This is the study document I used to study for the midterm. We were able to use an aid sheet during the exam, and you can use this to inspire the content and layout of yours. I would add more information on confidence intervals, and more code examples.
Modern Stats+DS software/programming/computational tools → mathematical+algorithmic data/statistical analysis methodologies →
explained+advocated w/ written+verbal communication → facilitate data-driven and evidence-based decision making
Learning first learning, structured course material is good → it’s faster to learn and troubleshoot problems yourself
Jupyterhub is a cloud-based service → run R/Rstudio from any web browser. Jupyterhub > Rstudio GUI IDE program that wraps… > R) > tidyverse
R Markdown Reproducibility (text+outputs+code)
R methods+algorithms usually built-in/loaded from packages → most R users don’t build algorithms/data types
tidyverse Key set of R packages that help facilitate modern stats+DS
bias survivorship bias → look at data that survived and doesn’t look at group with no data
alpha significance
Basic Functions glimpse() → summary printout shows variables vertically & shows no. of rows
head() → output is tibble & doesn’t show total no. of rows & can see n rows
c() → vector | all() → output is boolean | sum() → translate logical TRUE to numeric 1 and logical FALSE to numeric 0
help() | name() → column names
data/variable types numerical(cont, disc) | categorical (nom, ord, bin→categorical variables = logical T/F boolean variables)
123 & 1.23 same
for R (double)
Coercion
Visualisation Func coord_flip(), order geom_bar, labs(x= , y= )
Distributional 1st → centre/location: median, mean, mode
Characteristics
2nd → Spread/scale statistics: IQR, variance, SD
3rd/Higher order characteristics → skewness+modality+outliers
Truly tidy data Rows→ observations | columns→ variables | cell→ single measurement
Tidy data benefits Can use same tools in similar ways for diff datasets vs hard to reuse untidy data & one-time approaches
print vs head print → outputs n number of rows indicated.
Data Wrangling select() → extract subset of variables | remove variable w/ ‘-’ and rename w ‘=’ vs dplyr::rename(),
Functions (dplyr)
filter() → extract rows based on conditions in one+ columns & filter(is.na())
arrange() → sort observation based on values in one or more variables & desc()
mutate() → make new column w/ interesting variables & case_when(<condition eg. b>=a ~ “Female”,>) → ‘~` =
response (L) DEPEND ON explanatory variables (R)
Aggregation functions → summarise((n=n() → sample size *doesn’t know NA values, <obj>=sum(), median(), mean(),
var(), sd(), IQR(), quantile(<obj>, 0.75), min(), max())
group_by() %>% → group rows by column values
is.na() | !is.na()
na.rm() → ignores/excludes NA
Other: n_distinct()
%in% → see if an element is in dataframe/vector | levels() and nlevels()
Inference Theoretical populations vs Actual samples → population-(sampling)->sample-(inference)->population
Sample statistic
x̄ →
Hypothesis Testing
Functions
[i] → indexing into a vector, matrix, array, list or dataframe
Steps 1. Null Hypothesis → assumed value of parameter H0 : p=0.5 (sampling distribution to be compared against observed
test stat) & Alternative Hypothesis → H1 : p≠0.5 (Null is FALSE)
2. Set α-significance level (the probability we make a wrong decision about a chosen assumption) → reject H0 for
p-values less than α. It’s also probability→Type I error of rejecting a true H0 … Type II error failing to reject true NULL
3. Simulate Sampling Distribution assuming NULL is TRUE & 4. Compute p-value → The probability [can be
approximated] of observing a test statistic that is as or more extreme than the one we got if the NULL Hypothesis is
actually TRUE
5. “Reject H0 at α-significance level” if p-value is less than α OTHERWISE “fail to reject NULL at sig level”
Example Two 1. pick α=0.05 & placebo: 0.58 & actual: 0.75
Sample Hypothesis
Test
2. Test stat μ1=0.58 & μ2=0.75 → p=0.75-0.58
3. H0 : μ1=μ2 → μ1-μ2=0 & H1 : μ1≠μ2
4. Simulate sampling distribution assuming NULL is TRUE → set.seed() and n repetitions
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, creditcard of Stuvia-tegoed voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper ralwab. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €8,39. Je zit daarna nergens aan vast.
