The Statistical Analysis Using Certified Course is a comprehensive, hands-on program designed to equip learners with the skills required to perform effective statistical data analysis for real-world applications. This course covers the foundational and advanced techniques of descriptive and inferential statistics, hypothesis testing, regression modeling, and data interpretation using modern analytical tools and software.
Through a combination of theoretical understanding and practical implementation, participants will learn how to collect, analyze, visualize, and draw conclusions from data—enabling them to support data-driven decision-making in business, research, healthcare, social sciences, and more. Whether you’re a student, researcher, or working professional, this course provides the essential tools to enhance your analytical capabilities.
Introduction to Statistics Basics of statistics and its significance Types of data: Qualitative vs Quantitative Scales of measurement: Nominal, Ordinal, Interval, Ratio Population vs Sample Data collection techniques.
Descriptive Statistics Measures of central tendency: Mean, Median, Mode Measures of dispersion: Range, Variance, Standard Deviation Skewness and Kurtosis Frequency distribution tables. Data visualization: Histograms, Bar charts, Pie charts, Box plots
Probability and Probability Distributions Basic probability rules and concepts Conditional probability and Bayes’ Theorem Discrete and continuous distributions Binomial, Poisson, and Normal distributions Central Limit Theorem.
Sampling and Sampling Distributions Sampling techniques: Random, Stratified, Cluster, Systematic Sampling errors vs non-sampling errors Sampling distributions and the Law of Large Numbers Standard error and confidence intervals.
Hypothesis Testing Null and alternative hypothesis Type I and Type II errors Z-test, T-test (one-sample, two-sample), F-test P-values and significance levels Chi-square test for independence and goodness of fit.
Correlation and Regression Analysis Scatter plots and correlation coefficients Pearson and Spearman correlation Simple linear regression Multiple linear regression Model assumptions and diagnostics.
Analysis of Variance (ANOVA) One-way ANOVA Two-way ANOVA Post hoc analysis (Tukey’s HSD) Applications of ANOVA in real-world datasets
Time Series Analysis Components of time series: Trend, Seasonality, Cyclical Moving averages and exponential smoothing Autocorrelation and partial autocorrelation ARIMA models basics Forecasting techniques.
Statistical Tools and Software Introduction to Excel for stats SPSS for descriptive and inferential analysis R programming basics for stats Python libraries (Pandas, Numbly, Statsmodels, Seaborn) Case study: Tool-based statistical analysis.
Capstone Project and Case Studies Real-world industry case study implementation Hands-on project using real data Report preparation and result interpretation Domain-specific applications: Healthcare, Business, Engineering, Social Sciences.
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Email: coursedivine@gmail.com
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