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IUB CJUS-K 300 - Final Exam Study Guide

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CJUS-K300 1st EditionFinal Exam Study Guide Lectures: 20 - 23Lecture 20 (December 1)Bivariate Regression You read graphs as we read the English language, from left to right. So, when we look at the graph if it starts low on the left side and rises to the right then there is a positive relationship between the variables. If it looks like it is going down from left to right then the variables have a negative relationship. <- Positive Relationship <- Non Linear Relationship <- No RelationshipLine of Best Fit or Least Squares Regression Line:Population: y = α + βx + εSample: y = a + bxSlope:xy∑¿¿¿¿x∑¿¿¿2¿x2−¿∑¿¿xy−¿n∑¿b=¿ Lecture 21 (December 3)Bivariate linear regression analysis is the simplest linear regression procedure; simple leanera regression:- Explores the explanatory relationship for 2 variables - Examines only linear relationships- Simple linear regression focuses on explaining and predicting the dependent variables based on the information we know about he independent variable. The regression model examines the changes in the dependent variable as a function of changes or differences in values of the independent variable. This is what we would consider the interpretation of the answerDependent variable (DV) – variable that we are trying to explain or predict, it is usually very easily identifiable, we denote this as YIndependent variable (IV) – used to predict systematic changes in the dependent variable, we denote it as XThe regression aims to determine how and to what extent the dependent variable (Y) varies as afunction of changes in the independent variable (X)Best fitting straight line = least squares regression line: the extent to which the data points do not lie on the straight line indicate individual errors. Intercept (denoted as a): this is the point where the regression line crosses the Y axis Slope (denoted as b): this tells you how much a 1-unit increase in X affects the value of YLecture 22 (December 8)Pearson’s correlation – measures the strength of the linear relationship between the dependent variable and the independent variable. For samples we use (r) to denote pearson’s correlation, for populations we use (p) for pearson’s correlation. It is always between -1 and 1. - (-1) means there is a perfect negative relationship- (+1) means that there is a perfect positive relationship- (0) means that there is a perfect absence of any relationshipRule of thumb for Pearson’s r= +.70 or higher Strong positive relationship = +.40 to +.69 Moderate positive relationship = +.20 to +.39 Weak positive relationship = +.01 to +.19 No or negligible relationship = -.01 to -.19 No or negligible relationship = -.20 to -.39 Weak negative relationship = -.40 to -.69 Moderate negative relationship = -.70 or higher Strong negative relationship Interpretation of Pearson’s r will also be used for the interpretation of hypothesis testing. - We are (alpha value) % confident that there is (negative or positive 1) between the dependent variable and independent variableo Negative relationship or positive relationshipo You must write out the specific dependent and independent variables when doing the problemLecture 23 (December 10)R squared = the coefficient of determination- The amount of variance in the dependent variable which is associated with the independent variable- How well data points fit a statistical- In bivariate regression, R squared is the square of the sample correlation coefficient (pearson’s r)- In multivariate regression, R squared is the square of the coefficient of multiple correlation- In both cases R squared values range from 0 to 11. Generate Hypothesis2. Find the critical value3. Calculate obtained value 4. Make a conclusion 5.


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