Behavioral Statistics Midterm

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1. Definition
Statistics
• Numerical Summarize measure computed on data from a sample
• Numerical Characteristics of a Sample
• Ex: Mean, T-Test, Z-Score
2. Definition
Parameter
3. Definition
Population
• A target group of participants about which the researcher will make decisions
• Ex: OU Students, Am Citizens, 1999 Honda Civics
4. Definition
Population: Characteristics (3)
• 1. Large
• 2. Unobtainable-never can collect all data (CENSUS)
• 3. Hypothetical-we make assumptions because 1 &2 are as well as people are always changing
5. Definition
Sample
Subgroup of Population

Small group of participants from which the researcher makes decisions about the population
6. Definition
Random Sample
Random Sampling eliminates biases
7. Definition
Variables
• Quantity or property that takes on different values
• Continuous-infinite # of values
• Discrete-finite # of values
8. Definition
Independent Variable
variables manipulated by researchers
9. Definition
Dependant Variables
behavior observed and measured
10. Definitions
Extraneous Variables
• Not IV or DV but might effect the study
• a variable we need to control
• Ex: Gender
11. Ways to control Extraneous Variables
• 1 Randomize participants into groups
• 2 Keep all participants constantion EV
• 3 include EV in the design of experiment
12. Definition
Types of Experiment Designs
• True Experiment
• Observational Research
• Quasi-Experiemtn
13. Definition
True Experiment
• Manipulation of Independent Variable
• Randomization of Group
• Optional methods to control Extraneous variables
• Causal relationship between IV and EV

Problems:Taking it to the streets
14. Definition
Observational Research
• Predictive relationships
• Observation of prediction and criterion variable
• No Manipulation
• Minimal control of EV
• Predictive relationship btwn prediction of variable and criterion variable
15. Definition
Quasi Experimental Design
• Manipulation of IV
• No Randomization
• Some control of EV
• degree to which we can attribulte a causal relationship depends on consideration of possible outcomes

• 1)internal validity-applies outside the lab
• 2)External validity-applies in the lab only
16. Definiton
Scale of Measurment
• Nominal
• Ordinal
• Internal
• Ratio
17. Definition
Nominal-
• Data with identity only
• Qualitative in nature
• Ex: Gender, religion
18. Definition
Ordinal
• N+O
• Identity
• Order
• Ex:Ranking, Year in School, Scales!!!
19. Definition
Interval
• N+O+I
• Identity
• Order
• Equal Distance on # Scale
• Ex: Temperature, Not Scales!
20. Definition
Ratio
• N+O+I+R
• Identify
• Order
• Equal Distance
• True point 0
• Ex:Kelvin Temp, Height, Weight
21. Definition
Skew
• If Both halves of the distribution is said to be Normal Distributed
• Departure from Symmetry is defined as Skewness
22. Definition
Measures of Central Tendency
• A typical or Representative score
• Scores that represents the middle of the distribution
• Ex:Test scores in class, Average age
23. Definition
Measure of Central Tendency
Statistics and Parameters
• Mode:Most frequestn score
• Median-Middle valuse in distribution-50% below/above
• Mean-arithmetic average= Sum X/N
• Parameter Mean µ
• Statistic Mean= X_bar
24. Definitions
Measures of Variability
• Dispersion of the Scores
• EX: Variance, Standard Deviation,Range, Biased Sampled Variance
25. Definition
Measure of Variability
Statistics and Parameters
Variance
• Variance
• Parameter = σ2
• Statistic= S*2 and S2
26. Definition
Measure of Variability
Stats and Para
Standard Deviation
• Standard Deviation
• Parameter= σ
• Statistic= S* & S
27. Definition
Measure of Variablity
Range
• Range
• Maximize mean
• *Point of the class*
• Stat= obtain from sample
• Parameter= Exists in Population
28. Definition
Measure of Variability
Biased Sample Variance
• Biased Sample Variance
• Bias to small on average
• Bias sample variance (s*2) average2 Deviation from mean
29. Definition
Z-Score
Z=SOMETHING minus MEAN divided by STANDARD DEVIATION
30. Definition
Z-Score
Characteristics
• Each X to a Z
• Mean= Z_bar=0
• Variance= sz*2=1
• Standard Deviation= sz*=1
• Transformation to Z-Score does not change the shape of the distribution
31. Definition
Normal Distribution
• 1 Symmetric-same on both sides
• 2 Smooth-1 hump
• 3 Unimodal-1 mode
• 4 Bell Shaped
• -Tails of distrubution-never touch X-axis
• -Mode = Median=Mean
• -Infinite # of score values (continuous variable)
32. Definitions
Correlations
• The degree of linear relationship between two variables
• ↑↑ ↑↓ ↓↓
33. Definition
Correlation
Causation
34. Definition
Strengh of Association
• r = I 1 I
• Closer to 0= Weak
• Closer to 1 or -1 = Strong
35. Definition
Regression
• The area of statistics where a researcher is concerned with predicting one variable from another
• -Describing data for two variables
36. Definition
Regression
Characteristics
• Y'= predicted score of criteron variable
• b=slope of the line= Rise/run (Rise over Run)
• X=score of the predictor variable
• a= Y-intercept of the line
• (Y=observed point, Y' what come out of formula)
37. Definition
Sample Space
• Group of data points representing all possible outsomes of an experiment
• Ex: A population of a deck of Cards
38. Definition
Elementary Event
• Single member in a sample space
• (Ace of Diamonds in a deck of cards)
39. Definition
Event
• Any group of elementary events
• Ex: Kings
40. Definition
Probability
# in the event divided by total # of possible outcomes
41. Definition
Conditional Probability
• A probabilit of one event that is dependent of another event
• P(A/B)
42. Definition
Independence
One event occuring does not change the probability of another event occuring
43. Definition
Multiplication (AND) Rule
• INDEPENDENT
• P(A&B)= P(A) X P(B)
• NOT INDEPENDENT
• P(A&B)= P(A/B)*P(B)=P(A)*P(B/A)
44. Definition
Mutually Exclusive
• When A & B do not have an elementary event in common
• P(A&B)=0
45. Definition
P(A or B)= P(A) + P(B)- [P(A&B)]
46. Definition
Sampling Distributions
Distribution of all possible values of a statistic, sample mean, st devi, or variance
47. Definition
Sampling Distribution
Shape
sahpe of the mean is approximated by normal distribution
48. Definition
Sampling Distribution
Statistics and SD
Every statistic has a sampling distribution
49. Definition
Sampling Distrubution
Purpose
• 1 Pop and Param are typically unknow-we want to make decision about them
• 2 Sample and Stats are know and stats are estimates of parameter can't say stats is = to param and make decisions directly b/c stats have variability
• 3 Samp dis of stats is used to quantify into probability the info about variability of the stat
50. Definition
Central Limit Theorem
as N (Sample size) approaches infinity, the sampling distribution of X_Bar approches normality

N=30 is best (closest to infinity)
51. Application/ Identification
Statistics vs. Parameter
s* vs. σ
• Stats are used to estimate Parameter
• Stats- does vary
• Param-does not vary
52. Application/Identification
Types of Statistics (2)
• Descriptive Statistic
• Stat procedures used to describe a sample
• Ex: Mean, St Devi

• Inferential Statistic
• Statistic procedure used to make decisions about a population based upon result from a sample
• Ex: T-Score, ANOVA, Z-Score
53. Application/Identification
Variables
IV, DV, EV
• Indepentdent Variables-One manipulated by researcher
• Dependent Variables-Changes observed
• Extraneous Varibles- something that effects the experiment

Card Set Information

 Author: brittway ID: 138581 Filename: Behavioral Statistics Midterm Updated: 2012-03-01 00:59:39 Tags: Behavioral Statistics Folders: Description: Behavioral Statistics Show Answers:

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