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Bio 180 > Midterm 1 > Flashcards

Midterm 1 Flashcards

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1
Q

What are the four warnings in creating a histogram?

A

Choice of bin size has big affect
Changing axis range
Burying explanatory factors
How data is scaled

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2
Q

How are strip charts better than histograms?

A

Better for comparing multiple data series

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3
Q

What is the second step in sizing up data

A 
Calculate Numerical descriptors
Mean
Median 
Mode
Quantiles 
Variance 
Standard deviation 
Min and max
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4
Q

What are boxplots?

A

Graphical form of the quantiles

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5
Q

What does the line inside a box in a boxplot represent?

A

Line indicates the median

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6
Q

What does the box hold in a boxplot?

A

Box holds 50% of points

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7
Q

What are the whiskers in a boxplot?

A

The whiskers hold remaining points

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8
Q

What is a null hypothesis

A

Conservative statement saying that there isn’t an expected effect

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9
Q

What is a p-value

A

A measure of the strength of the evidence against a null hypothesis

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10
Q

How do we find the confidence level?

A

(1-p) x 100

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11
Q

What is sums of squares and how is it measured?

A

SSY is how we measure variability

Sum of each value minus grand mean squared

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12
Q

What is the relationship between SSY and n

A

SSY always increases with n

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13
Q

How to find variance

A

SSY/ n-1

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14
Q

what is the equation for standard variation

A

Square root of variance

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15
Q

What are the three variability arising in a data set?

A

Variability of the population (sigma)?
Variability by the sample (s)
Variability of the estimated mean

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16
Q

Why is standard error of the mean important

A

SEM can give us confidence intervals for our estimate of the population mean

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17
Q

How to find confidence intervals

A

Mean-tcrit (SD/ square root of sample size)

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18
Q

Relationship between estimate range and confidence level

A

A wider estimate range gives you a high confidence level

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19
Q

What is ANOVA

A

Analyze the difference among group means. Compare differences in values between treatments to the variation within a treatment group

20
Q

What is the response variable

A

A continuous variable that is being influenced

21
Q

What is a explanatory variable

A

Categorical or continuous variable that influences

22
Q

In ANOVA, how do you find the total mean square

A

SSY/ df

23
Q

What is linear regression

A

Can the value of he response variable(x) be predicted by the explanatory variable

24
Q

Differences between ANOVA and regression?

A

ANOVA: discrete x values, values are names, values are unordered
Regression: continuously varying, values have number meaning, values are ordered

25
Q

What is statistical elimination?

A

Including the second extra lavatory variable allowed us to eliminate its influence in the rest of our model

26
Q

What are the four principles of experimental design?

A

Replication
Randomization
Blocking
Orthonogonality

27
Q

What is replication

A

Multiple measures of the same thing
Appears in the # of error degrees of freedom(residuals)
Have at least 10 df for error

28
Q

What is randomization

A

Treatments need to be applied to experimental units randomly
Use uniformly distributed random numbers

29
Q

What cardinals sins does randomization avoid

A

Systemic design: similarity between plots that undermines replication

Unconscious bias in assigning treatment groups

Using haphazard bs random design

30
Q

What is blocking?

A

Tool to minimize error variation

Distribute individual data points into different “blocks” yo minimize biases due to known common features of subsets of the points

Acts as another explanatory variable

31
Q

What are the rules for block design

A

Blocks used to account for a factor that could influence response

Blocks should be used as internally homogeneous as possible

If possible, all treatments should be included in all blocks

32
Q

What is Latin square design

A

2 way blocking. Blocking so that each treatment appears exactly once in each row and column:

33
Q

What is orthogonality

A

The acknowledgement that one variable tell you nothing about the other variable

34
Q

What is the benefit of orthogonal design

A

There is no statistical elimination between orthogonal explanatory variables

35
Q

Which variables are easier for orthogonality

A

Easier for categorical variable than continuous variables

36
Q

What is the first step in sizing up data?

A

Make a graph

37
Q

In continuous explanatory variables what do the p-values in ANOVA table represent?

A

That each explanatory variable has no influence on the response variables

38
Q

With continuous variables, what do the p-values represent in the coefficients table

A

That each specific coefficient value equals zero

39
Q

In a continuous variable, what does the p-value mean overall?

A

Neither explanatory variables can be used to predict the response variable

40
Q

In a categorical variable, what does the p-value mean in the ANOVA table?

A

That each explanatory variable has no influence in the response variable

41
Q

In a categorical variable what does the p-value mean in the coefficients table mean?

A

Each specific coefficient value equals 0

42
Q

In a categorical variable, what does the overall p-value mean in the coefficients table?

A

That neither of the variables can be used to predict the response variable

43
Q

How does blocking affect residuals?

A

Blocking helps by reducing the size of the residuals. Increasing F but lowering P

44
Q

What is an interaction

A

Two x-variables interact of the effect of one x-variable on y depends on the level of the other

45
Q

Regarding interactions, what does non-parallel lines indicate

A

There is an interaction

46
Q

Regarding interactions, what does two parallel line indicate?

A

There are no interactions

47
Q

In which case are r-squared values high and low with and without interactions

A

Model without interactions have a low r- squared value while model with interactions have a higher-squared values

Bio 180 (2 decks)

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