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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

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

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.

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

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

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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