We want to demonstrate that a relationship exists between optimism and happiness (both adopt continuous interval-level measurement). We are NOT concerned with trying to demonstrate that one variable causes the other. What type of statistical test can be used to see if a relationship exists between the variables? O correlation O independent-samples t-test O one-way ANOVA O none of the aboveWe are interested in how (IV1) number of posts per day, (IV2) number of likes obtained per day, and (IV3) number of shares obtained per day by a company's Facebook account will predict this company's financial performance (DV). What statistical analysis should be conducted? (all of these variables are continuous variables) O t-tests O ANOVA O Correlation O Multiple regressionWhat does a 4 X 3 X 2 factorial design tell us? O We have 2 independent variables O We have 3 independent variables O We have 2 dependent variables O We have 3 dependent variablesA study of the effects of gender (1 = male, 2 = female) and educational level (1= less than high school, 2= high school, 3= some college, 4= college graduate or above) on income would be an example of a _factorial ANOVA 0 3 X2 O 2 X2 O 2X 3 O 2 X 4Correlation analysis can be conducted with two variables that are measured by nominal level. O True O FalseRegression is a method of finding an equation that describes the best-fitting line for a set of data O True O FalseA regression analysis used hours spent exercising to predict ounces of weight loss had a slope (b1) of 5 which is statistically significant. We would predict that O for every ounce lost, a person has to exercise for 5 hours O hours of exercise would have no effect on weight O we don't sufficient information to make a prediction O for every 1 hour of exercise, the weight loss is 5 ouncesIf our regression equation is performance score Y = 0.75xage + 0.50xexperience - 0.10xgrade point average - 2.0, what are the predictors? O age experience grade point average O age, experience, and grade point averageIf we know that the regression coefficient of predictor A is statistically significant and positive, we know that O with the increase of A, the dependent variable increases O with the increase of A, the dependent variable decreases O there is no relationship between the predictor A and the dependent variable. none of the above