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Sigma, or sum
μ, x̅
Population mean, sample mean
σ, s
Population standard deviation, sample standard deviation
p, p̅
Population proportion, sample proportion
N, N
Population size, sample size
σ_x ̅
Standard deviation of x̅, Dr. Crook calls it the STANDARD ERROR
E(any parameter)
Expected value
α
Level of significance
z
Z score
E
Margin of Error
H_o
Null Hypothesis
H_a
Alternate Hypothesis
σ_1,σ_2
Standard Deviations of Populations 1 and 2
μ_1,μ_2
Means of Populations 1 and 2
x ̅_1,x ̅_2
Means of Samples 1 and 2 from populations 1 and 2
df
Degrees of Freedom
p_1,p_2
Proportions of Populations 1 and 2
p ̅_1,p ̅_2
Proportions of Samples 1 and 2 from populations 1 and 2
σ_(p ̅1-p ̅2)
STANDARD ERROR of the difference of the sample proportions
β_0,β_1
Parameters in the Regression model/equation
ϵ
Error term
ŷ
Simple linear regression number
ŷ_i
Predicted value for element i
SSE
Sum of Squares Error
SST
Sum of Squares Total
SSR
Sum of squares regression
y̅
The linear regression line
r^2
Coefficient of Determination
r
Correlation coefficient
r_xy
Sample Correlation Coefficient
σ^2
Variance of the error term
t
T statistic
F
F statistic
MSR
Mean Square due to regression
MSE
Mean Square due to error
x^*
Given value of the independent variable x
y^*
The random variable denoting the possible values of the dependent variable y when x=x*
Multiple R
Sample correlation coefficient
R-square
Coefficient of determination
Adjusted R-square
Not for simple linear regression
Standard Error
Estimate of the population STD
Observations
Count of the sample
Note
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