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find the regression Equation Y=a+bx and draw the line corresponding to the equation on the scatter diagram by using the following data. Observation are 1,2,3,4,5,&6 ,Corresponding Test score X are 65,70,73,82,90,&60 and corresponding Expenditure(in Rs.) Y are 100,120,150,300,350,&90.
The regression coefficient of X on Y is 2.5 the regression coefficient of Y on X is 0.85.Are the above two statements consistent?
Que-1. The probability that a bulb produced by a factory will fuse after certain period of time is 0.05. Find the probability that out of 5 such bulbs
a. None fuses
b. Not more than 2 fuse
c. More than 2 fuse
Que-2. A supplier of components to an electronic industry makes a sophisticated product which sometimes fails immediately it is used. He controls his manufacturing process so that the proportion of faulty products is supposed to be only 5%. Out of 400 supplies in a batch, 26 prove to be faulty. Verify the manufacturer’s claim. Use 0.05 level of significance.
Que-3. The ages of husbands and wives in a community were found to have a correlation coefficient equal to +0.8; the average of husbands’ ages was 25 years and that of wives’ ages 22 years; their standard deviations were respectively 4 and 5 years. Find the two lines of regression and from the lines ,measure
a. The expected age of husband when wife’s age is 12 years
b. The expected age of wife when husband’s age is 33 years
2. From the data given below calculate the value of first and third quartiles, second and ninth deciles and forty-fifth and fifty-seventh percentiles.
Height in
centimeters Number of
students Cumulative
frequency
161-167 79 79
167-173 92 171
173-179 60 231
179-185 22 253
185-191 5 258
191-197 2 260
Total 260
You are given a diabetes data set (http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes). You are required to build a classification model to predict if a test result is positive or negative. (16)
(a) Explain the method you are going to use.
(b) Describe the data set and possible data pre-processing.
(c) Explain the process for classification model building.
(d) Show and explain your model.
(e) Discuss the accuracy of your model.
