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PSYC 2300 Week 1

by: Mary Kay

PSYC 2300 Week 1 PSYC2300

Mary Kay
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About this Document

This is Introductory to Statistics week 1 notes.
Introduction to Statistics
Hipp, Daniel
Class Notes




Popular in Introduction to Statistics

Popular in Psychology (PSYC)

This 3 page Class Notes was uploaded by Mary Kay on Friday September 16, 2016. The Class Notes belongs to PSYC2300 at University of Denver taught by Hipp, Daniel in Fall 2016. Since its upload, it has received 13 views. For similar materials see Introduction to Statistics in Psychology (PSYC) at University of Denver.


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Date Created: 09/16/16
 Different types of Statistics • Descriptive vs. Inferential • Frequency vs. Proportion Statistics Data is plural. Datum is the singular form Populations and Samples • Population: The entire set of individuals that you want to know about. Just number  change, no any condition change.  • Sample: The relatively small subset of scores or individuals that you have available to  observe.  What is a good sample? • One that allow you to generalize beyond your sample to a population. ◦ Randomly selected from the population of interest.  If truly random: each person in the population has an equal probability of being selected.  ◦ Representative of the population Characteristics of sample mirror those of the population of interest. Two types of statistics • Descriptive: Organizing, summarizing, and looking for relationships in a sample or a  population.  ◦ Average G.P.A ◦ Most typical college major • Inferential: techniques that tell us whether the strength of the relations in our data allow is to generalize beyond our sample to the population Variables: A variable is anything that can take on more than one value.  Relation: When a change in one variable systematically leads to a change in another  variable.  • Height and weight • Diet and disease • Alcohol and aggression Two ways to study relations between variables: • Correlational Study ◦ Measure two things and determine whether there is a relation between them.  ◦ In correlational studies, nothing is manipulated by the researcher.  ◦ Correlation does not equal causation. Third variable problem: there may be some other variable • Experiment Study ◦ Researcher manipulates a variable and measures the effect on another variable, while  holding everything else constant.  ◦ Researcher randomly assigns subjects to groups ◦ If everything else is controlled, you can (Tentatively) infer a causal relationship.  Types of Variables in Experiments: • Dependent Variable  ◦ The behavior that is measured by the experimenter.  Exam score Reaction Time • Independent Variable  ◦ The variable that is manipulated by the experimenter to see if it affects the behavior of  interest.  Hours of sleep Alcohol Consumption ◦ Called factors.  • E.g.: iv hours of sleep: condition levels are 4hrs, 6hrs, 8hrs. • Quasi ­ independent variables ◦ Gender ◦ Age ◦ Race ◦ Religion • Many of the same questions can be addressed by both correlational studies and  experiments, but stronger conclusions can typically be drawn from the experiment.  Classification some variables.  • IV vs. DV • Quantitative:specifics an amount ◦ Age, gpa • Qualitative: Specifies a category.  ◦ Gender, color Types of data Nominal Data • Nominal = name ◦ Differ only in kind (Different categories) e.g. Gender, religion, Major ◦ Can’t order the values.  Assigning numbers doesn’t mean an amount of something ◦ Qualitative Ordinal Data • Ordinal = order • Ranking, preferences ◦ e.g. sports standings, class rank, Olympic medals • Can't make assumption about the degree of difference. Interval Scale • Ordered categories of the same size.  • Equal space between interval • No “Real” 0 as origin of the scale (Can’t have “no temp”). ◦ On a scale of 1­7... Ratio Scale • Intervals are equally space • Has a “Real” 0 • Quantitative ◦ e.g., height, weight, unit of time, 10, 20, 30, GPA, books you read. The type of statistical procedures that you can do depend on the variables that you study.  Frequency ­ f • Describing A single variable • N ­ total set • Relative Frequency = f/N is between 0­1 • cf = cumulative frequency: the sum of all the frequencies of all scores at or below a  particular score.  • rel cf = relative cumulative frequency. (There are must have a 1) • Average = mean score Normal Distributions is, by far, the most frequently occurring type of distribution.  • Most human characteristics are normally distributed ◦ Height ◦ Intelligence ◦ Athletic Ability Positively Skewed • A few extremely high scores are raising the tail not the right. It is not balanced with  corresponding low scores.  • Company salaries. • High peak in left Negatively Skewed • High peak in right.  Bimodal Distribution • Two high frequency points.  


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