Quiz 6 Attached Files: Applying All The Code On Your Selecti ✓ Solved

Quiz6attached Filesapplying All The Code On Your Selected

Applying all the code on your selected dataset, complete all codes from Chapter 7 Time-dependent graphs. Make sure you submit to this link two things: 1. Your report file showing screenshots of all commands from Rstudio GUI. Make sure you show all Rstudio GUIs. 2. Submit your R script code. Quiz7 Applying all the code on your selected dataset, complete all codes from Chapter 8 Statistical Models. Make sure you submit to this link two things: 1. Your report file showing screenshots of all commands from Rstudio GUI. Make sure you show all Rstudio GUIs. 2. Submit your R script. Quiz8 Applying all the code on your selected dataset, complete all codes from Chapter 9 Other Graphs. Make sure you submit to this link two things: 1. Your report file showing screenshots of all commands from Rstudio GUI. Make sure you show all Rstudio GUIs. 2. Submit your R script code.

Paper For Above Instructions

This paper serves as a guide on how to apply R programming codes to your selected dataset, particularly focusing on the steps outlined in Chapters 7, 8, and 9 related to time-dependent graphs, statistical models, and other graph types. The application of these codes requires a methodical approach to ensure correctness and reproducibility in the analysis.

Chapter 7: Time-Dependent Graphs

In this chapter, we will explore various types of time-dependent graphs. The first step is to load your dataset into RStudio. This can be achieved using commands such as read.csv() or read.table(), depending on the format of your dataset. For illustration, let’s assume we're working with a dataset named data.csv.

Once the dataset is loaded, we can begin creating time-dependent graphs. A simple line plot can be generated using the plot() function. For example:

data 
plot(data$Time, data$Value, type="l", main="Time-Dependent Graph", xlab="Time", ylab="Value")

Remember to take a screenshot of this command in RStudio to include in your report.

Chapter 8: Statistical Models

Chapter 8 introduces statistical modeling using linear regression as a case study. To perform linear regression, we first create a linear model using the lm() function. For example, if we wanted to model Value based on Time, we would use:

model

After fitting the model, we can summarize the results with:

summary(model)

As with the previous chapter, be sure to capture a screenshot of this RStudio output for your report.

Chapter 9: Other Graphs

The final chapter covers a range of other graph types, such as scatter plots and bar charts. To create a scatter plot, you would use:

plot(data$Time, data$Value, main="Scatter Plot", xlab="Time", ylab="Value", pch=19)

And for a bar chart, consider using the barplot() function:

barplot(data$Value, names.arg=data$Time, main="Bar Chart", xlab="Time", ylab="Value")

Make sure to document these graphs with screenshots for your report as well.

Conclusion

Following the steps outlined in this paper will help you effectively complete the code applications for the three chapters. It is crucial to ensure every command and output is appropriately captured in your submitted report to meet the assignment requirements. The use of RStudio facilitates easy visualization and manipulation of your dataset, making the analysis straightforward.

References

  • R Development Core Team. (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
  • Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer.
  • Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage.
  • Davis, M. (2018). An Introduction to R for Quantitative Biology. Bioconductor.
  • Baker, S. (2019). Data Visualization with ggplot2: Your Guide to Creating Stunning Visuals with R. O'Reilly Media.
  • Givens, G. H., & Hoeting, J. A. (2013). Computational Statistics. Wiley.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
  • Ihaka, R., & Gentleman, R. (1996). R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics, 5(3), 299-314.
  • Kassambara, A. (2020). ggpubr: 'ggplot2' Based Publication Ready Plots. R package version 0.4.0.
  • Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed Effects Models and Extensions in Ecology with R. Springer.

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