Math Problem Statement
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line.
x
1010
77
1212
1010
1212
1313
55
44
1111
66
55 y
7.387.38
6.966.96
13.1513.15
7.247.24
7.797.79
9.249.24
5.815.81
5.465.46
8.178.17
6.576.57
5.715.71
Question content area bottom
Part 1
Create a scatterplot of the data. Choose the correct graph below.
A.
05101520250510152025xy
A scatterplot has a horizontal x-scale from 0 to 25 in intervals of 1 and a vertical y-scale from 0 to 25 in intervals of 1. Eleven points are plotted with approximate coordinates as follows: (7.5, 10); (7, 7); (13, 12); (7, 10); (8, 12); (9, 13); (6, 5); (5.5, 4); (8, 11); (6.5, 6); (5.5, 5).
B.
05101520250510152025xy
A scatterplot has a horizontal x-scale from 0 to 25 in intervals of 1 and a vertical y-scale from 0 to 25 in intervals of 1. Eleven points are plotted with approximate coordinates as follows: (10, 9); (7, 7); (12, 7); (10, 6); (12, 7); (13, 8); (5, 8); (4, 8); (11, 6); (6, 6); (5, 13).
C.
05101520250510152025xy
A scatterplot has a horizontal x-scale from 0 to 25 in intervals of 1 and a vertical y-scale from 0 to 25 in intervals of 1. Eleven points are plotted with approximate coordinates as follows: (10, 7.5); (7, 7); (12, 13); (10, 7); (12, 8); (13, 9); (5, 6); (4, 5.5); (11, 8); (6, 6.5); (5, 5.5).
D.
05101520250510152025xy
A scatterplot has a horizontal x-scale from 0 to 25 in intervals of 1 and a vertical y-scale from 0 to 25 in intervals of 1. Eleven points are plotted with approximate coordinates as follows: (4, 6.5); (5, 7); (5, 8); (6, 13); (7, 7); (10, 8); (10, 9); (11, 6); (12, 5.5); (12, 5.5); (13, 7.5).
Part 2
Find the equation of the regression line.
ModifyingAbove y with caretyequals=enter your response hereplus+enter your response herex
(Round the y-intercept two decimal places as needed. Round the slope to three decimal places as needed.)
Part 3
Identify a characteristic of the data that is ignored by the regression line.
A.
The data has a pattern that is not a straight line.
B.
There is no characteristic of the data that is ignored by the regression line.
C.
There is an influential point that strongly affects the graph of the regression line.
D.
There is no trend in the data.
Solution
import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.stats import linregress
Given data
x = np.array([10, 7, 12, 10, 12, 13, 5, 4, 11, 6, 5]) y = np.array([7.38, 6.96, 13.15, 7.24, 7.79, 9.24, 5.81, 5.46, 8.17, 6.57, 5.71])
Scatterplot
plt.figure(figsize=(8, 6)) plt.scatter(x, y, color='blue', label='Data points') plt.title('Scatterplot of x vs. y') plt.xlabel('x') plt.ylabel('y') plt.grid(True) plt.legend() plt.show()
Regression calculation
slope, intercept, r_value, p_value, std_err = linregress(x, y) regression_line_eq = f"y = {intercept:.2f} + {slope:.3f}x"
Identify ignored characteristics
ignored_characteristic = "A. The data has a pattern that is not a straight line."
regression_line_eq, ignored_characteristic <<ImageDisplayed>>('y = 3.29 + 0.498x', 'A. The data has a pattern that is not a straight line.')('y = 3.29 + 0.498x', 'A. The data has a pattern that is not a straight line.')success### Results:
Part 1: Scatterplot
The scatterplot is displayed above, showing the relationship between the variables and .
Part 2: Equation of the Regression Line
The regression line equation is:
Part 3: Ignored Characteristic
The characteristic of the data ignored by the regression line is:
A. The data has a pattern that is not a straight line.
Would you like a deeper explanation or further insights into the calculations?
Follow-up Questions:
- How can the regression equation be used to make predictions?
- Why does the regression line fail to capture non-linear patterns?
- What is the significance of the -value in this analysis?
- How can residual analysis help in evaluating the fit of the regression line?
- Are there other methods to model data that has a non-linear pattern?
Tip: Always examine the scatterplot first to assess whether a linear model is appropriate for the data.
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Math Problem Analysis
Mathematical Concepts
Regression Analysis
Linear Equations
Scatterplots
Formulas
y = mx + b (equation of a line)
Slope formula: m = Σ[(x - x̄)(y - ȳ)] / Σ(x - x̄)^2
Theorems
Least Squares Method
Suitable Grade Level
Grades 10-12
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