Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Contemporary Themes > Chemometrics > Flashcards

Chemometrics Flashcards

1
Q

Define the term principal component regression

A
  • finds relationships between two matrices (X and Y)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What are latent variables

A
  • underlying factors not directly measured but that defines the important variation / information in the data
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Define the term variance

A
  • a measure of the spread of the data

- the square of the standard deviation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

What is covariance and correlation

A
  • measures on how two variables vary in the same way

- formally correlation is normalised covariance

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What is the matrix equation and what do the various components represent

A
  • X = TP^T + E
  • T – scores (samples)
  • P^T – loadings ( variables)
  • E – residuals of unfitted data or noise
  • TP^T – contains the structure or information in the data
  • X – the raw data table
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What are PCs

A
  • latent variables that describe a new direction in n dimensional space that explains a large amount of the variation in the data
  • usually a smaller number of LVs are needed to capture the relevant information in the data
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What are outliers

A
  • samples that are numerically distant from the rest of the data
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

What are score plots

A
  • scores for the PC e.g. PC1 vs. PC3

- provides information about objects/ samples

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

What are loading plots

A
  • loadings for the the PC e.g. PC1 vs. PC3

- provides information about the variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

What is a biplot

A
  • an overlay of scores and loadings

- information about the relationship between variables and objects

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

What are some of the reasons that outliers occur

A
  • measurement error
  • labelling error
  • noise
  • unique/ extreme sample
    • interesting
    • unwanted
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

What are some ways of spotting and detecting outliers

A
  • scoreplot
  • residual
  • hotelling T^2
  • validation
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

What are the two kinds of outliers?

A

1) extreme but in model

2) outside the model

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

How are outliers found from scores

A
  • by inspecting score plots

- useful because peculiar behaviour can be easily spotted

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

How are outliers spotted using residuals

A
  • samples or variables not consistent with the model i.e. they are a different pattern than the model describes get high residuals
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

What is a model

A
  • a summary of your best knowledge of a system at the time of investigation
  • a good model can be used to predict future events with confidence
17
Q

What is validation

A
  • means texting if a model is general or only descriptive for the calibration samples
  • can be tested using only part of the samples for building the model and predicting the cost
18
Q

Describe the 3 types of validation

A
  • statistical - fit
    • confidence intervals
    • not used in chemometrics
  • empirical I - fit and prediction
    • cross validation or test set validation
    • often used in chemometrics
  • empirical II - fir to real life
    • external information, interview validation
      (Primarily PCA)
19
Q

How is cross validation performed

A
  • by systematically leaving some of the original samples out of the model and see how well they are predicted
  • the most simple method is leave one out where with n samples n models are built leaving every sample out once
20
Q

What are the benefits of preprocessing

A
  • models with lower prediction error

- simpler models that are more robust and/or more easy to interpret

21
Q

What is mean-centring

A
  • the mean value for each variable is substrated from the data
22
Q

What is scaling and what two categories does it fall under

A
  • multiplying (some) variables with a factor so that the variance gets similar numerical values
  • auto scaling and block scaling are the two types of scaling
23
Q

What is autoscaling

A
  • each column is divided by its standard deviation so that the variance gets similar numerical values
24
Q

What is block-scaling

A
  • giving blocks of data equal or modified importance

-

25
Q

What are consequences of mean centring

A
  • allows the PCs to capture the relevant variation in the data
  • without mean centring it is common that PC1 only describes the deviation from zero
26
Q

What are the consequences of correctly preformed scaling

A
  • gives each variable equal or comparable importance

- if scaling is not done variables with high numerical values maybe give inappropriate importance

27
Q

Define the term principle component analysis

A
  • describes the correlation structure X
  • uncovers the underlying variation in the data (X)
  • and provides a summary showing how observations are related and if there any deviating observations or groups of observations in the data

Contemporary Themes (4 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions