# Xiuchuan Zhang

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This is Xiuchuan's personal website.
I plan to post some of my current learning and review notes on it.
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These are the notes by learning the “Introduction to Probability and Data” from Coursera.org for future reviews.

## Introduction to Data

• Data matrix: data are organized in
• Observation (case): row
• Variable: column

### Types of variables

• Numerical
• numerical values (sensible to add, subtract, take averages, etc. with them) 1. continuous: infinite number of values within a given range 2. discrete: specific set of numeric values
• Categorical
• limited number of distinct categories (not sensible to do arithmetic operations)
1. ordinal: inherent ordering
2. Nominal: not ordering

### Relationships between variables

• associated (dependent) : positive or negative
• independent : not associated

### Observational study

• collect data in a way that does not directly interfere with how data arise (“observe”)
• only establish an association
• retrospective: use past data
• prospective: data are collected throughout the study

### Experiment study

• randomly assign subjects to treatments
• establish causal connections

### Why not Census

• some individuals are hard to locate or measure, and these people be different from the rest of the population
• populations rarely stand still

### Sources of Sampling bias

• Convenience sample: individuals who are easily accessible are more likely to be included in the sample
• Non-response: If only a (non-random) fraction of the randomly sampled people respond to survey such that the sample is no longer representative of the population
• Voluntary response: Occurs when the sample consists of people who volunteer to respond because they have strong opinions on the issue

### Sample Methods

• simple random sampling: randomly select cases from the population
• stratified sampling: first divide the population into homogenous groups called strata, and then randomly sample from within each stratum
• cluster sampling: divide the population into clusters, randomly sample a few clusters, and then sample all observation within these clusters
• multistage sampling: divide the population into clusters, randomly sample a few clusters, and then we randomly sample observations from within these clusters

### Experimental design

• Principles of Experimental Design:
1. control: compare treatment of interest to a control group
2. randomize: randomly assigning subjects to treatments
3. replicate: collect a sufficiently large sample, or replicate the entire study
4. block: block for variables known or suspected to affect the outcome
• confounding variable: is correlated with both the explanatory and response variables
• Explanatory variables (factors): conditions we can impose on our experimental units
• Blocking variables: characteristics that the experimental units come with, that we would like to control for
• Blocking is like stratifying:
• blocking during random assignment
• stratifying during random sampling

### Experimental terminology

• placebo: fake treatment, often used as the control group for medical studies
• placebo effect: showing change despite being on the placebo
• blinding: experimental units do not know which group they are in
• double-blind: both the experimental units and the researchers do not know the group assignment

### Random sampling and random assignment

ideal experiment $\searrow$ Random Assignment No Random Assignment most observational studies $\swarrow$
Random Sampling Causal and Generalizable not Causal, but Generalizable Generalizability
No Random Sampling Causal, but not Generalizable neither Causal nor Generalizable Np Generalizability
most experiments $\nearrow$ Causation Association bad observational studies $\nwarrow$

## Exploratory Data Analysis and Introduction to Inference

### Scatterplots

• explanatory variable on x axis
• response variable on y axis
• correlation, not causation

### Evaluate the relationship

• direction: positive or negative
• shape: linear or curved or others
• strength: strong or weak
• outliers

### Histogram

• provide a view of the data density
• especially useful for identifying shapes of distributions

### Skewness

• distributions are skewed to the side of the long tail
• left skewed: the longer tail is on the left on the negative end
• mean < median
• symmetric: no skewness is apparent
• mean $\approx$ median
• right skewed: the longer tail is on the right, the positive end
• mean > median

### Modality

• unimodal: one prominent peak (normal distribution or bell curve)
• bimodal: two prominent peak (might two distinct groups in data)
• uniform: no prominent peaks (no apparent trend)
• multimodal: more than two prominent peaks

### Bin width

the chosen bin width can alter the story the histogram is telling

• bin width too wide: might lose interesting details
• bin width too narrow: might be difficult to get an overall picture of the distribution
• ideal bin width depends on the data you are working with

### Dot plot

• useful when individual values are of interest
• can get too busy as the sample size increases

### Box plot

• useful for highlighting outliers, media, IQR(interquartile range)

### Intensity map

• useful for highlighting the spatial distribution

### Measures of spread

• range: (max - min)
• variance: roughly the average squared deviation from the mean
• sample variance: $s^2$
• population variance: $(\sigma)^2$
• $s^2 = \frac{\sum_{i=1}^{n} (x_i - \bar{x})^2}{n-1}$
• standard deviation: roughly the average deviation around the mean, and has the same units as the data
• sample sd: $s$
• population sd: $\sigma$
• inter-quartile range
• range of the middle 50% of the data, distance between the first quartile (25th percentile) and third quartile (the 75th percentile)
• most readily available in a box plot.
• $IQR = Q_3 - Q_1$

### Robust Statistics

• define: measures on which extreme observations have little effect
• robust measures of center & spread:
robust non-robust
center median mean
spread IQR SD, range

### Transforming data

• define: a rescaling of the data using a function
• When data are very strongly skewed, we sometimes transform them, so that they are easier to model
• (natural) log transformation:
• often applied when much of the data cluster near zero (relative to larger values in the dataset) and all observations are positive
• to make the relationship between the variables more linear, and hence easier to model with simple methods
• other transformations:
• square root
• inverse
• goals:
• to see the data structure differently
• to reduce skew assist in modeling
• to straighten a nonlinear relationship in a scatterplot

### Exploring Categorical Variables

• Bar plots
• Q: How are bar plots different than histograms?
• barplots for categorical variables, histograms for numerical variables
• x-axis on a histogram is a number line, and the ordering od the bars are not interchangeable
• Segmented bar plot
• useful for visualizing conditional frequency distributions
• compare relative frequencies to explore the relationship between the variables
• Relative frequency segmented bar plot
• Mosaicplot
• Side-by-side box plots

### Introduction to inference

• null hypothesis($H_0$): independent, “There is nothing going on”
• alternative hypothesis($H_A$): dependent, “There is something going on”

• hypothesis testing framework
• start with a null hypothesis($H_0$) that represents that status quo
• set an alternative hypothesis($H_A$) that represents our research question, i.e. what we’re testing for
• conduct a hypothesis test under the assumption that the null hypothesis is true, either via simulation or using theoretical methods
• If the test results suggest that the data do not provide convincing evidence for the alternative hypothesis, we stick with the null hypothesis
• If they do, then we reject the null hypothesis in favor of the alternative

### Inference summary

• set a null and an alternative hypothesis
• simulate the experiment assuming that the null hypothesis is true
• evaluated the p_value: probability of observing an outcome at least as extreme as the one observed in the original data
• if this probability is low, reject the null hypothesis in favor of the alternative

## Probability and Distribution

• random process: know what outcomes could happen, but don’t know which particular outcome will happen

• P (A) = Probability of event A
• 0≤P(A)≤1
• frequentist interpretation: The probability of an outcome would occur if we observed the random process an infinite number of times.

• Bayesian interpretation: A Bayesian interprets probability as a subjective degree of belief

• largely popularized by revolutionary advance in computational technology and methods during the last twenty years

• law of large members: sates that as more observations are collected, the proportion of occurrences with a particular outcome converges to the probably of that outcome

• common misunderstanding: gambler’s fallacy (law of averages)

• disjoint (mortally exclusive) events cannot happen at the same time
• P(A & B) = 0
• Union of disjoint events: P(A or B) = P(A) + P(B) - P(A & B)
• Complementary → disjoint; complementary !← disjoint
• non-disjoint events can happen at the same time
• P(A & B) != 0
• sample space: a collection of all possible outcomes of a trial

• probability distribution: all possibility outcomes in the sample space, and the probabilities with they occur

• Rules:
• the events listed must be disjoint
• each probability must be between 0 and I
• the probabilities must total I
• complementary events: two mentally exclusive events whose probabilities add up to l
• Independence: P(A/B) = P(A), P(A1, … & Ak<\sub>) = P(A1) × … × P(Ak)

• Dependence: P(A/B) = P(A & B)/ P(B), P(A & B) = P(A/B) × P(B)

• Posterior Probability: P(hypothesis / data) → P(hypothesis is true / observed data)

• P-value: P(data / hypothesis) → P(observed or more extreme outcome / H0 is true)

## Normal Distribution

### Normal distribution $N( \mu , \sigma )$

• unimodal and symmetric
• bell curve
• follows very strict guidelines about how variably the data are distributed around the mean
• Many variables are nearly normal, but none are exactly normal
• two parameters: mean μ and stand deviation σ
• Changing the center and the spread of the distribution changes the overall shape of the distribution • rules govern the variability of normally distributed data around the mean ### Standardizing with Z scores

• standardized (Z) score of an obervation is the number of standard deviations it falls above or below the mean
• $Z = \frac{observation - mean}{SD}$
• Z score of mean = 0 (normally: median ≈ 0 )
• unusual observation: $\lvert Z\rvert > 2$
• defined for distributions of any shape
• when the distribution is normal, Z scores can be used to calculate percentiles
• Percentile is the percentage of observations that fall below a given data point
• graphically, percentile is the area below the probability distribution curve to the left of that observation
• if the distribution does not follow the nice unimodal symmetric normal shape, you’d need to use calculus for that
• Methods for Z scores
1. Using R: pnorm(-1, mean = 0, sd = 1) (qnorm for quantiles or cutoff values)
2. Distribution Calculator
3. Table ### Evaluating

• anatomy of a normal probability plot
• Data are plotted on the y-axis of a normal probability plot, and theoretical quantiles (following a normal distribution) on the x-axis
• If there is a one-to-one relationship between the data and the theoretical quantiles, then the data follow a nearly normal distribution.
• Since a one-to-one relationship would appear as a straight line on a scatter plot, the closer the points are to a perfect straight line, the more confident we can be that the data follow a normal model.
• Constructing a normal probability plot requires calculating percentiles and corresponding z-scores for each observation, which is tedious. Therefore, we generally rely on software when making these plots. • Also can using 68-95-99.7% rule

## Binomial Distribution

• binomial distribution describes the probability of having exactly k successes in n independent Bernoulli trials with probability of success p
• # of scenarios × P(single scenario)
• $P(k = K) = {n \choose k} p^k (1-p)^{(n-k)}$

in R: dbinom(k, size, p) Distribution Calculator

• Choose function: ${n \choose k}=\dfrac{n!}{k!(n−k)!}$

in R: choose(n, k)

### Binomial conditions

1. The trials are independent.
2. The number of trials, n, is fixed.
3. Each trial outcome can be classified as a success or failure.
4. The probability of a success, p, is the same for each trial.
• Expected value (mean) of binomial distribution ($\mu = np$) and its standard deviation ($\sigma = \sqrt{np(1-p)}$)

### normal approximation

• Fact: when the number of trials increases, the shape of the binomial actually starts looking closer and closer to a full normal distribution

• Calculate the probabilities for each outcome from a to b and sum them up

in R: sum(dbinom(a:b, size = n, p =p))

• Success-failure rule: a binomial distribution with at least 10 expected successes and 10 expected failures closely follows a normal distribution
• $np \geq 10$
• $n( 1-p ) \geq 10$
• Normal approximation to the binomial: If the success-failure condition holds, then
• $Binomial(n,p) \thicksim Normal(\mu,\sigma)$
• where $\mu = np$ and $\sigma = \sqrt{np(1-p)}$

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