PSYCH 240 1st EditionExam # 1 Study Guide Lectures: 1 – 10For this exam you will need to know all of the definitions on this study guide and you will also need to know how to use the formulas on this study guide. Section 1: Variables Definitions-Statistics: mathematical tools for the organization, analysis, and interpretation of data-Descriptive statistics: summarize and describe a dataset-Inferential statistics: use data to make general conclusions (conclusions that go beyond the data at hand)-Variable: condition, characteristic, or measure that can take on different values-Value: one of the possible levels that a variable can take-Score: the value of a variable observed for a particular data-collection unit-Distribution: collection of scored across a number of data-collection units-Numeric/quantitative: values of the variable are numbers-Nominal/categorical/qualitative: values of the variable are verbal labels-Continuous: variable for which there are NO gaps in the values that the variable can take. There are an infinite number of possible values btw any two scores-Discrete: variable for which there ARE gaps in the values that the variable can take. Some scores simply never occur-Ratio Variable: equal-interval variable for which a value of zero truly indicates the complete absence of what is being measures-Equal Interval: equal-sized changes in the variable represent equal-sized changes in what is being measured-Ordinal: values of variable only indicate the rank of the score Section 2: Descriptives and Distributions Definitions-Mean/Average: sum of the scored divided by the number of scores-Median: value at the middle of a sorted set of scores-Range: difference between the largest and the smallest numberThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.-Variance: (approximately) the average squares deviation from the mean-Histogram: graph that shows the frequency (number) of scored in a data set that equal a given value or fall in a given range of values-Symmetrical Distribution: if you draw a line in the middle of the range of scores, one side of the distribution looks very similar to the other side-Positively Skewed Distribution: if you draw a line in the middle of the range of scores, scores willbe concentrated on the left side of the line, with fewer scores (a "tail") on the right side-Negatively Skewed Distribution: if you draw a line in he middle of the range of scored, scores will be concentrated on the right side of the line, with fewer scores (a "tail") on the left side-Floor Effects: many of the scores are concentrated near the lowest possible value for the variable. -Ceiling Effects: many of the scores are concentrated near the highest possible value for the variable-Mode: technically the one most common score, we also use it to mean a "high point" in the distribution more generally-Unimodal Distribution: only one peak in the distribution-Bimodal Distribution: two peaks in the distribution with a clear dip in between-Uniform/Regular Distribution: distribution with an approximately equal number of scores acrossthe entire range of values-Quantiles: set of cutoff scores that divides a distribution into N regions with an equal proportion of scores in each region (when it isn't convenient to show an entire graph)-Quartiles: quantiles that divide distribution into 4 regions, each with 25% of the scores-Inter-Quartile Range: the distance between the 1st and 3rd quartiles (the range in which half of the scores fall around the center of distribution)-Deciles: quantiles that divide distribution into 10 regions, each with 10% of the scores-Percentiles: quantiles that divide distribution into 100 regions, each with 1% of the scores Formulas-Mean: M=(sum of)X/N-Variance: s2=(sum of)(X-M)2/(N-1)-Standard Deviation: s=(square roof)s2 Section 3: Z-Scores Definitions-Z-scores: indicates the number of standard deviations that a score falls above or below the mean Formulas-Z-Scores: z=(X-M)/s-Raw Score: X = z(s) + M Section 4: Correlation and Prediction Definitions-Scatterplot: a graph in which the x-axis is the score on one variable and the y-axis is the score on the other variable; each point on the plot is one data collection unit-Least-Squares Regression Line: to define the relationship between the two variable mathematically, we'll put in a line that shows how one variable tends to change -Correlation: a measure of the degree AND direction of relationship between two variables-Non-linear relationship: relationship between variables approximately follows pattern that is nota straight line-Independent Variable (IV): variable we directly manipulate -Dependent Variable (DV): variable we measure the effects of on Formulas-Formula of a Line: y-hat=a + b(x)-Slope: b=Y-hat2 - Y-hat1/x2-x1 Section 5: Probability Definitions-Empirical Probability: the proportion of times that an outcome occurs in some number of observes attempts-Theoretical Probability (objective definition): the empirical probability we would get from an infinite number of attempts-Theoretical Probability (subjective definition): the extent to which you should expect or believe something-Addition Rule: the probability of getting any one of multiple mutually-exclusive outcome is the sum of the individual probabilities of each outcome-Multiplication Rule: the probability of simultaneously observing multiple independent outcome is the product of the probabilities of each individual outcome-Percentage Change: the difference btw the new and old value of a variable divided by the old value (times 100 to make it a percentage)-Marginal Probability: overall probability of an event in a population-Conditional Probability: probability of an event just for a subset of the population that has a certain characteristic-Contingency tables: good way to demonstrate the difference btw marginal and conditional probabilities-"Formal" Distributions: we can define "idealized" distributions that follow a particular mathematical function-Sample: smaller set of scores actually available to a researcher-Statistic: index that is computed from the sample data-- regular letters-Parameter: characteristic of a population as a whole-- Greek letters-Law of Large Numbers: sample estimates will tend to converge to population values as sample size increases Formulas-Percent Change: PC = 100 x (NV-OV)/OV-New Value: NV = OV + (PC/100 x OV)-Now this