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Two Classes Meet the Bell Curve

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Used every day by bench scientists to perform experiments, interpret data, and ... Frequencies of Different Measurements. But These Two Shade into Each Other ... – PowerPoint PPT presentation

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Title: Two Classes Meet the Bell Curve


1
Two Classes Meet the Bell Curve
  • December 2004
  • MUPGRET Workshop

2
Math and Science
  • Mathematics is an integral part of science.
  • Used every day by bench scientists to perform
    experiments, interpret data, and make predictions.

3
Statistics and Science
  • Necessity for analyzing datasets
  • Experiment must be well designed to be meaningful
  • Ex. replications and controls
  • Should know how youll analyze data before you
    start the experiment

4
Data Analysis
  • Data Come in Different Types
  • Testing How Well Data Fit Hypotheses

5
Data Types
  • Yes or No (Qualitative Discontinuous)
  • ---Ratios of Two or More Classes
  • How Much? (Quantitative Continuous)
  • ---Frequencies of Different Measurements
  • But These Two Shade into Each Other
  • ---Depending on Numbers Observed and on
    Measurement Discreteness

6
Statistical Testing for Fit
  • For Ratios, Chi-Square tests are often used
  • For Frequencies, means, standard deviations, and
    linear regression are often used

7
Chi-squared
  • Tests if your ratios are statistically different
    from your expectation. Can be applied to any set
    of ratios.
  • For example, do your data fit the 31 hypothesis?
  • Chi-squared
  • ?(observed-expected)2/expected

8
Replications
  • Give a better estimate of the true mean.
  • Help to remove environmental variation from
    measurements.
  • Reduce noise.
  • Reduce effect of outliers in the dataset.

9
Outliers
10
Mean
  • Average of a group of datapoints.
  • Treatment mean
  • Replicate mean
  • Grand mean

11
Standard Deviation
  • The difference between the mean treatment value
    and the grand mean.
  • Can think of it as the distance of the mean
    treatment value from the line of best fit.

12
Linear regression
  • Line of best fit.
  • Algebraic equation.

13
Genetic Models, Simple
14
One Gene, Two Genes,
15
Four Genes,
16
Six Genes, Twelve Genes
17
Genetic Models, Complex
18
Genetic Models, Whats This?
19
Continuous Distributions
  • Test if your distributions are statistically
    different from hypothetical distributions.
  • For example, do your measured data fit with
    chance, or are they biased?
  • Mean, Standard Deviation

20
The Bell Curve
21
Testing Selection Advance
22
High Heritability!
23
Lower Heritability!
24
Probability
  • Tests the likelihood that something will or will
    not occur.
  • Used extensively in everyday life.
  • Las Vegas type gaming
  • Lotto
  • Insurance amortization
  • Decisions regarding medical treatment

25
Everyday examples
  • Rolling the dice
  • 1 in 6 chance that you will roll a one with a
    single die.
  • (1/6)2 1/36 chance you will roll snake eyes.
  • Playing cards
  • 4 in 52 chance (1/13) of drawing an ace at random
    from a deck.
  • Whats the chance of a full house?

26
Biology examples
  • Punnett square
  • Nucleotide frequencies along a gene are used to
    examine evolutionary forces.
  • Mutation rates
  • Testing limits and sample sizes for transgenics.
  • DNA forensics

27
Mendels Results
Parent Cross F1 Phenotype F2 data
Round x wrinkled Round 5474 1850
Yellow x green Yellow 6022 2001
Purple x white Purple 705 224
Inflated x constricted pod Inflated 882 299
Green x yellow pod Green 428 152
Axial x terminal flower Axial 651 207
Long x short stem Long 787 277
28
Important Observations
  • F1 progeny are heterozygous but express only one
    phenotype, the dominant one.
  • In the F2 generation plants with both phenotypes
    are observed?some plants have recovered the
    recessive phenotype.
  • In the F2 generation there are approximately
    three times as many of one phenotype as the
    other.

29
3 1 Ratio
  • The 3 1 ratio is the key to interpreting
    Mendels data and the foundation for the the
    principle of segregation.

30
Punnett Square
A (½) a (½)
A (½) AA (½ x ½ ¼) Aa (½ x ½ ¼)
a(½) Aa (½ x ½ ¼) aa (½ x ½ ¼)
Male
Female
¼ AA ½ Aa ¼ aa
31
A Molecular View
Parents
F1
F2 Progeny
WW ww Ww ¼WW ¼Ww ¼wW ¼ww
1 2 1 Genotype 3 1 Phenotype
32
Alleles
  • People have thousands of genes.
  • Each gene has one to many alleles.
  • Each allele has a different DNA sequence.
  • Some DNA differences are small, some large.
  • Some allelic differences result in different
    phenotypes, e.g., brown vs. blue eyes.
  • Frequencies of alleles vary.

33
Molecularly Differing Alleles
34
Using and Predicting
  • How often is a given allele from a heterozygous
    parent transmitted to offspring?
  • How often is an allele in a population,
    occurring at a frequency of 0.1, found in a
    sample of individuals of size n?
  • How large a sample of individuals from a
    population is needed to be 95 sure of including
    at least one individual with an allele that is
    present at frequency p?
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