Hoofdstuk 2 - Frequency Distributions
𝑓
• Proportion: 𝑝 = 𝑛
f = frequentie
n = aantal
𝑓
• Percentage: 𝑝(100) = ∗ (100)
𝑛
Hoofdstuk 3 - Central Tendency
Σ𝑋 Σ𝑋
• Mean: (populatie) 𝜇 = 𝑁
(sample) 𝑀 = 𝑛
• Median = middelpunt
• Modus = vaakst voorkomende getal
Hoofdstuk 4 - Variability
• 𝑅𝑎𝑛𝑔𝑒 = 𝑋𝑚𝑎𝑥 − 𝑋𝑚𝑖𝑛
• Sum of Squares: SS
Berekenen op twee manieren
1. Definitial formula: 𝑆𝑆 = Σ(𝑋 − 𝜇)2
a. (𝑋 − 𝜇) voor elke score
b. Kwadrateer de uitkomsten (van stap a) voor elke score
c. Tel de waardes bij elkaar op
(Σ𝑋)2
2. Computational formula: 𝑆𝑆 = Σ𝑋 2 − 𝑁
a. Tel alle getallen bij elkaar op (Σ𝑋)
b. Tel alle gekwadrateerde uitkomsten bij elkaar op (Σ𝑋 2 )
c. Vul de waardes in in de formule
𝑆𝑆 𝑆𝑆
• Variance: (populatie) 𝜎 2 = 𝑁
(sample) 𝑠 2 = 𝑑𝑓
𝑆𝑆 𝑆𝑆
• Standard deviation: (populatie) 𝜎 = √ 𝑁 (sample) 𝑠 = √𝑑𝑓
Hoofdstuk 5 - Z-Scores: Location of Scores and Standardized Distributions
𝑋−𝜇 𝑋−𝑀
• Z-score: (populatie) 𝑧 = (sample) 𝑧 =
𝜎 𝑠
• Van z-score naar X: (populatie) 𝑋 = 𝜇 + 𝑧 𝜎 (sample) 𝑋 = 𝑀 + 𝑧 𝑠
• z-score ± 2.00 = extreme waarde
(Σ𝑧)2
• Sum of squares met z-scores: 𝑆𝑆 = Σ𝑧 2 − 𝑁
hoofdstuk 6 - Probability
, 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑢𝑡𝑐𝑜𝑚𝑒𝑠 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑠 𝑎𝑠 𝐴
• Probability of particular event A: 𝑝(𝐴) 𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟𝑠 𝑜𝑓 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑜𝑢𝑡𝑐𝑜𝑚𝑒𝑠
• p(A) = p en p(B) = q
• 𝜇 = 𝑝𝑛
• 𝜎 = √𝑛𝑝𝑞
𝑋−𝑝𝑛
• 𝑧=
√𝑛𝑝𝑞
• Proces hypothese testen
1. State hypothesis:
- State null hypothesis (H0)
- State alternative hypothesis (H1)
- Select alpha level (α)
2. Locate critical region (met α en z-score)
3. Transform sample mean into z-score
𝑀−𝜇
𝑧=
𝜎𝑀
(µ = H0)
4. Make a decision:
- Ligt de z-score in de critical region -> verwerp H0
- Ligt de z-score NIET in de critial region -> verwerp H0 NIET
• Type I error: H0 onterecht verwerpenM
• Type II error: onterecht geen effect vinden en H0 niet verwerpen
𝑚𝑒𝑎𝑛 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒
• Cohen’s d = 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛
Hoofdstuk 9 - Introduction to the t Statistic
𝑆𝑆
• Sample variance: 𝑠 2 = 𝑑𝑓
𝑠2
• Estimated standard error: 𝑠𝑀 = √ 𝑛
𝑀−𝜇
• t statistic: 𝑡 = 𝑠𝑀
𝑀−𝜇
• 𝑬𝒔𝒕𝒊𝒎𝒂𝒕𝒆𝒅 𝒅 = 𝑠
• Confidence interval (CI): 𝜇 = 𝑀 ± 𝑡(𝑠𝑀 )
Hoofdstuk 10 - The t Test for Two Independent Samples
(𝑀1− 𝑀2 ) − (𝜇1 − 𝜇2 )
• Independent-measures t statistic: 𝑡 = 𝑠(𝑀1− 𝑀2)
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