This is Business Statistics project.

A national insurance organization wanted to study the consumption pat- tern of cigarettes in all 50 states and the District of Columbia. The variables chosen for the study are

Variable Definition Age Median age of the a person living in a state HS Percentage of people over 25 years of age in a state who had completed high school Income Per capita personal income for a state in dollars Black Percentage of blacks living in a state Female Percentage of females living in a state Price Weighted average price (in cents) of a pack of cigarettes in a state Sales Number of packs of cigarettes sold in a state on a per capita basis

Find an appropriate model to predict sales using the variables Age, HS, Income, Black and Female. (make sure to eliminate the variables that are not significant). Find the 95% confidence intervals for the coefficients that are statistically different from 0 and write a short report in which you interpret your results. The data is in the file DATACIGARETTE.txt. Use again file.choose() to find where you saved it. it is a .txt file use sep=”” where reading it into R

> data<-read.table("/Users/HElbarmi/Desktop/EDA/Regressin/DATACIGARETTE.txt", header=TRUE, sep="")

> data

State Age HS Income Black Female Price Sales

1 AL 27.0 41.3 2948 26.2 51.7 42.7 89.8

2 AK 22.9 66.7 4644 3.0 45.7 41.8 121.3

3 AZ 26.3 58.1 3665 3.0 50.8 38.5 115.2

4 AR 29.1 39.9 2878 18.3 51.5 38.8 100.3

5 CA 28.1 62.6 4493 7.0 50.8 39.7 123.0

6 CO 26.2 63.9 3855 3.0 50.7 31.1 124.8

7 CT 29.1 56.0 4917 6.0 51.5 45.5 120.0

8 DE 26.8 54.6 4524 14.3 51.3 41.3 155.0

9 DC 28.4 55.2 5079 71.1 53.5 32.6 200.4

10 FL 32.3 52.6 3738 15.3 51.8 43.8 123.6

11 GA 25.9 40.6 3354 25.9 51.4 35.8 109.9

12 HI 25.0 61.9 4623 1.0 48.0 36.7 82.1

13 ID 26.4 59.5 3290 0.3 50.1 33.6 102.4

14 IL 28.6 52.6 4507 12.8 51.5 41.4 124.8

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15 IN 27.2 52.9 3772 6.9 51.3 32.2 134.6

16 IO 28.8 59.0 3751 1.2 51.4 38.5 108.5

17 KA 28.7 59.9 3853 4.8 51.0 38.9 114.0

18 KY 27.5 38.5 3112 7.2 50.9 30.1 155.8

19 LA 24.8 42.2 3090 29.8 51.4 39.3 115.9

20 ME 28.0 54.7 3302 0.3 51.3 38.8 128.5

21 MD 27.1 52.3 4309 17.8 51.1 34.2 123.5

22 MA 29.0 58.5 4340 3.1 52.2 41.0 124.3

23 MI 26.3 52.8 4180 11.2 51.0 39.2 128.6

24 MN 26.8 57.6 3859 0.9 51.0 40.1 104.3

25 MS 25.1 41.0 2626 36.8 51.6 37.5 93.4

26 MO 29.4 48.8 3781 10.3 51.8 36.8 121.3

27 MT 27.1 59.2 3500 0.3 50.0 34.7 111.2

28 NB 28.6 59.3 3789 2.7 51.2 34.7 108.1

29 NV 27.8 65.2 4563 5.7 49.3 44.0 189.5

30 NH 28.0 57.6 3737 0.3 51.1 34.1 265.7

31 NJ 30.1 52.5 4701 10.8 51.6 41.7 120.7

32 NM 23.9 55.2 3077 1.9 50.7 41.7 90.0

33 NY 30.3 52.7 4712 11.9 52.2 41.7 119.0

34 NC 26.5 38.5 3252 22.2 51.0 29.4 172.4

35 ND 26.4 50.3 3086 0.4 49.5 38.9 93.8

36 OH 27.7 53.2 4020 9.1 51.5 38.1 121.6

37 OK 29.4 51.6 3387 6.7 51.3 39.8 108.4

38 OR 29.0 60.0 3719 1.3 51.0 29.0 157.0

39 PA 30.7 50.2 3971 8.0 52.0 44.7 107.3

40 RI 29.2 46.4 3959 2.7 50.9 40.2 123.9

41 SC 24.8 37.8 2990 30.5 50.9 34.3 103.6

42 SD 27.4 53.3 3123 0.3 50.3 38.5 92.7

43 TN 28.1 41.8 3119 15.8 51.6 41.6 99.8

44 TX 26.4 47.4 3606 12.5 51.0 42.0 106.4

45 UT 23.1 67.3 3227 0.6 50.6 36.6 65.5

46 VT 26.8 57.1 3468 0.2 51.1 39.5 122.6

47 VA 26.8 47.8 3712 18.5 50.6 30.2 124.3

48 WA 27.5 63.5 4053 2.1 50.3 40.3 96.7

49 WV 30.0 41.6 3061 3.9 51.6 41.6 114.5

50 WI 27.2 54.5 3812 2.9 50.9 40.2 106.4

51 WY 27.2 62.9 3815 0.8 50.0 34.4 132.2

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