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DataScience_ISM_Ch04 – Instructor Solution Manual Chapter 4

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Access complete instructor solutions for Chapter 4 of Principles of Data Science by OpenStax. This Instructor Solution Manual (ISM) includes worked solutions, sample answers, and step-by-step explanations for exercises covering data cleaning, exploratory data analysis, and descriptive statistics. Ideal for instructors preparing lectures, grading assignments, or guiding students through Chapter 4. Official ISM access requires verified instructor credentials via OpenStax.

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Institution
Data Science
Course
Data Science

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, Principles of Data Science



Chapter 4
Inferential Statistics and Regression Analysis



Critical Thinking Questions
[4.1, LO 4.1.1]
1. An automotive designer wants to create a 90% confidence interval for the mean length of a
body panel on a new model electric vehicle. From a sample of 35 cars, the sample mean length
is 37 cm and the sample standard deviation is 2.8 cm.
a. Create a 90% confidence interval for the population mean length of the body panel
using Python.
b. Create a 90% confidence interval for the population mean length of the body panel
using Excel.
c. Create a 95% confidence interval for the population mean length of the body panel
using Python.
d. Create a 95% confidence interval for the population mean length of the body panel
using Excel.
e. Which confidence interval is narrower, the 90% or 95% confidence interval?

Solution a: A confidence interval for the mean when the population standard deviation is
unknown can be calculated using the Python t-interval() function.

import scipy.stats as stats
from scipy.stats import t
import numpy as np
import math

# Enter sample mean, sample standard deviation, and sample size
sample_mean = 37
sample_standard_deviation = 2.8
sample_size = 35

# Degrees of freedom (sample size - 1)
degrees_of_freedom = sample_size - 1

# Confidence level
confidence_level = 0.90

# standard error
standard_error = sample_standard_deviation / math.sqrt(sample_size)

# Calculate confidence interval using t.interval function
t.interval(confidence_level, degrees_of_freedom, sample_mean,
standard_error)

The resulting output will look like this:


11/11/24 For more free, peer-reviewed, openly licensed resources visit OpenStax.org. 2

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