To graph numerical data, one uses dot plots, stem and leaf graphs. For example, one study revealed a mean decrease of 1.54 centimetres (0.6 in) in the heights of 100 children from getting out of bed in the morning to between 4 and 5 p.m. Examples of numerical data are height, weight, age, number of movies watched, IQ, etc. inversely correlated), or an increase since lying down for a significant period of time (i.e.
Height can vary over the course of a day, due to factors such as a decrease from exercise done directly before measurement (i.e. As this case shows, data taken from a particular social group may not represent a total population in some countries. According to a study in France, executives and professionals are 2.6 centimetres (1.0 in) taller, and university students are 2.55 centimetres (1.0 in) taller than the national average. Different social groups can show different mean height. Under such circumstances, the mean height may not represent the total population unless sample subjects are appropriately taken from all regions with using weighted average of the different regional groups. For instance, one survey shows there is 10.8 centimetres (4.3 in) gap between the tallest state and the shortest state in Germany. Some countries may have significant height gaps between different regions. Test subjects may have been invited instead of chosen at random, resulting in sampling bias. I want to compare them at the same age ie when they were all five and on a separate chart to show their height relative to each other, that is their height on the same date. Generally speaking, self-reported height tends to be taller than measured height, although the overestimation of height depends on the reporting subject's height, age, gender and region. How do I plot a chart (graph) of height (cm) vs age (date value). Some studies may allow subjects to self-report values. Children under age 15, by world region 1950 to 2100, with UN projections. With regard to the second table, these estimated figures for said countries and territories in 2019 and the declared sources may conflict with the findings of the first table.įirst table: individual surveys and studies AccuracyĪs with any statistical data, the accuracy of the findings may be challenged. Child dependency ratio the ratio between under-19-year-olds and 20-to-69-year-olds. With regard to the first table, original studies and sources should be consulted for details on methodology and the exact populations measured, surveyed, or considered. It shows the female higher than the male and I'm not sure why the legend (or the histograms) aren't solid.Two tables which report the average adult human height by country or geographical regionīelow are two tables which report the average adult human height by country or geographical region. So what I've plotted in the chart doesn't make sense to me. Now if I just plotted the 2013 histograms in Excel with a binwidth of 20, the female plot would peak at 300 counts and the male would peak at 1800 counts. Ggplot(df, aes(df$cost,color=df$gender)) + #GRAPH TO COMPARE HEIGHTS AND GENDER CODE#
The code I put together is: library(ggplot2)Ĭosts<-read.table("cost_data.txt",header=TRUE) Here's a sample of the data: cost gender year As an example, for the year 2013 there are 10,949 data points for female and 53,351 data points for male. The one and only thing I’d compare your current strength levels to are your previous strength levels.
I wouldn’t compare your current strength levels to what someone claims they should be for you at this point. I'm trying to plot Female and Male data for each year in a facet wrap plot. I wouldn’t compare your current strength levels to a percentage of your body weight (e.g.
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