 # Analytics, Basic Statistics And Metrics Training

Learn the basics of extracting meaning from data sets.

## Course Overview

Our 4-day, instructor-led Analytics, Basic Statistics and Metrics course is designed for individuals hoping to understand the basics of extracting meaning from data sets. This course covers the following:

• The research process and statistical reasoning
• Populations and samples
• Planning and conducting a study
• Graphical representations of data using dotplots, stemplots, histograms, cumulative frequency plots, and boxplots
• Measuring position using quartiles, percentiles, and z-scores
• Measuring center using mean, median, and mode
• Measuring spread using range, interquartile range, and standard deviation
• Anticipating patterns using probability
• Standard normal distribution
• Extracting a random sample from a population
• Identifying the reliability of an estimate using confidence intervals
• Identifying relationships between variables with correlation and regression analysis

There is no prerequisite for this course.

## Schedule

Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 240-667-7757. #### Not seeing a good fit?

Let us know. Our team of instructional designers, curriculum developers, and subject matter experts can create a custom course for you.

## Course Outline

### Introduction to Social Science Research Principles and Terminology

• Populations, samples, parameters and statistics
• Inferential and descriptive statistics
• Sampling issues
• Types of variables and scales of measurement
• Research design
• Making sense of distributions and graphs
• Wrapping up and looking forward
• Work problems

### Measures of Central Tendency

• Measures of central tendency in depth
• Example: the mean, median and mode of skewed distributions
• Writing it up
• Wrapping up and looking forward
• Work problems

### Measures of Variability

• Range
• Variance
• Standard deviation
• Measures of variability in depth
• Why have variance?
• Examples: examining the range, variance, and standard deviation
• Worked examples
• Wrapping up and looking forward
• Work problems

### The Normal Distribution

• Characteristics of the normal distribution
• Why is the normal distribution so important?
• The normal distribution in depth
• The relationship between the sampling method and the normal distribution
• Skew and Kurtosis
• Example 1: applying normal distribution probabilities to a normal distribution
• Example 2: applying normal distribution probabilities to a nonnormal distribution
• Wrapping up and looking forward
• Work problems

### Standardization and z Scores

• Standardization and z scores in depth
• Interpreting z scores
• Examples: comparing raw scores and z scores
• Worked examples
• Wrapping up and looking forward
• Work problems

### Standard Errors

• What is standard error?
• Standard errors in depth
• How to calculate the standard error of the mean
• The central limit theorem
• The normal distribution and t distributions: comparing z scores and t values
• The use of standard errors in inferential statistics
• Example: sample size and standard deviation effects on the standard error
• Worked examples
• Wrapping up and looking forward
• Work problems

### Statistical Significance, Effect, Size, and Confidence Intervals

• Statistical Significance in depth
• Limitations of statistical significance testing
• Effect size in depth
• Confidence intervals in depth
• Example: statistical significance, confidence interval. and effect size for a one-sample t test of motivation
• Wrapping up and looking forward
• Work problems

### t Tests

• What is a t test?
• t distributions
• The one-sample t test
• The independent samples t test
• Dependent (paired) samples t test
• Independent samples t tests in depth
• The standard error of the difference between independent sample means
• Determining the significance of the t value for an independent samples t test
• Paired of dependent samples t tests in depth
• Example 1: comparing boys’ and girls’ grade point averages
• Example 2: comparing fifth- and sixth- grade GPAs
• Writing it up
• Worked examples
• Wrapping up and looking forward
• Work problems

### One-Way Analysis of Variance

• ANOVA vs. independent t tests
• One-way ANOVA in depth
• Deciding if the group means are significantly;y different
• Post-Hoc tests
• Effect size
• Example: comparing the sleep of 5-, 8-, and 12-year-olds
• Writing it up
• Worked example
• Wrapping up and looking forward
• Work problems

### Factorial Analysis of Variance

• When to use factorial ANOVA
• Some cautions
• Factorial ANOVA in depth
• Interpreting main effects in the presence of an interaction effect
• Testing simple effects
• Analysis of covariance
• Illustration of factorial ANOVA, ANCOVA, and effect size with real data
• Example: performance, choice, and public vs. private evaluation
• Writing it up
• Wrapping up and looking forward
• Work problems

### Repeated-Measures Analysis of Variance

• When to use each type of repeated-measures technique
• Repeated-measures ANOVA in depth
• Repeated-measures analysis of Covariance (ANCOVA)
• Adding an independent group variable
• Example: changing attitudes about standarized tests
• Writing it up
• Wrapping up and looking forward
• Work problems

### Correlation

• When to use correlation and what it tells us
• Pearson correlation coefficients in depth
• A brief word on other types of correlation coefficients
• Example: the correlation between grades and test scores
• Writing it up
• Wrapping up and looking forward
• Work problems

### Regression

• Simple vs. multiple regression
• Variables used in regression
• Regression in depth
• Multiple regression
• ExampleL Predicting the use of self-handicapping strategies
• Writing it up
• Worked examples
• Wrapping up and looking forward
• Work problems

### The Chi-Square Test of Independence

• Chi-square test of independence in depth
• Example: generational status and grade level
• Writing it up
• Worked example
• Wrapping up and looking forward
• Work problems

### Factor Analysis and Reliability Analysis: Data Reduction Techniques

• Factor analysis in depth
• A more concrete example of exploratory factor analysis
• Confirmatory factor analysis: a brief introduction
• Reliability analysis in depth
• Writing it up
• Work Problems
• Wrapping up

## Analytics, Basic Statistics And Metrics Training FAQs

Who should enroll in Analytics, Basic Statistics and Metrics Training?

This course is ideal for Organizational Analysts, Functional Managers, IT Specialists, Statisticians, or Business Intelligence Professionals.

### \$1,348 – \$1,595

• ##### Price Match Guarantee

We’ll match any competitor’s price quote. Call us at 240-667-7757.

• #### This training course includes:

• 4 days of instructor-led training
• Analytics, Basic Statistics and Metrics Training book