5. Discuss when you would use discriminant analysis instead of multiple regression analysis. Explain the difference between metric and non-metric variables.
Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable. In many ways discriminant analysis matches multiple regression analysis. The main difference between these two is that regression analysis deals with a continuous dependent variable, while discriminant analysis must have a discrete dependent variable and they are divided into groups. You plot each independent variable versus the group variable.
Equation of multiple regression is:
Discriminant analysis is: Suppose you have data for K groups, with
Nk observations per group. Let N represent the total number of
observations. Each observation consists of the measurements of p
variables. The i th observation is represented by Xki. Let M
represent the vector of means of these variables across all groups
and Mk the vector of means of observations in the kth group. Define
three sums of squares and cross products matrices, ST, SW, and SA,
as follows,
A discriminant function is a
weighted average of the values of the independent variables. The
weights are selected so that the resulting weighted average
separates the observations into the groups.
Metric data is what most people mean when they talk about numbers( cordinal ), the sorts of numbers we collect when we measure something while Non - metric data refers to all the structured data market researchers use that is not metric data i.e. non - metric data includes information that is ranked(ordinal data)
5. Discuss when you would use discriminant analysis instead of multiple regression analysis. Explain the difference...
explain when you want to use an IV regression instead of the OLS regression.
If prediction is the goal of analysis, the researcher might use discriminant function scores in order to describe group differences. T F The main analysis obtained from a discriminant analysis is the summary of the discriminant functions. T F A discriminant score is analogous to a factor score. T F A canonical correlation is a value that is equivalent to the correlation between the discriminant scores and the levels of the independent variables. T F A high value for this...
2. In a multiple regression analysis, describe how to detect each of the following phenomenon and indicate the steps you would take to deal with any problems to which they may give rise: i) lack of fit ii) heteroscedasticity (non-constant variables) ii multicolinearity ii influential points
2. In a multiple regression analysis, describe how to detect each of the following phenomenon and indicate the steps you would take to deal with any problems to which they may give rise: i)...
4 & 5
QUESTION 4 What is a major difference between linear regression and logistic regression? a. The nature of the independent variable(s) b. The nature of the dependent variable c. The number of independent variables d. The number of dependent variables QUESTION 5 Which one of the following statistical tests would the researcher hope to have a non-significant result (p > .05) in a logistic regression analysis? a. The likelihood ratio test b. The logit step test C. The...
Describe a research effort where you could use a Multiple Regression analysis. It could be something related to work productivity, or perhaps a student’s performance in school. List three variables (X1, X2, X3) you’d include in a Multiple Regression Model in order to better predict an outcome (Y) variable. For example, you might list three variables that could be related to how long a person will live (Y). Or you might list three variables that contribute to a successful restaurant....
Think about a healthcare scenario where multiple regression might be useful in a healthcare organization. Consider what your dependent and independent variables might be for conducting a multiple regression analysis. Build a small example, and run the regression analysis. Post a description of the dependent and independent variables you will use for your multiple regression analysis, and then explain your regression model in terms of your dependent and independent variables. Explain how you might measure your variables. Be specific and...
Question 1 (-110 Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations. Coefficients Standard Error Constant 12.924 4.425 -3.682 2.630 x2 45.216 12.560 Analysis of Variance Source of Variation Degrees of Freedom Sum of Squares Mean Square Regression 4853 2426.5 Error 485.3 Carry out the test to determine if there is a relationship among the variables at the 5% level. The null hypothesis should be rejected not be rejected...
What is the difference between cost-effectiveness evaluation and utility analysis? When, if ever, would you use utility rather than cost-effectiveness? Why?
43. A multiple regression analysis is conducted with 5 independent variables and an intercept on a sample of 100 observations. Suppose you want to conduct a hypothesis to test whether the coefficient of the first variable is statistically significant. What will be the degrees of freedom for this test? A.98 B. 99
Is Regression Analysis always useful in predicting values? Discuss with examples. Question 2 A regression line, derived from the least squares mentod (OLS), has only two properties. True or false. If yes, explain. If no, explain with examples Question 3. There is no difference(s) between the standard error of the sample mean and the standard error of the regression. If true, explain. If false, explain. Question 4. Does the correlation coefficient and the regression r-squared measure the same concepts. Explain