Final Material Flashcards
A distribution may be positively skewed (to the right), symmetrical, or negatively skewed (to the left). Note where mean, median, and mode are.
Product of the Binomial, or Gaussian Distribution: The area under the normal curve
In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is. Normal distribution.
Kurtosis
Positive kurtosis indicates heavier tails and a more peaked distribution, while negative kurtosis suggests lighter tails and a flatter distribution. Kurtosis helps in analyzing the characteristics and outliers of a dataset. The measure of Kurtosis refers to the tailedness of a distribution
- Leptokurtic (thin)
- Mesokurtic
- Platykurtic (flat)
Algebra
y = mx + b
y = (slope times x ) plus the y intercept
Monotonic & Nonmonotonic Functions
Quadratic Equation
y= x^2 + x + b
Cubic Equation
y = x^3 + x^2 + x + b
Positive & Negative Accelerated Increasing and Decreasing Function
Scatterplots
Visually show correlations
- careful not to confuse correlation with slopeSS
Different ways to analyze data
- one-sample t-test
- independent samples t-test
- dependent (or repeated measures, or correlated) samples t-test
- one way ANOVA
- two way ANOVA
- chi-square
- regression
Three tests relevant to “single-factor design”
- independent samples t-test
- dependent (or repeated measures, or correlated) samples t-test
- one way ANOVA
Single Factor Experimental Design
single factor experimental design- two levels
- between-subjects
- within-subjects
single factor- more than two levels
- between-subjects
- within-subjects
Factors, Levels, Cells (or Treatments)
- factors is the thing we compare
- levels looks at treatments
- each cell represents an amount of people
- 1 by 2
- 1 by 4
- 2 by 4
Between subjects vs within-subjects
Between-subjects:
- Different levels of the IV are administered to different groups of participants (each participant is administered only one level)
- Each participant has only one score on the DV so scores cannot vary “within” each participant, they can only vary between participants
Within-subjects:
- Different (multiple) levels of the IV are administered to each participant (each participant usually receives every level)
- Each participant has as many scores on the DV as there are two or more levels so scores can vary “within” each participant
- Scores (average across all levels) can also vary between participants
Different statistical tests correspond to different experimental designs
Tests associated with “Single Factor–Two Levels” design:
Independent Samples t-test
Dependent Samples t-test
Independent Samples t-test
- Corresponds to “single factor—two levels, between-subject” design
- Use this when you have two separate groups of participants and you want to compare means from these groups
- Goodwin makes distinction between “independent groups design” (with random assignment) and “nonequivalent groups design” (quasi experiments where random assignment is not possible).
- Find the two means, find the difference between these means, and see if this difference is “significantly” greater than zero
Example of a nonequivalent group design
Sex Aggression
male 23
male 12
male 10
male 7
female 18
female 49
female 31
female 25
Dependent Samples t-test
- Corresponds to “single factor—two levels, within-subject” design
- Use this when you have one group of participants being tested 2 times and you want to compare means at the 2 different times (“within-subjects design”)
- And use it with “matched groups”
- Find the mean of each participant’s or pair’s “difference score” and see if this is “significantly” greater than zero
- Counterbalancing…
Tests associated with “single Factor-More Than Two Levels” design
One-Way ANOVAs
Factor, Levels, Cells (or Treatments)
One-Way ANOVAs
Regular One-Way ANOVA
One Way ANOVA Example
Repeated Measures One-Way ANOVA