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Steps to solve it
Cohen’s d formula
If the confidence interval does not include the value of zero effect, it can be assumed that there is
a statistically significant result.
IF YOU CHANGE ONE VALUE IN A SAMPLE, MEAN
CHANGES
IF YOU ADD / REMOVE AN OBSERVATION TO / FROM A SAMPLE, MEAN
CHANGES, UNLESS THAT OBSERVATION HAS THE VALUE OF THE MEAN.
IF YOU MULTIPLY/DIVIDE ALL VALUES IN A SAMPLE BY A CONSTANT,
THE MEAN WILL ALSO BE MULTIPLIED/DIVIDED BY THAT CONSTANT.
IF YOU ADD / SUBTRACT A CONSTANT TO ALL VALUES IN A SAMPLE,
YOU ADD / SUBTRACT THAT SAME CONSTANT FROM THE MEAN.
THE MEDIAN (MD):
IS A VALUE FROM A SAMPLE OR A POPULATION DIVIDING ALL OBSERVATIONS INTO TWO EQUAL HALVES. MEDIAN CAN BE AN AVERAGE OF TWO VALUES WHEN A SAMPLE OR A POPULATINO HAS AN EVEN NUMBER OF OBSERVATIONS.
How to solve it?
y REPRESENTS
VALUES OF A DEPENDENT VARIABLE
x REPRESENTS
VALUES OF AN INDEPENDENT VARIABLE
ŷ REPRESENTS
PREDICTED VALUES OF D.V.
e IS A
RESIDUAL TERM
REGRESSION LINE IS A SET OF ALL
ŷ
a IS AN INTERCEPT TERM:
A VALUE OF y, WHEN x = 0
a=
B COEFFICIENT IS
THE EFFECT OF X ON Y
COEFFICIENTS DO NOT REPRESENT RELTIVE STRENGTH OF EFFECTS, BECAUSE
SIZE OF B DEPENDS NOT ONLY ON THE STRENGTH OF EFFECT, BUT ALSO ON MEASUREMENT UNITS.
TO OBTAIN RELATIVE COMPARISSON OF EFFECT SIZE WE NEED TO Z STANDARDIZE ALL OF OUR VARIABLES.
r kvadratu formulė
ASSUMPTIONS OF REGRESSION ANALYSIS
- Linearity: the relationship between X and the mean of Y is linear.
- Independence: observations are independent of each other.
- Homoscedasticity: variance of residuals is the same for all values of X.
- Normality of residuals: for any fixed value of X, Y is normally distributed
What are the effect sizes of Cohen’s D?
How to solve it?
SSt=
SSw=
SS1+SS2+SS3
SSb=
SSt-SSw
DFt=
N-1
DFb=
k-1
DFw=
N-k
Bimodal
Positively skewed
Symetric-unimodal
If we increase s,
value of t will be lower
If we increase n,
value of t will increase too
Jei didėja p,
didėja galimybė atmesti H0
How to solve it?
Solve it
A larger n would lead to
a larger df and smaller tc. Also, lead to smaller s and smaller sM, producing a larger tT. This would also make rejecting H0 more likely
Smaller SS would lead to
smaller s and smaller sM, producing larger tT. This would also make rejecting H0 more likely
A larger difference between M and miu - the effect size - would also
increase tT, making rejection more likely
Spearman rho gali būti tarp
-1 ir 1
Ni lygu
SSb/SSt
Standard deviation formula
Solve it
Solve it
If You will look at this table carefully, You will see a miracle called “pitakas”
WITH SKEWED FREQUENCY DISTRIBUTIONS.
A MEAN SHIFTS QUITE STRONGLY IN THE DIRECTION OF OUTLIERS. A MEDIAN, IN THE MEANTIME, STAYS CLOSER TO TYPICAL VALUES. (SEE CHART OF A SKEWED DISTRIBUTION ABOVE).
ADDING / SUBTRACTING A CONSTANT TO EACH VALUE
DOES NOT AFFECT STD.
MULTIPLYING / DIVIDING EACH VALUE BY A CONSTANT, STD
ALSO GETS MULTIPLIED / DIVIDED BY THAT CONSTANT.
