# Fabulous Tips About How To Build A Proportionate Stratified Sample

**This video shows how to allocate proportionally for stratified random sampling.**

**How to build a proportionate stratified sample**.
The stratified random sampling is a way of creating the sample based on the groups share in the entire population.
The sample proportion is estimated as follows.
Proportionate stratified sampling = (population size of stratum*number of elements in population)/sample size 1 sampleproportionate stratified = (nh*n)/n1 this formula uses 4.

Sample proportions let the size of the sample from stratum hbe 𝑛𝑛ℎ. Τ ^ h = n h y ¯ h v ^ a r ( τ ^ s t) = ∑ h = 1 l n h ⋅ ( n h − n h) ⋅ s h 2 n h s h 2 = 1 n h − 1 ∑ i = 1 n h ( y h i − y ¯ h) 2. The population is divided into groups and the number of samples from each.

For stratified random sampling, i.e., take a random sample within each stratum: 𝑝𝑝= 𝑁𝑁ℎ 𝑁𝑁 𝑌𝑌ℎ𝑖𝑖 𝑛𝑛ℎ = 𝑁𝑁ℎ 𝑁𝑁 𝑝𝑝ℎ 𝐿𝐿 ℎ=1 𝑛𝑛ℎ 𝑖𝑖=1 𝐿𝐿 ℎ=1 where 𝑝𝑝ℎ= 𝑌𝑌ℎ𝑖𝑖 𝑛𝑛ℎ 𝑛𝑛ℎ 𝑖𝑖=1 is the sample response. From 2:1 (6 men to 3 women) to 3:1 (8 men to 2 women).

Proportionate stratified sample is a version of a sampling method call stratified sample. It would be natural to implement proportionate stratified sampling in pyspark via the sampleby method with fractions. Estimating the population proportion similarly, estimating the proportion of the population with a particular trait (p) using stratified random sampling involves combining estimates from.

Refer to the example we have presented in class.note: