Final

Question 1

Repeated-Measures ANOVA

• used when? (2)

Answer 1

1. independant assumption violated
2. 3+ conditions

Question 2

Repeated Measures ANOVA

• SS calculated? (4)

Answer 2

SS_total

SS_{within treatments}

SS_subjects

SS_error

Question 3

Repeated Measures ANOVA

formulas:

• SS total
• SS within treatments
• SS subjects
• SS error

Answer 3

SS _total = Σx² - (Σx)²/N

SS _{<strong>within treatments</strong>} = n Σ(x̄_w - x̄)²

SS _subjects = k Σ(x̄_s - x̄)²

SS _error = SS_total - SSwithin treatments - SS_subjects

Question 4

Repeated Measures Anova

• MS formulas
• df formulas

Answer 4

MS_treatment

df_treatment = k - 1

MS_error

df_error = (k-1)(n-1)

When calculating SS_error, eliminates effects of MS_subjects

Question 5

Repeated Measures Anova

• assumptions (2)

Answer 5

1) Normality
2) Sphericity

Question 6

Repeated Measures ANOVA ASSUMPTIONS

2) Sphericity

Answer 6

assumption of equal variances & equal covariances

(NOT robust to violations)

Question 7

Factorial ANOVA

• simplest case?

Answer 7

2 b/w-subject IVs of 2 levels

Question 8

Main effect

Answer 8

mean difference among levels of one factor

Question 9

Interaction

Answer 9

mean differences between treatment conditions (cells) are different from what would be predicted from overall main effects of factors

• effect of one factor depends on different levels of 2nd factor

Question 10

Two-Factor ANOVA

(3) hypothesis tests?

Answer 10

1) main effect of A
2) main effect of B
3) A x B interaction

Question 11

Two-Factor ANOVA

• SS

Answer 11

SS total

SS _BG

SS WG

SS _A

SS _B

SS _AxB

Question 12

Two Factor ANOVA

Formulas:

• SS total
• SS BG
• SS WG
• SS A
• SS B
• SS AxB

Answer 12

SS total = ΣX_total² - ( ΣX_total)²/N

SS _BG = ΣT²/n - G²/N

SS _WG = SS_T - SS_BG

SS _A = ΣT_row²/n_row - G²/N

SS _B = ΣTcolumn²/n_column - G²/N

SS _AxB = SS_BG - SS_A -SS_B

Question 13

Two-Factor ANOVA

• MS
• df
• F

Answer 13

MS for:

• A → a -1
• B → b -1
• AxB → (a-1)(b-1)
• WG → ab(n-1)

F = MS/MS_WG

• F_A, F_B, F_AxB

Question 14

Correlation

Answer 14

describes linear relationship between 2 ordinal/interval level variables

Question 15

Correlation

1. relationship must be?
2. restricted range & reliability of measures limits…?

Answer 15

1) linear
2) magnitude of correlation coefficient

Question 16

Correlation

• (3) assumptions

Answer 16

1) Normality
2) Linearity
3) Homoscedasticity

Question 17

Correlation ASSUMPTIONS

3) Homoscedasticity

Answer 17

assumes variance around regression line is same for all X values

(equal spread)

Question 18

Regression

Answer 18

technique to fine line of best fit

Question 19

Correlation suggests we can….

Answer 19

predict Y values for given values of X

Question 20

If correlation is perfect, all points will..

If NOT?

Answer 20

• If correlation is perfect, all points will*..fall on regression line
• If NOT,* regression line must be calculated.

Question 21

Regression

Notation for:

1. predicted values of Y
2. residuals
3. regression coefficient (slope)
4. Y intercept

Answer 21

1) Y’
2) Y-Y’
3) b₁
4) b_o

Question 22

Regression

• residuals

Answer 22

errors of underprediction & overprediction

Question 23

Sum of squared residuals is?

Answer 23

Minimal

(regression line = best-fitting line)

Question 24

Regression

• formula for predicted values of Y (Y’)

Answer 24

Y’ = b₁x + b_o

Question 25

Correlation & Regression

formulas:

• b₁
• b_o

Answer 25

b₁ = r (S_y/S_x)

b_o = y-bar - b₁ x̄

Question 26

Correlation & Regression

calculate?

Answer 26

1. r
2. b₁
3. b_o
4. Y’
5. SS_{Y<strong>’</strong>} = Σ(Y’-Ybar)²
6. SS_Y-Y’ = Σ(Y-Y’)²
7. MS_predicted
8. MS_residual
9. Fobtained

Question 27

Regression

• predicted/explained variance

Answer 27

Variability in Y predicted by X

SS_Y’ = Σ(Y’-Ybar)²

Question 28

Regression

• Residual/Error Variance

Answer 28

Variability in Y NOT predicted by X

Question 29

Non-Parametric Tests

Answer 29

make no assumptions about shape of distribution

Question 30

Chi-Square Test for Goodness of Fit

• uses?
• determines?

Answer 30

uses sample data to test hypothesis about shape/proportion of population distribution

• determines how well sample proportions fit population proportions specified by null hypothesis

Question 31

Chi-Square Test for Goodness of Fit

• null hypothesis
• alternative hypothesis

Answer 31

Ho: change in frequency due to chance

H1: change in frequency NOT due to chance

Question 32

Chi-Square Test for Goodness of Fit

• formula
• df?

Answer 32

X² = Σ [(o - e)²/e]

df = c -1

• c = # of categories

Question 33

Chi-Square Test for 2 Independent Samples

Answer 33

used to test whether there is association between 2 frequency variables

Question 34

Chi-Square Test for 2 Independent Samples

• null & alternative hypotheses

Answer 34

Ho: treatment is independant of outocme

H1: treatment is NOT independant of outocme

Question 35

Chi-Square Test for 2 Independent Samples

• formula
• df?

Answer 35

X² = Σ [(o - e)²/e]

e = [(R)(C)]/N

df = (R-1)(C-1)