Question

4) The regression equation predicting the average weight of a male age 18=24 (y) based on his height (x) is giben by y= 172.63+4.842x. Describe the correlation between height and weight based on the slop of the regression line.

a) positive correlation since the magnitude of the slope is large relative to the intercept.

b) negative correlation since the magnitude of the slope is small relative to the intercept.

c) positive correlation since the slope is positive. d) positive correlation since the magnitude of the slope is large relative to the intercept.

5) The regression equation predicting the average weight of a male age 18=24 (y) based on his height (x) is giben by y= 172.63+4.842x. What is the pest prediction for the weight of a male age 18-24 who is 70 inches tall? round your answer to the nearest pound.

a) 173

b) 339

c) 166

d) 70

6) The regression equation predicting the average weight of a male age 18=24 (y) based on his height (x) is giben by y= 172.63+4.842x. Interpret the slope of the regression line.

a) For every unit increase in weight, the predicted hight decrease by 4.842 pounds.

b) For every unite increase in height, the predicted weight decrease b 4.842 pounds.

c) For every unit in weight, the predicted height increase by 4.842 pounds.

d) for every unit increase in height, the predicted weight increases by 4.842 pounds.

7) A regression line for predicting Internet usage (%) of 39 countries in y=3.16 + 1.55x, where x is the pre capita GDP, in thousand of dollars , and y is internate usage. What is the risidual for country with a per capita of $28,000 and actual Internet use of 38 percent?

a) 5.4

b)-1.79

c) -5.4

d) 1.79

Answer 1

4) Answer: c) positive correlation since the slope is positive.

--------

5) Regression equation :

y= 172.63 + 4.842x.

Prediction weight of a male age 18-24 who is 70 inches tall

y= 172.63 + 4.842*70 = 511.57

Note: This is the correct answer but it is not given in options.

-------

6)

d) for every unit increase in height, the predicted weight increases by 4.842 pounds.

-----

7) Regression equation:

y = 3.16 + 1.55x,

Predicted value of y at x =    28

ŷ = 3.16 + (1.55) * 28 = 46.56  

Residual = y - ŷ = 38 - 46.56 = -8.56  

-------

If any doubt ask me in comments.