Introduction to Statistical Analysis using IBM SPSS Statistics (v24)
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1 to Statistical Analysis using IBM SPSS Statistics (v24) to Statistical Analysis Using IBM SPSS Statistics is a two day instructor-led classroom course that provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, the assumptions made by each method, how to set up the analysis, as well as how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing underlying relationships. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results. Further to this, students will get to spend one day with the software for hands-on training. Audience: This basic course is for students with: Anyone who has worked with IBM SPSS Statistics and wants to become better versed in the basic statistical capabilities of IBM SPSS Statistics Base Anyone with limited or no statistical background Anyone who wants to refresh their knowledge and statistical experience that were gained many years ago High-level Curriculum Course Assessment Students will be assessed through 3 random exercises selected from the pool of 12 learning activities covering the following topics: to Statistical Analysis Understanding Data Distributions Theory Data Distributions for Categorical Variables Data Distributions for Scale Variables Making Inferences about Populations from Samples Relationships between Categorical Variables The Independent-Samples T Test The Paired-Samples T Test One-Way ANOVA Bivariate Plots and Correlations for Scale Variables Regression Analysis Non-Parametric Tests
2 Assessments will be marked and scores sent to the client after the training. Course Contents Lesson 0: Course Course About IBM Business Analytics Supporting Materials Course Assumptions References Lesson 1: to Statistical Analysis Basic Steps of the Research Process Populations and Samples Research Design Independent and Dependent Variables Lesson 2: Understanding Data Distributions Theory Levels of Measurement and Statistical Methods Measures of Central Tendency and Dispersion Normal Distributions Standardized (Z-) Scores Requesting Standardized (Z-) Scores Standardized (Z-) Scores Output Procedure: Descriptives for Standardized (Z-) Scores Demonstration: Descriptives for Z-Scores
3 Lesson 3: Data Distributions for Categorical Variables Using Frequencies to Summarize Nominal and Ordinal Variables Requesting Frequencies Frequencies Output Procedure: Frequencies Demonstration: Frequencies Lesson 4: Data Distributions for Scale Variables Summarizing Scale Variables Using Frequencies Requesting Frequencies Frequencies Output Procedure: Frequencies Demonstration: Frequencies Summarizing Scale Variables Using Descriptives Requesting Descriptives Descriptives Output Procedure: Descriptives Demonstration: Descriptives Summarizing Scale Variables Using the Explore Procedure Requesting Explore Procedure: Explore Demonstration: Explore Lesson 5: Making Inferences about Populations from Samples Basics of Making Inferences About Populations from Samples Influence of Sample Size Hypothesis Testing The Nature of Probability Types of Statistical Errors Statistical Significance and Practical Importance
4 Lesson 6: Relationships between Categorical Variables Crosstabs Crosstabs Assumptions Requesting Crosstabs Crosstabs Output Procedure: Crosstabs Example: Crosstabs Chi-Square Test Requesting the Chi-Square Test Chi-Square Output Procedure: Chi-Square Test Example: Chi-Square Test Clustered Bar Chart Requesting Clustered Bar Chart with Chart Builder Clustered Bar Chart from Chart Builder Output Procedure: Clustered Bar Chart with Chart Builder Example: Clustered Bar Chart with Chart Builder Adding a Control Variable Requesting a Control Variable Control Variable Output Procedure: Adding a Control Variable Example: Adding a Control Variable Syntax-Only Crosstabs Features Extensions: Beyond Crosstabs Association Measures Lesson 7: The Independent-Samples T Test The Independent-Samples T Test Independent-Samples T Test Assumptions Requesting the Independent-Samples T Test Independent-Samples T Test Output Procedure: Independent-Samples T Test Demonstration: Independent-Samples T Test Error Bar Chart Requesting an Error Bar Chart with Chart Builder Error Bar Chart Output Demonstration: Error Bar Chart with Chart Builder
5 Lesson 8: The Paired-Samples T Test The Paired-Samples T Test Assumptions for the Paired-Samples T Test Requesting a Paired-Samples T Test Paired-Samples T Test Output Procedure: Paired-Samples T Test Demonstration: Paired-Samples T Test Lesson 9: One-Way ANOVA One-Way ANOVA Assumptions of One-Way ANOVA Requesting One-Way ANOVA One-Way ANOVA Output Procedure: One-Way ANOVA Demonstration: One-Way ANOVA Post Hoc Tests with a One-Way ANOVA Requesting Post Hoc Tests with a One-Way ANOVA Post Hoc Tests Output Procedure: Post Hoc Tests with a One-Way ANOVA Demonstration: Post Hoc Tests with a One-Way ANOVA Error Bar Chart with Chart Builder Requesting an Error Bar Chart with Chart Builder Error Bar Chart Output Procedure: Error Bar Chart with Chart Builder Demonstration: Error Bar Chart with Chart Builder Lesson 10: Bivariate Plots and Correlations for Scale Variables Scatterplots Requesting a Scatterplot Scatterplot Output Procedure: Scatterplot Demonstration: Scatterplot Adding a Best Fit Straight Line to the Scatterplot Pearson Correlation Coefficient Requesting a Pearson Correlation Coefficient Bivariate Correlations Output
6 Procedure: Pearson Correlation with Bivariate Correlations Demonstration: Pearson Correlation with Bivariate Correlations Lesson 11: Regression Analysis Simple Linear Regression Simple Linear Regression Assumptions Requesting Simple Linear Regression Simple Linear Regression Output Procedure: Simple Linear Regression Demonstration: Simple Linear Regression Multiple Regression Multiple Linear Regression Assumptions Requesting Multiple Linear Regression Multiple Linear Regression Output Procedure: Multiple Linear Regression Demonstration: Multiple Linear Regression Automatic Linear Modeling Lesson 12: Non-Parametric Tests Non Parametric Analyses The Independent Samples Nonparametric Analysis Requesting an Independent Samples Nonparametric Analysis Independent Samples Nonparametric Tests Output Procedure: Independent Samples Nonparametric Tests Demonstration: Independent Samples Nonparametric Tests The Related Samples Nonparametric Analysis Requesting a Related Samples Nonparametric Analysis Related Samples Nonparametric Tests Output Procedure: Related Samples Nonparametric Tests Demonstration: Related Samples Nonparametric Tests Cape Town Johannesburg Durban Contact: Priscilla.doig@synergy.co.za
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