Sampling Techniques

STAT 200 - Chapter 9

Population Concepts

emojis = ["female-nurse1", "female-nurse2", "female-nurse3", "male-nurse1",
  "male-nurse2", "female-doc1","female-doc2", "female-doc3", "male-doc1",
  "male-doc2", "female-staff1", "female-staff2", "female-staff3", "male-staff1",
  "male-staff2", "male-staff3"];

// Loading the data
workers = await d3.json("https://ubc-stat.github.io/stat-200/data/workers_data.json");
  
//calculate the parameters
asc = arr => arr.sort((a, b) => a - b);
sum = arr => arr.reduce((a, b) => a + b, 0);
mean = arr => sum(arr) / arr.length;

/**
 * Computes the sample standard deviation of an array of numbers.
 *
 * @function
 * @param {number[]} arr - An array of numbers for which the sample standard deviation is to be calculated.
 * @returns {number} The sample standard deviation of the input array, rounded to two decimal places.
 *
 * @example
 * std([1, 2, 3, 4, 5]); // Returns 1.58
 * std([10, 20, 30, 40, 50]); // Returns 15.81
 */
std = (arr) => {
    const mu = mean(arr); 
    const diffArr = arr.map(a => (a - mu) ** 2);
    return Math.sqrt(sum(diffArr) / (arr.length - 1));
};



/**
 * Computes the q-th quantile of a given array of numbers.
 *
 * @function
 * @param {number[]} arr - An array of numbers for which the quantile is to be calculated.
 * @param {number} q - The quantile to compute, where 0 <= q <= 1. For example, 0.25 represents the first quartile (25th percentile).
 * @returns {number} The calculated quantile value, rounded to two decimal places.
 *
 * @example
 * quantile([1, 2, 3, 4, 5], 0.25); // Returns 2
 * quantile([10, 20, 30, 40, 50], 0.5); // Returns 30
 */
quantile = (arr, q) => {
    const sorted = asc(arr); 
    const pos = (sorted.length - 1) * q;
    const base = Math.floor(pos);
    const rest = pos - base;
    if (sorted[base + 1] !== undefined) {
        return sorted[base] + rest * (sorted[base + 1] - sorted[base]);
    } else {
        return sorted[base]
    }
};


pop_mean = mean(workers.map(d => d.income)).toFixed(2); 
pop_sd = std(workers.map(d => d.income)) 
pop_25q = quantile(workers.map(d => d.income), 0.25)
pop_50q = quantile(workers.map(d => d.income), 0.50)
pop_75q = quantile(workers.map(d => d.income), 0.75)
pop_99q = quantile(workers.map(d => d.income), 0.99)


// Filtering data
worker_filtered = {
  const worker_filtered = {
    'female': {
       'nurse': workers.filter(worker => worker.sex == 'female' && worker.job == 'nurse'),
       'staff': workers.filter(worker => worker.sex == 'female' && worker.job == 'staff'),
      'doctor': workers.filter(worker => worker.sex == 'female' && worker.job == 'doctor')
    },
    'male': {
       'nurse': workers.filter(worker => worker.sex == 'male' && worker.job == 'nurse'),
       'staff': workers.filter(worker => worker.sex == 'male' && worker.job == 'staff'),
      'doctor': workers.filter(worker => worker.sex == 'male' && worker.job == 'doctor')
    }
  }
  
  return worker_filtered;
}


/**
 * Generates a random number from a uniform distribution within a specified range [min, max).
 *
 * @function
 * @param {number} min - The lower bound of the range.
 * @param {number} max - The upper bound of the range.
 * @returns {number} A random number from a uniform distribution within the range [min, max).
 *
 * @example
 * getRandom(1, 5); // Returns a random number between 1 (inclusive) and 5 (exclusive)
 * getRandom(10, 20); // Returns a random number between 10 (inclusive) and 20 (exclusive)
 */
function getRandom(min, max) {
    return Math.random() * (max - min) + min;
}


/**
 * Randomly selects an element from a given array.
 *
 * @function
 * @param {Array} elements - An array of elements from which to select.
 * @returns {*} A randomly selected element from the input array.
 *
 * @example
 * getRandomElement([1, 2, 3, 4, 5]); // Returns one of the numbers from the array
 * getRandomElement(['apple', 'banana', 'cherry']); // Returns one of the strings from the array
 */
function getRandomElement(elements) {
    return elements[Math.floor(getRandom(0, elements.length))];
}


/**
 * Extracts the sex and job information from a given emoji name.
 * 
 * @param {string} randomElement - The name of the emoji from which to extract the sex and job information.
 * @returns {string[]} - An array containing the extracted sex ('male' or 'female') and job ('nurse', 'doctor', or 'staff') information.
 *
 * @example
 *
 * extract_sex_job("female_nurse_emoji"); // Outputs: ['female', 'nurse']
 */
function extract_sex_job(randomElement){
  // The ternary operator checks if "female" is included in the name, assigning 'female' to sex if true, and 'male' if false.
  const sex = randomElement.includes("female") ? 'female': 'male';

  let job; 
  if (randomElement.includes("nurse")){
    job = 'nurse';
  } else if (randomElement.includes("doc")){
    job = 'doctor'
  } else if (randomElement.includes("staff")){
    job = 'staff'
  } 
  
  // Return the extracted information as an array with two elements: sex and job.
  return [sex, job];
}

console.log(pop_mean);

Population

• Population: the group containing all elements you want to study.
  • The population is fixed;
  • You don’t have access to all elements of the population;
• Examples:
  • All penguins in the world;
  • All Adelie penguins in the world;
  • All iPhones;
  • All Google’s employees;

Parameters

• Parameters: quantities that summarize the population.
  • Parameters are fixed but unknown;
  • We want to estimate them because they give us useful information about the population;
• Examples:
  • The average body mass of all penguins in the world;
  • The median flipper length of all Adelie penguins in the world;
  • The average lifetime of all iPhones 16 Pro Max;
  • The IQR of all Google employees’ salaries;

Census

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• Retrieving data from the entire population is called census;

• In a census, we have to measure all elements of the population.

  • Unfortunately, this is often impossible, too costly (moneywise or timewise) or unethical.

• Example 1: You want to learn the effectiveness of a new drug for HIV. Can you imagine infecting the entire population with HIV so we can give them an untested drug with unknown side effects?

• Example 2: Ford wants to crash-test its vehicles to measure some safety metrics. Should they test every single car they produce?

Iihs Crash Test GIFfrom Iihs GIFs

• What would they sell?

Parameters vs Variables

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Caution

A common mistake many students make is to mix up a variable of interest with a parameter of interest.

• A variable can be measured for each individual in the population;

• Parameter is a summary of these measurements (e.g., mean, median, etc…)

• For example, one might be interested in the average dolphin weight, which is a parameter;
  • the average weight is a quantity of the population, not of a single dolphin.
• However, the dolphin weight is what is being measured, i.e., the variable of interest.

Population distribution

• The population distribution is obtained by measuring all the elements in the population.

• The population distribution is unknown!

  • remember: we don’t have access to all elements in the population, so we can never get the population distribution.

Sample Concepts

Sample

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• Sample: a subset (part) of the population;

  • You do have access to all elements of the sample;

• We hope that the sample represents well the population, but this is not always the case.

• We use samples to obtain information about the population (i.e., to estimate parameters).

• Example 1: You’re making soup and want to know if it has enough salt. Then, you taste a spoonful (a sample!) of the soup. If that portion lacks salt, you conclude that the whole soup lacks salt.
  • You are extrapolating results from a sample to the entire population.

• Example 2: Imagine you order a basket of french fries. You take one piece to see if you have put enough salt. But, just by chance, you ended with a piece that got too much salt on top of it. You might conclude wrongly that the whole basket of french fries is salty.
  • In this case, you got a sample that doesn’t represent the population well. But it’s still a sample!

Random Samples

• There are many different strategies we can use for sampling! We will cover some of them today.

• But they all have one thing in common: they have a random component!

• Randomness is crucial in sampling and statistical theory.

• Randomization tends to give samples that are fairly representative of the population.

Sample Distribution

• The sample distribution is obtained by measuring all the elements in the sample.

• The sample distribution is known!

• We hope that the sample distribution resembles the population distribution;

  • remember: we don’t know the population distribution, so we will never know.

Statistics

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• Statistics: quantities that summarize the sample.
  • Samples are random, so statistics are also random;
  • Statistics can be calculated because we can measure the entire sample;
  • Statistics give information about the parameters;
• Statistics are the sample counterpart of parameters;

Sampling Techniques

Example: BC’s Health System

• Suppose we want to know the average income of all workers that work in BC’s hospitals.

Example: BC’s Health System

• The first thing is to properly define our population;
  • part time workers?
  • temporary workers?
  • casual workers?

Example: BC’s Health System

• Second, the parameter(s) of interest.

• What population quantities are you interested in?

  • population mean income (\(\mu\))?
  • population median income (\(Q_2\))?
  • population Std. Dev. (\(\sigma\))?

• Finally, how to select our sample?

Simple Random Sampling (SRS)

• In SRS, all individuals have the same chance of being selected;

• The steps are:
  1. obtain the list with the names of all hospital workers (sampling frame);
  2. select a few names from the list at random;
  3. go to the field and collect the data;

Simple Random Sampling (SRS)

Simple Random Sampling (SRS)

You might need to refresh this page to show the plot

Population (\(\mu = ?\))

{
  // This code append the images to the population container.
  const N = 750; // how many images to append
  const div = document.querySelector("#pop-srs1");
  //div.style.height=`${0.10*screen.height}px`;
  
  for (let i=0; i < N; i++){
     let randomElement = getRandomElement(emojis);
     let img = html`<img src="imgs/${randomElement}.svg" height="45px" width=auto style='position: absolute; left: ${getRandom(0, 90)}%; top: ${getRandom(0, 82)}%; padding:0; margin:0;'></img>`;
     div.append(img);
  }
}

{
  const button = document.querySelector("#srs-truth-button");
  const truth_srs = document.querySelector("#truth-container");

  button.onclick = e => {
    
    if (truth_srs.style.visibility == 'visible'){
      truth_srs.style.visibility = 'collapse';
    }
    else {
      truth_srs.style.visibility = 'visible';
    }
  };

}

{
  // Creates the SRS Population Histogram 
  var margin = {top: 10, right: 10, bottom: 30, left: 25},
    width = document.querySelector("#pop-srs1").clientWidth - margin.left - margin.right,
    height = 250 - margin.top - margin.bottom;

  d3.select("#truth-container")
    .append("p")
    .text('Population distribution')
    .style('font-size', '0.7em')
    .style('margin', 0)

  // append the svg object to the body of the page
  var svg = d3.select("#truth-container")
    .append("svg")
      .attr("width", width + margin.left + margin.right)
      .attr("height", height + margin.top + margin.bottom)
    .append("g")
      .attr("transform",
            "translate(" + margin.left + "," + margin.top + ")");
  

  // X axis: scale and draw:
  var x = d3.scaleLinear()
      .domain([d3.min(workers, d => d.income), d3.max(workers, d => d.income)])
      .range([margin.left, width - margin.right]);

  svg.append("g")
      .attr("transform", "translate(0," + `${height - margin.bottom}` + ")")
      .call(d3.axisBottom(x).tickSizeOuter(0))
      .call(g => g.append("text")
        .attr("x", width / 2)
        .attr("fill", "currentColor")
        .attr("font-weight", "bold")
        .attr("text-anchor", "bottom")
        .attr('font-size', '16px')
        .attr("class", "axis")
        .attr("dy", "2.5em")
        .text("Income (in thousands of $)")
        .attr("class","axes-label"));
  
  // set the parameters for the histogram
  var histogram = d3.histogram()
      .value(d => d.income)   // I need to give the vector of value
      .domain(x.domain())  // then the domain of the graphic
      .thresholds(x.ticks(20)); // then the numbers of bins
      

  // And apply this function to data to get the bins
  var bins = histogram(workers);

  // Y axis: scale and draw:
  var y = d3.scaleLinear()
      .range([height - margin.bottom, 0])
      .domain([0, d3.max(bins, d => d.length + 100)]);   // d3.hist has to be called before the Y axis obviously

  svg.append("g")
      .attr("transform", `translate(${margin.left},0)`)
      .call(d3.axisLeft(y))
      .call(g => g.select(".tick:last-of-type text").clone()
        .attr("x", -(height - margin.bottom)/2)
        .attr("y", -40)
        .attr("font-weight", "bold")
        .attr('font-size', '16px')
        .attr('transform', 'rotate(270)')
        .attr("text-anchor", "middle")
        .text("Frequency")
        .attr("class","axes-label"));

  // append the bar rectangles to the svg element
  svg.selectAll("rect")
      .data(bins)
      .enter()
      .append("rect")
        .attr("x", 1)
        .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; })
        .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; })
        .attr("height", function(d) { return height - y(d.length) - margin.bottom; })
        .style("fill", "steelblue")

  d3.select("#truth-container")
    .append("p")
    .text('A few parameters:')
    .style('font-size', '0.7em')
    .style('margin', 0)
  
  let ul = d3.select("#truth-container")
             .append('ul')
             .style('font-size', '0.5em');

  ul.append('li')
    .text(`Mean: ${pop_mean}`)
    .attr("style", 'margin-bottom: 0 !important;');
  ul.append('li')
    .text(`Median: ${pop_50q}`)
    .attr("style", 'margin-bottom: 0 !important;');
  ul.append('li')
    .text(`0.99-quantile: ${pop_99q}`)
    .attr("style", 'margin-bottom: 0 !important;');
  ul.append('li')
    .text(`Std. Dev.: ${pop_sd}`)
    .attr("style", 'margin-bottom: 0 !important;');
  ul.append('li')
    .text(`IQR: ${Math.round(100*(pop_75q-pop_25q))/100}`)
    .attr("style", 'margin-bottom: 0 !important;');
 
}

viewof sample_size_srs1 = {

  let input = Inputs.range([15, 504], 
                           {value: 15,
                            step: 1, 
                            label: "Sample size: "});
  //d3.select(input).select('input[type="number"]').style("display", "none");
  return input;
}

Sample

function append_sample_element(div, element, fontSize){
  let info_element = extract_sex_job(element);
  let worker = getRandomElement(worker_filtered[info_element[0]][info_element[1]]);
  let img = html`<img src="imgs/${element}.svg" height="45px" width="45px" data-income='${worker.income}' style='margin: 0 auto;'></img>`;
  
  const container = document.createElement("div");
  let name = html`<div style='margin-left: auto; margin-right:auto; font-size: ${fontSize};'>${worker.first_name}</div>`
  let income = html`<div style='margin-left: auto; margin-right:auto; font-size: ${fontSize};'>$${worker.income}k </div>`
  
  container.append(name);
  container.append(img);
  container.append(income);
  
  container.style.fontSize = '0.27em';
  container.style.display = 'flex'
  container.style.flexDirection = 'column';
  container.style.width = '60px';
  container.style.margin = '0';
  container.style.marginBottom = '1px';
  div.append(container);
  
  return worker;
}

function take_srs(size, div_selector){
  const div = document.querySelector(div_selector);
  div.innerHTML = '';
  
  let sample_elements = Array(size);
  for (let i=0; i < size; i++){
       
       let randomElement = getRandomElement(emojis);
       sample_elements[i] = append_sample_element(div, randomElement, '0.95em');
    }
    
  return sample_elements;
}

selected_elements_srs = take_srs(sample_size_srs1, "#sample-srs1");

srs_mean = Math.round(selected_elements_srs.reduce((partialSum, a) => partialSum + a.income, 0)/selected_elements_srs.length, 2);

{
  let sample_size = sample_size_srs1;
  // Creates the Histogram
  var margin = {top: 10, right: 10, bottom: 30, left: 25},
    width = document.querySelector("#pop-srs1").clientWidth - margin.left - margin.right,
    height = 200 - margin.top - margin.bottom;

  document.querySelector("#sample-dist-srs").innerHTML = '';
  
  d3.select("#sample-dist-srs")    
    .append("p")
    .text('Sample distribution')
    .style('font-size', '0.7em')
    .style('margin', 0);

  var svg = d3.select("#sample-dist-srs")
    .append("svg")
      .attr("width", width + margin.left + margin.right)
      .attr("height", height + margin.top + margin.bottom)
    .append("g")
      .attr("transform",
            "translate(" + margin.left + "," + margin.top + ")");
  

  // X axis: scale and draw:
  var x = d3.scaleLinear()
      .domain([d3.min(selected_elements_srs, d => d.income-10), d3.max(selected_elements_srs, d => d.income+10)])
      .range([margin.left, width - margin.right]);

  svg.append("g")
      .attr("transform", "translate(0," + `${height - margin.bottom}` + ")")
      .call(d3.axisBottom(x).tickSizeOuter(0))
      .call(g => g.append("text")
        .attr("x", width / 2)
        .attr("fill", "currentColor")
        .attr("font-weight", "bold")
        .attr("text-anchor", "bottom")
        .attr('font-size', '16px')
        .attr("class", "axis")
        .attr("dy", "2.5em")
        .text("Income (in thousands of $)")
        .attr("class","axes-label"));
  
  // set the parameters for the histogram
  var histogram = d3.histogram()
      .value(d => d.income)   // I need to give the vector of value
      .domain(x.domain())  // then the domain of the graphic
      .thresholds(x.ticks(20)); // then the numbers of bins
      

  // And apply this function to data to get the bins
  var bins = histogram(selected_elements_srs);

  // Y axis: scale and draw:
  var y = d3.scaleLinear()
      .range([height - margin.bottom, 0])
      .domain([0, d3.max(bins, d => d.length+10)]);   // d3.hist has to be called before the Y axis obviously

  svg.append("g")
      .attr("transform", `translate(${margin.left},0)`)
      .call(d3.axisLeft(y))
      .call(g => g.select(".tick:last-of-type text").clone()
        .attr("x", -(height - margin.bottom)/2)
        .attr("y", -40)
        .attr("font-weight", "bold")
        .attr('font-size', '16px')
        .attr('transform', 'rotate(270)')
        .attr("text-anchor", "middle")
        .text("Frequency")
        .attr("class","axes-label"));

  // append the bar rectangles to the svg element
  svg.selectAll("rect")
      .data(bins)
      .enter()
      .append("rect")
        .attr("x", 1)
        .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; })
        .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; })
        .attr("height", function(d) { return height - y(d.length) - margin.bottom; })
        .style("fill", "steelblue")
        .on("mouseenter", (d, i, nodes) => { 
            // Mouse-over event: turns the bin red and add the number of data points in the bin to the top of the bin
            d3.select(d.target).style("fill", "red");
            d3.select(d.target.parentNode)
                .append("text")
                .attr("x", (x(i.x0) + x(i.x1)) / 2)
                .attr("text-anchor", "middle")
                .attr("y", y(i.length + 1))
                .attr("class", "freq")
                .attr('font-size', '0.5em')
                .text(i.length)
                .property("bar", d.target);

            d3.select(d.target).style("cursor", "pointer"); // change the cursor
            
            document.getElementById("sample-srs1")
                    .querySelectorAll("img")
                    .forEach(entry => {
                        if (+entry.dataset.income >= d.target.__data__.x0 &&
                            +entry.dataset.income <= d.target.__data__.x1){
                              entry.parentNode.style.border = 'solid';
                              entry.parentNode.style.borderColor = 'red';
                        }
            });
        })
        .on("mouseout", (d, i, nodes) => { 
              // Mouse-out event: returns to the original configuration
              if (!d.target.flag) {
                  d3.select(d.target).style("fill", "steelblue")
                  d3.select(d.target).style("cursor", "default");
                  d3.selectAll(".freq")
                    .filter((e, j, texts) => {
                        return texts[j].bar === d.target;
                    }).remove();
                  document.getElementById("sample-srs1")
                      .querySelectorAll("img")
                      .forEach(entry => {
                        if (+entry.dataset.income >= d.target.__data__.x0 &&
                            +entry.dataset.income <= d.target.__data__.x1){
                              entry.parentNode.style.border = 'none';
                        }
                      });
              }
         })
        

  d3.select("#sample-dist-srs")
    .append("p")
    .text('A few statistics:')
    .style('font-size', '0.7em')
    .style('margin', 0)
  

  let srs_mean = mean(selected_elements_srs.map(d => d.income)).toFixed(2);
  let srs_sd   = std(selected_elements_srs.map(d => d.income)).toFixed(2);
  let srs_25q  = quantile(selected_elements_srs.map(d => d.income), 0.25).toFixed(2);
  let srs_50q  = quantile(selected_elements_srs.map(d => d.income), 0.50).toFixed(2);
  let srs_75q  = quantile(selected_elements_srs.map(d => d.income), 0.75).toFixed(2);
  let srs_99q  = quantile(selected_elements_srs.map(d => d.income), 0.99).toFixed(2);

  let ul = d3.select("#sample-dist-srs").append('ul');
  ul.append('li')
    .text(`Mean: ${srs_mean}`)
    .attr("style", 'margin-bottom: 0 !important;');

  ul.append('li')
    .text(`Median: ${srs_50q}`)
    .attr("style", 'margin-bottom: 0 !important;');

  ul.append('li')
    .text(`0.99-quantile: ${srs_99q}`)
    .attr("style", 'margin-bottom: 0 !important;');

  ul.append('li')
    .text(`Std. Dev.: ${srs_sd}`)
    .attr("style", 'margin-bottom: 0 !important;');
    
  ul.append('li')
    .text(`IQR: ${Math.round((srs_75q - srs_25q) * 100) / 100}`)
    .attr("style", 'margin-bottom: 0 !important;');

  ul.style('font-size', '0.5em')
    .style('margin', 0);  
}

Simple Random Sampling (SRS)

• Use the previous slides to investigate the following questions:
  1. What happens to the statistics when a new sample is taken?
  2. What happens to the parameters when a new sample is taken?
  3. Contrast the Sample Distribution with the Population Distribution for small and large sample sizes. What do you notice?

Stratified Sampling

• We are investigating the income of hospital workers in BC;

• The idea is to divide the population into groups, called strata;

  • Individuals in the same stratum are similar to each other (in terms of the variables being measured);

• Then, we draw a SRS from each stratum separately;

Stratified Sampling

• For example, we could split the population into staff, nurse, and doctors.
  • or use even more groups: IT staff, Admin staff, licensed nurse, registered nurse, general doctor, specialist doctor, surgeons.
• It is expected that the income within each stratum (job category) to be somewhat similar;

Stratified Sampling

• In stratified sampling, we:
  1. split the population into subpopulations - called strata.
  2. draw a SRS from each stratum;
  3. estimate the parameters of interest of each stratum separately;
  4. combine the strata’s estimates to build an overall estimate;

Stratified Random Sampling

Stratified Random Sampling

You might need to refresh this page to show the plot

Population

Nurses

Staffs

Doctors

{
  const div_staff = document.querySelector('#pop-stratified1').querySelector('#pop-str-staff1');
  const div_doctor = document.querySelector('#pop-stratified1').querySelector('#pop-str-doctor1');
  const div_nurse = document.querySelector('#pop-stratified1').querySelector('#pop-str-nurse1');
  div_staff.innerHTML = '';
  div_doctor.innerHTML = '';
  div_nurse.innerHTML = '';

  const N = 750
  for (let i=0; i < N; i++){
      let randomElement = getRandomElement(emojis);

      if (randomElement.includes('staff')){
        let img = html`<img src="imgs/${randomElement}.svg" height="45px" width=auto style='position: absolute; left: ${getRandom(0, 90)}%; top: ${getRandom(0, 35)}%; padding:0; margin:0;'></img>`;
        div_staff.append(img);
      }
      if (randomElement.includes('doc')){
        let img = html`<img src="imgs/${randomElement}.svg" height="45px" width=auto style='position: absolute; left: ${getRandom(0, 90)}%; top: ${getRandom(0, 35)}%; padding:0; margin:0;'></img>`;
        div_doctor.append(img);
      }
      if (randomElement.includes('nurse')){
        let img = html`<img src="imgs/${randomElement}.svg" height="45px" width=auto style='position: absolute; left: ${getRandom(0, 90)}%; top: ${getRandom(0, 35)}%; padding:0; margin:0;'></img>`;
        div_nurse.append(img);
      }
    }
}

31b8e172-b470-440e-83d8-e6b185028602:t y p e : O A B l A G Y A N Q B h A D c A N w A y A C 0 A Z A B k A D Y A M w A t A D Q A N g A y A D I A L Q A 4 A D Q A Y g B m A C 0 A Y Q B m A D E A O Q A 5 A D U A Y Q A x A G I A M g B i A D k A 
 p o s i t i o n : M w A x A D I A N g A 0 A A = = 
 p r e f i x : 
 s o u r c e : P A B 0 A G E A Y g B s A G U A I A B p A G Q A P Q A n A H A A b w B w A C 0 A c w B 0 A H I A L Q B w A G E A c g B h A G 0 A Z Q B 0 A G U A c g B z A C c A I A B j A G w A Y Q B z A H M A P Q A n A H M A d A B y A G E A d A B p A G Y A a Q B l A G Q A L Q B 0 A G E A Y g B s A G U A J w A g A C A A I A A g A C A A c w B 0 A H k A b A B l A D 0 A J w B 2 A G k A c w B p A G I A a Q B s A G k A d A B 5 A D o A I A B j A G 8 A b A B s A G E A c A B z A G U A O w A g A G Y A b w B u A H Q A L Q B z A G k A e g B l A D o A I A A w A C 4 A N Q B l A G 0 A O w A n A D 4 A C g A 8 A H Q A a A B l A G E A Z A A + A A o A I A A g A D w A d A B y A D 4 A C g A g A C A A I A A g A D w A d A B o A D 4 A U A B h A H I A Y Q B t A G U A d A B l A H I A P A A v A H Q A a A A + A A o A I A A g A C A A I A A 8 A H Q A a A A + A F M A d A B h A G Y A Z g A 8 A C 8 A d A B o A D 4 A C g A g A C A A I A A g A D w A d A B o A D 4 A T g B 1 A H I A c w B l A D w A L w B 0 A G g A P g A K A C A A I A A g A C A A P A B 0 A G g A P g B E A G 8 A Y w B 0 A G 8 A c g A 8 A C 8 A d A B o A D 4 A C g A g A C A A I A A g A D w A d A B o A D 4 A T w B 2 A G U A c g B h A G w A b A A 8 A C 8 A d A B o A D 4 A C g A g A C A A P A A v A H Q A c g A + A A o A P A A v A H Q A a A B l A G E A Z A A + A A o A P A B 0 A G I A b w B k A H k A P g A K A C A A I A A 8 A H Q A c g A g A G k A Z A A 9 A C I A c A B v A H A A L Q B y A G 8 A d w A t A G 0 A Z Q B h A G 4 A I g A + A A o A I A A g A C A A I A A 8 A H Q A Z A A + A E 0 A Z Q B h A G 4 A P A A v A H Q A Z A A + A A o A I A A g A C A A I A A 8 A H Q A Z A A g A G M A b A B h A H M A c w A 9 A C I A c w B 0 A G E A Z g B m A C 0 A Y w B l A G w A b A A i A D 4 A P A A v A H Q A Z A A + A A o A I A A g A C A A I A A 8 A H Q A Z A A g A G M A b A B h A H M A c w A 9 A C I A b g B 1 A H I A c w B l A C 0 A Y w B l A G w A b A A i A D 4 A P A A v A H Q A Z A A + A A o A I A A g A C A A I A A 8 A H Q A Z A A g A G M A b A B h A H M A c w A 9 A C I A Z A B v A G M A d A B v A H I A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B v A H Y A Z Q B y A G E A b A B s A C 0 A Y w B l A G w A b A A i A D 4 A P A A v A H Q A Z A A + A A o A I A A g A D w A L w B 0 A H I A P g A K A C A A I A A 8 A H Q A c g A g A G k A Z A A 9 A C I A c A B v A H A A L Q B y A G 8 A d w A t A G 0 A Z Q B k A G k A Y Q B u A C I A P g A K A C A A I A A g A C A A P A B 0 A G Q A P g B N A G U A Z A B p A G E A b g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B z A H Q A Y Q B m A G Y A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B u A H U A c g B z A G U A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B k A G 8 A Y w B 0 A G 8 A c g A t A G M A Z Q B s A G w A I g A + A D w A L w B 0 A G Q A P g A K A C A A I A A g A C A A P A B 0 A G Q A I A B j A G w A Y Q B z A H M A P Q A i A G 8 A d g B l A H I A Y Q B s A G w A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A P A A v A H Q A c g A + A A o A I A A g A D w A d A B y A C A A a Q B k A D 0 A I g B w A G 8 A c A A t A H I A b w B 3 A C 0 A O Q A 5 A H E A d Q B h A G 4 A d A B p A G w A Z Q A i A D 4 A C g A g A C A A I A A g A D w A d A B k A D 4 A M A A u A D k A O Q A t A H E A d Q B h A G 4 A d A B p A G w A Z Q A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B z A H Q A Y Q B m A G Y A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B u A H U A c g B z A G U A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B k A G 8 A Y w B 0 A G 8 A c g A t A G M A Z Q B s A G w A I g A + A D w A L w B 0 A G Q A P g A K A C A A I A A g A C A A P A B 0 A G Q A I A B j A G w A Y Q B z A H M A P Q A i A G 8 A d g B l A H I A Y Q B s A G w A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A P A A v A H Q A c g A + A A o A I A A g A D w A d A B y A C A A a Q B k A D 0 A I g B w A G 8 A c A A t A H I A b w B 3 A C 0 A c w B 0 A G Q A L Q B k A G U A d g A i A D 4 A C g A g A C A A I A A g A D w A d A B k A D 4 A U w B 0 A G Q A L g A g A E Q A Z Q B 2 A C 4 A P A A v A H Q A Z A A + A A o A I A A g A C A A I A A 8 A H Q A Z A A g A G M A b A B h A H M A c w A 9 A C I A c w B 0 A G E A Z g B m A C 0 A Y w B l A G w A b A A i A D 4 A P A A v A H Q A Z A A + A A o A I A A g A C A A I A A 8 A H Q A Z A A g A G M A b A B h A H M A c w A 9 A C I A b g B 1 A H I A c w B l A C 0 A Y w B l A G w A b A A i A D 4 A P A A v A H Q A Z A A + A A o A I A A g A C A A I A A 8 A H Q A Z A A g A G M A b A B h A H M A c w A 9 A C I A Z A B v A G M A d A B v A H I A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B v A H Y A Z Q B y A G E A b A B s A C 0 A Y w B l A G w A b A A i A D 4 A P A A v A H Q A Z A A + A A o A I A A g A D w A L w B 0 A H I A P g A K A C A A I A A 8 A H Q A c g A g A G k A Z A A 9 A C I A c A B v A H A A L Q B y A G 8 A d w A t A G k A c Q B y A C I A P g A K A C A A I A A g A C A A P A B 0 A G Q A P g B J A F E A U g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B z A H Q A Y Q B m A G Y A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B u A H U A c g B z A G U A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A I A A g A D w A d A B k A C A A Y w B s A G E A c w B z A D 0 A I g B k A G 8 A Y w B 0 A G 8 A c g A t A G M A Z Q B s A G w A I g A + A D w A L w B 0 A G Q A P g A K A C A A I A A g A C A A P A B 0 A G Q A I A B j A G w A Y Q B z A H M A P Q A i A G 8 A d g B l A H I A Y Q B s A G w A L Q B j A G U A b A B s A C I A P g A 8 A C 8 A d A B k A D 4 A C g A g A C A A P A A v A H Q A c g A + A A o A P A A v A H Q A Y g B v A G Q A e Q A + A A o A P A A v A H Q A Y Q B i A G w A Z Q A + A A = = 
 s u f f i x : :31b8e172-b470-440e-83d8-e6b185028602

{
  const button = document.querySelector("#str-truth-button");

  button.onclick = e => {
    const truth_str = document.querySelector("#str-truth-container");
    const pop_param = document.querySelector("#pop-str-parameters");
    if (truth_str.style.visibility === 'visible'){
      truth_str.style.visibility = 'collapse';
      pop_param.style.visibility = 'collapse';
    }
    else {
      truth_str.style.visibility = 'visible';
      pop_param.style.visibility = 'visible';
    }
  };
}

{
  // Creates the Histogram
  let margin = {top: 10, right: 10, bottom: 30, left: 25};
  let width = document.querySelector("#pop-stratified1").clientWidth - margin.left - margin.right;
  let height = 250 - margin.top - margin.bottom;

  let div = document.querySelector("#str-truth-container").innerHTML = '';
  
  d3.select("#str-truth-container")    
    .append("p")
    .text('Population distribution')
    .style('font-size', '0.7em')
    .style('margin', 0);

  // append the svg object to the body of the page
  var svg = d3.select("#str-truth-container")
    .append("svg")
      .attr("width", width + margin.left + margin.right)
      .attr("height", height + margin.top + margin.bottom)
    .append("g")
      .attr("transform",
            "translate(" + margin.left + "," + margin.top + ")");
  
  // X axis: scale and draw:
  var x = d3.scaleLinear()
      .domain([d3.min(workers, d => d.income-10), d3.max(workers, d => d.income+10)])
      .range([margin.left, width - margin.right]);

  svg.append("g")
      .attr("transform", "translate(0," + `${height - margin.bottom}` + ")")
      .call(d3.axisBottom(x).tickSizeOuter(0))
      .call(g => g.append("text")
        .attr("x", width / 2)
        .attr("fill", "currentColor")
        .attr("font-weight", "bold")
        .attr("text-anchor", "bottom")
        .attr('font-size', '16px')
        .attr("class", "axis")
        .attr("dy", "2.5em")
        .text("Income (in thousands of $)")
        .attr("class","axes-label"));
  
  // set the parameters for the histogram
  var histogram = d3.histogram()
      .value(d => d.income)   // I need to give the vector of value
      .domain(x.domain())  // then the domain of the graphic
      .thresholds(x.ticks(30)); // then the numbers of bins
      

  // And apply this function to data to get the bins
  var bins1  = histogram(workers.filter(d => d.job === 'staff'));
  var bins2  = histogram(workers.filter(d => d.job === 'nurse'));
  var bins3  = histogram(workers.filter(d => d.job === 'doctor'));

  // Y axis: scale and draw:
  var y = d3.scaleLinear()
      .range([height - margin.bottom, 0])
      .domain([0, d3.max(bins1, d => d.length+100)]);   // d3.hist has to be called before the Y axis obviously

  svg.append("g")
      .attr("transform", `translate(${margin.left},0)`)
      .call(d3.axisLeft(y))
      .call(g => g.select(".tick:last-of-type text").clone()
        .attr("x", -(height - margin.bottom)/2)
        .attr("y", -40)
        .attr("font-weight", "bold")
        .attr('font-size', '16px')
        .attr('transform', 'rotate(270)')
        .attr("text-anchor", "middle")
        .text("Frequency")
        .attr("class","axes-label"));

  // append the bar rectangles to the svg element
  svg.selectAll("rect")
      .data(bins1)
      .enter()
      .append("rect")
        .attr("x", 1)
        .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; })
        .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; })
        .attr("height", function(d) { return height - y(d.length) - margin.bottom; })
        .style("fill", "orange")
        .style("opacity", '0.5')
        .on("mouseenter", (d, i, nodes) => { 
            // Mouse-over event: turns the bin red and add the number of data points in the bin to the top of the bin
            d3.select(d.target).style("opacity", 1);
            d3.select(d.target.parentNode)
                .append("text")
                .attr("x", (x(i.x0) + x(i.x1)) / 2)
                .attr("text-anchor", "middle")
                .attr("y", y(i.length + 1))
                .attr("class", "freq")
                .attr('font-size', '0.5em')
                .text(i.length)
                .property("bar", d.target);

            d3.select(d.target).style("cursor", "pointer"); // change the cursor
        })
        .on("mouseout", (d, i, nodes) => { 
              // Mouse-out event: returns to the original configuration
              if (!d.target.flag) {
                  d3.select(d.target).style("opacity", 0.6)
                  d3.select(d.target).style("cursor", "default");
                  d3.selectAll(".freq")
                    .filter((e, j, texts) => {
                        return texts[j].bar === d.target;
                    }).remove();
              }
         })

  // append the bar rectangles to the svg element
  svg.selectAll("rect2")
      .data(bins2)
      .enter()
      .append("rect")
        .attr("x", 1)
        .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; })
        .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; })
        .attr("height", function(d) { return height - y(d.length) - margin.bottom; })
        .style("fill", "steelblue")
        .style("opacity", '0.5')
        .on("mouseenter", (d, i, nodes) => { 
            // Mouse-over event: turns the bin red and add the number of data points in the bin to the top of the bin
            d3.select(d.target).style("opacity", 1);
            d3.select(d.target.parentNode)
                .append("text")
                .attr("x", (x(i.x0) + x(i.x1)) / 2)
                .attr("text-anchor", "middle")
                .attr("y", y(i.length + 10))
                .attr("class", "freq")
                .attr('font-size', '0.5em')
                .text(i.length)
                .property("bar", d.target);

            d3.select(d.target).style("cursor", "pointer"); // change the cursor
        })
        .on("mouseout", (d, i, nodes) => { 
              // Mouse-out event: returns to the original configuration
              if (!d.target.flag) {
                  d3.select(d.target).style("opacity", 0.6);
                  d3.select(d.target).style("cursor", "default");
                  d3.selectAll(".freq")
                    .filter((e, j, texts) => {
                        return texts[j].bar === d.target;
                    }).remove();
              }
         })
         
         
  // append the bar rectangles to the svg element
  svg.selectAll("rect3")
      .data(bins3)
      .enter()
      .append("rect")
        .attr("x", 1)
        .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; })
        .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; })
        .attr("height", function(d) { return height - y(d.length) - margin.bottom; })
        .style("fill", "#69b3a2")
        .style("opacity", '0.5')
        .on("mouseenter", (d, i, nodes) => { 
            // Mouse-over event: turns the bin red and add the number of data points in the bin to the top of the bin
            d3.select(d.target).style("opacity", 1);
            d3.select(d.target.parentNode)
                .append("text")
                .attr("x", (x(i.x0) + x(i.x1)) / 2)
                .attr("text-anchor", "middle")
                .attr("y", y(i.length + 1))
                .attr("class", "freq")
                .attr('font-size', '0.5em')
                .text(i.length)
                .property("bar", d.target);

            d3.select(d.target).style("cursor", "pointer"); // change the cursor
        })
        .on("mouseout", (d, i, nodes) => { 
              // Mouse-out event: returns to the original configuration
              if (!d.target.flag) {
                  d3.select(d.target).style("opacity", 0.6);
                  d3.select(d.target).style("cursor", "default");
                  d3.selectAll(".freq")
                    .filter((e, j, texts) => {
                        return texts[j].bar === d.target;
                    }).remove();
              }
         })    
  // Legend
  svg.append("circle").attr("cx",300).attr("cy",20).attr("r", 6).style("fill", "orange")
  svg.append("circle").attr("cx",300).attr("cy",40).attr("r", 6).style("fill", "steelblue")
  svg.append("circle").attr("cx",300).attr("cy",60).attr("r", 6).style("fill", "#69b3a2")
  svg.append("text").attr("x", 310).attr("y", 25).text("Staff").style("font-size", "15px").attr("alignment-baseline","middle")
  svg.append("text").attr("x", 310).attr("y", 45).text("Nurse").style("font-size", "15px").attr("alignment-baseline","middle")
  svg.append("text").attr("x", 310).attr("y", 65).text("Doctor").style("font-size", "15px").attr("alignment-baseline","middle")
  
  d3.select("#str-truth-container")
    .append("p")
    .text('A few parameters:')
    .style('font-size', '0.7em')
    .style('margin', 0)

  //Append the table parameters
  let pop_table = document.querySelector("#pop-str-parameters");
  document.querySelector('#str-truth-container').append(pop_table);
  
  ['staff', 'nurse', 'doctor'].forEach(c => {
    pop_table.querySelector("#pop-row-mean")
             .querySelector(`.${c}-cell`)
             .append(document.createTextNode(mean(workers.filter(d => d.job == c).map(d => d.income)).toFixed(2)));

    pop_table.querySelector("#pop-row-median")
             .querySelector(`.${c}-cell`)
             .append(document.createTextNode(quantile(workers.filter(d => d.job == c).map(d => d.income), 0.5).toFixed(2)));
    
    pop_table.querySelector("#pop-row-99quantile")
             .querySelector(`.${c}-cell`)
             .append(document.createTextNode(quantile(workers.filter(d => d.job == c).map(d => d.income), 0.99).toFixed(2)));  
    
    pop_table.querySelector("#pop-row-std-dev")
             .querySelector(`.${c}-cell`)
             .append(document.createTextNode(std(workers.filter(d => d.job == c).map(d => d.income)).toFixed(2)));

    let p25q = quantile(workers.filter(d => d.job == c).map(d => d.income), 0.25).toFixed(2);
    let p75q = quantile(workers.filter(d => d.job == c).map(d => d.income), 0.75).toFixed(2);
    pop_table.querySelector("#pop-row-iqr")
             .querySelector(`.${c}-cell`)
             .append(document.createTextNode((p75q - p25q).toFixed(2)));
  });
  
    pop_table.querySelector("#pop-row-mean")
             .querySelector(`.overall-cell`)
             .append(document.createTextNode(mean(workers.map(d => d.income)).toFixed(2)));

    pop_table.querySelector("#pop-row-median")
             .querySelector(`.overall-cell`)
             .append(document.createTextNode(quantile(workers.map(d => d.income), 0.5).toFixed(2)));
    
    pop_table.querySelector("#pop-row-99quantile")
             .querySelector(`.overall-cell`)
             .append(document.createTextNode(quantile(workers.map(d => d.income), 0.99).toFixed(2)));  
    
    pop_table.querySelector("#pop-row-std-dev")
             .querySelector(`.overall-cell`)
             .append(document.createTextNode(std(workers.map(d => d.income)).toFixed(2)));

    let p25q = quantile(workers.map(d => d.income), 0.25).toFixed(2);
    let p75q = quantile(workers.map(d => d.income), 0.75).toFixed(2);
    pop_table.querySelector("#pop-row-iqr")
             .querySelector(`.overall-cell`)
             .append(document.createTextNode((p75q - p25q).toFixed(2)));
}

viewof sample_size_str1 = {

  let input = Inputs.range([30, 500], 
                           {value: 15,
                            step: 1, 
                            label: "Sample size: "});
  //d3.select(input).select('input[type="number"]').style("display", "none");
  return input;
}

Sample

Nurses

Staffs

Doctors

function take_stratified(size, div_selector){
  
  let div_staff = document.querySelector('#sample-str-staff1');
  let div_nurse = document.querySelector('#sample-str-nurse1');
  let div_doctor = document.querySelector('#sample-str-doctor1');
  div_doctor.innerHTML = '';        
  div_staff.innerHTML = '';
  div_nurse.innerHTML = '';

  let sample_elements = Array(size);
  for (let i=0; i < size; i++){
      let randomElement = getRandomElement(emojis);
      if (randomElement.includes('staff')){
        sample_elements[i] = append_sample_element(div_staff, randomElement, '14px');
      }
      if (randomElement.includes('doc')){
        sample_elements[i] = append_sample_element(div_doctor, randomElement, '14px');
      }
      if (randomElement.includes('nurse')){
        sample_elements[i] = append_sample_element(div_nurse, randomElement, '14px');
      }
    }
    
  return sample_elements;
}
selected_elements_stratified = take_stratified(sample_size_str1, "#sample-stratified");

{
  let text_nodes = document.querySelector('#sample-stratified').querySelectorAll('span')
  text_nodes[0].innerHTML = `Nurse  (n<sub>nurse</sub> = ${selected_elements_stratified.filter(d => d.job == 'nurse').length})`;

  text_nodes[1].innerHTML = ` Staff  (n<sub>staff</sub> = ${selected_elements_stratified.filter(d => d.job == 'staff').length})`;

  text_nodes[2].innerHTML = `Doctor  (n<sub>doctor</sub> = ${selected_elements_stratified.filter(d => d.job == 'doctor').length})`;
}

Statistics	Staff	Nurse	Doctor	Overall
Sample Mean
Sample Median
Sample 0.99-quantile
Sample Std. Dev.
Sample IQR

sample_size_str1;

{
  let sample_size = sample_size_str1;
  
  // Creates the Histogram
  var margin = {top: 10, right: 10, bottom: 30, left: 25},
    width = document.querySelector("#sample-dist-str1").clientWidth - margin.left - margin.right,
    height = 200 - margin.top - margin.bottom;
  
  document.querySelector("#sample-dist-str1").innerHTML = '';
  
  d3.select("#sample-dist-str1")    
    .append("p")
    .text('Sample distribution')
    .style('font-size', '0.7em')
    .style('margin', 0);


  var svg = d3.select("#sample-dist-str1")
    .append("svg")
      .attr("width", width + margin.left + margin.right)
      .attr("height", height + margin.top + margin.bottom)
    .append("g")
      .attr("transform",
            "translate(" + margin.left + "," + margin.top + ")");

  // X axis: scale and draw:
  var x = d3.scaleLinear()
      .domain([d3.min(selected_elements_stratified, d => d.income-10), d3.max(selected_elements_stratified, d => d.income+10)])
      .range([margin.left, width - margin.right]);

  svg.append("g")
      .attr("transform", "translate(0," + `${height - margin.bottom}` + ")")
      .call(d3.axisBottom(x).tickSizeOuter(0))
      .call(g => g.append("text")
        .attr("x", width / 2)
        .attr("fill", "currentColor")
        .attr("font-weight", "bold")
        .attr("text-anchor", "bottom")
        .attr('font-size', '16px')
        .attr("class", "axis")
        .attr("dy", "2.5em")
        .text("Income (in thousands of $)")
        .attr("class","axes-label"));
  
  // set the parameters for the histogram
  var histogram = d3.histogram()
      .value(d => d.income)   // I need to give the vector of value
      .domain(x.domain())  // then the domain of the graphic
      .thresholds(x.ticks(40)); // then the numbers of bins
      

  // And apply this function to data to get the bins
  var bins1  = histogram(selected_elements_stratified.filter(d => d.job === 'staff'));
  var bins2  = histogram(selected_elements_stratified.filter(d => d.job === 'nurse'));
  var bins3  = histogram(selected_elements_stratified.filter(d => d.job === 'doctor'));

  // Y axis: scale and draw:
  var y = d3.scaleLinear()
      .range([height - margin.bottom, 0])
      .domain([0, d3.max(bins1, d => d.length+10)]);   // d3.hist has to be called before the Y axis obviously

  svg.append("g")
      .attr("transform", `translate(${margin.left},0)`)
      .call(d3.axisLeft(y))
      .call(g => g.select(".tick:last-of-type text").clone()
        .attr("x", -(height - margin.bottom)/2)
        .attr("y", -40)
        .attr("font-weight", "bold")
        .attr('font-size', '16px')
        .attr('transform', 'rotate(270)')
        .attr("text-anchor", "middle")
        .text("Frequency")
        .attr("class","axes-label"));

  // append the bar rectangles to the svg element
  svg.selectAll("rect")
      .data(bins1)
      .enter()
      .append("rect")
        .attr("x", 1)
        .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; })
        .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; })
        .attr("height", function(d) { return height - y(d.length) - margin.bottom; })
        .style("fill", "orange")
        .style("opacity", '0.5')
        .on("mouseenter", (d, i, nodes) => { 
            // Mouse-over event: turns the bin red and add the number of data points in the bin to the top of the bin
            d3.select(d.target).style("opacity", 1);
            d3.select(d.target.parentNode)
                .append("text")
                .attr("x", (x(i.x0) + x(i.x1)) / 2)
                .attr("text-anchor", "middle")
                .attr("y", y(i.length + 1))
                .attr("class", "freq")
                .attr('font-size', '0.5em')
                .text(i.length)
                .property("bar", d.target);

            d3.select(d.target).style("cursor", "pointer"); // change the cursor
        })
        .on("mouseout", (d, i, nodes) => { 
              // Mouse-out event: returns to the original configuration
              if (!d.target.flag) {
                  d3.select(d.target).style("opacity", 0.6)
                  d3.select(d.target).style("cursor", "default");
                  d3.selectAll(".freq")
                    .filter((e, j, texts) => {
                        return texts[j].bar === d.target;
                    }).remove();
              }
         })

  // append the bar rectangles to the svg element
  svg.selectAll("rect2")
      .data(bins2)
      .enter()
      .append("rect")
        .attr("x", 1)
        .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; })
        .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; })
        .attr("height", function(d) { return height - y(d.length) - margin.bottom; })
        .style("fill", "steelblue")
        .style("opacity", '0.5')
        .on("mouseenter", (d, i, nodes) => { 
            // Mouse-over event: turns the bin red and add the number of data points in the bin to the top of the bin
            d3.select(d.target).style("opacity", 1);
            d3.select(d.target.parentNode)
                .append("text")
                .attr("x", (x(i.x0) + x(i.x1)) / 2)
                .attr("text-anchor", "middle")
                .attr("y", y(i.length + 1))
                .attr("class", "freq")
                .attr('font-size', '0.5em')
                .text(i.length)
                .property("bar", d.target);

            d3.select(d.target).style("cursor", "pointer"); // change the cursor
        })
        .on("mouseout", (d, i, nodes) => { 
              // Mouse-out event: returns to the original configuration
              if (!d.target.flag) {
                  d3.select(d.target).style("opacity", 0.6);
                  d3.select(d.target).style("cursor", "default");
                  d3.selectAll(".freq")
                    .filter((e, j, texts) => {
                        return texts[j].bar === d.target;
                    }).remove();
              }
         })
         
         
  // append the bar rectangles to the svg element
  svg.selectAll("rect3")
      .data(bins3)
      .enter()
      .append("rect")
        .attr("x", 1)
        .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; })
        .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; })
        .attr("height", function(d) { return height - y(d.length) - margin.bottom; })
        .style("fill", "#69b3a2")
        .style("opacity", '0.5')
        .on("mouseenter", (d, i, nodes) => { 
            // Mouse-over event: turns the bin red and add the number of data points in the bin to the top of the bin
            d3.select(d.target).style("opacity", 1);
            d3.select(d.target.parentNode)
                .append("text")
                .attr("x", (x(i.x0) + x(i.x1)) / 2)
                .attr("text-anchor", "middle")
                .attr("y", y(i.length + 1))
                .attr("class", "freq")
                .attr('font-size', '0.5em')
                .text(i.length)
                .property("bar", d.target);

            d3.select(d.target).style("cursor", "pointer"); // change the cursor
        })
        .on("mouseout", (d, i, nodes) => { 
              // Mouse-out event: returns to the original configuration
              if (!d.target.flag) {
                  d3.select(d.target).style("opacity", 0.6);
                  d3.select(d.target).style("cursor", "default");
                  d3.selectAll(".freq")
                    .filter((e, j, texts) => {
                        return texts[j].bar === d.target;
                    }).remove();
              }
         })    
  // Legend
  svg.append("circle").attr("cx",300).attr("cy", 20).attr("r", 6).style("fill", "orange")
  svg.append("circle").attr("cx",300).attr("cy", 40).attr("r", 6).style("fill", "steelblue")
  svg.append("circle").attr("cx",300).attr("cy", 60).attr("r", 6).style("fill", "#69b3a2")
  svg.append("text").attr("x", 310).attr("y", 25).text("Staff").style("font-size", "15px").attr("alignment-baseline","middle")
  svg.append("text").attr("x", 310).attr("y", 45).text("Nurse").style("font-size", "15px").attr("alignment-baseline","middle")
  svg.append("text").attr("x", 310).attr("y", 65).text("Doctor").style("font-size", "15px").attr("alignment-baseline","middle")
        

  
  d3.select("#sample-dist-str1")
    .append("p")
    .text('A few statistics:')
    .style('font-size', '0.7em')
    .style('margin', 0)  

  let sample_str_table = document.querySelector("#sample-str-statistic");
  ['staff', 'nurse', 'doctor'].forEach(c => {
      sample_str_table.querySelector("#sample-row-mean")
                .querySelector(`.${c}-cell`)
                .innerHTML = mean(selected_elements_stratified.filter(d => d.job == c).map(d => d.income)).toFixed(2);

      sample_str_table.querySelector("#sample-row-median")
                .querySelector(`.${c}-cell`)
                .innerHTML = quantile(selected_elements_stratified.filter(d => d.job == c).map(d => d.income), 0.5).toFixed(2);
      
      sample_str_table.querySelector("#sample-row-99quantile")
                .querySelector(`.${c}-cell`)
                .innerHTML = quantile(selected_elements_stratified.filter(d => d.job == c).map(d => d.income), 0.99).toFixed(2);  
      
      sample_str_table.querySelector("#sample-row-std-dev")
                .querySelector(`.${c}-cell`)
                .innerHTML = std(selected_elements_stratified.filter(d => d.job == c).map(d => d.income)).toFixed(2);

      let p25q = quantile(selected_elements_stratified.filter(d => d.job == c).map(d => d.income), 0.25).toFixed(2);
      let p75q = quantile(selected_elements_stratified.filter(d => d.job == c).map(d => d.income), 0.75).toFixed(2);
      sample_str_table.querySelector("#sample-row-iqr")
                .querySelector(`.${c}-cell`)
                .innerHTML = (p75q - p25q).toFixed(2);
  });
  
    sample_str_table.querySelector("#sample-row-mean")
             .querySelector(`.overall-cell`)
             .innerHTML = mean(selected_elements_stratified.map(d => d.income)).toFixed(2);

    sample_str_table.querySelector("#sample-row-median")
             .querySelector(`.overall-cell`)
             .innerHTML = quantile(selected_elements_stratified.map(d => d.income), 0.5).toFixed(2);
    
    sample_str_table.querySelector("#sample-row-99quantile")
             .querySelector(`.overall-cell`)
             .innerHTML = quantile(selected_elements_stratified.map(d => d.income), 0.99).toFixed(2);  
    
    sample_str_table.querySelector("#sample-row-std-dev")
             .querySelector(`.overall-cell`)
             .innerHTML = std(selected_elements_stratified.map(d => d.income)).toFixed(2);

    let p25q = quantile(selected_elements_stratified.map(d => d.income), 0.25).toFixed(2);
    let p75q = quantile(selected_elements_stratified.map(d => d.income), 0.75).toFixed(2);
    sample_str_table.querySelector("#sample-row-iqr")
             .querySelector(`.overall-cell`)
             .innerHTML = (p75q - p25q).toFixed(2);

}

Stratified Random Sampling

• In stratified sampling, we study each subpopulation separately and then combine the results for the entire population.

• Stratified Sampling tends to perform better than SRS (i.e., there is less variability across samples);

  • The more homogeneous the groups are, the better the Stratified Sampling is in comparison to SRS.

Cluster Sampling

• SRS and Stratified sampling can be prohibitively expensive;

• A more convenient way (but potentially less precise), is cluster sampling;

• In cluster sampling, we split the population into groups, called clusters.

  • Different from a stratum, a cluster is supposed to be heterogenous;
  • Ideally, each cluster has similar composition as the population as a whole;

Cluster Sampling

• For example, we could use as clusters hospital units.
  • Each hospital should have similar composition as the population;

Cluster Sampling

• In cluster sampling, we:
  1. split the population into subpopulations - called clusters.
  2. get a list of all clusters in the population;
  3. draw a SRS of clusters;

Cluster Sampling

Once we have a sample of clusters we can:

1. Collect the data from all units in the selected clusters; this is called one-stage cluster;

2. Select a sample of units within each selected cluster using SRS or Stratified Sampling; two-stage cluster;

Systematic Sampling

Scroll down

• A systematic sample is obtained by selecting every kth individual from the sampling frame;

• The effectiveness of this method depends on the structure of the sampling frame.

• It could be better, worse, or the same as SRS or even stratified sampling.

Multistage sampling

• Multistage sampling involves more than one stage or more than one sampling procedure in obtaining a sample.

• Two-stage cluster sampling is an example of multistage sampling.

Sampling problems

Biased samples

• If our sampling approach systematically gives us nonrepresentative samples, we say that the sampling method is biased.

• Remember, we don’t know if a sample is representative or not since we don’t know the population;

• Biased sampling is a property of the approach, not of a given sample.

• Let’s check a few things that can compromise our sample data;

Undercoverage

• It occurs when a sampling frame or a sampling procedure completely excludes or underrepresents certain kinds of individuals from the population.

• For example, a librarian wants to find out how often UBC students use library service. She only surveys students visiting the Woodward Biomedical Library.

Convenience Sampling

• The selection of individuals from the population based on easy availability and accessibility.

• For example, a market researcher wants to estimate the average price of housings in Vancouver. He collects information on the prices by sending out a survey to 50 households in his neighbourhood.

Voluntary Response Bias

• If the participation in survey is voluntary, individuals with strong opinions tend to respond more often and thus will be overrepresented.

• For example, call-in polls, UBC’s optional teaching evaluations, etc…

Nonresponse Bias

• Individuals who do not respond in a survey might differ from the respondents in certain aspects (e.g.,mail-in questionnaires);

• Voluntary response bias is a form of nonresponse bias; but nonresponse may occur for other reasons.

• For example, those who are at work during the day won’t respond to a telephone survey conducted only during working hours.

Response Bias

• When a surveyed subject’s response is influenced by how a question is phrased or asked, or due to misunderstanding of a question or unwillingness to disclose the truth, response bias has occurred.

• For example, the question, “Have you ever committed a crime?” could pressure the respondents into lying to avoid compromising themselves.

References

Image Attributions

Female Nurse 1: Twitter, CC BY 4.0, via Wikimedia Commons.

Female Nurse 2: Twitter, CC BY 4.0, via Wikimedia Commons.

Female Nurse 3: Twitter, CC BY 4.0, via Wikimedia Commons.

Male Nurse 1: Twitter, CC BY 4.0, via Wikimedia Commons.

Male Nurse 2: Twitter, CC BY 4.0, via Wikimedia Commons.

Female Doctor 1: Google, Apache License 2.0, via Wikimedia Commons.

Female Doctor 2: Google, Apache License 2.0, via Wikimedia Commons.

Female Doctor 3: Google, Apache License 2.0, via Wikimedia Commons.

Male Doctor 1: Google, Apache License 2.0, via Wikimedia Commons.

Male Doctor 2: Google, Apache License 2.0, via Wikimedia Commons.

Female Staff 1: Google, Apache License 2.0, via Wikimedia Commons.

Female Staff 2: Google, Apache License 2.0, via Wikimedia Commons.

Female Staff 3: Google, Apache License 2.0, via Wikimedia Commons.

Male Staff 1: Google, Apache License 2.0, via Wikimedia Commons.

Male Staff 2: Google, Apache License 2.0, via Wikimedia Commons.

Male Staff 3: Google, Apache License 2.0, via Wikimedia Commons.