Statistical inference is very essential in making decisions based on available data. Many organizations these days now rely on data driven decision making to increase their productivity and service provision. In this post, we look at how we can use R to make inferences on data.

The data used for this work is an extract from the General social survey (GSS) 1972–2012 data. The data extract is part of the final project used by Duke University’s Inferential Statistics course on Coursera.

We begin our work by loading in the required libraries.

`library(ggplot2)`

library(dplyr)

library(statsr)

library(tidyr)

In performing our inference, we…

This is an analysis of a dataset using python. The data being used was obtained from ‘https://www.kaggle.com/jacobbaruch/basketball-players-stats-per-season-49-leagues'. The data set contains information on players from 49 different basketball leagues in the world which spans from seasons 1999 to 2020.

The general idea is to use data analysis modules like **pandas, matplotlib and seaborn** in python to find some statistical information from the dataset. This is a project being undertaken as part of the course Data Analysis with Python: Zero to Pandas, which has thought me a lot of useful features and function in python.

Pytorch is a library for processing tensors. A tensor can be a number, vector, matrix or any ndarray. There are various functions in the

module that can be used to create and manipulate tensors. The five(5) functions we will learn are as follows:*torch*

*torch.eye**torch.complex**torch.chunk**torch.dstack**torch.movedim*

I will provide **three** examples each for each function. **Two** of the examples I provide will work and the **last one** will throw an ** error**. We will find out why the error is returned and take note of why it happened.

Before we begin, let’s install and import PyTorch

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