# I need help creating a thesis and an outline on Element of economics. Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is required.

I need help creating a thesis and an outline on Element of economics. Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is required. Question: An insurance manager believes that the number of new policies written annually by his agents is related to the number of years of selling experience these agents have. A random sample of 12 agents revealed the following data:

No. of years of experience

No. of new policies written annually

2

17

5

22

7

34

6

37

12

50

9

41

5

13

20

48

13

39

4

20

10

35

20

63

a) Find the least-squares regression line of number of new policies on number of years of experience.

b) Find the coefficient of determination for the line in (a) and comment.

c) Estimate the number of new policies written by an agent with 15 years of experience.

d) What can be said about the slope of a least-squares regression line if the two variables involved have a correlation coefficient of r=0?

e) In the exercise 9.1, can we reliably estimate the number of policies written annually by an agent with 1 year experience? Explain.

f) Interpret the slope of the least-squares regression line in exercise 9.1.

No. of yrs. of Experience (x)

No. of new policies (y)

x2

y2

xiyi

2

17

4

289

34

5

22

25

484

110

7

34

49

1156

238

6

37

36

1369

222

12

50

144

2500

600

9

41

81

1681

369

5

13

25

169

65

20

48

400

2304

960

13

39

169

1521

507

4

20

16

400

80

10

35

100

1225

350

20

63

400

3969

1260

113

419

1449

17067

4795

N = 12

a) The best fit line associated with the n points (x1, y1), (x2, y2), . . . , (xn, yn) has the form

y = b0 + b1x

where

slope = b1

=

n(xy)  (x)(y)

n(x2)  (x)2

intercept = b0

=

y  m(x)

n

So using our calculated values, we put them in formulae:

b1 =

12(4795)  (113)(419)

12(1449)  (113)2

b1 =

10193

4619

=

2.2068

And

b0 =

419-2.2068(113)

12

=

14.136

So the least-squares regression line is

y = 14.136 + 2.2068x

b) Coefficient of determination is calculated as

r

2 =

b1y + b0xy – (y)2/n

y2  (y)2/n

So we calculate it using the given data,

r2

=

14.136(419)+2.2068(4795)-(419)/12

17067-(419)2/12

r2

=

1874.51

2436.917

r2 = 0.7692

So, r2 = 0.7692 indicates that 76.92% of the variability in y is explained by its linear relationship with the independent variable x and 23.08% of the variation is due to chance or other factors.

c) The estimated number of new policies by an agent having 15 years of experience is

y = 2.2068*15+14.136

=47.2804

d) If r = 0, i.e., the coefficient of correlation is zero, than there is no linear relationship between the dependent and the independent variable. This means that the slope of least-squares regression line will change from point to point and will not be constant.

e) The coefficient of correlation is r = 0.887, this means that there is a strong positive relationship between the independent and dependant variable. Hence the least-squares regression line fitted is very good. So if we estimate the number of policies written by an agent with one year experience, it will be reliable.

f) The slope of the line, i.e., b1=2.2068, indicates that the values of y increase by 2.2068 units for a unit increase in x.

Bibliography

Chatterjee, Samprit and Hadi, Ali S. Regression Analysis by Example. 4th Edition. Wiley Series in Probability and Statistics. 2006. 