Description

SAS Statistical Business Analyst Overview:

The SAS Statistical Business Analyst exam training is designed for individuals aiming to enhance their statistical analysis and business intelligence proficiency using SAS software. SAS (Statistical Analysis System) is a powerful analytics platform widely used across industries for data management, advanced analytics, and business intelligence.

This training program provides comprehensive instruction on utilizing SAS tools and techniques for statistical analysis, data manipulation, predictive modeling, and reporting. Participants learn how to effectively apply statistical methods to extract valuable insights from data, optimize decision-making processes, and drive business growth.

Key topics covered in the SAS Statistical Business Analyst exam training may include:

1. SAS programming: Learning the fundamentals of SAS programming language and syntax for data manipulation, analysis, and reporting.
2. Statistical analysis techniques: Understanding various statistical methods such as descriptive statistics, hypothesis testing, regression analysis, and multivariate analysis.
3. Predictive modeling: Exploring predictive modeling techniques using SAS tools such as SAS Enterprise Miner to develop predictive models for forecasting and risk assessment.
4. Data visualization: Creating insightful data visualizations and reports using SAS Visual Analytics or other SAS reporting tools to communicate findings effectively.
5. Business applications: Applying statistical analysis and predictive modeling techniques to address real-world business challenges across various industries, such as finance, healthcare, marketing, and retail.

By completing the SAS Statistical Business Analyst exam training, individuals gain the skills and knowledge necessary to pass the SAS Statistical Business Analyst certification exam. This certification validates their expertise in using SAS software for statistical analysis and business intelligence, enhancing their credibility and career opportunities in the field of analytics and data-driven decision-making.

SAS Statistical Business Analyst Exam Details & Instructions

Exam Content:

These objectives will be tested on the exam. For more information about each objective, download the complete exam content guide.

1. 10% – ANOVA.
2. 20% – Linear regression.
3. 25% – Logistic regression.
4. 20% – Prepare inputs for predictive model performance.
5. 25% – Measure model performance.

Exam Details

SAS Statistical Business Analysis Using SAS®9: Regression and Modeling Exam

Use this exam ID to register:

1. A00-240
2. This exam is administered by SAS and Pearson VUE.
3. 60 multiple choice and short-answer questions.
4. In addition to the 60 scored items, there may be up to five unscored items.
5. Two hours to complete exam.
6. Passing score is 68%.

TABLE OF CONTENTS

1. Chapter 1: Statistics and Making Sense of Our World
2. Introduction
3. What Is Statistics?
4. Variable Types and SAS Data Types
5. The Data Analytics Process
6. Getting Started with SAS
7. Key Terms
8. Chapter 2: Summarizing Your Data with Descriptive Statistics
9. Introduction
10. Measures of Center
11. Measures of Variation
12. Measures of Shape
13. Other Descriptive Measures
14. The MEANS Procedure
15. Key Terms
16. Chapter Quiz
17. Chapter 3: Data Visualization
18. Introduction
19. View and Interpret Categorical Data
20. View and Interpret Numeric Data
21. Visual Analyses Using the SGPLOT Procedure
22. Key Terms
23. Chapter Quiz
24. Chapter 4: The Normal Distribution and Introduction to Inferential Statistics
25. Introduction
26. Continuous Random Variables
27. The Sampling Distribution of the Mean
28. Introduction to Hypothesis Testing
29. Hypothesis Testing for the Population Mean (σ Known)
30. Hypothesis Testing for the Population Mean (σ Unknown)
31. Key Terms
32. Chapter Quiz
33. Chapter 5: Analysis of Categorical Variables
34. Introduction
35. Testing the Independence of Two Categorical Variables
36. Measuring the Strength of Association between Two Categorical Variables
37. Key Terms
38. Chapter Quiz
39. Chapter 6: Two-Sample t-Test
40. Introduction
41. Independent Samples
42. Paired Samples
43. Key Terms
44. Chapter Quiz
45. Chapter 7: Analysis of Variance (ANOVA)
46. Introduction
47. One-Factor Analysis of Variance
48. The Randomized Block Design
49. Two-Factor Analysis of Variance
50. Key Terms
51. Chapter Quiz
52. Chapter 8: Preparing the Input Variables for Prediction
53. Introduction
54. Missing Values
55. Categorical Input Variables
56. Variable Clustering
57. Variable Screening
58. Key Terms
59. Chapter Quiz
60. Chapter 9: Linear Regression Analysis
61. Introduction
62. Exploring the Relationship between Two Continuous Variables
63. Simple Linear Regression
64. Multiple Linear Regression
65. Variable Selection Using the REG and GLMSELECT Procedures
66. Assessing the Validity of Results Using Regression Diagnostics
67. Concluding Remarks
68. Key Terms
69. Chapter Quiz
70. Chapter 10: Logistic Regression Analysis
71. Introduction
72. The Logistic Regression Model
73. Logistic Regression with a Categorical Predictor
74. The Multiple Logistic Regression Model
75. Scoring New Data
76. Key Terms
77. Chapter Quiz
78. Chapter 11: Measure of Model Performance
79. Introduction
80. Preparation for the Modeling Phase
81. Assessing Classifier Performance
82. Adjustment to Performance Estimates When Oversampling Rare Events
83. The Use of Decision Theory for Model Selection
84. Key Terms
85. Chapter Quiz
86. References

Pricing & Discounts

1. $180
2. Exam fees in the US and most other countries.

For additional information regarding this training, please visit here.

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