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Introduction to Statistic
Course Overview
AP Statistics is a college-level course that introduces students to the principles and methods of statistical analysis. The course covers a wide range of topics, including data collection and organization, probability theory, hypothesis testing, and data interpretation. Students will develop the skills necessary to analyze and interpret data, make informed decisions, and communicate statistical information effectively.
Develop ability to collect, organize, and analyze data using appropriate statistical methods.
Enhance understanding of probability theory and its applications in real-world scenarios.
Skills to conduct hypothesis tests, make inferences, and draw conclusions based on sample data.
To foster critical thinking and problem-solving skills through hands-on data analysis and interpretation.
Statistics, Probability, Inference, ANOVA and Regression.
Lesson 1: Introduction to Statistics
Lesson 2: Descriptive Statistics
Lesson 3: Sampling and Experimental Design
Lesson 4: Probability
Lesson 5: Probability Distributions
Lesson 6: Sampling Distributions
Lesson 7: Confidence Intervals
Lesson 8: Hypothesis Testing
Lesson 9: Inference for Means
Lesson 10: Inference for Proportions
Lesson 11: Chi-Square Tests
Lesson 12: Regression Analysis
Lesson 13: Correlation Analysis
Lesson 14: Multiple Regression
Lesson 15: Analysis of Variance (ANOVA)
Lesson 16: Nonparametric Methods
Lesson 17: Confidence Intervals and Hypothesis Testing for Regression
Lesson 18: Experimental Design and Analysis
Lesson 19: Simulation and Probability Models
Lesson 20: Review and Exam Preparation
Introduction to SPSS
Course Overview
Our SPSS and statistics course provides a comprehensive overview of using SPSS for data analysis. Students will learn data entry, manipulation, and visualization techniques. They will master descriptive and inferential statistics, including hypothesis testing and regression analysis. By the end, students will have the skills to confidently analyze data, interpret results, and make informed decisions using SPSS.
Confidently navigate and utilize the SPSS software for statistical analysis.
They will gain proficiency in applying descriptive statistics and data visualization techniques to summarize and interpret data effectively.
Students will acquire the skills to conduct various inferential statistical tests and hypothesis testing using SPSS.
They will learn to analyze relationships between variables through correlation and regression analysis
SPSS, Data Collection, Data Analysis, Theory and Practical.
Lesson 1: Introduction to SPSS
Lesson 2: Descriptive Statistics
Lesson 3: Data Visualization
Lesson 4: Inferential Statistics Part 1
Lesson 5: Inferential Statistics Part 2
Lesson 6: Correlation Analysis
Lesson 7: Regression Analysis Part 1
Lesson 8: Regression Analysis Part 2
Lesson 9: Data Transformation
Lesson 10: Factor Analysis
Lesson 11: Cluster Analysis
Lesson 12: Multivariate Analysis
Introduction to Excel and Advance Excel
Course Overview
Master Excel with our comprehensive Base Excel and Advanced Excel courses. In Base Excel, learn essential functions, data manipulation, and basic analysis. Advance your skills in Advanced Excel with pivot tables, advanced functions, macros, and automation. Become an Excel expert and boost your productivity and data management capabilities.
Proficiency in essential Excel functions, formulas, and data manipulation techniques.
Ability to analyze data using functions like VLOOKUP, SUMIF, and COUNTIF.
Skills in creating dynamic reports and charts, and applying conditional formatting.
Proficiency in pivot tables for data summarization and analysis.
Knowledge of advanced Excel features like macros, data validation, and automation for enhanced productivity.
Start-up with MS Excel, Quick review on MS Excel Customize Ribbon, Quick Access Toolbar, Mini
Toolbar.
1. Introduction of Excel shortcut keys: Chart will be provided by Advanced Excel.
2. Introduction to Excel Worksheet, Row, Column, Cells, etc.
3. Detailed discussion on Excel design, Back-End working structure, and Excel Options.
4. Use of Basic Operators Like: + – / * ^ %.
5. Introduction to the Data and Data Formats.
6. Copy, Cut, Paste, Hide, Unhide, Link the Data in Rows, Columns, and Sheets.
7. Inserting, Deleting, Moving and linking the data in between the multiple sheets.
Introduction to R and Statistic
Course Overview
Learn R programming and data analytics in this comprehensive 10hr course. Master data manipulation, cleaning, and visualization techniques using R. Explore statistical analysis, Regression and time series analysis. Develop advanced visualization skills. Apply your knowledge to real-world projects and gain practical experience in data analytics
Develop ability to collect, organize, and analyze data using appropriate statistical methods.
Proficiency in R programming language, including data manipulation, cleaning, and visualization techniques.
Understanding of statistical analysis concepts and how to apply them using R, including hypothesis testing, regression analysis, and analysis of variance.
Knowledge of predictive modeling and techniques for regression and time series tasks using R.
Statistics, R-Markdown, R, Time Series and Regression.
Session 1: Introduction to R and Data Analytics
Session 2: Data Manipulation and Cleaning in R
Session 3: Data Visualization in R
Session 4: Statistical Analysis in R
Session 7: Time Series Analysis in R
Introduction to Statistic
Course Overview
AP Statistics is a college-level course that introduces students to the principles and methods of statistical analysis. The course covers a wide range of topics, including data collection and organization, probability theory, hypothesis testing, and data interpretation. Students will develop the skills necessary to analyze and interpret data, make informed decisions, and communicate statistical information effectively.
Develop ability to collect, organize, and analyze data using appropriate statistical methods.
Enhance understanding of probability theory and its applications in real-world scenarios.
Skills to conduct hypothesis tests, make inferences, and draw conclusions based on sample data.
To foster critical thinking and problem-solving skills through hands-on data analysis and interpretation.
Statistics, Probability, Inference, ANOVA and Regression.
Lesson 1: Introduction to Statistics
Lesson 2: Descriptive Statistics
Lesson 3: Sampling and Experimental Design
Lesson 4: Probability
Lesson 5: Probability Distributions
Lesson 6: Sampling Distributions
Lesson 7: Confidence Intervals
Lesson 8: Hypothesis Testing
Lesson 9: Inference for Means
Lesson 10: Inference for Proportions
Lesson 11: Chi-Square Tests
Lesson 12: Regression Analysis
Lesson 13: Correlation Analysis
Lesson 14: Multiple Regression
Lesson 15: Analysis of Variance (ANOVA)
Lesson 16: Nonparametric Methods
Lesson 17: Confidence Intervals and Hypothesis Testing for Regression
Lesson 18: Experimental Design and Analysis
Lesson 19: Simulation and Probability Models
Lesson 20: Review and Exam Preparation
Introduction to Statistic
Course Overview
AP Statistics is a college-level course that introduces students to the principles and methods of statistical analysis. The course covers a wide range of topics, including data collection and organization, probability theory, hypothesis testing, and data interpretation. Students will develop the skills necessary to analyze and interpret data, make informed decisions, and communicate statistical information effectively.
Develop ability to collect, organize, and analyze data using appropriate statistical methods.
Enhance understanding of probability theory and its applications in real-world scenarios.
Skills to conduct hypothesis tests, make inferences, and draw conclusions based on sample data.
To foster critical thinking and problem-solving skills through hands-on data analysis and interpretation.
Statistics, Probability, Inference, ANOVA and Regression.
Lesson 1: Introduction to Statistics
Lesson 2: Descriptive Statistics
Lesson 3: Sampling and Experimental Design
Lesson 4: Probability
Lesson 5: Probability Distributions
Lesson 6: Sampling Distributions
Lesson 7: Confidence Intervals
Lesson 8: Hypothesis Testing
Lesson 9: Inference for Means
Lesson 10: Inference for Proportions
Lesson 11: Chi-Square Tests
Lesson 12: Regression Analysis
Lesson 13: Correlation Analysis
Lesson 14: Multiple Regression
Lesson 15: Analysis of Variance (ANOVA)
Lesson 16: Nonparametric Methods
Lesson 17: Confidence Intervals and Hypothesis Testing for Regression
Lesson 18: Experimental Design and Analysis
Lesson 19: Simulation and Probability Models
Lesson 20: Review and Exam Preparation
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