Statistics Training Courses and Workshops

Schools providing training courses, certificates, diplomas or degree programs of Statistics




Total 578 training courses and degree programs available around the world.

United States - United Kingdom - Canada - Australia - India

Popular courses:
Statistics
Business Statistics
Biostatistics
Applied Statistics
Survey Analysis Using IBM SPSS Statistics
Data Management and Manipulation with IBM SPSS Statistics


Statistics

Course Format: Online
School/Trainer: Anne Arundel Community College
Training Center(s)/Venue(s): Annapolis, Arnold, Baltimore, Edgewater, Gambrills, Hanover, Laurel, Severn, United States
  V

Use meaningful data to explore concepts in probability and statistics including measures of central tendency and dispersion. Develop statistical literacy by studying graphical representations of data, discrete and continuous probability distributions, and sampling techniques and theory. Construct and interpret confidence intervals, find lines of best-fit, and perform hypothesis tests for means, proportions, and independence. Technology use is required throughout the course for statistical analyses.

Business Statistics

Course Format: Online
School/Trainer: Anne Arundel Community College
Training Center(s)/Venue(s): Annapolis, Arnold, Baltimore, Edgewater, Gambrills, Hanover, Laurel, Severn, United States

Learn statistical analysis as an aid in business decision making through the use of descriptive statistics, probability, confidence intervals, hypothesis testing, chi square, analysis of variance, regression and correlation analysis.

Financial Data/Statistics Management

Course Format: Classroom
School/Trainer: Globe University - Wisconsin
Training Center(s)/Venue(s): Appleton, Eau Claire, La Crosse, Madison, Wausau, United States

Students will investigate information technology solutions
used to manage financial data/statistics and their applications. Research
topics include qualitative and quantitative approaches, validity and reliability
testing, and related practices.

Financial Data/Statistics Management

Course Format: Classroom
School/Trainer: Globe University Sioux Falls Campus
Training Center(s)/Venue(s): Sioux Falls, United States

Students will investigate information technology solutions
used to manage financial data/statistics and their applications. Research
topics include qualitative and quantitative approaches, validity and reliability
testing, and related practices.

Financial Data/Statistics Management

Course Format: Classroom
School/Trainer: Minnesota School of Business & Globe University
Training Center(s)/Venue(s): Blaine, Minneapolis, Richfield, Rochester, Woodbury, United States

Students will investigate information technology solutions
used to manage financial data/statistics and their applications. Research
topics include qualitative and quantitative approaches, validity and reliability
testing, and related practices.

SAS Visual Statistics: Interactive Model Building

Course Format: Classroom
School/Trainer: Global Knowledge USA
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

In this course, you will learn about SAS Visual Statistics for building predictive models in an interactive, exploratory way. Exploratory model fitting is a critical step in modeling big data.

Who Needs To Attend

Predictive modelers
Business analysts
Data scientists who want to take advantage of SAS Visual Statistics for highly interactive, rapid model fitting

Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression (Certificate)

Course Format: Classroom
School/Trainer: Global Knowledge USA
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

This course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t-tests, ANOVA, linear regression, and logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum.

Certification:
SAS Certified Clinical Trials Programmer Using SAS 9
SAS Statistical Business Analysis Using SAS 9: Regression and Modeling

Survey Analysis Using IBM SPSS Statistics

Course Format: Classroom
School/Trainer: Global Knowledge USA IBM Training Centers
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

This course reviews the standard methods that are used to analyze survey data, beginning with simple methods, such as crosstabulations, and moving toward the advanced, such as logistic regression. Appropriate methods of analysis are discussed for both categorical and continuous data. Also included are discussions of qualitative data analysis and the reporting and presentation of survey results.

Course Content
## The Logic of Survey Analysis
## Data checking and data validation
## Data transformations: create new variables
## Testing for Reliability and Validity
## Analyzing Categorical Variables
## Analyzing Interval Variables
## Analyzing Text Data
## Reporting Survey Results for Categorical and Scale Data
## Clustering Respondents
## Multivariate Analysis using Regression Techniques
## Special Issues: Missing Data
## Special Issues: Complex Samples and Sample Weights
## Measuring Change over Time with Surveys
## Decision Tree Analysis

Statistical Analysis Using IBM SPSS Statistics

Course Format: Classroom
School/Trainer: Global Knowledge USA IBM Training Centers
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

Introduction to Statistical Analysis
Explain the difference between a sample and a population
Explain the difference between an experimental research design and a non-experimental research design
Explain the difference between independent and dependent variables

Examine Individual Variables
Describe the levels of measurement used in IBM SPSS Statistics
Use graphs to examine variables
Use summary measures to examine variables
Explain normal distributions
Explain standardized scores and their use

Test Hypotheses-Theory
Explain the difference between a sample and a population
Design a test of a hypothesis
Explain the alpha level
Explain the difference between statistical and practical significance
Describe the two types of errors in testing a hypothesis

Test Hypotheses about Individual Variables
Explain the sampling distribution of a statistic
Explain the difference between the standard deviation and the standard error
Use the One-Sample T Test to test a hypothesis about a population mean
Use the Paired-Samples T Test to test on an &,quot,&,quot,before-... [Read More]

IBM SPSS Statistics

Course Format: Classroom
School/Trainer: Global Knowledge USA IBM Training Centers
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

Introduction to IBM SPSS Statistics (V23) guides you through the fundamentals of using IBM SPSS Statistics for typical data analysis process. You will learn the basics of reading data, data definition, data modification, and data analysis and presentation of analytical results. You will also see how easy it is to get data into IBM SPSS Statistics so that you can focus on analyzing the information. In addition to the fundamentals, you will learn shortcuts that will help you save time. This course uses the IBM SPSS Statistics Base features.

IBM SPSS Statistics: Exploratory Data Analysis V19

Course Format: Classroom
School/Trainer: Global Knowledge USA IBM Training Centers
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

a one day instructor-led online course that provides a practical, application-oriented introduction to some of the advanced statistical methods available in IBM®SPSS® Statistics for data analysts and researchers. Students will review several advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, as well as how to interpret the results. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence and interpret their output.

Factor Analysis
## Explain the basic theory of factor analysis and the steps in factor analysis
## Explain the assumptions and requirements of factor analysis
## Specify a factor analysis and interpret the output

K-Means Cluster Analysis
## Explain the basic theory of cluster analysis and the steps in doing a cluster analysis
## Explain the approach of K-Means cluster analysis
## Specify a K-Means cluster analysis and interpret the output

TwoStep Cluster Analysis
## Explain the basic approach of TwoStep cluster analys... [Read More]

IBM SPSS Statistics Syntax

Course Format: Classroom
School/Trainer: Global Knowledge USA IBM Training Centers
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

Syntax is the command language that gives instructions to SPSS Statistics on how to modify, manage, and analyze your data. Syntax programs can be used to automate repetitive tasks and perform additional options that are not available in the dialog boxes. You will learn the rules of SPSS syntax, how to generate syntax from the dialog boxes, modify and optimize its use. The approach of the course will be that the dialog boxes and syntax are complementary.

Course Content
## Why Syntax?
## Working with Syntax ## Generating Syntax from the GUI
## The Syntax Editor
## Managing Syntax (e.g., log syntax in the Viewer and/or a journal file, Insert syntax from file)

## Syntax for Reading and Saving Data (e.g. for reading and saving SPSS Statistics data files, ExcelTM files, text files)
## Syntax for Defining Variables (e.g. variable and value labels, missing values)
## Syntax for Selecting Cases (e.g. filtering cases, copying selected to new dataset)
## Syntax for Transformations (e.g. Compute, Visual Binning, Recode)

Data Management and Manipulation with IBM SPSS Statistics

Course Format: Classroom
School/Trainer: Global Knowledge USA IBM Training Centers
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

Helpful Data Management Features
Read from a database using the Database Wizard
Customize variable attributes
Compare datasets
Use Variable Sets
Rename datasets

Use Functions
Identify the general form of a function
Use statistical functions
Use logical functions
Use missing value functions
Use conversion functions
Use system variables

Additional Data Transformations
Use Automatic Recode to recode string variables into numeric variables
Use Count Values within Cases to count values across variables

Set the Unit of Analysis
Remove duplicate cases
Create aggregated datasets
Restructure datasets

Merge Files
Add cases from one dataset to another
Add variables from one dataset to another
Enrich a dataset with aggregated information

Analyze Multiple Response Questions
Describe the two ways to encode a multiple response set
Define multiple response sets
Use the Multiple Response Frequencies and Crosstabs procedures

Edit Tables and Charts
Use the features of the Pivot Table Editor
Create and apply ... [Read More]

Advanced Statistical Analysis Using IBM SPSS Statistics (V19)

Course Format: Classroom
School/Trainer: Global Knowledge USA IBM Training Centers
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

Factor Analysis
## Explain the basic theory of factor analysis and the steps in factor analysis
## Explain the assumptions and requirements of factor analysis
## Specify a factor analysis and interpret the output

K-Means Cluster Analysis
## Explain the basic theory of cluster analysis and the steps in doing a cluster analysis
## Explain the approach of K-Means cluster analysis
## Specify a K-Means cluster analysis and interpret the output

TwoStep Cluster Analysis
## Explain the basic approach of TwoStep cluster analysis
## Specify a TwoStep cluster analysis
## Use the Model Viewer to study and interpret the output

Binary Logistic Regression
## Explain the basic theory and assumptions of logistic regression
## Specify a logistic regression analysis
## Interpret model fit, logistic regression coefficients and model accuracy

Multinomial Logistic Regression
## Explain the basic theory of multinomial logistic regression
## Specify a multinomial logistic regression analysis
## Interpret model fit, logistic regression coefficients and model accuracy

D... [Read More]

IBM Cognos Statistics: Author Statistical Reports in Report Studio

Course Format: Classroom
School/Trainer: Global Knowledge USA IBM Training Centers
Training Center(s)/Venue(s): Arlington, Atlanta, Cary, Irving, Morristown, New York City, Santa Clara, Schaumburg, Seattle, United States
  V

Introduction to IBM Cognos Statistics
Explain what IBM Cognos Statistics is
Explain the architecture of IBM Cognos Statistics
Explain data types used in IBM Cognos Statistics
Explain the use of the case variable in statistical objects

Examine Descriptive Statistical Reports
Compare descriptive statistics and inferential statistics
Examine descriptive statistics (mean, count, minimum, maximum, standard deviation)

Examine Data Distribution with Statistical Charts
Explain histograms and normal distribution curves
Explain boxplots
Explain grouping variables
Explain normality tests using Q-Q plot

Examine Curve Estimation, Correlation and Regression
Compare categorical data and numeric data
Explain statistical significance
Explain hypothesis testing
Demonstrate the relationship between two variables using basic correlation
Explain linear and logarithmic curves in curve estimation
Explain regression coefficients

Examine the Means Comparison Tests
Explore a one-sample t-test
Examine the one-way ANOVA technique

Examine Nonparametric Tests... [Read More]

Survey Analysis Using IBM SPSS Statistics

Course Format: Classroom
School/Trainer: Global Knowledge Canada IBM Training Centres
Training Center(s)/Venue(s): Halifax, Mississauga, Montreal, Ottawa, Toronto, Winnipeg, Canada
  V

This course reviews the standard methods that are used to analyze survey data, beginning with simple methods, such as crosstabulations, and moving toward the advanced, such as logistic regression. Appropriate methods of analysis are discussed for both categorical and continuous data. Also included are discussions of qualitative data analysis and the reporting and presentation of survey results.

Course Content
## The Logic of Survey Analysis
## Data checking and data validation
## Data transformations: create new variables
## Testing for Reliability and Validity
## Analyzing Categorical Variables
## Analyzing Interval Variables
## Analyzing Text Data
## Reporting Survey Results for Categorical and Scale Data
## Clustering Respondents
## Multivariate Analysis using Regression Techniques
## Special Issues: Missing Data
## Special Issues: Complex Samples and Sample Weights
## Measuring Change over Time with Surveys
## Decision Tree Analysis

Statistical Analysis Using IBM SPSS Statistics

Course Format: Classroom
School/Trainer: Global Knowledge Canada IBM Training Centres
Training Center(s)/Venue(s): Halifax, Mississauga, Montreal, Ottawa, Toronto, Winnipeg, Canada
  V

Introduction to Statistical Analysis
Explain the difference between a sample and a population
Explain the difference between an experimental research design and a non-experimental research design
Explain the difference between independent and dependent variables

Examine Individual Variables
Describe the levels of measurement used in IBM SPSS Statistics
Use graphs to examine variables
Use summary measures to examine variables
Explain normal distributions
Explain standardized scores and their use

Test Hypotheses-Theory
Explain the difference between a sample and a population
Design a test of a hypothesis
Explain the alpha level
Explain the difference between statistical and practical significance
Describe the two types of errors in testing a hypothesis

Test Hypotheses about Individual Variables
Explain the sampling distribution of a statistic
Explain the difference between the standard deviation and the standard error
Use the One-Sample T Test to test a hypothesis about a population mean
Use the Paired-Samples T Test to test on an &,quot,&,quot,before-... [Read More]

IBM SPSS Statistics

Course Format: Classroom
School/Trainer: Global Knowledge Canada IBM Training Centres
Training Center(s)/Venue(s): Halifax, Mississauga, Montreal, Ottawa, Toronto, Winnipeg, Canada
  V

Introduction to IBM SPSS Statistics (V23) guides you through the fundamentals of using IBM SPSS Statistics for typical data analysis process. You will learn the basics of reading data, data definition, data modification, and data analysis and presentation of analytical results. You will also see how easy it is to get data into IBM SPSS Statistics so that you can focus on analyzing the information. In addition to the fundamentals, you will learn shortcuts that will help you save time. This course uses the IBM SPSS Statistics Base features.

IBM SPSS Statistics: Exploratory Data Analysis V19

Course Format: Classroom
School/Trainer: Global Knowledge Canada IBM Training Centres
Training Center(s)/Venue(s): Halifax, Mississauga, Montreal, Ottawa, Toronto, Winnipeg, Canada
  V

a one day instructor-led online course that provides a practical, application-oriented introduction to some of the advanced statistical methods available in IBM®SPSS® Statistics for data analysts and researchers. Students will review several advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, as well as how to interpret the results. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence and interpret their output.

Factor Analysis
## Explain the basic theory of factor analysis and the steps in factor analysis
## Explain the assumptions and requirements of factor analysis
## Specify a factor analysis and interpret the output

K-Means Cluster Analysis
## Explain the basic theory of cluster analysis and the steps in doing a cluster analysis
## Explain the approach of K-Means cluster analysis
## Specify a K-Means cluster analysis and interpret the output

TwoStep Cluster Analysis
## Explain the basic approach of TwoStep cluster analys... [Read More]

IBM SPSS Statistics Syntax

Course Format: Classroom
School/Trainer: Global Knowledge Canada IBM Training Centres
Training Center(s)/Venue(s): Halifax, Mississauga, Montreal, Ottawa, Toronto, Winnipeg, Canada
  V

Syntax is the command language that gives instructions to SPSS Statistics on how to modify, manage, and analyze your data. Syntax programs can be used to automate repetitive tasks and perform additional options that are not available in the dialog boxes. You will learn the rules of SPSS syntax, how to generate syntax from the dialog boxes, modify and optimize its use. The approach of the course will be that the dialog boxes and syntax are complementary.

Course Content
## Why Syntax?
## Working with Syntax ## Generating Syntax from the GUI
## The Syntax Editor
## Managing Syntax (e.g., log syntax in the Viewer and/or a journal file, Insert syntax from file)

## Syntax for Reading and Saving Data (e.g. for reading and saving SPSS Statistics data files, ExcelTM files, text files)
## Syntax for Defining Variables (e.g. variable and value labels, missing values)
## Syntax for Selecting Cases (e.g. filtering cases, copying selected to new dataset)
## Syntax for Transformations (e.g. Compute, Visual Binning, Recode)

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