10.4.1 About Poisson regression for count.

10.3 Prepare R Environment for Analysis.

9.11 Presentation of multinomial regression model.

9.7.3 Model with interaction term between independent variables.

9.6.5 Create new categorical variable from fbs.

9.5 Estimation for Multinomial logit model.

9.4 Models for multinomial outcome data.

9.3 Examples of multinomial outcome variables.

8.18 Presentation of logistic regression model.

8.16 Prediction from binary logistic regression.

8.12 Convert the log odds to odds ratio.

8.11 Multiple binary logistic regression.

6.6.4 Three variables: Plotting a numerical and two categorical variables.

6.6.3 Two variables: Plotting a numerical and a categorical variable.

6.6.2 One variable: Distribution of a numerical variable.

6.6.1 One variable: Distribution of a categorical variable.

5.9.4 Changing the level of categorical variable.5.9.2 Conversion from numeric to factor variables.5.9 Data transformation for categorical variables.5.7.2 Summary statistic using summarize().5.7 Group data and get summary statistics.5.6.2 Select observation using filter().5.6 Sorting data and selecting observation.5.5.2 Generate new variable using mutate().5.5.1 Select variables using dplyr::select().5.5 Select variables, generate new variable and rename variable.5.4.1 Create a new project or set the working directory.4.7.2 Important questions before plotting graphs.4.3 History and objectives of data visualisation.2.4.2 Checking availability of R package.2.3.2 Function, Argument and Parameters.1.8.3 Environment, History, Connection and Build Pane.

1.8.2 Files, Plots, Packages, Help and Viewer Pane.1.7.4 TinyTeX, MiKTeX or MacTeX (for Mac OS) and TeX Live.1.7.3 Checking R and RStudio Installations.

1.7 Installing R and RStudio on Your Local Machine.

1.5 Point and click R Graphical User Interface (GUI).

Data Analysis in Medicine and Health using R.

Rstudio cloud account