statistical analysis frequency distribution # indivi duals median mean medianmean median figure...
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STATISTICAL ANALYSIS
Figure 1. Histogram of final exam scores.
Frequency Distribution# In
divi
dual
s
MedianMean
MedianMean MeanMedian
Figure 2.Frequency distributions of three different samples.
A B C
negative normal positive
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Descriptive Statistics: used to describe, simplify, and summarize a collection of data in a clear understandable way.Ex. Mean, standard deviation, frequency
Inferential Statistics: allows you to make inferences about a population from a sample. It’s used to test the Ho.Ex. t-test, ANOVA, ANCOVA
Raw Data: the data you collect directly from the organisms or environment you are studying.
Population (N): the total # of individuals in the population of interest.
Sample (n): the number of observations or individuals measured
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Mean: n
XX
_X = 1.2+3.0+0.5+2.3+1.5 5
= 1.7 m
Median: Middle number in a data set. Order from smallest to largest. 0.5 1.2 1.5 2.3 3.0
Range: Difference between the largest and smallest data points in a sample. 3.0 - 0.5 = 2.5 m
Fern Height (m)
1.2 3.0 0.5 2.3 1.5
Hand calculations
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Sample Variance1
)( 22
n
XXs
_ _ X X-X (X-X)2 0.5 -1.2 1.441.2 -0.5 0.251.5 -0.2 0.042.3 0.6 0.363.0 1.3 1.69
3.78
n = 5_X = 1.7
3.78 4
S2 = = 0.945
Standard Deviation1
)( 2
n
XXs S = 0.945 = 0.972
Standard Errorn
s
n
sSE
2435.
5
972.
5
945.SE
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Confidence Interval
)(CI df95 SEtX
95% confidence interval
mean
Mean = 1.7SE = 0.435
Standard Error
df = n-1
df = 5-1 = 4
CI95 = 1.7 ± (2.78*0.435)
CI95 = 1.7 ± 1.21 m
df α (2-tail) α (1-tail)
.10
.05 .05 .025
.02
.01 .01 .005
.005 .0025
.002
.001 .001
.0005
1 6.314 12.71 31.82 63.66 127.3 318.3 636.6 2 2.920 4.303 6.965 9.925 14.09 22.33 31.60 3 2.353 3.182 4.541 5.841 7.453 10.21 12.92 4 2.132 2.776 3.747 4.604 5.598 7.173 8.610 5 2.015 2.571 3.365 4.032 4.773 5.893 6.869
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df = n-1
_X - u s n
t =
Testing Ho using t-test
Distribution must be normalUse when n > 30
Ho = 0Ha > 0
t = 1.7 - 00.972/ 5 = 0.39 df=5-1=4
Ignore “-” signs
df α (2-tail) α (1-tail)
.10
.05 .05 .025
.02
.01 .01 .005
.005 .0025
.002
.001 .001
.0005
1 6.314 12.71 31.82 63.66 127.3 318.3 636.6 2 2.920 4.303 6.965 9.925 14.09 22.33 31.60 3 2.353 3.182 4.541 5.841 7.453 10.21 12.92 4 2.132 2.776 3.747 4.604 5.598 7.173 8.610 5 2.015 2.571 3.365 4.032 4.773 5.893 6.869 6 1.943 2.447 3.143 3.707 4.317 5.208 5.959
t-Table
two tail is more strict
one-tail two-tail2.7760.39
Your t-value is less than t-critical. Fail to reject Ho.
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Comparing two sample means
All right. It is time to use Excel!
X1 X2
5 64 73 55 94 62 85 7
t =
_ _X 1 - X2
s12 + s2
2
n1 n2
df = n1+ n2-2
Variances must be equal
Ho: X1=X2
Ha: X1=X2
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Using Excel
Click Tools, Data Analysis, t-test: Two-sample Assuming Unequal Variances
Enter datax1 x2
5 64 73 55 94 62 85 7
Data Set
0
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x1 x2 t-Test: Two-Sample Assuming Unequal Variances5 64 7 x1 x23 5 Mean 4 6.865 9 Variance 1.33 1.814 6 Observations 7 72 8 Hypothesized Mean Difference 05 7 df 12
t Stat -4.26P(T<=t) one-tail 0.00t Critical one-tail 1.78P(T<=t) two-tail 0.00t Critical two-tail 2.18
t-critical Value
Your Value
Reporting results in the literature: Sample X1 and sample X2 are significantly different, t(12) = -4.26, p < 0.05, 2-tail.
df
Do you accept or reject the Ho?
Lets the reader know that you set alpha to 0.05. The p-value is less than 0.05, therefore the results are significant.
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When you compare more than 2 samples, you need to do an Analysis of Variance (ANOVA).
The relationship between an ANOVA and t-test: F=t2
An ANOVA is the similar to a t-test in the sense that you compare the F value to the critical F value.
You also need to look at the p-value. If you set alpha to 0.05 and your p value is less that alpha, you can reject the Ho; if greater than alpha then fail to reject the Ho.
ANOVA
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Descriptive Stats
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Correlation & Regression
Correlation: describe the strength of association between two variables. The correlation coefficient is designated as r. r can range from -1 to +1.
+1 = a strong positive correlation 0 = no correlation
-1 = a strong negative correlation
r = +1 r = -1
r = 0
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This relationship can be represented by a regression equation.
Table 1. Sample data of tree fern age, height, and basal area. Age (years) Height (m) Basal Area
(m2) 10 0.5 .025 25 1.2 .053 30 1.5 .052 40 2.3 .110 60 3.0 .180
Equation for a straight line:y = bx + a y = DV, b = slope, x = IV, a = intercept
Simple Linear Regression: shows relationship between variables.
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Correlation of Tree Fern Height & Basal Area
r = .967, p < .01
0
0.5
1
1.5
2
2.5
3
3.5
0 0.05 0.1 0.15 0.2
Basal Area ~ Meters Sqr
Hei
gh
t ~
Met
ers
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Tree Fern Height vs. Age
0
0.5
1
1.5
2
2.5
3
3.5
0 20 40 60 80
Age ~ Years
Hei
gh
t ~
Met
ers
Tree Fern Height vs. Age
y = 0.0518(± .02) x - 0.0098R2 = 0.98, p<.01
0
0.5
1
1.5
2
2.5
3
3.5
0 20 40 60 80
Age ~ Years
Hei
gh
t ~
Met
ers