Lecture

Introduction to Data Analysis

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In course

CH-234: Organic functions and reactions II

To develop basic understanding of the reactivity of aromatic and heteroaromatic compounds. To develop a knowledge of a class of pericyclic reactions. To apply them in the context of the synthesis. ...

Description

This lecture covers the basics of data analysis, including data cleaning, visualization, and interpretation. It also introduces statistical concepts such as correlation, regression, and hypothesis testing. The instructor explains how to use Python libraries for data analysis and provides examples of real-world applications.

Instructors (2)

Felix Daniel Bobbink

Nicolai Cramer

Nicolai Cramer was born in Stuttgart, Germany; he studied chemistry at the University of Stuttgart where he graduated in 2003, and earned his PhD in 2005 under the guidance of Professor Sabine Laschat. After a research stage at Osaka University, Japan, he joined the group of Professor Barry M. Trost at Stanford University as a postdoctoral fellow in 2006. From 2007 on, he worked on his habilitation at the ETH Zurich associated to the chair of Professor Erick M. Carreira and recieved the venia legendi in 2010. In 2010, he started as Assistant Professor at the EPF Lausanne and was promoted to Associate Professor in 2013 and to Full Professor in 2015. His main research program encompasses enantioselective metal-catalyzed transformations and their implementation for the synthesis of biologically active molecules. Author profile (Angew. Chem. Int. Ed.) CV

Official source

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related concepts (32)

Correlation

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Pearson correlation coefficient

In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations.

Segmented regression

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions.

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