PY 211 - Exam 1

Frequency

Describes the number of times or how often a category, score, or range of scores occurs

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Purpose: to make the presentation and interpretation of the distribution of data much clearer

Relative Frequency

Divide the number of scores by the total number of data points (N)

Cumulative Relative Frequency

Add the relative frequency for the current score to the cumulative relative frequency for the previous score

Grouped Data

How often scores occur in intervals

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summarized by group frequency tables. Data is often grouped when the data set is large.

Histogram

used for group data especially

Bar Graph

used for data that is not grouped into intervals

Pie Charts

summarizes relative percent (relative frequency) of discrete and categorical data into sectors

Unimodal

If graph has one high point

Bimodal

Graph has two high points

Multimodal

3+ peaks on a graph

Symmetrical Distribution

Equal number on both sides of the middle line

Skewed Distribution

Anything clearly not symmetrical. Has one side that is long and spread out like a tail.

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The side with the fewer scores is considered to be the direction of the skew.

Nominal Level of Measurement

No information about order; no information about distance between categories

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ex: age/gender

Ordinal

Contain information about order; no information about distance between categories.

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ex: finishing places in a race, education level

Interval

Differences between values correspond to differences in the underlying variable

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ex: happiness, anger (-5 to +5)

Ratio

Differences between values are absolute; veins at 0

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ex: class standing, birth order

Central Tendency

Statistical measures for locating a single score that is most representative or descriptive of all scores of distribution

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mean, median, mode

Mean

the balance point in a distribution represented by the letter M

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M = SigmaX/N

Median

The middle value in a distribution of data listed in numeric order

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Median position = (N+)/2

Mode

The value in a data set that occurs most often

When is mean appropriate?

1. appropriate when data are normally distributed
2. appropriate when data is measured on an interval or ratio scale

When is Median appropriate

1. appropriate when data has a skewed distribution
2. appropriate when the data is measured on an ordinal scale

Variability

Provides a quantitative measure of the degree to which scores in a distribution are spread out or clustered together

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purpose: to describe the situation, measure how well an individual score represents the distribution

How to Calculate Variance

calculate the mean, subtract mean from each score the data set to get you the deviation score, square each deviation score, add up each squared deviation score and divide them by the amount of scores that you have.

Standard Deviation

The most widely used number to describe a group of scores. It is simply the square root of the variance.

Range

The spread of your data from the lowest to the highest value in distribution

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the difference between the smallest score and the highest score.

Normal Curve

\-1 to +1 = 68% of data

\-2 to +2 = 95% of data

\-3 to +3 = 99% of data

Z-Scores

Specifies the precise location of each x value within a distribution. The numerical value specifies the distance or standard deviation of a value from the mean

Purpose of z-scores

1. tells you the exact location of the raw score within a distribution
2. forms a standardized distribution that can be directly compared to other distributions that have also been transformed

Population

an entire group of people to which a researcher intents the results of a study to apply

Sample

scores of a particular group studied

Random Selection

You start with the whole population and then randomly select some participants to be in the study.

Population Parameters

the mean, variance, and standard deviation of a population

Sample Statistics

The mean, variance and standard deviation for the scores in a sample

Probability

expected relative frequency of a particular outcome

Outcome

Observed results of an experiement

Addition Rule

Used when there are two or more mutually exclusive outcomes (which means if one outcome happens the other cannot). The total probability of either outcomes is the sum of the individual probabilities

Multiplication Rule

Use this to figure the probability of getting both of two (or more) independent outcomes. The probability of getting two or more independent outcomes is the product of the individual probabilities.