**Making changes? Want to evidence their impact and measure improvement?**

** Variation itself is neither good nor bad. It is something natural of life. The aim of the Statistical Process Control is to keep the process under control, understand its performance and make decisions. **

- Detect and eliminate special causes of variation.
- Establishment of the process specification and control limits.
- Identification of how much a process conforms to the specification limits.
- Continuous hypothesis testing to check if the process is under control.
- Know the cost of each error to act first where the worse losses are being generated.
- Measure the performance of your processes in the short and long-term.
- Determine the qualitative and quantitative quality characteristics.
- Control of proportions, populations, events within the same process, ...
- Monitorization.
- Representation of deviations (and accumulations)
- And much more...

Control Charts

Principal Component Analysis, PCA

**Making changes? Want to evidence their impact and measure improvement?**

** Variation itself is neither good nor bad. It is something natural of life. The aim of the Statistical Process Control is to keep the process under control, understand its performance and make decisions. **

- Detect and eliminate special causes of variation.
- Establishment of the process specification and control limits.
- Identification of how much a process conforms to the specification limits.
- Continuous hypothesis testing to check if the process is under control.
- Know the cost of each error to act first where the worse losses are being generated.
- Measure the performance of your processes in the short and long-term.
- Determine the qualitative and quantitative quality characteristics.
- Control of proportions, populations, events within the same process, ...
- Monitorization.
- Representation of deviations (and accumulation of them).
- And much more...

**The first step to reducing variability is to understand how the data is. **

**This is where Exploratory Analysis comes in, providing simplicity with the use of simple graphics that include relevant information about the data and its characteristics, helping us to make decisions.**

- Explore the data distribution.
- Identify outliers, discontinuities, concentrations of variables, relations between variables, ...
- Population studies, randomization, ...
- Detect relations between pairs of quantitative. variables with scatter plots.

Skim

Balance

Scatter plots

Histograms

Boxplots

Sequential charts

Normality test

**The first step to reducing variability is to understand how the data is. **

**This is where Exploratory Analysis comes in, providing simplicity with the use of simple graphics that include relevant information about the data and its characteristics, helping us to make decisions.**

- Explore the data distribution.
- Identify outliers, discontinuities, concentrations of variables, relations between variables, ...
- Population studies, randomization, ...
- Detect relations between pairs of quantitative. variables with scatter plots.

**Let us forget for a moment technical concepts and the most common definitions of control, monitorization, and improvement, and ask ourselves… What is the basis of quality management? Isn’t it detecting problems early and fixing them before they start to have a direct impact on productivity?**

**This type of analysis focuses on managing the processes to detect recurring and relevant events, known as patterns, and before they become major problems.**

**More proactivity and less reactivity.**

- Data mining techniques.
- Reduction of dimensionality (summarize the structure of the data) by describing them in new uncorrelated variables and without losing relevant information.
- Predicts behavior of either individuals or groups.
- Use different characteristics to discover probabilities of occurrence.
- Avoid multicollinearity in multiple regression by eliminating variables and classifying the information considering the essential.
- Clustering methods to constitute groups of similar individuals (basing on the variables).

Principal Component Analysis, PCA

Clustering techniques:

∴ Hierarchical

∴ K-means

**Let us forget for a moment technical concepts and the most common definitions of control, monitorization, and improvement, and ask ourselves… What is the basis of quality management? Isn’t it detecting problems early and fixing them before they start to have a direct impact on productivity?**

**This type of analysis focuses on managing the processes to detect recurring and relevant events, known as patterns, and before they become major problems.**

**More proactivity and less reactivity.**

- Data mining techniques.
- Reduction of dimensionality (summarize the structure of the data) by describing them in new uncorrelated variables and without losing relevant information.
- Predicts behavior of either individuals or groups.
- Use different characteristics to discover probabilities of occurrence.
- Avoid multicollinearity in multiple regression by eliminating variables and classifying the information considering the essential.
- Clustering methods to constitute groups of similar individuals (basing on the variables).

**Among the many ways in which statistics help us to improve, one of the most demanded by companies is to predict what will happen in the future to reduce costs, increase profits, and detect market trends.**

**Predictive models help to infer the probability that certain situations will occur before they happen and to deduce future results. These data analysis methods make it possible to have a better understanding of the information we collect at our facilities, predict performance and failures, and respond in time.**

- Determine the factors that have the greatest influence on your results.
- Establish dependency relations between variables (dependent and explanatory) to estimate the population average value of the first in terms of known values.
- Find out if something you want to occur or not can happen.
- Probability that several events will occur in a fixed interval of time and / or space if these events occur each a constant time and independently of the time elapsed since the last event.
- Analysis of variance (measure that represents the variability) and its evaluation, for example, the effect of a treatment on the variability of the response variable.

Lineal

Logistic

Poisson

ANOVA

**Among the many ways in which statistics help us to improve, one of the most demanded by companies is to predict what will happen in the future to reduce costs, increase profits, and detect market trends.**

**Predictive models help to infer the probability that certain situations will occur before they happen and to deduce future results. These data analysis methods make it possible to have a better understanding of the information we collect at our facilities, predict performance and failures, and respond in time.**

- Determine the factors that have the greatest influence on your results.
- Establish dependency relations between variables (dependent and explanatory) to estimate the population average value of the first in terms of known values.
- Find out if something you want to occur or not can happen.
- Probability that several events will occur in a fixed interval of time and / or space if these events occur each a constant time and independently of the time elapsed since the last event.
- Analysis of variance (measure that represents the variability) and its evaluation, for example, the effect of a treatment on the variability of the response variable.

Measure what can be measured, and make measurable what cannot be measured

Galileo Galilei

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