Analytics in SPSS: from beginner to confident user

About Course

The course very gradually, from simple to complex, immerses professionals from non-technical sciences (management, business, humanities, linguists, psychologists, sociologists, cultural scientists, economists, political strategists, etc.) in the exciting analysis of data and the search for hidden patterns and methods of predictive analytics – and will help you easily navigate, use and not be afraid.

The course is also suitable for engineering professionals who have not studied data analysis, but want to understand it – without unclear formulas and cumbersome calculations.

The course is based on the most modern materials demonstrating the possibilities of using the SPSS program in various fields (marketing and sociological research, personnel research, opinion polls, development of psychodiagnostic tools and tests, analysis and forecasting, etc.)

Loading and importing data from different sources
Transforming and cleaning data, preparing the array for analysis
Descriptive statistics: mean, mode, median, quartiles, etc.
Predictive analytics
Finding differences between groups
Identifying hidden relationships between variables
Classification tasks (will not give credit, buy will not buy goods, etc.), construction of neural networks
Time series analysis, search for patterns and forecasting
Syntax Basics
Other SPSS Features

Course Content

Introduction

Author’s word
Just came to inquire for fun?
Warning before the course
How will we learn

Not about spss: basic non-technical concepts

Introduction to the section
Descriptive and analytical statistics
The Importance of Models in Analytics
Without a model: exploratory data analysis (rad) and data mining
Sample and population
Sample size calculation
Complex case: representativeness and sample size
Data array
Analysis objects (strings)
Variable characteristics
Types of scales for variables
Data type for variable values
Requirements for writing values in an array
Array Awareness
The concept of a statistical hypothesis
Formulation of hypotheses
Probability of error and significance level
Normal distribution
Параметрика и непараметрика
Functional and probabilistic relationships
Data analysis process in an organization
Section results
Testing on the results of the section

Introduction to spss

Preparing the workspace: installing tools
Basic interface elements
Bookmarkview data
TabView Variables
Invisible element: context menu
The ribbon is the main control part of the program (+ quick access panel)
Analysis results window: output
Your own language: syntax window
Section results
Interface
Get to know the interface yourself

Basics for a quick start

Introduction to the section
Entering variables: creating an array passport
Passportization
Data entry: variable values
Massiveness
Reverse: loading the finished array and editing variables
The essence of preparing data for analysis
Readiness
The essence of data analysis
Analyzer
The essence of data visualization
Visualizer: not just tables
Exporting analysis results
Export
Section results

Preparing data for analysis: loading, cleaning and transformation

Introduction
Extracting and Loading Data: Reading and Importing
Loading a finished array
Editing Variables
Adjust the variables yourself
Ordering Variables
Let’s move it around
Removing variables (columns)
Delete unnecessary
Displaying Variable Values
Two sides of the same coin
Filtering Selecting observations (rows) for analysis
(un)natural selection
Repairing samples using observation selection
Array splitting: automatic selection of observations in the context of virtual groups
Split
Sorting observations (rows)
Sorter
Removing rows
Lowercase “annihilation”
Detection and cleaning of duplicates
Duplication
Detecting and dealing with input errors
Primitive: a simple way to find errors
Detecting input errors in a meaningful way
Smyslovukha
About working with missing values
Missing values: casescasesobjects (strings)
Gaps in observations of objects (rows)
Missing values: variables (columns)
Passes on variables (columns)
Descriptive and analytical statistics
Ranking
Multiple Answers: Virtual Variable
Virtual set
Transpose an array
Transposition (switching columns and rows)
Weighing data: working with an aggregated array
Weigher for the unit

Descriptive Statistics

The essence of descriptive statistics
Frequency analysis (frequency distribution)
Interesting frequencies
Frequencies for several variables: contingency tables (cross-classification)
Interesting frequencies: crosstabs
Frequencies for Multiple Choice Responses
Many to unity
Turf analysis
Turfology
4 groups of measures in descriptive statistics
Measures of central tendency: mean, mode, median
Pp-qq
Comprehensive data explore
Review approach
Metrics ratio
Percentages
Custom Pivot Tables reports (custom tables)
Summary
Visualizations: diagrams
Visual
Section results

Analytical statistics: what is it?

Analytical statistics: differences between groups

What is it used for?
Parametrics and non-parametrics again
Once again about the significance of differences between groups
Dependent (paired, related) and independent samples
Basic group comparison tool: contingency tables and chi-square test
Group Comparison: Chi-Square
Comparison of 2 independent groups (samples): t-test, parametrics
T-test for means (parametrics)
Comparison of 2 independent groups (samples): nonparametrics
Non-parametric for comparing 2 independent samples: mann-whitney
Lots of independent
Comparison of Groups in Analysis of Missing Values
Comparison of 2 paired (related) groups/samples: t-test, parametrics
Pair parametrics
Comparison of 2 paired (related) groups/samples: nonparametrics
Paired nonparametrics
Comparison of multiple paired samples
Paired set
Section results

A special case of group comparison: one-sample comparison tests

One-Sample Comparison Tests
T-test for means (parametrics)
Parameter for one sample
General overview of the window with one-sample tests for nonparametrics
Binomial test (non-parametric)
Binomial
Chi-square test (non-parametric)
Square chi
Testing the shape of the distribution (nonparametrics)
Ks-norm
Wilcoxon signed (median) test
Wilcoxon sign
Is the sequence of values random or not (non-parametric)
Exchange
Section results

Analytical statistics: relationships between variables

Causality and dependent and independent variables
Reasoning about relationships between variables
And once again about what a significant statistical relationship is
The essence of correlation of variables
Contingency tables again: just to check the connection
Communication via crossstabs
Checking the contact form
Form
Correlation analysis: strength, direction, significance
The strength of the connection is not important
Dealing with spurious correlations
Recognize a lie
The essence of regression analysis
Factor analysis
Factorization
Reliability-consistency (suitability) analysis
Suitability
Multidimensional scaling
Mnogomerka
Results of the section on searching for hidden relationships between variables

Analytical statistics: object classification

What is it used for?
Logistic regression
Logistics
Multinomial logistic regression
Multiple Logistics
Ordinal regression
Order
Probit analysis
Try
Discriminant
Decision trees classifications
Tree
Roc classifier (receiver operating characteristic)
Roc
Neural Networks: Multilayer Perceptron (mlp)
Neuronka
Results of classification from a “bird’s eye view””

Analytical Statistics: Basics of Time Series Forecasting

Introduction, purpose and subject area disclaimers
The main pitfall of forecasting over time
Main tasks of time series analysis
Time series components: trend, seasonality, cycle, burst
The starting point for trend analysis is to look at the chart by eye”
Start of time
Time series analysis: trend line with forecast and forecast “corridor”
Trendovik
““manifestation” of a trend using a moving average
Slip
Seasonal decomposition
Season
Auto- and cross-correlations
Temporal correlation
Section results

Syntax Basics: Introducing the Internal Spss Language

What is syntax and what can be useful for a business user
Opening, populating, launching and saving the syntax window
Rename variables and delete variables
Removing variables and changing their names
Calculation of variables (compute)
Calculation
Conditional statements if, and and or
Conventions
Recoding variables (recode with to, into and else)
Translation
Basic aggregator functions (sum, mean, count, min, max)
Aggregations
Comments in syntax (* or /*)
Commentator
Why take the syntax directly from the user interface and be able to edit it?
Section results

A brief overview of the individual capabilities of spss and its “relatives””

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