PSYC 203 1st Edition Lecture 6Outline of Last Lecture I. Outliers II. Review (Variance & Standard Deviation)III. Z Scores & LocationIV. Visual Approaches to Understanding Datasets a. Frequency distributionV. In class exampleOutline of Current Lecture I. Review II. Bar Graphs III. SkewnessIV. CorrelationCurrent LectureI. Review a. Rule of Thumb about Outliersi. usually from 2 to 3 is good b. Attributes of the Normal Curvei. 68% of data lies from -1 to 1 ii. 95% from -2 to 2 iii. 99.7% from -3 to 3iv.c. z Scoresi. z= (X-μ)/σd. Frequency DistributionsII. Bar Graphs These 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.a. There are spaces between adjacent bars because it reinforces the idea that they aren’t continuous b. Remember that bar graphs are used when there are categories or types)c. How you set up your data can have a huge impact on how the information is taken. III. Skewnessa. Mean>Median = Positive Skewb. Mean<Median = Negative Skewi. Formula is Skewness = (3(Mean-Median))/ standard deviation1. Mean3,Median6,SD=5. Skew= -1.82. Mean6,Median3,SD=5. Skew= 1.8IV. Correlationa. We can compute a correlation when everyindividual has 2 scores on 2 continuous variables.b.c. by plotting the points, we can see the correlation between 2 things d. it really doesn’t matter what variable (x or y) we assign to what item. i. For example X can be for Family income OR for student’s average grade e. A correlation captures two aspects of the linear association between X & Y:i. the direction : tells us if it’s a positive or negative correlation, depending on its slope. 1. Note that just because it is a positive correlation, it doesn’t mean that it is a good thing. It just means they are directly correlated. Same goes for a negative correlation. ii. the degree: tells us the size of the
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