Plot Column
Plot Column - Related Videos
02:34
Arrhenius Plots
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2020
The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules. The Arrhenius equation can be used to...
09:15
Column Chromatography - Student Protocol
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2020
Source: Lara Al Hariri and Ahmed Basabrain at the University of Massachusetts Amherst, MA, USA Column Chromatography of Food DyeExpand In this experiment, you'll use column chromatography to separate green food coloring into its component blue and yellow dyes, called erioglaucine and tartrazine. First, you'll use 95% ethanol to elute the dye with a weaker affinity for silica gel. Then, you'll flush the second dye from the column with water. To recover the purified dyes, you will collect each...
11:14
Buckling of Steel Columns
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2023
Source: Roberto Leon, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA In the design of civil works, it is important to deliver structures that are not only safe under unexpected loads, but also provide excellent performance under everyday loads at a reasonable economic cost. The latter is often tied to minimum use of materials, ease of fabrication, and rapid construction in the field. Structures made of steel members can be very economical because of the great...
01:07
Residual Plots
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2023
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation. When the residual values are plotted against the variable x, it is called a residual...
01:18
Microsoft Excel: Plotting Mean, SD, and SE
0 Views •
2025
In Microsoft Excel, plotting the mean along with standard deviation (SD) and standard error (SE) helps visualize data variability and reliability. To plot these values, follow these steps: First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size. To plot these values, use a bar...
FAQs
Accurate comparison depends on linking bar height to a clearly scaled vertical axis and using consistent measurement units. The horizontal axis should identify the categories being compared, while the vertical scale communicates numerical magnitude. In environmental datasets, this design lets readers distinguish meaningful differences among sites, sampling periods, or measured variables without confusing scale with results.
Category selection determines the comparison being made. Grouping observations by monitoring site emphasizes spatial differences, whereas grouping them by sampling period emphasizes change across time. Categories should remain clearly defined and consistently labeled so that apparent patterns, differences, or outliers reflect the environmental measurements rather than ambiguity about which observation each bar represents.
Labels and units provide the reference needed to interpret bar heights correctly. A pollutant concentration, species count, precipitation value, and temperature are different kinds of measurements, so readers need to know what each axis and category represents. Clear labeling supports accurate comparison and prevents a visually prominent bar from being interpreted without its measurement context.
A sequence of bars can make a rising or falling pattern visible across sampling periods, while an isolated bar may draw attention as a possible outlier among sites or observations. These visual signals help analysts identify where closer examination is warranted, but the chart presents the measured comparison rather than replacing the underlying environmental analysis.
Begin by organizing each environmental measurement with its category, then assign categories to the horizontal axis and values to a consistently scaled vertical axis. Draw one vertical bar for each category, making height proportional to the value. Add descriptive axis labels and units before using the figure in an analysis or report.
Environmental researchers can use Plot Column displays to compare pollutant concentrations, species counts, precipitation, temperature, or other measurements across sites and sampling periods. Selecting categories that match the research question makes the comparison more useful, whether the goal is to summarize monitoring results, identify variation, or communicate observations from multiple locations.
In reports and presentations, the chart condenses numerical results into a form that readers can compare quickly. It can highlight differences among categories, make trends or outliers easier to notice, and provide a clear summary of monitoring data. Because interpretation depends on scale, labels, and units, those elements should accompany the visual whenever results are communicated.