Descriptive Statistics Excel, Bar Chart, Pie Chart and More
Statistics give research work credibility and authority. If there are two research articles - one without statistics and the other that backs each claim with statistical analysis, people would give importance to the latter. Furthermore, Descriptive Statistics can tell you a lot of information without using too many words. Oftentimes, researchers cannot see a simple truth from a given data. It is only after statistical analysis, they can conclude the given data. Creating a statistical analysis, however, is quite hard. This is where the usage of statistical tools comes in. Statistical tools used in research can help the researchers back their claim, make sense of a large set of data, graphically visualise the complex data or explain a lot of things within a short amount of time.
Important Statistical Tools Used in Research
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As said, mere text-based analysis of a certain economic aspect is quite limiting. A reader has to read the entire analysis to understand what a researcher wants to say. With the help of statistical tools, you can represent data in a concise yet detailed manner. Statistical can also help in revealing more information from the data. Furthermore, you can use statistical tools to make your data collection work easier.
There are broadly two categories of statistical tools:
Traditional tools
Software based tools
Traditional tools are those statistical tools used in research that are not any computer program. Usually, these tools use Arithmetic, logic, permutation and combination etc to present and organise data. There are many such statistical tools. Some of the important ones are:
Central Tendency: Mean, Median, Mode
Mean is the summation of all the numbers in a dataset divided by the total number of values. We use this to find a middle point. This is useful when the data-set has numbers that are not too far from each other.
When the data-set has numbers that are too far away from each other, we use the median to find a middle point. To find a median we arrange the numbers in a data-set in ascending order. And then, we just pick the exact middle number as the Median.
Finally, Mode is the most frequently occurring value in a set of observations.
Standard Deviation
Standard Deviation is, as the name suggests, used to find what numbers in the data-set deviate from the ‘standard.’ Suppose you want to find which pupils in your class have weights that are greater and lower than the ‘standard’ weight. You can find that using the Standard Deviation.
Method - First, you find the average weight of the students in your class. Then you subtract the mean from each of the students’ weights separately. Now, square the numbers that you get after subtraction. Then find the average of these squared numbers. What you get is Variance. Now, if you find the square root of the variance you will get the Standard Deviation. So now, you have a standard against which you can measure which students are undernourished and which students are overweight.
Statistical Control Charts
Process Control Charts
Suppose it takes 10 minutes for the morning assembly to complete in your school. Some day it takes 12 minutes, someday it takes 7 minutes. But over time if you collect the data and average it, you get a 10 minute average assembly time.
Now, to make a Statistical Process Control Chart, you first mark the average time (i.e 10 mins) as the middle point and draw a line. Now you set three-sigma limits based on the variation of the time it takes to complete the assembly. Thus you get an upper threshold and a lower threshold of the time limit. If someday one of your classmates feels sick in between the morning assembly prayer, your teachers would come and help him. So that the day the variation in time to complete the assembly will not be within the threshold. And in the process control chart, this will be depicted as a spike.
Process Control Chart is a nice way to identify normal variation and abnormal variation. This will help us identify the abnormal variation so that we make sure that the abnormal variation never happens again and the process remains within control.
Statistical Quality Control Charts
The Statistical Quality Control Chart is by and large similar to the Process ControlChart. The only difference is that it is used by the QC personnel. For example, a battery manufacturer can see if the quantity of nickel-cadmium is more or less the same in each of the units. Statistical Quality control methods are used to keep the quality of products within the accepted range.
Histogram and Bar Chart
Suppose you want to know how many of your classmates ( not who) have a height between 4ft and 5ft and how many classmates have a height of more than 5ft. What you would use to statistically present the data is a Histogram.
When you want to know exactly which friends have how much heights, you use a bar chart - showing their names and height.
(Note: Both are represented with the help of bars)
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There are countless other traditional statistical tools that you will come across if you keep on learning Statistics.
Software Based Tools
Today, we have much software that helps visualise and analyse large data-set within a short period of time. You can use these tools to analyze descriptive statistics.
SPSS
IBM’s SPSS is an easy to use Statistical tool that you can use to analyse data easily. The software is quite intuitive and the learning curve is not as steep as that of MS Excel. Broadly speaking, there are two basic categories - Variable View and Data View. In the variable view, you type in the variables (serial number, gender, the question asked, age and so on). After you finish, you go to the Data View and type in the data against the variable (serial number = 1, age = 5 and so on). Now based on the data SPSS will show you various statistical figures and charts like histogram, bar chart, pie chart, median, mean etc. SPSS analyze the given data and spit out statistical graphics and discoveries quite easily. Analyzing data in SPSS is quite straightforward - much of the heavy lifting is done by the software itself.
Excel Statistics
Microsoft’s Excel is another excellent tool for statisticians. However, SPSS is more useful to statisticians. Excel is used more as a data storage software. However, the Excel formulas can be of great value to the researchers. Using the Excel formula, the statisticians can predict the future trend of an event or process. You can also use Excel to create various charts. However, for descriptive statistics, Excel is not as good as SPSS. Sometimes Excel can give inaccurate results when it comes to Statistical analysis.
Did You Know?
IBM paid US\[$\] 1.2 billion to buy SPSS!
FAQs on Statistical Tools in Business: Applications and Examples
1. What are statistical tools in the context of business?
In business, statistical tools are the methods and techniques used to collect, organise, analyse, interpret, and present data. Their primary purpose is to transform raw business data, such as sales figures or customer feedback, into meaningful insights that help managers make informed, data-driven decisions rather than relying on intuition alone.
2. What are some key applications of statistical tools in business decision-making?
Statistical tools are applied across various business functions. Key applications include:
- Market Research: Analysing survey data to understand customer preferences and market trends.
- Financial Analysis: Using tools like regression to predict stock prices or assess investment risks.
- Quality Control: Employing control charts to monitor and maintain product quality within acceptable standards.
- Demand Forecasting: Predicting future sales of a product based on historical data.
- Performance Management: Evaluating employee or store performance using measures of central tendency and dispersion.
3. What are some common examples of statistical tools used in business?
Common statistical tools can be categorised into two main types. Descriptive tools summarise data, including Mean (average), Median (middle value), Mode (most frequent value), and Standard Deviation (measure of data spread). Inferential tools make predictions, such as Regression Analysis (to model relationships between variables) and Hypothesis Testing (to validate assumptions).
4. How do descriptive statistics differ from inferential statistics in business analysis?
The core difference lies in their purpose. Descriptive statistics are used to summarise and describe the main features of a dataset. For example, calculating the average monthly sales for the last year. In contrast, inferential statistics use a sample of data to make predictions or draw conclusions about a larger population. For instance, using the last year's sales data to forecast sales for the upcoming quarter.
5. Why is 'Standard Deviation' an important tool for a business manager?
Standard Deviation is crucial because it measures the volatility or risk associated with a business process. A low standard deviation in production output indicates a stable and predictable process. Conversely, a high standard deviation in a stock's return signals high risk and uncertainty. It helps a manager understand the consistency of operations and make decisions to manage variability.
6. How does a business use a tool like a Histogram versus a Bar Chart for analysis?
While both are visual tools, they are used for different types of data. A Histogram is used to show the frequency distribution of continuous data, like grouping customers by age ranges (e.g., 20-30, 31-40). A Bar Chart is used to compare discrete categories, such as the total sales figures for different products (e.g., Product A vs. Product B vs. Product C).
7. Why is proper data collection essential before applying any statistical tool?
Proper data collection is fundamental because of the principle of 'garbage in, garbage out'. Even the most advanced statistical tool will produce misleading or incorrect results if the initial data is biased, inaccurate, or incomplete. The reliability of any business decision, forecast, or conclusion drawn from statistical analysis directly depends on the quality and integrity of the data collected.
