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 xx and yy.

Part 2: Equation of the Regression Line
The regression line equation is:
y=3.29+0.498xy = 3.29 + 0.498x

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:

  1. How can the regression equation be used to make predictions?
  2. Why does the regression line fail to capture non-linear patterns?
  3. What is the significance of the rr-value in this analysis?
  4. How can residual analysis help in evaluating the fit of the regression line?
  5. 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