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:

y=mx+by = mx + b

where mm is the slope and bb is the y-intercept.

Given Data Points:

  • x:[1,2,3,4,5,6]x: [1, 2, 3, 4, 5, 6]
  • y:[553,5449,19372,49543,112085,190802]y: [553, 5449, 19372, 49543, 112085, 190802]

Steps to Calculate:

  1. Calculate the means of xx and yy: xˉ=1+2+3+4+5+66=3.5\bar{x} = \frac{1 + 2 + 3 + 4 + 5 + 6}{6} = 3.5 yˉ=553+5449+19372+49543+112085+1908026=61100.67\bar{y} = \frac{553 + 5449 + 19372 + 49543 + 112085 + 190802}{6} = 61100.67

  2. Calculate the slope mm: m=(xixˉ)(yiyˉ)(xixˉ)2m = \frac{\sum{(x_i - \bar{x})(y_i - \bar{y})}}{\sum{(x_i - \bar{x})^2}}

  3. Calculate the y-intercept bb: b=yˉmxˉb = \bar{y} - m \bar{x}

Calculation:

  1. Numerator and Denominator for mm:
    • Compute (xixˉ)(yiyˉ)(x_i - \bar{x})(y_i - \bar{y}) and (xixˉ)2(x_i - \bar{x})^2:
xxyyxxˉx - \bar{x}yyˉy - \bar{y}(xxˉ)(yyˉ)(x - \bar{x})(y - \bar{y})(xxˉ)2(x - \bar{x})^2
1553-2.5-60547.67151368.6756.25
25449-1.5-55651.6783477.5052.25
319372-0.5-41728.6720864.3350.25
4495430.5-11557.67-5778.3350.25
51120851.550984.3376476.4952.25
61908022.5129701.33324252.3256.25
  1. Sum up the columns: (xxˉ)(yyˉ)=151368.675+83477.505+20864.3355778.335+76476.495+324252.325=494661.005\sum{(x - \bar{x})(y - \bar{y})} = 151368.675 + 83477.505 + 20864.335 - 5778.335 + 76476.495 + 324252.325 = 494661.005 (xxˉ)2=6.25+2.25+0.25+0.25+2.25+6.25=17.5\sum{(x - \bar{x})^2} = 6.25 + 2.25 + 0.25 + 0.25 + 2.25 + 6.25 = 17.5

  2. Calculate mm: m=494661.00517.528293.53m = \frac{494661.005}{17.5} \approx 28293.53

  3. Calculate bb: b=61100.67(28293.53×3.5)1241.75b = 61100.67 - (28293.53 \times 3.5) \approx -1241.75

Final Linear Regression Equation:

y=28293.53x1241.75y = 28293.53x - 1241.75

Rounding to two decimal places: y28293.53x1241.75y \approx 28293.53x - 1241.75

Summary:

The equation for the linear function that best fits the data is: y=28293.53x1241.75y = 28293.53x - 1241.75

Would you like further details on any step, or do you have any questions? Here are some related questions you might consider:

  1. How can I interpret the slope and intercept of this linear regression equation?
  2. What are the limitations of using linear regression for this data?
  3. How would you check the goodness of fit for this regression model?
  4. What other regression techniques could be applied to this dataset?
  5. 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