Publications by Xiao Ling
STAT3000 1009
Normal distribution: https://mathlets.org/mathlets/probability-distributions/ Confidence Interval: https://mathlets.org/mathlets/confidence-intervals/ Example Suppose you give a test to a history class. After you have graded them, you compute the mean and standard deviation of the distribution of grades to be \(\mu\) = 80 and \(\sigma\) = 12. T...
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STAT3000 100724
What is a Random Variable? Let’s start by breaking it down: The word “random” suggests something that happens by chance. The word “variable” is something we use in math or science to represent a number that can change. So, a random variable is a quantity that can take on different values depending on the outcome of some random event. ...
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STAT3000 1002
Setting the random seed set.seed(2) sample(1:100) ## [1] 85 79 70 6 32 8 17 93 81 76 41 50 75 65 3 80 89 55 ## [19] 63 95 33 54 43 38 40 16 45 97 9 74 73 2 67 86 1 48 ## [37] 90 71 15 100 18 92 36 82 30 37 34 21 13 7 84 77 44 20 ## [55] 28 4 72 42 98 96 62 52 35 59 64 ...
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STAT3000 093024
Common Terms and Statistics We introduce concepts briefly and then provide detailed case studies demonstrating how statistics is used in data analysis along with R code implementing these ideas. It is important for a data analyst to have an in-depth understanding of statistics. Distributions: a quick way to summarize a list with lots of values....
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STAT3000 092524
Background The Instacart dataset consists of multiple CSV files that provide detailed information on customer orders, products, aisles, and departments. It is commonly used for analyzing e-commerce customer behaviors and developing recommendation systems. This report provides a brief overview of the data and sets up the analysis for further ex...
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STAT3000 092324
Pipes Pipes are a new tool for expressing a sequence of multiple operations. “object %>% function1() %>% function2()” ### The point of the pipe is to help you write code in a way that is easier to read and understand. lapply(c("ggplot2","tidyverse"),library,character.only=1) ## [[1]] ## [1] "ggplot2" "stats" "graphics" "grDevices" ...
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STAT3000 091824
In this section, we will learn how to make presentation ready plots using ggplot2 and base R. We will learn how to use ggplot to make presentations. lapply(c("ggplot2","readr","tidyverse","RColorBrewer"),library,character.only=1) #load multiple packages in one line ## [[1]] ## [1] "ggplot2" "stats" "graphics" "grDevices" "utils" "data...
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STAT3000 091624
More on coorelationship You may want to present a pairwise scatterplot matrix of the multiple variables. Every column of input is plotted against every other column of input. pairs(mtcars) crabs <- read.csv("./crabs.csv") cor(mtcars) ## mpg cyl disp hp drat wt ## mpg 1.0000000 -0.8521620 -0.8475...
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STAT3000 091124
Mutate Function mutate() is used to create new variables of modify existing ones within a data frame. It is used with other dplyr functions, like filter(), select(). Arguments: - data: The data frame you’re working with. - new_variable: The name of the new or modified variable. - expression: A mathematical or logical expression to create the ...
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STAT3000 090924
Data Summarization We will learn the basics of exploratory data analysis in R We will learn how to summarize one categorical variable, a character vector in R, one quantitative variable, a numeric vector in R, and summaries of bivariate data. We will cover both numeric and the basics of graphical summaries. Let’s explore some of these numer...
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