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A series of questions and answers related to statistical concepts, primarily focusing on correlation, regression, and chi-square tests. It covers topics such as the interpretation of correlation coefficients, the purpose of regression analysis, the assumptions of multiple regression, and the application of chi-square tests. The material is presented in a question-and-answer format, making it useful for review and self-assessment. It includes key definitions and concepts relevant to understanding statistical relationships and hypothesis testing in psychological research. Designed to help students reinforce their knowledge of these statistical methods and their applications.
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correlation - Answers - a linear relationship between two continuous variables scatterplots - Answers - used to plot correlational data can help to see whether a linear relationship exists between two variables correlation coefficient - Answers - a measure of the strength AND direction of a linear relationship between two variables. represented by r population correlation coefficient - Answers - represented by ρ (Roh) The range of the correlation coefficient - Answers - - 1 to 1 What is the correlation when r is 0? - Answers - there is no correlation What r value(s) are associated with a small ("weak") effect - Answers - less than 0. What r value(s) are associated with a medium effect? - Answers - 0.3-0. if there is a significant correlation between two variables which possibilities should be considered? - Answers - 1. is a cause-and-effect relationship possible?
the equation of a regression line - Answers - y-hat =mx+b a point on a scatterplot that falls on the regression line has a correlation of - Answers - +/- 1 When picking x values to to predict y always - Answers - pick an x value that is inbetween your data. because you cant tell if any value outside of your data still follows a linear relationship Explained variation is due to - Answers - variable x (outcome/criterion variable) Unexplained variation is due to - Answers - unknown factors The more variation we can explain, the ______________ a variable (x) predicts the outcome of another variable (y) - Answers - stronger coefficient of determination - Answers - the ratio of the explained variation to the total variation denoted by r2 (r-squared) Standard error of estimate - Answers - a measure of variability around the regression line - its standard deviation. A common measure of the accuracy of our predictions The closer observed y-values are to the predicted y-values, the __________ the standard error of estimate will be - Answers - smaller Ideally, we want the standard error of estimate to be ___________ - Answers - as small as possible Multiple regression - Answers - a statistical technique that allows us to predict values on an outcome variable from values on more than one predictor variable What r values(s) are associated with a large ("strong") effect? - Answers - 0.5 and above A positive correlation indicates a __________ relationship - Answers - direct A negative correlation indicates a ___________ relationship - Answers - inverse When we are expecting a negative correlation we conduct a ________-tailed test - Answers - left When we are expecting a positive correlation we conduct a _______-tailed test - Answers - right
Chi-square Goodness of fit test - Answers - used to test whether a frequency distribution fits an expected distribution that is predicted by theory when observations are sorted on one dimension. When to use a Chi-square goodness of fit test - Answers - when you have one variable that you are categorizing people on If any of your calculated expected frequencies for chi-square goodness of fit test are less than five, then ___________ - Answers - you cannot run a chi-square Assumptions of the Chi-square goodness of fit test - Answers - 1. Observed frequencies must be obtained by using a random sample