Introduction to
Statistics
Course Overview
Statistics is the branch of mathematics that involves collecting, analyzing, interpreting, presenting, and organizing data. It plays a crucial role in decision-making across various fields such as economics, science, business, and social sciences. By using techniques such as probability theory, hypothesis testing, and regression analysis, statistics helps in making informed predictions, identifying patterns, and drawing conclusions from data. Its applications are vast, from understanding trends in markets to analyzing scientific experiments and making policy decisions.
Course lessons
- Types of data
- Data collection methods
- Descriptive vs. inferential statistics
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (variance, standard deviation, range)
- Data visualization (graphs, histograms, box plots)
- Probability concepts and rules
- Conditional probability
- Probability distributions (binomial, normal, etc.)
- Sampling methods and sampling distributions
- Confidence intervals
- Hypothesis testing (t-tests, chi-square tests)
- Correlation coefficients
- Simple and multiple linear regression
- Model fitting and interpretation
- One-way ANOVA
- Two-way ANOVA
- Post hoc tests
- Chi-square tests
- Wilcoxon tests
- Rank-based methods
- Trend analysis
- Forecasting models
- Seasonal variations
- Using tools like SPSS, R, Excel, and Python for data analysis
- Interpreting output from statistical software
Skills Acquired
Data Analysis – Ability to analyze data using both descriptive and inferential statistics, applying measures of central tendency, variability, and distribution patterns.
Statistical Software Proficiency – Hands-on experience with statistical tools like SPSS, R, Excel, and Python to perform data analysis, generate visualizations, and interpret results.
Probability Theory – Understanding of probability concepts and the ability to apply probability distributions to real-world data for decision-making.
Hypothesis Testing – Proficiency in conducting hypothesis tests, analyzing p-values, and determining statistical significance for various data sets.
Regression and Correlation Analysis – Skill in performing regression analysis (both simple and multiple) and evaluating relationships between variables.
Data Visualization – Ability to present data effectively through charts, graphs, and plots, making complex data accessible and understandable.
Sampling and Survey Methods – Knowledge of sampling techniques and the ability to design surveys or experiments that collect reliable and valid data.
Predictive Analytics – Capability to build models for forecasting future trends based on historical data and apply techniques like time series analysis.
Course Features
The Statistics course offers a comprehensive curriculum that covers key concepts like data analysis, probability theory, hypothesis testing, and regression analysis.
Students will gain hands-on experience with industry-standard statistical tools such as R, SPSS, and Excel. The course also emphasizes practical applications, enabling learners to conduct real-world data analysis, make data-driven decisions, and effectively present findings using data visualization techniques. Additionally, students will explore predictive analytics and advanced statistical methods, preparing them for roles in research, business, and analytics.
Designed for all levels
hands-on learning
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