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# Importing libraries
import scipy.stats as st
import numpy as np

# Creating random normal distribution
dist = st.norm.rvs(size=1000, loc=50, scale=2)

# Finding confidence interval
confidence = st.norm.interval(confidence=0.95, loc=np.mean(dist), scale=st.sem(dist))

print(confidence)

Everything was clear?

Thanks for your feedback!

Section 5. Chapter 3

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Course Content

Learning Statistics with Python

1. Basic Concepts

Sample vs Population Types of Statistics Types of Data Mean Value Median Value Median Value of the Even Number of Values Mean or Median Mode Value Descriptive Statistics Quiz

2. Mean, Median and Mode with Python

Examine the Dataset Calculating Mean and Median Values with Python Statistics with pandas Calculate the Mean and Median Salary

3. Variance and Standard Deviation

Population Variance Sample Variance Calculate Variance with Python Standard Deviation Standard Deviation with Python Calculating Variance and Standard Deviation

4. Covariance vs Correlation

Covariance Correlation Covariance and Correlation Quiz Calculate Covariance and Correlation

5. Confidence Interval

6. Statistical Testing

What is t-test Hypotheses t-test Mathematically One-Tailed And Two-Tailed Test t-test Assumptions Performing a t-test in Python Conduct a t-test Paired t-test

Calculating Confidence Interval with Python

What Values Can We Estimate Using a Confidence Interval?

In this course, we will estimate mean values, but you can also estimate other statistics such as variances, mathematical expectations, and more.

Now, let's dive into the function for calculating confidence intervals.


python

The st.norm.interval() function is used to compute a confidence interval with the following parameters:

The confidence parameter represents the confidence level;
The loc parameter signifies the mean value of the distribution;
The scale is the standard error of the mean.

What Is Standard Error of the Mean?

The standard error of the mean, often called standard error, measures how likely the population mean is to deviate from a sample mean.

Try to change the confidence parameter and observe the changes.


              1234567891011
            
# Importing libraries
import scipy.stats as st
import numpy as np

# Creating random normal distribution
dist = st.norm.rvs(size=1000, loc=50, scale=2)

# Finding confidence interval
confidence = st.norm.interval(confidence=0.95, loc=np.mean(dist), scale=st.sem(dist))

print(confidence)

Everything was clear?

Thanks for your feedback!

Section 5. Chapter 3