PSYC 230 midterm study guide

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Descriptive Statistics
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- The mode is used almost exclusively with nominal-level data, as it is the only measure of central tendency available for such variables.
- The mean is the arithmetic average, and it is probably the measure of central tendency that you are most familiar.
- The median is the middle value. It is the value that splits the dataset in half. To find the median, order data from smallest to largest and then find data point has an equal amount of values above it and below it.
- What is commonly used the most is the mean because it includes all data in the calculations. If you have a skewed distribution median is often the best measure of central tendency.
- Range is based on only two most extreme values in the dataset, which it makes it very susceptible to outliers if the numbers are high or low it can affect the entire range even it its atypical.
- Variance is the average squared difference of values of the mean. There are two formulas for the variance depending on whether you calculate the - variance of an entire population or using a sample to estimate the population variance.
What is a partial correlation?- a technique that involves examining correlation after controlling for one or more variables.Scatterplots are used to judge the strength and direction of a correlation. How can you judge the strength of the correlation by examining the scatterplot? How can you tell the direction of a correlation by examining a scatterplot?- can judge the strength by looking at how linear the scatterplot seems to be. - The closer all the data points are the stronger the - correlation if spread out they are weak. If scatterplot starts high then goes low it is negativeIn multiple regression, what is the partial regression coefficient?- based on the problem your predictors correlated they probably overlap each other. If you want to know how well each predictor predict the outcome you need to separate them out. - The idea of partial regression coefficient it is the correlation between that predictor and the outcome when you're ignoring the overlap of the other predictors.