For example one variable that you want to describe statistically is the Mathematics Grade Score of 14 students in 4th grade.
Here your one variable = Mathematics Grade Score of 4th graders.
Because the procedure and calculation are the same for any one variable that you want to describe statistically, mathematician often use to generalize the equation or formula for computing the mean, median, mode, and standard deviation, so they often use the one variable, x = to represent the variable that you want to describe using statistical analysis of minimum, maximum, mean, median, deviation, and quartile.
Boxplot is also called Box and Whisker plot. Why do you need to learn this? Because if you are using enterprise corporation software like Tableau software for data visualization, you will encounter Box and Whisker plot as one of the options. So as a student to become college and career ready, you need to acquire this knowledge. Important to remember
1. Always arrange your data point from lowest to highest, hence called ordered statistical analysis
2. Count the number of your data points or records or observation. The number of count is represented by letter variable "n". In Excel, using Rank and Percentile data analysis, you see the default column name "Point" = 7 ; Value = 2589 ; Rank = 1 ; Percent = 100% . It means Record number 7 from your original data list . If you are using SAS statistical analysis, this will be called "Observation" = 7. It means the same thing record number 7 when you count your original data list from top to bottom.
Now you can answer the question what is the value of the lower whisker? = 70 is the answer
3. minimum value is also called the lower whisker
Can you answer the question what is the value of maximum whisker? = 78 is the answer
4. maximum value is also called the maximum whisker
Now if you are ask,"Tell me what is the median or middle value of your ordered data point ?" You see the middle is between 73 and 75. So you are confused, which one? To solve that confusion, statistician created the rule to get a uniform answer. The rule is you add the two middle number and divide it by 2. Now everybody agreed the median value is 74.
Median is also called the second quartile meaning 2/4 simplifying the fraction becomes 1/2 which correspond to the middle or median of your ordered list.
5. First quartile (1/4) , represented by variable name, Q1 means from overall median (1/2) to the minimum , you find the middle (1/2) value. In fraction 1/2 * 1/2 = 1/4. The word quarter means 1/4. From your ordered list of student's grade, the first quartile (Q1) student has a grade of 71.
6. Third quartile (3/4), represented by variable name, Q3 means from overall median (1/2) to the maximum, you find the middle (1/2) value. Why it is called third quartile? because you are counting the equal sharing from the minimum up to the third quartile line. Mathematically you added the first quartile (1/4) + second quartile (1/4) + third quartile (1/4) = 3/4 . So what is the grade of the third quartile (Q3) student? = 76 answer
7. the rectangular box is also called the inter-quartile, the range between Q3-Q1. Mathematically 3/4 - 1/ 4 = 2/4 = 1/2 = 0.50 in decimal = 50% in percentage
Why is the box or inter-quartile important? Because some decision makers want to know the range of the 50% of the population of data being analyze, " Tell me what is the range of grade of 50% of the student population ? ". Just by looking at the box, decision maker can answer that question quickly, the range of student's grade representing 50% of the total population is from 71 to 76. The short cut is just read the first quartile (Q1) value and the third quartile (Q3) value from the box and whisker plot. You should know by now where is the Q1 and Q3 location from the graph.
1. To open probability distribution calculator click the ABC icon. Then select probability calculator. Statistics calculator comes with probability calculator.
2. To view the probability calculator only. Select the three horizontal line icon, select view. Then uncheck algebra view, uncheck graphics view, and check the probability calculator.
INTERACTIVE PROBABILITY AND STATISTICS FROM GEOGEBRA
Desmos Statistical Calculator
Please wait while GeoGebra is downloading the interactive program. Follow the instructions below to view the statistics function from Geogebra.
Step 1. Click the icon three horizontal line
then select view tab
Step 2. From view tab click algebra, to turn off the algebra function
Step 3. From view tab click spreadsheet, to turn on the spreadsheet
Step 4. From view tab click graphics, to turn off the graphics
Step 5. From view tab click input bar, to turn off the input bar
Step 6. In the spreadsheet view enter all the grades
Step 7. Follow the four steps shown above
By Apolinario "Sam" Ortega, 14 January 2013, Created with GeoGebra



| Feature | Mahalanobis Distance | Signal-to-Noise Ratio (SNR) |
|---|---|---|
| Definition | Measures how far a point is from the mean, accounting for correlations between variables | Ratio of signal strength to noise level |
| Mathematical Basis | Uses covariance matrix and multivariate statistics | Based on variance or standard deviation |
| Formula | 𝐷 2 = ( 𝑥 − 𝜇 ) 𝑇 ⋅ Σ − 1 ⋅ ( 𝑥 − 𝜇 ) | SNR = 𝜇 𝜎 or Power signal Power noise |
| Aspect | Mahalanobis | SNR |
|---|---|---|
| Strengths | Accounts for feature correlation; effective in multivariate settings | Simple, fast, intuitive; good for real-time systems |
| Limitations | Sensitive to covariance estimation; assumes normality | Ignores feature relationships; limited to univariate or low-dimensional data |
| Algorithm | Core Idea | Best For | Limitations |
|---|---|---|---|
| Z-Score | Flags points with standardized values beyond a threshold (e.g., |Z| > 3) | Univariate, normally distributed data | Fails on skewed or non-normal data |
| Interquartile Range (IQR) | Uses Q1 and Q3 to define outliers beyond 1.5×IQR | Simple, robust for small datasets | Limited to univariate data |
| Mahalanobis Distance | Measures distance from mean accounting for covariance | Multivariate, correlated features | Assumes normality; sensitive to covariance estimation |
| Local Outlier Factor (LOF) | Compares local density of a point to its neighbors | Non-linear, high-dimensional data | Requires tuning of neighborhood size |
| Isolation Forest | Randomly partitions data; outliers isolate faster | Large, high-dimensional datasets | Less interpretable; random behavior |
Sources: Spot Intelligence