Statistical Aid
Statistical Aid is a site that provides statistical content, data analysis content, and also discusses the various fields of statistics.
Lean more about:
1. Machine learning & Data Science
Basic Statistics
Basic statistics is a branch of mathematics that focuses on the collection, analysis, interpretation, and presentation of data. It involves understanding key concepts like descriptive statistics, which summarize data using measures such as the mean, median, mode, range, and standard deviation to provide insights into data trends and variability. Additionally, inferential statistics allows us to make predictions or generalizations about a population based on sample data, using methods like hypothesis testing and confidence intervals. Data visualization, through tools such as graphs and charts, plays an important role in presenting information clearly, helping identify patterns or anomalies. This foundation of statistical techniques is widely applicable in fields like economics, medicine, engineering, social sciences, and business, enabling researchers and professionals to make informed decisions, evaluate risks, and draw meaningful conclusions from both small and large datasets.
- Definition and scope of statistics
- Statistical Data
- Population vs Sample
- Random Variable
- Central tendency
- Arithmetic, Geometric and harmonic mean
- Measures of Dispersion
- Variance and Standard Deviation
- Skewness and Kurtosis
- Correlation analysis
- Intra vs Inter class correlation
- Regression Analysis
- Data levels (Nominal, ordinal, Interval and Ratio)
- Hypothesis Testing
- Multicollinearity
- correlation vs causation
- standard deviation calculation
- Residuals
- Bayes theorem
- random vs systematic error
- Stem and leaf plot
- Graphical representation of data
- Range rule of Thumb
- Relative frequency distribution
- Elementary Statistics
- Statistics Symbols
- Z table
- Standardization
- Error Propagation
- choosing the right regression analysis
- Permutation vs combination
- Interaction Effects
- T distribution table
- Chebysheve's theorem
- Trimmed Mean
- Two way table
- Critical value
- Benford's law
- Reliability and validity
- cronbache's Alpha
- Data Driven Decision making
- Type I and Type II error
- Box and whisker plot
- OLS, Ordinary Least Squares
- P-value
- Interquertile Range
- Covariates
A probability distribution is a mathematical function that describes the likelihood of different outcomes in a random experiment or event.
- Bernoulli Distribution
- Binomial Distribution
- Negative Binomial distribution
- Poission Distribution
- Exponential Distribution
- Normal distribution
- Gamma Distribution
- Geometric Distribution
- Hypergeometric Distribution
- Uniform Distribution
- Power Series Distribution
- Logarithmic Series Distribution
- Skewed Distribution
- Bomidal Distribution
Probability sampling ensures that every individual in the population has a known, non-zero chance of being selected.
- Simple Random Sampling
- Stratified Sampling
- Systematic Sampling
- Multi-Stage Sampling
- Cluster Sampling
- Quadrat Sampling
Non-probability sampling doesn't offer each member of the population an equal chance of selection. Methods like convenience sampling, where participants are selected based on availability, or purposive sampling, where specific individuals are chosen for a particular purpose, fall under this category.
Data analysis using SPSS (Statistical Package for the Social Sciences) involves the use of this software to organize, analyze, and interpret data in a variety of fields, including social sciences, healthcare, and business.
Data analysis using spss
Data input in spss
Import data in spss
Sort data using spss
Merge data file in spss
Combine data set in spss
Missing value in spss
Variable transformation in spss
Univariate analysis in spss
Bivariate analysis in spss
Normality of data in spss
Data Analysis Using Stata
- Introduction to data analysis using Stata
- Data cleaning and editing in Stata
- Variable manipulation and reliability in Stata
- Multiple Regression analysis in Stata
Data Analysis Using R/R Studio
- Import data into R
- Principal component analysis (PCA) code
- Canonical correlation analysis (CCA) code
- Independent component analysis (ICA) code
- Cluster Analysis using R
- One-way ANOVA using R
- Two-way ANOVA using R
- Paired sample t-test using R
- One sample T-test using R
- Random forest in R
- Chi-square test in R
- Pearson correlation test in R
- Two way repeated measure ANOVA
- One way repeated measure ANOVA
Machine Learning and Data Science
- Introduction to Machine
- Data Science
- Python Programming for Data Science
- Exploratory Data Analysis
- Data Cleaning and Preprocessing
- Data Visualization with Matplotlib and Seaborn
- Big Data Analytics
- Supervised Learning
- Unsupervised Learning
- Reinforcement learning
- Deep Learning
- Decision tree classifier
- Support vector machines
- convolutional neural networks
- Artificial neural network
- Natural language processing
There are also some other topics as follows:
- Machine Learning and Data Science
- Non-Parametric Tests
- Parametric vs Non-parametric test
- Time Series Analysis
- Statistical Inference
- Experimental Design