You have three variables: response variable=respond (yes or no) and the predictor variables are gender (male or female) and year in school (fr, so, jr, sr). Would you use ANOVA, Linear regression, or Logistic regression in this situation and why?
You have three variables: response variable=respond (yes or no) and the predictor variables are gender (male...
You have three variables: response variable= student loan debt amount (measured in dollars) and the predictor variables are year in school (fr,so,jr,sr) and parents assisting (yes or no). Would you use ANOVA, Linear regression, or Logistic regression in this situation and why?
You have two variables: response variable=donate (yes or no) and the predictor variable is income (measured in dollars). Would you use ANOVA, Linear regression, or Logistic regression in this situation and why?
You have three variables: response variable=income and the predictor variables are years of college (measured in years) and years of experience (measured in years). Would you use ANOVA, Linear regression, or Logistic regression in this situation and why?
Regression - response variable versus predictor variable. Provide examples of predictor variables that would be helpful in "predicting" a response variable. Also, what happens if these two are switched, that is, the "y" variable is used as the "x" variable.
You want to determine if salary is affected by gender and whether the employee has a graduate degree or not (taken together). The correct statistical technique/test would be: Chi-square Correlation One-tailed ttest Two-tailed ttest Simple linear regression Multiple regression Logistic regression Dummy variable regression ANOVA One-way ANOVA
What is the correlation between Female and
ln(MarketValue),PXU is ________ (Round your response to
three decimal places)
Exercise 8.7 -Question Help This problem is inspired by a study of the "gender gap* in earnings in top corporate jobs [Bertrand and Hallock (2001) The study compares total compensation among top executives in a large set of U.S. public corporations in the 1990s (Each year these publicly traded corporations must report total compensation levels for their top five executives.) Let Fermale be...
3. (20 pts) Suppose that we have 4 observations for 3 variables y , x\, X2 and consider a problem of regressing y on two (qualitative) variables x\, xz. Data y (Income) x (Gender) X2 (Management Status) obs no. Female None 2 Male None 3 Female Yes 4 Male Yes Y4 To handle the qualitative variables x\, x2, we define dummy variables z1, 22 as Male for for 1, 1, T2= Yes Z1= Z2= -1 for for 1 1 =...
As a statistical consultant, you have been asked to develop a linear model which shall be given to first year executive MBA students. Variable Name Description X1 Gender Male or Female X2 GMAT Score Score on GMAT Test X3 College Degree Previously Earned Bachelors, Masters, or PhD Y Income Annual Income in Thousand Dollars Where 1=Male and 2= Female for X1 and B = Bachelors Degree, M=Master’s Degree, and P= PhD for X3. Given this data, recode the variables...
For this problem, you will add another independent variable, the variable “gender” to the data. In this case the variable is set to 0 if the purchaser of the camera body is a male and set to 1 if the purchaser of the camera body is not a male (is a female). Rerun the regression model using three independent variables, sales of the camera body (x1), price of the lens (x2) and gender (x3). The dependent variable is still number...
As a statistical consultant, you have been asked to develop a linear model which shall be given to first year executive MBA students. Variable Name Description X1 Gender Male or Female X2 GMAT Score Score on GMAT Test X3 College Degree Previously Earned Bachelors, Masters, or PhD Y Income Annual Income in Thousand Dollars Where 1=Male and 2= Female for X1 and B = Bachelors Degree, M=Master’s Degree, and P= PhD for X3. Given this data, recode the variables...