The RfR-Studio programming software contains a plethora of in-built datasets that are available for use. Many of these datasets are already clean and as such one can skip the process of cleaning the data and start with the analysis of data. Use the command 'data(package = .packagestallavailable = TRUE))' to list the data sets in all available packages. Choose one data set in R that you can analyse using either Multiple linear regression or two-factor ANOVA or ANCOVA. Do not consider any dataset that was used in any of the previous assignments or in the prescribed textbook. Analyse the chosen dataset using the relevant model and report the results from the data analysis in the form of an essay. Append the R-codes to the essay. do not include them in the body of the discussion. You must on y include the R-outputs. Make sure you label all the figures (tables and plots). The essay should be structured as follows: 1. Introduction . Give a brief background of the data . Present and interpret the numerical andi'or graphical summaries of the data 2. Discussion of methodology and data analysis results 0 Introduce the chosen method of analysis and motivate why it is suitable to analyse the data. . Briefly discuss the assumptions underlying the chosen model. 0 Fit the model to the data using the statistical software R. o Fit the model to the data using the statistical software R. If your chosen model is a Multiple regression model answer the questions that follow: 0 ii) Determine whether the assumptions of the model are met; Use proper linear regression model diagnostics to identify outliers i inuential points in the data and propose appropriate remedies; Determine the best model or a subset of the predictors that model the response well using appropriate model selection criteria; Produce condence intervals for the model parameters and interpret the results; Formulate hypotheses in terms of the model parameters and construct test procedures for testing such hypotheses. If your chosen model is a two factor ANOVA, answer the following questions. Determine whether the assumptions of the model are met: Use proper analysis of variance diagnostics to verify the assumptions and identify outliers I influential points in the data and propose appropriate remedies; Formulate hypotheses in terms of treatment means comparisons, e.g. linear contrasts of treatment means, and in terms of variance components. iii) If your chosen model is ANCOVA answer the following questions. - Determine whether the assumptions of the model are met; - Use proper analysis of covariance diagnostics to verify the assumptions and identify outliers l influential points in the data and propose appropriate remedies; - Formulate hypotheses in terms of treatment means comparisons and hypotheses to compare two or more regression models