Math Problem Statement
x 1 2 3 4 5 6 y 553 5449 19372 49543 112085 190802
Use linear regression to find the equation for the linear function that best fits this data. Round to two decimal places.
y
Solution
To find the linear regression equation that best fits the provided data points, we can use the formula for a linear regression line, which is given by:
where is the slope and is the y-intercept.
Given Data Points:
Steps to Calculate:
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Calculate the means of and :
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Calculate the slope :
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Calculate the y-intercept :
Calculation:
- Numerator and Denominator for :
- Compute and :
1 | 553 | -2.5 | -60547.67 | 151368.675 | 6.25 |
2 | 5449 | -1.5 | -55651.67 | 83477.505 | 2.25 |
3 | 19372 | -0.5 | -41728.67 | 20864.335 | 0.25 |
4 | 49543 | 0.5 | -11557.67 | -5778.335 | 0.25 |
5 | 112085 | 1.5 | 50984.33 | 76476.495 | 2.25 |
6 | 190802 | 2.5 | 129701.33 | 324252.325 | 6.25 |
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Sum up the columns:
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Calculate :
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Calculate :
Final Linear Regression Equation:
Rounding to two decimal places:
Summary:
The equation for the linear function that best fits the data is:
Would you like further details on any step, or do you have any questions? Here are some related questions you might consider:
- How can I interpret the slope and intercept of this linear regression equation?
- What are the limitations of using linear regression for this data?
- How would you check the goodness of fit for this regression model?
- What other regression techniques could be applied to this dataset?
- How does increasing the number of data points affect the regression line?
Tip: Always visualize your data and the regression line to better understand the fit and any potential outliers.
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Math Problem Analysis
Mathematical Concepts
Statistics
Linear Regression
Algebra
Formulas
y = mx + b
m = Σ((x_i - x̄)(y_i - ȳ)) / Σ((x_i - x̄)²)
b = ȳ - m * x̄
Theorems
Least Squares Method
Suitable Grade Level
Grades 11-12