Mathematics and Data Analysis Training

Mathematics and Data Analysis Training

Mathematics and data analysis Training, Engineering statistics  and Statistical Data Reduction

The Mathematics and data analysis/Engineering statistics /Statistical Data reduction training course will provide you the sufficient knowledge to understand the statistics which is required in uncertainty calculations, statistical and probability. This training course covers the main topics of engineering statistics and data analysis such as: different types of data and their representations, data analysis, data reduction, descriptive and inferential statistics, probability, random variables and regression topics.

The Mathematics and data analysis/Engineering statistics /Statistical Data reduction training provides you the in depth knowledge of all the basic topics in reliability and statistics and explains how the theory is applied in engineering which you can perform in your organization. By learning the fundamentals of data analysis and engineering statistics, you will be able to tackle real projects along with statistical concepts. During the training course, you will learn about the concept of samples and population in statistics and how to present them. Moreover, you will be introduced to the main topics of data analysis such as: organization of data, analyzing the quantitative or qualitative data, clinical significance and interpreting the results of data analysis.

By taking the Mathematics and data analysis/Engineering statistics /Statistical Data reduction training course, you will master the concepts of statistic engineering including descriptive and inferential statistics, probability concepts and random variables. You will be able to organize the quantitative and qualitative data, understand the descriptive measure of population and calculation of variance for descriptive data.  Moreover, the engineering aspect of probability in statistics will be taught which gets you familiar with different theories behind the probability analysis.

The Mathematics and data analysis/Engineering statistics /Statistical Data reduction training course will help you to understand the inferential statistics topics such as confidence intervals, margin of error, hypothesis testing, critical value approach and inference of population. Furthermore, different types of distributions which is a vital part of engineering statistics will be introduced and discussed in detail.

Audience

The Mathematics and data analysis/Engineering statistics /Statistical Data reduction training is a 2-day course designed for:

• Anyone who is making a product of process assessment to make effective decision for different situations
• Product design managers, engineers, or quality managers
• Individuals seeking to attain the certified statistic status
• clients interested in developing Statistical Engineering competencies within their own companies
• Personnel involved with product quality assurance and regulatory compliance testing
• Engineers to manipulate the data, create data visualizations and make strategic predictions
• Mangers and individuals dealing with uncertainty in their products or processes.
• Faculty members who want to teach the engineering statistics or data analysis course

Training Objective

Upon the completion of Mathematics and data analysis/Engineering statistics /Statistical Data reduction, the attendees are able to:

• Understand the basic concepts in statistics
• Explain different types of data in statistics
• Lean the methods to represent different types of data
• Understand the main topics of data analysis
• Describe the procedures in statistical data reduction
• Explain the different between descriptive and inferential statistics
• Understand the uncertainty theory and fundamentals of probability analysis
• Describe the random variables with their probability functions
• Understand the correlation and regression concepts in statistics engineering
• Differentiate the different types of distributions in statistics

Training Outline

The outline of Mathematics and data analysis/Engineering statistics /Statistical Data reduction is mentioned in the following which can be revised and tailored to the client’s need:

Introduction to statistics

• Definition
• Population
• Data and variables
• Class intervals
• Descriptive statistics
• Inferential statistics

Data

• Qualitative data
• Quantitative data
• Distribution shapes
• Discrete and continuous data
• Analog and digital data

Representing the data

• Bar graphs
• Pie charts
• Dot plots
• Line graphs
• Scatter plots
• Pictograph
• Histograms
• Frequency distribution
• Cumulative tables

Data Analysis

• Organizing data
• Analyzing quantitative data
• Mean, median, mode and range
• Inferential analysis
• Statistical significance
• Clinical significance
• Analyzing qualitative data
• Interpreting the results

Data Reduction

• Measures of location/ measures of central tendency
• Mean versus expected value
• Properties of the mean
• Measures of dispersion
• Properties of variance
• Standard deviation
• Coefficient of variation
• Measures of association
• Covariance
• Correlation coefficient

Descriptive statistics

• Organizing quantitative and qualitative data
• Measure of center
• Concept of mean, mode and median
• Measure of variation
• Descriptive measure for populations

Inferential statistics

• Estimation of population mean
• Confidence intervals
• Margin of error
• Hypothesis testing
• Critical value approach
• P-value approach
• Inference for two population means
• Sampling distribution of two sample means
• Mann-Whitney test
• Inference for population standard deviation
• Inference for population proportions

Probability and random variables

• Basics of probability
• Events
• Rules of probability
• Contingency tables
• Conditional probability
• Theory of intersection
• Rules of addition
• The multiplication rule and independence
• Bayes’s rule
• Test statistics
• Counting rule
• Discrete random variables and probability distribution
• Mean and standard deviation of a random variable

Distributions

• Binomial distribution
• Continuous distribution
• Normal distribution
• Gaussian distribution
• Statistical significance
• Confidence intervals
• Chi-Square distribution
• F-distribution
• Poisson distribution

Correlation and regression

• Linear equations with one independent variable
• The regression equation
• Linear correlation
• Regression model
• Estimation and prediction
• Inferences in correlation
• Analysis of variance (ANOVA)
• One way ANOVA
• multiple comparisons