QMB 3200 Final Part A: Symbols

Σ

Σ

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