Syllabus

1. Basics R:

• Introduction of R
• Interacting with R and R studio
• R studio Handling
• Data types
• Data structure
• Packages in R and its installation
• Set working directory
• Import various types of files in R (such as Excel, STATA, and SPSS)
• Data cleaning functions -Select, rename, mutate using dplyr, filter, arrange, summaries, group_by, slice,s etc
• Exporting the data to excel
• Create a contingency table.
• The basic concept to create the graph using ggolot2
• Argument and properties of ggplot2
• Scatter plot
• Add some elements to the theme and apply the pre-built theme.
• Boxplot
• Bar chart
• Histogram
• Line chart

2. Basics Statistics Concept:

• Concept of statistical tests and their terminology (Hypothesis, model fit, Population and Sample, Standard Error, Confidence interval, Effect Size, type of Error) Check Assumptions
• Exploring the assumption of the parametric test
• Normality test- Histogram,
• Density Curve,
• Q-Q Plot, Skewness, Kurtosis
• Shapiro Wilk Test,
• Kolmogorov Smirnov test
• Homogeneity of variance test- Leven’s Test
• Dealing with Outliers

3. Correlation:

• Carl Pearson’s Correlation Coefficient and correlation test.
• Concept of Coefficient of Determination
• Spearman and Kendall tau Correlation Test
• Partial Correlation test
• Point Biserial and Biserial Correlation test

4. Linear Regression:

• Basics Concept of Regression -Intercept, Coefficient, SST, SSM, SSR, etc
• Simple regression model Fit and interpret the outcome.
• Multiple Linear Regression
• Test the accuracy of the Model
• Detect the outliers of sample data: Residuals, and standardized residuals.
• Detect the influential points in sample data
• Run the multiple regression and interpret the result
• Durbin Watson test- for Independence of error check
• VIF- Variance influence factor- For multicollinearity
• Breusch Pagan test- Homogeneity
• Graphical Method for assumptions check.
• Regression with dummy Coding.

5. Logistic Regression:

• Logistics regression: Concepts
• Terminology: Log Likelihood, Wald Test, odds, odd ratio
• Probable Error in Logistics regression: Incomplete Information, Complete separation, overdispersion
• Binary logistics regression Model fitting
• Test the accuracy: chi-square test, Pseudo R square
• Interoperate the model coefficient and odd ratio
• Case wise diagnostic
• Testing for multicollinearity

6. Comparing Means(T-test):

• T-test and its assumptions
• Independent and dependent t-test
• Welch t-test
• Checking the assumption of the t-test
• Interpretation and application of t-test

7. Analysis of Variance (ANOVA):

• Introduction to ANOVA
• Theory of ANOVA
• Checking the Assumptions of ANOVA
• Interpretation of ANOVA result
• Dummy Coding and Contrast setting
• Post hoc test
• Effect Size

8. Analysis of Covariance (ANCOVA):

• Introduction to ANCOVA
• Procedure of ANCOVA
• Concept of Sum of squares
• Interaction effects
• Interpretation of ANCOVA
• Contrast in ANCOVA

9. Two-way analysis of Variance:

• Introduction to two-way ANOVA
• The procedure of two-way ANOVA
• Interpretation of two-way ANOVA

Details of the dates and times for the programs: Click here