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Statistics > Week 1 content > Flashcards

Week 1 content Flashcards

1
Q

When we study the relationship between two variables, what do we refer to?

A

a bivariate analysis

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2
Q

What graphical technique shows the relationship between variables?

A

scatter diagram

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3
Q

What do you need to draw a scatter diagram?

A

two variables

scale one variable along the horizontal axis (X axis)

scale the other variable along the vertical axis (Y axis)

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4
Q

What is the dependent variable?

A

the variable being predicted or estimated

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5
Q

What is the independent variable?

A

the predictor variable

it provides the basis for estimation

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6
Q

What is the coefficient of correlation also known as?

A

r

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7
Q

What is the coefficient of correlation (r)?

A

a measure of the strength of the relationship between two variables

it can range from -1 to +1

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8
Q

When r = -1 or 1, what does this indicate?

A

perfect and strong correlation

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9
Q

If r = -1, and there is a negative slope, what is the correlation?

A

perfect negative correlation

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10
Q

If r = =1, and there is a positive slope, what is the correlation?

A

perfect positive correlation

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11
Q

What do positive values of r indicate?

A

a direct relationship

eg there are two variables, as values assumed by A increase, then the values of B increase as well

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12
Q

What do negative values of r indicate?

A

an inverse relationship

eg there are two variables e and f, as the values assumed by e increase, then the values of the f decrease

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13
Q

What is the equation for r?

A

r = ∑(Xi - X̄) (Yi - ȳ) / (n-1) SxSy

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14
Q

What does the correlation coefficient (r) depend on?

A

r depends entirely on dispersion

the product of the total dispersions of each variable

the product of the standard deviations of each variable

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15
Q

What does x̄ = ?

A

x̄ =(ΣXi) / n

the mean of variable X

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16
Q

What does Sx = ?

A

Sx=√((ΣXi - x̄)^2 / √(n-1))

the standard deviation of variable X

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17
Q

What does ȳ = ?

A

ȳ=(∑Yi)/n

the mean of variable Y

18
Q

What does Sy = ?

A

Sy=√((∑Yi- ȳ)^2 / (n-1))

the standard deviation of variable Y

19
Q

What does n stand for?

A

the number of observations

20
Q

What is r if:

n = 10
x̄ = 22
ȳ = 45
Sx = 9.189
Sy = 14.337

A

r = ∑(Xi - X̄) (Yi - ȳ) / (n-1) SxSy

–> r = 900 /(10-1) (9.189) (14.337)
–> r = 0.759

21
Q

When using excel, what is the function to calculate (r) the correlation coefficient?

A

=CORREL()

22
Q

What does a correlation of 0.759 mean?

A

it is positive, therefore there’s a positive relationship between the variables

0.759 is close to +1, thus the correlation is strong

23
Q

What does the knowledge of the existing casual relationship between two variables imply?

A

relationship between X and Y is described by a linear function

changes in Y are assumed to be related to changes in X

We can predict the value of a dependent variable based on the value of at least one independent variable

we can explain the impact of changes in an independent variable on the dependent variable

24
Q

What does correlation NOT mean?

A

causation

25
Q

What does casual relationship mean?

A

one variable is determined by another

26
Q

What is the linear regression model?

A

an equation with only two variables plus an error term

27
Q

What does the error term do?

A

marks the difference between a deterministic equation and a regression equation

28
Q

What is the linear regression model equation?

A

Yi = b0 + b1Xi + ei

where:
Yi = dependent variable
b0 = population y intercept
b1 = population slope coefficient
Xi = independent variable
ei = random error term

29
Q

What is the random error component in the linear regression equation?

Yi = b0 + b1Xi + ei

A

ei

30
Q

What is the linear component in the linear regression equation?

Yi = b0 + b1Xi + ei

A

b0 + b1Xi

31
Q

Why do errors occur?

A

not every point will be on the regression line, most are scattered around it

on the contrary, any prediction based on the regression line will be exactly on the line

thus, we can expect an error to occur when comparing the true values to the predicted values

32
Q

What does the simple linear regression model provide?

A

an estimate of the observed values

33
Q

What is the simple linear regression equation / prediction line?

A

Ŷi = b0 + b1Xi

where:
Ŷ = estimated/predicted Y value for observation i
b0 = estimate of the regression intercept
b1 = estimate of the regression slope
Xi = value of X for observation i

34
Q

What is b1 and how do you work it out?

A

the slope

b1 = r (Sy / Sx)

where:
r = the correlation coefficient between Y and X
Sy = the standard deviation of Y
Sx= the standard deviation of X
ȳ = the average of y
x̄ = the average of x

35
Q

What is b0 and how do you work it out?

A

the intercept

b0 = ȳ - b1x̄

where:
r = the correlation coefficient between Y and X
Sy = the standard deviation of Y
Sx= the standard deviation of X
ȳ = the average of y
x̄ = the average of x

36
Q

When is the b0 the estimated mean value of Y?

A

when the value of X is zero

37
Q

What is b1 being the estimated change in the mean value of Y a result of?

A

a one-unit increase in X

38
Q

What is the interpretation of a positive slope?

A

an increase in X corresponds an increase in Y

ΔY = b1ΔX

39
Q

What is the interpretation of a negative slope?

A

an increase in X corresponds a decrease in Y

ΔY = -b1ΔX

40
Q

What is the interpretation of the slope (b1) when it equals zero?

A

there is no relationship between Y and X

41
Q

What is the difference between the predicted and observed values equal to?

A

the error term

ei = Yi - Ŷi

42
Q

What can the linear regression model be used to make and how?

A

predictions

if the intercept (b0) and the slope (b1) of the prediction line are known, then the quantitative relationship between the dependent variable (y) and the independent variable (x) is known
–> then we can predict the value of Y given a value of X

Statistics (5 decks)

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