Publications by Rabby
Document_Landslides_2
Goal We are assessing and comparing the impact of 1m and 30m DEM-based factors on landslide susceptibility assessment. Previously, we used logistic regression and random forest models to assess two scenarios: Only DEM-based causal factors (a total of 8) were used. DEM-based causal factors were complemented with 8 non-DEM-based causal factors (a...
2854 sym R (20151 sym/31 pcs) 4 img
Document_Landslides
Goal Landslide susceptibility refers to the spatial probability of landslides occurring over a specific area. While it can predict where a landslide may occur, it cannot determine the timing of the event, thus it remains a spatial probability. For landslide susceptibility mapping accurate locations of past landslides are necessary. This task i...
5363 sym R (28674 sym/117 pcs) 7 img 12 tbl
Lab 4_STA_111
How to Submit Do not submit the RMarkdown file. Please submit final documents in html or PDF on Canvas. If you have any question please email me or meet me during office hours. Goal We’ve been talking about ways that we take samples from a population. We are now going to transition in our course into talking about ways we can use samples to ...
15408 sym
Lab 3_STA_111_03_04_2024
Goal In this lab, we are going to explore the normal distribution, which is also called the Gaussian distribution. We are going to see a few examples of things that are normally distributed, as well as seeing how we can use properties of the normal distribution to work with data. If you want a reminder on how to use R or R markdown, look back a...
7315 sym R (120 sym/1 pcs)
Lab_2_STA_Spring_2024
Goal This lab will explore how statistical computing can help us answer questions using data. Last week, we discussed different statistical measures like IQR, standard deviation, and variance. We also learned how to plot box plots, histograms, and scatter plots. We will apply these measures and plots in this lab to examine numerical data. This ...
18801 sym R (398 sym/9 pcs)
Lab_1_STA_111_Spring_2024
Goal Today we are going to be exploring the foundations of coding in R. As we move through this course, you will get experience with different things that R can do. For today, we want to start getting familiar with the set up and how to run codes using R. Note: This lab assumes that you have already installed the software and gone through the R...
9747 sym
Setting a working directory
What is a working directory in R The working directory is just a file path on your computer that sets the default location of any files you read into R, or save out of R. In other words, a working directory is like a little flag somewhere on your computer which is tied to a specific analysis project. If you ask R to import a data set from a cs...
2221 sym
Lab 4_Fall_2023
How to Submit Do not submit the RMarkdown file. Please submit final documents in html or PDF on Canvas. If you have any question please email me or meet me during office hours. Goal We’ve been talking about ways that we take samples from a population. We are now going to transition in our course into talking about ways we can use samples to ...
15416 sym
Lab 3_Fall_2023
Goal In this lab, we are going to explore the normal distribution, which is also called the Gaussian distribution. We are going to see a few examples of things that are normally distributed, as well as seeing how we can use properties of the normal distribution to work with data. If you want a reminder on how to use R or R markdown, look back a...
7315 sym R (120 sym/1 pcs)
STA_111_Fall_2023_Lab_2_Updated
Goal This lab will explore how statistical computing can help us answer questions using data. Last week, we discussed different statistical measures like IQR, standard deviation, and variance. We also learned how to plot box plots, histograms, and scatter plots. We will apply these measures and plots in this lab to examine numerical data. This ...
18840 sym R (465 sym/9 pcs)