day each year.

Advertising sales by myself have to lead to revenues that are more than three times what they were two years ago from marketing new line merchandise…though total advertising spending has been 20% higher during this time period (a ratio of 2 dollars spent on advertising for every 10 dollars generated).

Business applications such as the association between output and cost, advertising and sales can be traced back to general principles found in microeconomic theory such as supply and demand.

Supply refers to the number of goods or services that a business wants to sell at various possible prices.

Demand refers to the number of goods and services customers want to buy at various possible prices. The intersection between supply and demand gives rise to market price, leaving equilibrium in place.

In microeconomics, supply is generally depicted as an upward-sloping curve and demand as a downward sloping curve, intersecting each other at their respective maxima. These two curves combine into one single curve which demonstrates the relationship between price and quantity sold for all possible levels of output.

This information can be used by executives in strategic decision-making and allows them to calculate how changing variables will affect profit levels; sales are also greatly affected by changes in advertising.

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Evaluating the use of software such as Excel and SPSS to perform raw data analysis

It’s not uncommon to be able to use Excel and SPSS on the same data set, in tandem. If you’re analyzing data from an experiment that was done on a computer-based platform like LabView, you might need to use SPSS.

Excel is also useful for extracting certain results of pre-existing statistical calculations by someone else.

However, if your research project required considerable computation and various analyses with different conclusions as a result (as well as repeated changes or corrections), then using platforms such as R may do the job better than either Excel or SPSS.

Applying the appropriate methods and tools for the evaluation of raw data

The two primary categories of methods for evaluating raw data are qualitative and quantitative.

Qualitative data relies on the researcher to interpret the meaning of observations by collecting information about subjective emotional attitudes and impressions, perceptions, and worldviews about a particular topic.

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Quantitative data relies on mathematical analysis of physical quantities that rely on calculations to produce numeric values. Quantitative methods are frequently used when studying phenomena such as climate change, solar energy output, economic growth rates, or mortality rates which can be effectively represented with numbers.

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