Week one Assignment Descriptive Statistics

Week One Assignment

Descriptive Statistics

ALY 6015 21284 Intermediate Analytics

Date: 2019/01/13

Q1: Set the working directly to the location of the file, and import the dataset using R code. Using descriptive statistics, visualize this data numerically and graphically.

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1. Summary statistics (mean, median, standard deviation, min, max, Q1, and Q3) for variable “Sepal.Width”

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1. Create a histogram for “Sepal.Width”

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Q2: Using height_weight_bygender.csv data file, plot a histogram of men’s and women’s height.

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1. The histogram of men's height

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1. The histogram of women's height

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Q3: Using the height_weight_byGender.csv file, plot a scatterplot of height by weight for either men or women using the plot function.

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1. The scatterplot of height by weight for men

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1. The scatterplot of height by weight for women

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Q4: What is an example of a statistical inference question we may ask next?

I want to ask what is the significant correlation between height and weight for either men or women, and can we describe them by R function?

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1. R output

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From the results, we can see that there is indeed a correlation between height and weight and the correlation coefficient is higher for men than for women.

R Code:

#1

setwd('/Users/jonas/Desktop/Informatics/Intermediate Analytics/Week 1')

task_data <- read.csv('assignment1.csv', header = TRUE)

task_data

Sepal.Width <- task_data$Sepal.Width

mean(Sepal.Width)

median(Sepal.Width)

sd(Sepal.Width)

min(Sepal.Width)

max(Sepal.Width)

quantile(Sepal.Width, probs = c(0.25,0.75))

hist(Sepal.Width, main = "The Histogram of Sepal.Width")

#2

setwd('/Users/jonas/Desktop/Informatics/Intermediate Analytics/Week 1')

height_weight <- read.csv('height_weight_by_gender.csv', header = TRUE)

head(height_weight)

male <- subset(height_weight, height_weight$X...Gender == "Male")

head(male)

hist(male$Height..inches.,main = "The Histogram of Men's Height")

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