for judy only

 

1. Review the student’s regression equation (include it in your post). What is the independent variable name? (The answer is not “x” 🙂 What is the dependent variable name? Choose any other value for the independent variable (represented by the letter x in the equation) and plug that value in to solve for an estimate of the dependent variable (y in the equation). Show all steps and work.
2. Review the correlation (r value) that the student calculated between the two variables. Is this correlation strong, medium, or weak and why? Based on the correlation strength, do you think that the regression equation will offer a fair estimate? Why or why not?

1.

Choose any Excel Discussion dataset. Include the name of the dataset. From that dataset, select any two quantitative variables that you suspect will be related (such as age and height for example). What is the name of the dataset you have chosen? Which two variables did you choose? I chose the Female Health Data set. The variables I chose was weight and

BMI

.

2. Next, using Excel, calculate the relationship (r value) between the two variables. Recall that the Excel “formula” for correlation is “=CORREL.” What is the r value for the two variables that you have chosen? Is it positive or negative? Is it strong, medium, or weak? Note that it is best to have an r value that is medium or strong. It is recommended that you try a few different variables until you find two variables with an r value between .5 and 1 (or between -.5 and -1). Using Excel to calculate for my r and r squared I obtained an r value of 0.936 and an r squared of .876. This is a strong positive correlation.

3. Next, use Excel to create a scatterplot for the two variables. You decide which variable will be dependent (y) and which will be independent (x). On the scatterplot, include the “trendline” and the “equation for the line” using Excel options. Attach your scatterplot to your post.

4. Finally, using the equation of the line that you generated above, plug in any reasonable value for x (your chosen independent variable) and solve the equation for y (your chosen dependent variable). It is up to you to determine which of your two variables is x and which is y. What prediction do you get? Show all your work. In other words, type out the equation, plug in a value for x, and show your solution for y.

Using the equation y = .1534x + 3.309 and a weight of 140 pounds (x) I would expect a BMI of:

.1534(140) + 3.309 = 24.785

114.8 149.30000000000001 107.8 160.1 127.1 123.1 111.7 156.30000000000001 218.8 110.2 188.3 105.4 136.1 182.4 238.4 108.8 119 161.9 174.1 181.2 124.3 255.9 106.7 149.9 163.1 94.3 159.69999999999999 162.80000000000001 130 179.9 147.80000000000001 112.9 195.6 124.2 135 141.4 123.9 135.5 130.4 100.7 19.600000000000001 23.8 19.600000000000001 29.1 25.2 21.4 22 27.5 33.5 20.6 29.9 17.7 24 28.9 37.700000000000003 18.3 19.8 29.8 29.7 31.7 23.8 44.9 19.2 28.7 28.5 19.3 31 25.1 22.8 30.9 26.5 21.2 40.6 21.9 26 23.5 22.8 20.7 20.5 21.9

Weight

BMI

2

>Sheet1 FEMALE ID # AGE HEIGHT WEIGHT WAIST PULSE CHOL

BMI

WRIST Month of Birth of First Child BODY TEMPERATURE (F) 29 5 23 64

.3 11

4.

8 67

.2 76 26

4 19

.6 4.6 January 99.02 r value 0.

93 60

515816 27

39 32 66.4 149

.3 82.5 72 181 23.8 5.5 February 98

.93 r squared 0.87619

25

6

34 2992

25

62

.3 107.8 66.7 88 267

19.6 4.6

March 98.61 37 45 55

62.3

160.1

93 60

384 29.1

5

NA 98.67 44

86

27

59

.6 127.1 82.6

72 98

2

5.2 4.8

January

98.

68 4

48

8

29

63.6 123

.1 7

5.4

68 62

21.4 4.9 December 98.

89 4878

25

59.8 111.7 73.6 80 126 22 5.1 April 98.91 4880

22

63.3 1

56

.3 81.4

64 89

27.5

5.5 March

98.66 4881 41 67.9 218.8 99.4

68

5

31 33.5 5.8

February

98.81 4835

32

61.4 110.2 67.7

68

130 20.6

5 January

98.84 4842

31 66.7

188.3 100.7

80

175 29.9

5.2

May 98.75 6225

19

64.8 105.4 72.9

76 44

17.7

4.8

June 99.01 8680

19

63.1 1

36

.1 85

68 8

24

5.1 March

98.69 8681

23 66.7

182.4 85.7

72

112 28

.9 5.6

January

99.05 12348 40 66.8 238.4

126

96 462 37.7

5.4 February

98.63 146

51

23

6

4.7 108.8 74.5

72 62

18.3

5.2 January

98.99 16767

27

65

.1 119

74.5 68 98

19.8 5.3 October 98.60 17765

45

61.9 161.9 94

72

4

47 29.8

5 April 98.84
19377

41

64.3 174.1 92.8

64

125 29.7

4.7 NA

98.72 19378

56

63.4 181.2 105.5

80

318 31.7

5.4 May

98.89 19382

22

60.7 124

.3 75.5

64

325

23.8 5

September 98.90 20278 57

63.4

255.9 1

26.5

80

600 44.9

5.6 February 98.90
21626

24

62.6 106.7 70

76

237 19.2

5

August 99.00 3

223

3

37

60.6 149.9

98 76

173 28.7

5.1 August 98.90
33

104

59

63.5 163.1 104.7

76

309 28.5

5.1 March

98.77 33106

40

58.6 94.3 67.8

80 94

19.3 4.2

February 98.69
33334

45

60.2 159.7 99.3

104

280

31 5.2

November 98.92 33335 52 67.6 162.8 91.1

88

254 25.1

5.3 NA

98.96 34779

31 63.4 130 74.5 60 123

22.8

5.1 April

98.98 35035

32

64.1 179.9 95.5

76

596 30.9

5 January

98.82 35272

23

62.7 147.8 79.5

72

301

26.5 4.9

July

99.05
35273

23

61.3 112.9 69.1

72 223

21.2

4.7 December

98.94 35505

47

58.2 195.6

105.5 88

293 40.6

5.5 March 98.81
35506

36

63.2 124.2 78.8

80 146

21.9

4.7 May

98.95 35507

34

60.5 135

85.7 60 149 26 5.2 February

98.73 35984

37 65

141.4

92.8 72 149

23.5

4.8 May 98.75
35988

28

61.8 123.9 72.7

88

920

22.8 5 March 98.89
36115

29 68

135.5 75.9

88

271 20.7

4.9 NA

98.97 36502

48 67

130.4 68.6

124

207 20.5

5.3 November

98.88 38089

25 57 100.7

68.7

64 2 21.9 4.6 June 98.88

114.8 149.30000000000001 107.8 160.1 127.1 123.1 111.7 156.30000000000001 218.8 110.2 188.3 105.4 136.1 182.4 238.4 108.8 119 161.9 174.1 181.2 124.3 255.9 106.7 149.9 163.1 94.3 159.69999999999999 162.80000000000001 130 179.9 147.80000000000001 112.9 195.6 124.2 135 141.4 123.9 135.5 130.4 100.7 19.600000000000001 23.8 19.600000000000001 29.1 25.2 21.4 22 27.5 33.5 20.6 29.9 17.7 24 28.9 37.700000000000003 18.3 19.8 29.8 29.7 31.7 23.8 44.9 19.2 28.7 28.5 19.3 31 25.1 22.8 30.9 26.5 21.2 40.6 21.9 26 23.5 22.8 20.7 20.5 21.9

Weight

BMI

1.

Choose any Excel Discussion dataset. Include the name of the dataset. From that dataset, select any two quantitative variables that you suspect will be related (such as age and height for example). What is the name of the dataset you have chosen? Which two variables did you choose?

I’m choosing the data set

Male Health

Data. I chose to use the weight and waist.

2. Next, using Excel, calculate the relationship (r value) between the two variables. Recall that the Excel “formula” for correlation is “=CORREL.” What is the r value for the two variables that you have chosen? Is it positive or negative? Is it strong, medium, or weak? Note that it is best to have an r value that is medium or strong. It is recommended that you try a few different variables until you find two variables with an r value between .5 and 1 (or between -.5 and -1).

The R value is .95, I believe it is positive and strong. I predict that the two variables rely heavily on each other and as the waist size goes up so will the weight.

Male Health

119.96226420000001 135.0044268 141.19131909999999 119.22340389999999 142.28929690000001 114.4375618 139.1223177 135.58635469999999 135.8082393 122.9209881 122.06009539999999 124.8414934 127.225424 123.84089109999999 118.7168471 136.3587775 119.9469526 122.6357484 136.0056878 128.89569359999999 137.83491950000001 122.9765491 139.068039 137.8156712 133.7541994 138.76177480000001 119.75264660000001 125.184156 119.8783387 140.4206092 145.3173396 122.3573772 142.6854621 143.74413480000001 145.843414 127.2613503 123.5906377 145.91798059999999 118.9596816 137.250652 38.433652180000003 43.255973959999999 45.624743100000003 37.253182459999998 45.598719490000001 36.884449129999993 45.032341340000002 42.349801380000002 42.840190839999998 39.834869099999992 39.59747187 38.637281489999992 41.29002337 39.557648110000002 38.183893559999987 42.387276959999987 38.214214329999997 39.515691859999997 43.550812219999997 42.900475040000003 44.41314088 37.822132699999997 45.607338759999998 45.551081839999988 43.155114709999999 44.106740930000001 37.340144479999992 40.59829938 38.442786699999999 43.58904613 45.877517249999997 37.579955120000001 44.28848301 44.736077809999998 45.841451879999987 42.34511732 40.17630243 45.377223729999997 38.30346715999999 42.844168519999997

WEIGHT

WAIST