How to Install and Uninstall perl-Statistics-Descriptive.noarch Package on Fedora 35
Last updated: November 29,2024
1. Install "perl-Statistics-Descriptive.noarch" package
Please follow the instructions below to install perl-Statistics-Descriptive.noarch on Fedora 35
$
sudo dnf update
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$
sudo dnf install
perl-Statistics-Descriptive.noarch
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2. Uninstall "perl-Statistics-Descriptive.noarch" package
This guide let you learn how to uninstall perl-Statistics-Descriptive.noarch on Fedora 35:
$
sudo dnf remove
perl-Statistics-Descriptive.noarch
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$
sudo dnf autoremove
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3. Information about the perl-Statistics-Descriptive.noarch package on Fedora 35
Last metadata expiration check: 1:50:24 ago on Wed Sep 7 08:25:01 2022.
Available Packages
Name : perl-Statistics-Descriptive
Version : 3.0800
Release : 4.fc35
Architecture : noarch
Size : 75 k
Source : perl-Statistics-Descriptive-3.0800-4.fc35.src.rpm
Repository : fedora
Summary : Perl module of basic descriptive statistical functions
URL : https://metacpan.org/release/Statistics-Descriptive
License : (GPL+ or Artistic) and MIT
Description : This module provides basic functions used in descriptive statistics. It has
: an object oriented design and supports two different types of data storage
: and calculation objects: sparse and full. With the sparse method, none of
: the data is stored and only a few statistical measures are available. Using
: the full method, the entire data set is retained and additional functions
: are available.
Available Packages
Name : perl-Statistics-Descriptive
Version : 3.0800
Release : 4.fc35
Architecture : noarch
Size : 75 k
Source : perl-Statistics-Descriptive-3.0800-4.fc35.src.rpm
Repository : fedora
Summary : Perl module of basic descriptive statistical functions
URL : https://metacpan.org/release/Statistics-Descriptive
License : (GPL+ or Artistic) and MIT
Description : This module provides basic functions used in descriptive statistics. It has
: an object oriented design and supports two different types of data storage
: and calculation objects: sparse and full. With the sparse method, none of
: the data is stored and only a few statistical measures are available. Using
: the full method, the entire data set is retained and additional functions
: are available.