Question
Ls4202_endsem_2017
please answer only question no.1. (5 marks)
Answer any 10 questions. Please write brief and precise answers. Marks: 5 x 10- 50 1. Considering dependent (or response) and independent (or predictor) variables that can be categorical or continuous, what are the different statistical nodels.you would use for differenst combinations of these variables? a n 2. A researcher has a large number of data pairs (age, height) of humans from birth to 70 years. Sho / how you can compute the Pearson's Correlation between the two variables. Would you expect it to be positive or negative? Why? What would you suggest to be a major problem with this approach? 3. The regression function is the conditional expectation of Y for any given values of X, X2,X denoted as E( Ylri,22, ,r.) = A, + 1x1 + ,-+ . Given this, state the assumptions of the lines regression model regarding the Y, and X,. What is the rationale for fitting the least squares line, an how would you test whether your assumptions regarding the distribution of Y, are reasonably met? 4. For n independent observations Yi. Ys.Yn, which are plant growth responses to different horn concentrations Xi, a student derives the least squares regression line Y, + 1ZQ What would typical null hypothesis, and what is the basis for computing confidence intervals for Bo and B for hypothesis testing? onditions or properties of a response variable Y, is a Generalized Linear Model (G 5. Under what needed over a simple linear regression model? Given the need for a GLM to model your data, what the basic components of a GLM that you would need? 6. A lab technician is required to carry out quick serening for the preence of malaria in patiemst to a more conclusive test. It is known that the probability of detection of malaria in the screening n one occasion, the technician tests a single patient and obtains a negative in the screening test. data, illustrate how you can test hypotheses that the patient has malaria or not using Bayesia
Explanation / Answer
Answer:
The model for Diffrent combinations of dependent and independent variable
Sr No
Dependent Variable
Independent Variable
Model
1
categorical
categorical
1) spearman correlation
2)logistic regression
2
categorical
Contineous
1) logistic Regression
3
Contineous
Sr No
Dependent Variable
Independent Variable
Model
1
categorical
categorical
1) spearman correlation
2)logistic regression
2
categorical
Contineous
1) logistic Regression
3
Contineous
Contineous Regression
