Learn statistics
Dave Tang
2024-03-18
Last updated: 2024-03-18
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Knit directory: bioinformatics_tips/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5ed2a80 | Dave Tang | 2024-03-18 | Include ToC |
| html | 02c4da1 | Dave Tang | 2024-03-18 | Build site. |
| Rmd | 6a40a22 | Dave Tang | 2024-03-18 | The normal distribution |
| html | ad4c626 | Dave Tang | 2024-03-18 | Build site. |
| Rmd | 9fc987b | Dave Tang | 2024-03-18 | Standard deviation |
| html | 52ab7ed | Dave Tang | 2024-03-18 | Build site. |
| Rmd | 2c2f5ea | Dave Tang | 2024-03-18 | Why n-1 and why squared deviations? |
| html | e8368ef | Dave Tang | 2024-03-18 | Build site. |
| Rmd | cfd0f35 | Dave Tang | 2024-03-18 | Statistics page |
Basics
Statistics are summaries or collections of numbers. The mean, median, and mode are all summary statistics. When we can’t take every single possible measurement (the population), which is often the case, we take an estimation using a sample.
Statistics uses a lot of notation (usually algebraic), which is simply a shorthand way of expressing statistical computations. Most statistical calculations simply involve addition, subtraction, multiplication, division, and exponentiation. Common calculations include the “sum of cross products” and “sum of squares”:
\[ \frac{(\sum xy)^2}{\sum x^2 + \sum y^2} = \frac{(sum\ of\ cross\ products)^2}{sum\ of\ squares\ for\ x + sum\ of\ squares\ for\ y} \]
Calculating the mean is simply:
\[ \bar{x} = \frac{\sum x}{n} \]
Variance
The mean as a summary statistic is useful but it does not provide any information on the variance. For example, the two datasets below have the same mean but as you can see have different variances.
x1 <- c(48, 49, 49, 47, 48, 47)
x2 <- c(12, 62, 3, 50, 93, 68)
mean(x1) == mean(x2)[1] TRUEThe easiest (but least useful) way to summarise variation is by using the range; it’s not that useful because it uses just two values (the lowest and highest values) from the total dataset.
range(x1)[1] 47 49range(x2)[1] 3 93To make the best use of the datasets, we need a statistic that uses all the numbers. One key piece of intuition is to realise that if all numbers are identical, there would be no variation and the numbers would all equal the mean.
x3 <- rep(48, 6)
mean(x3)[1] 48Therefore, if the numbers are not the same, each number’s contribution to the total variation is its deviation (difference) from the mean. We could add all these differences (ignoring whether the difference is + or -) and come up with a “total deviation”: \(\sum(x - \bar{x})\).
sum(abs(x1 - mean(x1)))[1] 4sum(abs(x2 - mean(x2)))[1] 162However the main problem with this method is that we can only compare total deviations between datasets with the same number. We need a deviation statistic that is independent of sample size, in the same way the mean is.
We can divide the total deviation by the total number of numbers to get the mean (average) deviation:
\[ \frac{\sum(x - \bar{x})}{n} = \frac{The\ sum\ of\ all\ the\ deviations\ from\ the\ mean}{The\ number\ of\ numbers} \]
sum(abs(x1 - mean(x1))) / length(x1)[1] 0.6666667sum(abs(x2 - mean(x2))) / length(x2)[1] 27The mean average deviation is very close to the standard measure of variation used in statistics, which is the variance.
\[ variance = \frac{\sum(x - \bar{x})^2}{n - 1} = \frac{The\ sum\ of\ all\ the\ squared\ deviations\ from\ the\ mean}{One\ less\ than\ the\ number\ of\ numbers} \]
var(x1)[1] 0.8var(x2)[1] 1189.2The bottom part of the variance equation is known as the degrees of freedom (d.f.) and the top part, \(\sum(x - \bar{x})^2\) is known as the sum of squares but should really be called the sum of squares of deviations since that is what we are calculating.
It is essential to remember that:
- Sum of squares in statistics is the technical term for summed squared deviations from the mean.
Why n - 1?
The two important basics of degrees of freedom are:
- We are calculating statistics from just a small sample rather than the entire population of numbers and
- The mean that is used as the basis for the deviations that contribute to the sum of squares is based on the total of just that small sample.
In ignorance of the true mean of the population, we are forced into using the total of our sample to calculate a mean from which the deviations are then calculated. It is that use of the total which restricts our freedom from \(n\) to \(n - 1\). If we used the true mean of the population and not the mean of the sample in calculating the sum of squares, we would divide by \(n\) instead.
As an analogy imagine a bookshelf and that we are trying to work out the mean thickness of all the books. We take a small sample of six books. and their combined thickness is 158 mm giving a mean of 26.3 mm. We need to know the individual deviation from the mean for each of the six books to calculate the variance.
We measure the thickness of one book (1 d.f.) and it is 22mm. The remaining five books must total \(158 - 22 = 136mm\) in thickness. We measure one more book (2 d.f.) and it is 24 mm thick. By the time, we measured the fifth book (fifth degree of freedom), the combined thickness is 129 mm. There are no more degrees of freedom for the six books! We do not need to pick up and measure the last book; it must be \(158 - 129 = 29mm\). Therefore, given the mean of the sample, we know the total thickness of the six books and from this the individual thicknesses of all six books after measuring only five (hence 5 d.f. for a sample of six!).
By using the sample mean as the base from which to calculate the deviations for the sum of squares, we have lost 1 d.f. Thus variance is not the simple mean of the squared deviations, it is the squared deviation per opportunity for variation once we have been given the sample mean.
Why are the deviations squared?
You might look at your data as measured in a multidimensional space, where each subject is a dimension and each item is a vector in that space from the origin towards the items’ measurement over the full subject’s space.
Additional remark: this view of things has an additional nice flavour because it uncovers the condition, that the subjects are assumed independent of each other. This is to have the data-space Euclidean; changes in that independence-condition require then changes in the mathematics of the space: it has correlated (or “oblique”) axes.
Now the distance of one vector-arrowhead to another is just the formula for distances in the Euclidean space, the square root of squares of distances-of-coordinates (from the Pythagorean theorem):
\[ d = \sqrt{(x_1 - y_1)^2 + (x_2 - y_2)^2 + \cdots + (x_n - y_n)^2} \]
And the standard deviation is that value, normalised by the number of subjects, if the mean-vector is taken as the y-vector.
\[ s = \sqrt{\frac{(x_1 - \bar{x})^2 + (x_2 - \bar{x})^2 + \cdots + (x_n - \bar{x})^2}{n}} \]
The standard deviation
Standard deviation is the square root of variance.
sd(x1) == sqrt(var(x1))[1] TRUEsd(x2)[1] 34.48478In the case of x2 the mean and standard deviation is
\(48.0 \pm 34.5\), which suggests
variation between \(48.0-34.5=13.5\)
and \(48.0+34.5=82.5\).
The standard deviation was once called root mean square deviation. This was a helpful name as it reminds us of the calculation in reverse (deviation, square, mean, root), where mean is \(n -1\).
\[ s = \sqrt{\frac{\sum (x - \bar{x})^2}{n - 1}} \]
The normal distribution
The word distribution is short for frequency distribution, i.e., the frequency with which different numbers are found in a population. The normal distribution is a particular pattern of variation of numbers around the mean. It is symmetrical with the frequency of individual numbers falling off equally away from the mean in both directions.
One argument (and demonstration) for why normal distributions are so common:
because many variables in the real world are the sum of other independent variables. And, when independent variables are added together, their sum converges to a normal distribution.
Read in example dataset containing the height (inches) and weights (pounds) of 25,000 different humans of 18 years of age (that are converted back to cms and kgs).
readr::read_csv(
file = "data/SOCR-HeightWeight.csv.gz",
show_col_types = FALSE,
col_names = c('index', 'height', 'weight'),
skip = 1
) |>
dplyr::mutate(height = height * 2.54) |>
dplyr::mutate(weight = weight / 2.205) -> socr
tail(socr)# A tibble: 6 × 3
index height weight
<dbl> <dbl> <dbl>
1 24995 171. 57.9
2 24996 177. 53.5
3 24997 164. 54.5
4 24998 164. 53.6
5 24999 172. 60.0
6 25000 175. 56.6Plot distribution of heights.
library(ggplot2)
ggplot(socr, aes(height)) +
geom_histogram(
aes(y = ..density..),
colour = "#000000",
fill = "#FFFFFF"
) +
geom_density() +
geom_vline(
xintercept = mean(socr$height),
colour = "#990000",
lty = 2
) +
theme_minimal() +
ggtitle("Distribution of heights in 25,000 18 year olds") +
NULLWarning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
| Version | Author | Date |
|---|---|---|
| 02c4da1 | Dave Tang | 2024-03-18 |
We can see that the mean height coincides with the most frequent
height and that the heights are symmetrical about the mean. The
qqnorm function produces a normal QQ plot and
qqline adds a line to a “theoretical” QQ plot.
qqnorm(socr$height, pch = 16)
qqline(socr$height, col = 2, lty = 2)
| Version | Author | Date |
|---|---|---|
| 02c4da1 | Dave Tang | 2024-03-18 |
The mean and standard deviation is \(172.70 \pm 4.83\).
mean(socr$height)[1] 172.7025sd(socr$height)[1] 4.830264In a normal distribution, 68% of the data lie between the mean \(\pm\) 1 \(s\), which is what we observe in this dataset.
x <- mean(socr$height)
s <- sd(socr$height)
prop.table(table(socr$height > x - s & socr$height < x + s))
FALSE TRUE
0.31644 0.68356 95% of the data lie between the mean \(\pm\) 2 \(s\), which is true again in this dataset.
x <- mean(socr$height)
s <- sd(socr$height)
prop.table(table(socr$height > x - 2 * s & socr$height < x + 2 * s))
FALSE TRUE
0.0454 0.9546 Visualise
eg1 <- datasets::anscombe
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.4.4 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.7 utf8_1.2.4 generics_0.1.3 stringi_1.7.12
[5] hms_1.1.3 digest_0.6.33 magrittr_2.0.3 evaluate_0.22
[9] grid_4.3.2 fastmap_1.1.1 rprojroot_2.0.3 jsonlite_1.8.7
[13] processx_3.8.2 whisker_0.4.1 ps_1.7.5 promises_1.2.1
[17] httr_1.4.7 fansi_1.0.5 scales_1.2.1 jquerylib_0.1.4
[21] cli_3.6.1 rlang_1.1.1 crayon_1.5.2 munsell_0.5.0
[25] bit64_4.0.5 withr_2.5.1 cachem_1.0.8 yaml_2.3.7
[29] tools_4.3.2 parallel_4.3.2 tzdb_0.4.0 dplyr_1.1.3
[33] colorspace_2.1-0 httpuv_1.6.12 vctrs_0.6.4 R6_2.5.1
[37] lifecycle_1.0.3 git2r_0.32.0 stringr_1.5.0 fs_1.6.3
[41] bit_4.0.5 vroom_1.6.4 pkgconfig_2.0.3 callr_3.7.3
[45] pillar_1.9.0 bslib_0.5.1 later_1.3.1 gtable_0.3.4
[49] glue_1.6.2 Rcpp_1.0.11 xfun_0.40 tibble_3.2.1
[53] tidyselect_1.2.0 rstudioapi_0.15.0 knitr_1.44 farver_2.1.1
[57] htmltools_0.5.6.1 labeling_0.4.3 rmarkdown_2.25 readr_2.1.4
[61] compiler_4.3.2 getPass_0.2-2