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Cards (20)
Parameter
Number describing a whole population
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Population Mean
μ = Σx/N, Mean =
summation
of x / no. of
elements
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Population Variance
σ²
= Σ (x-y)²/n, Summation of data - mean squared / no. of elements
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Population
SD
Square root
of
variance
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Statistic
Number
describing the sample
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Sample Mean
x̄ =
Σx/n
, X bar = sample mean, Summation of x divided by no. of elements in the
sample
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Sample Variance
s² = Σ (x-x̄ )²/n-1, Summation of data - sample mean squared / no. of elements -
1
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Sample
SD
Square root
of sample
variance
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Sampling Distribution of the Sample Mean
Frequency
distribution, Specific
size
taken from a population
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Sampling Error
The difference between the sample
mean
and the
population
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Constructing the Sample Distribution of the means
1. Set all possible random sample
2. List all the possible samples and compute the mean of each sample
3. Construct the
sampling
distribution
4. Construct a
histogram
of the
sampling distribution
of the means
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nCr
N!/n!(N-n)!, N =
population
, n =
sample
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distribution
Probability distribution that is used to estimate
population parameters
when the sample
size
is small or population variance and standard deviation is unknown
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distribution developed by William Sealy Gosset
1908
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distribution
Symmetrical about 0
Bell shaped
but has
heavier
tails
Mean, median, and mode = 0
Variance
is always
greater
than 1, equal to V/V-2 where V is the degrees of freedom
As the degrees of freedom increase, the t-distribution curve looks more like the
normal
distribution
The
SD
varies with the
sample size
Total area is
1
or
100
%
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Finding the t-value
t=x̄ - μ / s / √n, T is equal to sample mean -
population mean
/ sample standard deviation /
square root
of sample size
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Degrees of Freedom (df)
The number of
independent
pieces of information needed to calculate something
Single =
n-1
Two
groups = n₁ + n₂ -
2
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The
median
is the middle number when all numbers are arranged from smallest to largest.
The mean is the
average
value.
The
mode
is the
most frequently
occurring score or scores.
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