Stats and Modeling Flashcards

1
Q

What is stats used for?

A

discern justifiable differences in quantitative patterns

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2
Q

What are some examples of stats tests?

A

Basic t-tests, ANOVA, Chi-squared (X2) are
useful

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3
Q

How many outcomes do Chi-squared tests measure?

A

Two

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4
Q

When are these basic stats tests too burdensome?

A

situations where multiple variables and their interactions are in question/confounding!

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5
Q

T-tests

A

This tests whether significant differences, not attributable to random chance, are present in continuous data

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6
Q

X2 Test

A

Test significance between groups of non-continuous data.
Infected vs. not infected, or vaccinated vs. non-vaccinated. Useful with 2 x 2 contingency tables.

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7
Q

Fisher’s Exact test

A

This tests for significance in much the same way as Chi-squared test does, but works with any size data set. It also gives an “exact” P-value, rather than an approximation; Applicable to 2x2 tables.

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8
Q

Multivariable regression

A

Multiple independent variables are regressed on a single dependent variable. All variables are considered together to discern correlations either individually or in combinations

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9
Q

Multivariate analysis (MVA)

A

Techniques allow more than two variables to be analyzed at once

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10
Q

What is not usually included under MVAs?

A

Multiple regression is not typically included under this heading, but can be thought of as a multivariate analysis

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11
Q

Two general types of MVA technique?

A

Analysis of dependence and analysis of interdependence

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12
Q

Analysis of dependence

A

Where one (or more) variables are dependent variables, to be explained or predicted by others; E.g.: Multiple regression, PLS, MDA

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13
Q

Analysis of interdependence

A

No variables thought of as “dependent”; Look at the relationships among variables, objects or cases; E.g. : cluster analysis, factor analysis

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14
Q

What is important to bring to statistician?

A

The question you are trying to answer

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15
Q

In most cases data analysis is done to test?

A

the association between two or more variables (bivariate)

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16
Q

In health research the aim is to establish the association between?

A

risk factor and disease or between therapy and outcome

17
Q

Simple linear regression

A

Y = a + bX

18
Q

Y

A

Dependent variable

19
Q

a

A

intercept

20
Q

b

A

slope/regression coefficient (change in Y with a one unit
change in X)

21
Q

Multiple Linear Regression equation example

A

predictor value

22
Q

Multiple linear regression model

A

Y=a+b1X1+b2X2+b3X3 + ..

23
Q

What is the slope of the regression line mean?

A

Strength of correlation

24
Q

Model

A

A set of theoretically and or evidence based associations between variables

25
Q

What does data analysis test?

A

the extent to which
the proposed theoretical associations are observed in the data

26
Q

What is model development based on?

A

Previous knowledge (previous research evidence about associations) and new and hypothetical ones.

27
Q

What is model development about?

A

It is mostly about deciding which independent variables (risk factors) should be in the multivariate analysis.

28
Q

What does SIR stand for?

A

Susceptible, infectious, recovered

29
Q

Susceptible S(t)

A

Disease susceptible people in population

30
Q

Infectious I(t)

A

Those capable of transmitting an infection to others

31
Q

Recovered R(t)

A

Those that have been infected and recovered

32
Q

Kermack-McKendrick Model Assumptions

A

Assumes a well-defined and closed and infective and this lasts the duration of the disease state. An infective person is instantly infective and this lasts the duration of the disease state. Homogenous population with no genetic variation or demographic differences.

33
Q

What does a vaccine do to a SIR model?

A

Takes people from susceptible to recovered