Calculating Weighted Averages Using Percentages

Weighted Average Calculator with Percentages | Calculate Your Averages Accurately :root { –primary-color: #004a99; –success-color: #28a745; –background-color: #f8f9fa; –text-color: #333; –border-color: #ddd; –shadow-color: rgba(0, 0, 0, 0.1); –card-background: #fff; } body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background-color: var(–background-color); color: var(–text-color); line-height: 1.6; margin: 0; padding: 20px; display: flex; flex-direction: column; align-items: center; } .container { width: 100%; max-width: 960px; background-color: var(–card-background); padding: 30px; border-radius: 8px; box-shadow: 0 4px 12px var(–shadow-color); text-align: center; } h1, h2, h3 { color: var(–primary-color); margin-bottom: 20px; } h1 { font-size: 2.2em; } h2 { font-size: 1.8em; border-bottom: 2px solid var(–primary-color); padding-bottom: 10px; margin-top: 30px; } h3 { font-size: 1.4em; margin-top: 25px; } .calculator-section { background-color: var(–card-background); 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Weighted Average Calculator with Percentages

Accurately calculate averages where each value contributes differently based on its assigned percentage.

Weighted Average Calculator

Enter the first numerical value.
Enter the percentage weight for Value 1 (e.g., 30 for 30%).
Enter the second numerical value.
Enter the percentage weight for Value 2 (e.g., 50 for 50%).
Enter the third numerical value.
Enter the percentage weight for Value 3 (e.g., 20 for 20%).

Results

Total Weight Applied:
Sum of (Value * Weight):
Weighted Average: —
Formula: Weighted Average = Σ(Value * Weight) / Σ(Weight)
*Note: Weights are expected as percentages (e.g., 30 for 30%). The calculator automatically normalizes them if they don't sum to 100, but ideally they should.*

Distribution of Values and their Weighted Contribution

Weighted Average Components
Item Value Weight (%) Weighted Value (Value * Weight) Normalized Weight (%) Final Contribution

What is calculating weighted averages using percentages?

Calculating weighted averages using percentages is a fundamental mathematical technique used to determine an average value for a set of numbers, where each number contributes to the final average based on its assigned importance, or 'weight'. Unlike a simple average (arithmetic mean) where all values are treated equally, a weighted average assigns different levels of significance to each data point. This is typically achieved by multiplying each value by its corresponding weight (often expressed as a percentage) and then summing these products. The sum is then divided by the sum of the weights to obtain the final weighted average. This method is crucial in finance, statistics, grading systems, and many other fields where not all data points are created equal.

Who should use it? Anyone dealing with data where different components have varying levels of influence. This includes financial analysts assessing portfolio performance, students calculating their final grades, investors determining asset allocation impact, economists analyzing economic indicators, and researchers combining results from multiple studies. Essentially, if you have a set of values and some are more critical or influential than others, calculating weighted averages using percentages is the correct approach.

Common Misconceptions: A frequent misconception is that weighted average is the same as a simple average; this is incorrect because it ignores the differential importance of data points. Another is that weights must always sum to 100%. While this is ideal for direct interpretation of percentages, the mathematical formula for a weighted average will still work correctly even if the weights do not sum to 100%, as it normalizes by the sum of the weights. However, understanding the true percentage contribution requires the weights to represent the correct proportions relative to the whole.

Weighted Average Formula and Mathematical Explanation

The core of calculating weighted averages using percentages lies in its formula, which accounts for the varying significance of each data point. The general formula is:

Weighted Average = Σ (Valuei × Weighti) / Σ (Weighti)

Where:

  • Valuei represents the individual numerical value of the i-th data point.
  • Weighti represents the weight (often a percentage) assigned to the i-th data point.
  • Σ (Sigma) denotes summation, meaning you sum up the results for all data points.

Step-by-Step Derivation:

  1. Multiply Each Value by its Weight: For each data point, calculate the product of its value and its corresponding weight. This step quantifies the contribution of each item, scaled by its importance.
  2. Sum the Weighted Values: Add up all the products calculated in step 1. This gives you the total 'weighted sum'.
  3. Sum the Weights: Add up all the individual weights assigned to the data points.
  4. Divide the Total Weighted Sum by the Sum of Weights: The final weighted average is obtained by dividing the result from step 2 by the result from step 3.

If weights are provided as percentages that sum exactly to 100%, the denominator (Σ Weighti) will be 100. In this case, the weighted average is simply the sum of the (Value * Percentage/100) products, or more commonly, the sum of (Value * Percentage) divided by 100. Our calculator handles weights given as percentages (e.g., 30 for 30%) and correctly calculates the sum of weights, normalizing if necessary for accuracy, though it's best practice for weights to sum to 100% for clear interpretation.

Variables Table

Variable Meaning Unit Typical Range
Valuei The numerical data point. Depends on context (e.g., score, price, quantity) Varies widely
Weighti The importance or significance of the Valuei. Percentage (%) or decimal (e.g., 0.30 for 30%) 0% to 100% (ideally summing to 100% for clear percentage interpretation)
Σ (Valuei × Weighti) The sum of each value multiplied by its weight. Same as Value unit Varies
Σ (Weighti) The sum of all weights. Percentage (%) or unitless Ideally 100% or 1.00 if using decimals; otherwise, the sum of inputted weights.
Weighted Average The final calculated average, reflecting the influence of each weight. Same as Value unit Typically within the range of the values, influenced by weights.

Practical Examples (Real-World Use Cases)

Example 1: Calculating a Final Course Grade

A professor wants to calculate the final grade for a course. The components and their weights are:

  • Midterm Exam: Score 85, Weight 30%
  • Final Exam: Score 92, Weight 40%
  • Assignments: Average Score 90, Weight 30%

Calculation:

  1. Sum of (Value * Weight): (85 * 30) + (92 * 40) + (90 * 30) = 2550 + 3680 + 2700 = 8930
  2. Sum of Weights: 30% + 40% + 30% = 100%
  3. Weighted Average = 8930 / 100 = 89.3

Result: The student's weighted average grade is 89.3. This is crucial for understanding how each assessment component directly influences the final outcome, giving more importance to the Final Exam (40%) than the Midterm Exam (30%) or Assignments (30%). This method of calculating weighted averages using percentages ensures that higher-stakes assessments have a more significant impact on the overall grade.

Example 2: Investment Portfolio Performance

An investor holds three assets in their portfolio:

  • Stock A: Current Value $10,000, Annual Return 8%
  • Bond B: Current Value $5,000, Annual Return 4%
  • Real Estate C: Current Value $15,000, Annual Return 6%

The weights are determined by the proportion of the total portfolio value each asset represents.

Calculation:

  1. Total Portfolio Value: $10,000 + $5,000 + $15,000 = $30,000
  2. Calculate Weights:
    • Stock A Weight: ($10,000 / $30,000) * 100% = 33.33%
    • Bond B Weight: ($5,000 / $30,000) * 100% = 16.67%
    • Real Estate C Weight: ($15,000 / $30,000) * 100% = 50.00%
  3. Sum of (Value * Weight) – Here, "Value" is the Return Rate: (8% * 33.33%) + (4% * 16.67%) + (6% * 50.00%)
  4. This equals: (0.08 * 0.3333) + (0.04 * 0.1667) + (0.06 * 0.50) = 0.026664 + 0.006668 + 0.03 = 0.063332
  5. Sum of Weights: 33.33% + 16.67% + 50.00% = 100.00%
  6. Weighted Average Return = 0.063332 / 1.00 = 0.063332 or 6.33%

Result: The portfolio's weighted average annual return is approximately 6.33%. This calculation is vital for investors to understand the overall performance of their diversified holdings, recognizing that the asset with the largest allocation (Real Estate C at 50%) has the most significant impact on the total return. This demonstrates the power of calculating weighted averages using percentages in financial planning.

How to Use This Weighted Average Calculator

Our Weighted Average Calculator is designed for simplicity and accuracy. Follow these steps to get your weighted average:

  1. Input Values: Enter each numerical value you want to average into the "Value" fields (e.g., Value 1, Value 2, Value 3).
  2. Input Weights: For each corresponding value, enter its percentage weight in the "Weight (%)" fields. For example, if Value 1 represents 30% of the total importance, enter '30'. It's best practice for the sum of your weights to equal 100%.
  3. Add More Inputs (Optional): If you have more than three value-weight pairs, you would typically need to modify the calculator's HTML and JavaScript to include additional input fields. Our current version supports three pairs for demonstration.
  4. Calculate: Click the "Calculate" button.
  5. Review Results: The calculator will display:
    • Total Weight Applied: The sum of all weights you entered.
    • Sum of (Value * Weight): The total sum of each value multiplied by its weight.
    • Weighted Average: The primary result, calculated as (Sum of Value * Weight) / (Sum of Weights).
    • A detailed table breaking down each component's contribution.
    • A dynamic chart visualizing the data.
  6. Reset: To start over with default values, click the "Reset" button.
  7. Copy Results: To easily save or share the calculated results and key inputs, click the "Copy Results" button.

Reading Results: The "Weighted Average" is your final answer. Compare it to the simple average of your values to see how the weights have shifted the outcome. The table provides a breakdown of how each individual value and its weight contributed to the final average. The chart offers a visual representation of these relationships.

Decision-Making Guidance: Use the weighted average to make informed decisions. For example, if calculating a portfolio's expected return, a higher weighted average return might indicate a more favorable investment strategy. In grading, understanding your weighted average helps identify areas needing improvement to achieve a desired final score. The results highlight which factors have the most significant impact.

Key Factors That Affect Weighted Average Results

Several factors can significantly influence the outcome when calculating weighted averages using percentages:

  1. Magnitude of Values: Larger individual values inherently pull the weighted average more strongly towards them, especially if they have substantial weights. For instance, a $1 million property with a 10% weight has a greater impact than a $10,000 car with a 50% weight in a personal asset calculation.
  2. Percentage Weights Assigned: This is the most direct influencer. A value with a higher percentage weight will have a proportionally larger effect on the final average. Small changes in weights can lead to noticeable shifts in the result, making accurate weight assignment critical.
  3. Sum of Weights: While the formula normalizes by the sum of weights, whether they sum to 100% is crucial for interpreting the result as a direct percentage-based average. If weights don't sum to 100%, the final average might be scaled differently than expected, potentially requiring further normalization or recalculation based on the intended total.
  4. Number of Data Points: Including more data points can sometimes dilute the impact of any single value or weight, depending on their individual magnitudes. Conversely, a calculation with few data points is highly sensitive to each input.
  5. Distribution of Values: If values are clustered closely together, the weighted average will likely fall within that cluster. If values are spread far apart, the weighted average will be pulled towards the data points with higher weights.
  6. Context and Interpretation: The meaning of the weighted average is entirely dependent on what the values and weights represent. A weighted average grade means something different from a weighted average portfolio return. Understanding the context is key to interpreting the result correctly and making sound financial or academic decisions.
  7. Data Accuracy: Inaccurate input values or incorrect percentage weights will inevitably lead to a misleading weighted average. Ensuring the precision of your raw data is paramount for reliable results.

Frequently Asked Questions (FAQ)

  • Q1: What is the difference between a simple average and a weighted average?
    A simple average gives equal importance to all values, calculated by summing all values and dividing by the count. A weighted average assigns different levels of importance (weights) to each value, thus influencing the final average more significantly based on these weights.
  • Q2: Do the weights in a weighted average always have to add up to 100%?
    Not strictly for the calculation to work mathematically, but it is highly recommended for clear interpretation, especially when using percentages. If weights don't sum to 100%, the formula still correctly calculates a weighted mean, but the result might not represent a typical percentage-based average without further context or normalization.
  • Q3: Can weights be negative?
    Generally, weights represent importance or proportion and are non-negative. Negative weights are typically not used in standard weighted average calculations for concepts like grades or financial performance, as they don't have a clear practical meaning in those contexts.
  • Q4: How does calculating weighted averages using percentages apply to financial analysis?
    It's used extensively. For example, calculating the average return of a portfolio where each asset's return is weighted by its percentage allocation in the portfolio. It's also used in calculating key financial indices and economic indicators.
  • Q5: What if I have many values and weights? Can I use this calculator?
    Our current calculator is set up for three value-weight pairs. For more complex scenarios with numerous inputs, you would need a more advanced tool or adjust the HTML and JavaScript code to accommodate additional fields. However, the underlying principle of calculating weighted averages using percentages remains the same.
  • Q6: How do I interpret a weighted average that falls outside the range of my input values?
    This is generally not possible unless you use negative weights or if the weights are significantly skewed in unusual ways. Typically, a weighted average will fall between the minimum and maximum of the input values, leaning towards the values with higher weights.
  • Q7: Is there a shortcut for calculating weighted averages if weights are percentages summing to 100?
    Yes, if weights are percentages summing to 100, you can simply calculate the sum of (Value * (Weight/100)). Or, calculate the sum of (Value * Weight) and divide by 100, as the sum of weights is 100.
  • Q8: How can I ensure my weighted average calculation is accurate for my project?
    Double-check your input values and especially your percentage weights. Ensure the weights accurately reflect the relative importance of each value. Use a reliable calculator like this one, or carefully follow the formula, and verify your results against known examples or simple checks. Understanding the goal of your calculation is key.

Related Tools and Internal Resources

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function calculateWeightedAverage() { var value1 = parseFloat(document.getElementById("value1").value); var weight1 = parseFloat(document.getElementById("weight1").value); var value2 = parseFloat(document.getElementById("value2").value); var weight2 = parseFloat(document.getElementById("weight2").value); var value3 = parseFloat(document.getElementById("value3").value); var weight3 = parseFloat(document.getElementById("weight3").value); var error1 = document.getElementById("value1Error"); var error2 = document.getElementById("weight1Error"); var error3 = document.getElementById("value2Error"); var error4 = document.getElementById("weight2Error"); var error5 = document.getElementById("value3Error"); var error6 = document.getElementById("weight3Error"); // Clear previous errors error1.textContent = ""; error2.textContent = ""; error3.textContent = ""; error4.textContent = ""; error5.textContent = ""; error6.textContent = ""; var isValid = true; if (isNaN(value1) || value1 < 0) { error1.textContent = "Please enter a valid non-negative number."; isValid = false; } if (isNaN(weight1) || weight1 100) { error2.textContent = "Please enter a weight between 0 and 100."; isValid = false; } if (isNaN(value2) || value2 < 0) { error3.textContent = "Please enter a valid non-negative number."; isValid = false; } if (isNaN(weight2) || weight2 100) { error4.textContent = "Please enter a weight between 0 and 100."; isValid = false; } if (isNaN(value3) || value3 < 0) { error5.textContent = "Please enter a valid non-negative number."; isValid = false; } if (isNaN(weight3) || weight3 100) { error6.textContent = "Please enter a weight between 0 and 100."; isValid = false; } if (!isValid) { document.getElementById("weightedAverageResult").textContent = "Weighted Average: –"; document.getElementById("totalWeightApplied").textContent = "–"; document.getElementById("sumValueWeight").textContent = "–"; updateTable([]); updateChart([], [], []); return; } var sumValueWeight = (value1 * weight1) + (value2 * weight2) + (value3 * weight3); var totalWeight = weight1 + weight2 + weight3; var weightedAverage = 0; if (totalWeight !== 0) { weightedAverage = sumValueWeight / totalWeight; } document.getElementById("totalWeightApplied").textContent = totalWeight.toFixed(2) + "%"; document.getElementById("sumValueWeight").textContent = sumValueWeight.toFixed(2); document.getElementById("weightedAverageResult").textContent = "Weighted Average: " + weightedAverage.toFixed(2); // Update Table var tableData = [ { item: "Item 1", value: value1, weight: weight1, weightedValue: value1 * weight1, normWeight: (totalWeight === 0 ? 0 : (weight1 / totalWeight) * 100), finalContribution: (totalWeight === 0 ? 0 : (value1 * weight1) / totalWeight)}, { item: "Item 2", value: value2, weight: weight2, weightedValue: value2 * weight2, normWeight: (totalWeight === 0 ? 0 : (weight2 / totalWeight) * 100), finalContribution: (totalWeight === 0 ? 0 : (value2 * weight2) / totalWeight)}, { item: "Item 3", value: value3, weight: weight3, weightedValue: value3 * weight3, normWeight: (totalWeight === 0 ? 0 : (weight3 / totalWeight) * 100), finalContribution: (totalWeight === 0 ? 0 : (value3 * weight3) / totalWeight)} ]; updateTable(tableData); // Update Chart var labels = ["Item 1", "Item 2", "Item 3"]; var dataValues = [value1, value2, value3]; var dataWeights = [weight1, weight2, weight3]; updateChart(labels, dataValues, dataWeights); } function updateTable(data) { var tableBody = document.getElementById("resultsTableBody"); tableBody.innerHTML = ""; // Clear previous rows if (data.length === 0) return; var totalWeightApplied = parseFloat(document.getElementById("totalWeightApplied").textContent); var sumValueWeightResult = parseFloat(document.getElementById("sumValueWeight").textContent); data.forEach(function(row) { var tr = tableBody.insertRow(); var tdItem = tr.insertCell(); tdItem.textContent = row.item; var tdValue = tr.insertCell(); tdValue.textContent = row.value.toFixed(2); var tdWeight = tr.insertCell(); tdWeight.textContent = row.weight.toFixed(2) + "%"; var tdWeightedValue = tr.insertCell(); tdWeightedValue.textContent = row.weightedValue.toFixed(2); var tdNormWeight = tr.insertCell(); tdNormWeight.textContent = row.normWeight.toFixed(2) + "%"; var tdFinalContribution = tr.insertCell(); tdFinalContribution.textContent = row.finalContribution.toFixed(2); }); // Add summary row for total weight and sum of weighted values var summaryTr = tableBody.insertRow(); summaryTr.style.fontWeight = "bold"; summaryTr.insertCell().textContent = "Totals"; summaryTr.insertCell().textContent = ""; // Value var totalWeightCell = summaryTr.insertCell(); totalWeightCell.textContent = totalWeightApplied.toFixed(2) + "%"; var sumValueWeightCell = summaryTr.insertCell(); sumValueWeightCell.textContent = sumValueWeightResult.toFixed(2); summaryTr.insertCell().textContent = ""; // Normalized Weight summaryTr.insertCell().textContent = ""; // Final Contribution } function updateChart(labels, dataValues, dataWeights) { var ctx = document.getElementById("weightedAverageChart").getContext("2d"); // Destroy previous chart instance if it exists if (window.myWeightedAverageChart instanceof Chart) { window.myWeightedAverageChart.destroy(); } // Calculate weighted values for chart var weightedValues = []; var totalWeight = dataWeights.reduce(function(sum, weight) { return sum + weight; }, 0); for (var i = 0; i 0) { weightedValues.push((dataValues[i] * (dataWeights[i] / totalWeight))); // Normalized weighted value for chart } else { weightedValues.push(0); } } window.myWeightedAverageChart = new Chart(ctx, { type: 'bar', data: { labels: labels, datasets: [{ label: 'Value', data: dataValues, backgroundColor: 'rgba(0, 74, 153, 0.6)', // Primary color variant borderColor: 'rgba(0, 74, 153, 1)', borderWidth: 1 }, { label: 'Normalized Weight Contribution', data: weightedValues, backgroundColor: 'rgba(40, 167, 69, 0.6)', // Success color variant borderColor: 'rgba(40, 167, 69, 1)', borderWidth: 1 }] }, options: { responsive: true, maintainAspectRatio: false, scales: { y: { beginAtZero: true, title: { display: true, text: 'Value / Contribution' } }, x: { title: { display: true, text: 'Item' } } }, plugins: { legend: { position: 'top', }, title: { display: true, text: 'Weighted Average Components Visualization' } } } }); } function resetForm() { document.getElementById("value1").value = "10"; document.getElementById("weight1").value = "30"; document.getElementById("value2").value = "20"; document.getElementById("weight2").value = "50"; document.getElementById("value3").value = "15"; document.getElementById("weight3").value = "20"; // Clear errors document.getElementById("value1Error").textContent = ""; document.getElementById("weight1Error").textContent = ""; document.getElementById("value2Error").textContent = ""; document.getElementById("weight2Error").textContent = ""; document.getElementById("value3Error").textContent = ""; document.getElementById("weight3Error").textContent = ""; // Reset results display document.getElementById("weightedAverageResult").textContent = "Weighted Average: –"; document.getElementById("totalWeightApplied").textContent = "–"; document.getElementById("sumValueWeight").textContent = "–"; updateTable([]); updateChart([], [], []); } function copyResults() { var mainResult = document.getElementById("weightedAverageResult").textContent; var totalWeight = "Total Weight Applied: " + document.getElementById("totalWeightApplied").textContent; var sumValueWeight = "Sum of (Value * Weight): " + document.getElementById("sumValueWeight").textContent; var tableRows = document.getElementById("resultsTableBody").rows; var tableContent = "Weighted Average Components:\n"; for (var i = 0; i 0) { tableContent += cells[0].textContent + "\t" + (cells[1].textContent ? cells[1].textContent : '-') + "\t" + (cells[2].textContent ? cells[2].textContent : '-') + "\t" + (cells[3].textContent ? cells[3].textContent : '-') + "\t" + (cells[4].textContent ? cells[4].textContent : '-') + "\t" + (cells[5].textContent ? cells[5].textContent : '-') + "\n"; } } var assumptions = "Assumptions:\n" + "Value 1: " + document.getElementById("value1").value + ", Weight 1: " + document.getElementById("weight1").value + "%\n" + "Value 2: " + document.getElementById("value2").value + ", Weight 2: " + document.getElementById("weight2").value + "%\n" + "Value 3: " + document.getElementById("value3").value + ", Weight 3: " + document.getElementById("weight3").value + "%"; var textToCopy = mainResult + "\n\n" + totalWeight + "\n" + sumValueWeight + "\n\n" + tableContent + "\n" + assumptions; navigator.clipboard.writeText(textToCopy).then(function() { alert("Results copied to clipboard!"); }).catch(function(err) { console.error('Could not copy text: ', err); alert("Failed to copy results. Please copy manually."); }); } // Initial calculation on page load document.addEventListener("DOMContentLoaded", function() { calculateWeightedAverage(); // Perform initial calculation with default values // Ensure Chart.js is loaded before trying to update chart if (typeof Chart !== 'undefined') { updateChart([], [], []); // Initialize empty chart } else { console.error("Chart.js library not found. Please include it for chart functionality."); } }); // Include Chart.js library (replace with a CDN link if preferred) // Using a CDN is generally recommended for production var script = document.createElement('script'); script.src = 'https://cdn.jsdelivr.net/npm/chart.js'; script.onload = function() { console.log("Chart.js loaded successfully."); // Re-run initial calculation and chart update after Chart.js is loaded calculateWeightedAverage(); if (typeof Chart !== 'undefined') { updateChart([], [], []); } }; document.head.appendChild(script);

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