Data preparation and Statistical analysisMert Demir
Descriptive Statistics: This involves summarizing and describing the features of a data set. It includes measures like mean, median, mode, range, variance, and standard deviation. Descriptive statistics give a quick overview of the data and its characteristics.
Inferential Statistics: Unlike descriptive statistics, inferential statistics are used to make predictions or inferences about a population based on a sample of data. It involves techniques like hypothesis testing, confidence intervals, and regression analysis.
Regression Analysis: This method is used to examine the relationship between two or more variables. The most common types are linear regression and multiple regression, which are used to predict the value of a dependent variable based on the value of one or more independent variables.
Analysis of Variance (ANOVA): ANOVA is used to compare the means of three or more groups to determine if at least one mean is significantly different from the others. It's particularly useful in experimental and observational studies.
Time Series Analysis: This involves analyzing data points collected or observed at successive points in time. It's often used in economics, weather forecasting, and stock market analysis to identify trends, cycles, and seasonal variations.
Factor Analysis: This technique is used to reduce a large number of variables into fewer numbers of factors. This method is common in social sciences, where researchers deal with large datasets.
Cluster Analysis: Used for grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. It’s widely used in market research, pattern recognition, data analysis, and image processing.
Principal Component Analysis (PCA): PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
Non-parametric Methods: These are methods that do not assume a specific distribution for the data. They are useful when you have data that doesn’t fit traditional statistical models. Examples include the Mann-Whitney U test and the Kruskal-Wallis test.
Bayesian Statistics: This is an approach to statistics in which all evidence about the true state of the world is expressed in terms of degrees of belief or Bayesian probabilities.
What's included
Clean dataset
Cleaning and preparing all the messy data
Statistical analysis
After cleaning and preparing the dataset, using several methods for statistical analysis.
Mert's other services
Contact for pricing
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PyTorch
R
AI Developer
Data Analyst
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Mert Demir Rome, Italy
Data preparation and Statistical analysisMert Demir
Contact for pricing
Tags
Keras
Python
PyTorch
R
AI Developer
Data Analyst
Data Scientist
Descriptive Statistics: This involves summarizing and describing the features of a data set. It includes measures like mean, median, mode, range, variance, and standard deviation. Descriptive statistics give a quick overview of the data and its characteristics.
Inferential Statistics: Unlike descriptive statistics, inferential statistics are used to make predictions or inferences about a population based on a sample of data. It involves techniques like hypothesis testing, confidence intervals, and regression analysis.
Regression Analysis: This method is used to examine the relationship between two or more variables. The most common types are linear regression and multiple regression, which are used to predict the value of a dependent variable based on the value of one or more independent variables.
Analysis of Variance (ANOVA): ANOVA is used to compare the means of three or more groups to determine if at least one mean is significantly different from the others. It's particularly useful in experimental and observational studies.
Time Series Analysis: This involves analyzing data points collected or observed at successive points in time. It's often used in economics, weather forecasting, and stock market analysis to identify trends, cycles, and seasonal variations.
Factor Analysis: This technique is used to reduce a large number of variables into fewer numbers of factors. This method is common in social sciences, where researchers deal with large datasets.
Cluster Analysis: Used for grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. It’s widely used in market research, pattern recognition, data analysis, and image processing.
Principal Component Analysis (PCA): PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
Non-parametric Methods: These are methods that do not assume a specific distribution for the data. They are useful when you have data that doesn’t fit traditional statistical models. Examples include the Mann-Whitney U test and the Kruskal-Wallis test.
Bayesian Statistics: This is an approach to statistics in which all evidence about the true state of the world is expressed in terms of degrees of belief or Bayesian probabilities.
What's included
Clean dataset
Cleaning and preparing all the messy data
Statistical analysis
After cleaning and preparing the dataset, using several methods for statistical analysis.
Mert's other services
Contact for pricing