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.
Explore the function used to calculate confidence intervals.
st.norm.interval(confidence=0.95, loc=np.mean(dist), scale=st.sem(dist))
The st.norm.interval() function is used to compute a confidence interval with the following parameters:
- The
confidenceparameter represents the confidence level; - The
locparameter signifies the mean value of the distribution; - The
scaleis 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)
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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.
Explore the function used to calculate confidence intervals.
st.norm.interval(confidence=0.95, loc=np.mean(dist), scale=st.sem(dist))
The st.norm.interval() function is used to compute a confidence interval with the following parameters:
- The
confidenceparameter represents the confidence level; - The
locparameter signifies the mean value of the distribution; - The
scaleis 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)
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