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I need help with tabs 3-5, I'm going out of town and don't have
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I need help with tabs 3-5, I'm going out of town and don't have time to work on them. I already completed tabs 1-2 on another copy bu I don't completely understand regression. Help please!

Housing prices

House

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45 Value

119.37

148.93

130.39

135.70

126.30

137.08

123.49

150.83

123.48

132.05

148.21

139.53

114.34

140.04

136.01

140.93

132.42

118.30

122.14

149.82

128.91

134.61

121.99

150.50

142.87

155.55

128.50

143.36

119.65

122.57

145.27

149.73

147.70

117.53

140.13

136.57

130.44

118.13

130.98

131.33

141.10

117.87

160.58

151.10

120.15 Price

121.87

150.25

122.78

144.35

116.20

139.49

115.73

140.59

120.29

147.25

152.26

144.80

107.06

147.47

135.12

140.24

129.89

121.14

111.23

145.14

139.01

129.34

113.61

141.05

152.90

157.79

135.57

151.99

120.53

118.64

149.51

146.86

143.88

118.52

146.07

135.35

121.54

132.98

147.53

128.49

141.93

123.55

162.03

157.39

114.55 A real estate agent has collected a random sample o

sold in a suburban community. She is particularly in

appraised value and recent selling price of the hous

values of these two variables for each of the 75 ran

provided in the table. Using these sample data, test

statistically significant mean difference between the

prices of the houses sold in the suburban communi

significance is it appropriate to conclude that no diff

two values? 46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75 133.17

140.16

124.56

127.97

101.93

131.47

121.27

143.55

136.89

106.11

137.54

134.33

127.59

137.44

114.09

145.46

141.90

116.34

149.20

141.81

116.44

137.74

144.70

149.66

118.17

137.66

119.70

143.12

129.91

141.78 139.54

149.92

122.08

136.51

109.41

127.29

120.45

151.96

132.54

114.33

141.32

83.76

118.20

140.20

113.55

156.52

137.35

110.61

153.69

153.33

111.95

143.46

142.13

155.46

135.44

127.30

113.77

141.11

130.08

139.35 ted a random sample of 75 houses that were recently

y. She is particularly interested in comparing the

elling price of the houses in this particular market. The

for each of the 75 randomly chosen houses are

hese sample data, test whether there exists a

difference between the appraised values and selling

he suburban community. Report a p-value. For which

o conclude that no difference exists between these Salaries of men and women

Woman

26

28

30

32

34

48

52

22

27 Man

29

31

33

29

33

56

54

28

33 You are trying to determine whether male and fem

different salaries. The data contain the salaries (in t

salaries are normally distributed. a. Assume that each row of data represents paired

members of different genders are paid equally? Be b. How would you collect data to ensure that the o hether male and female Central Bank employees, having equal qualifications, receive

ntain the salaries (in thousands of dollars) for 9 male and 9 female employees. Assume

ed. ta represents paired observations, and using alpha=0.05, can you conclude that

are paid equally? Be sure to write down your hypothesis.

to ensure that the observations are actually paired? , receive

s. Assume hat Date

22-May-06

15-May-06

8-May-06

1-May-06

24-Apr-06

17-Apr-06

10-Apr-06

3-Apr-06

27-Mar-06

20-Mar-06

13-Mar-06

6-Mar-06

27-Feb-06

21-Feb-06

13-Feb-06

6-Feb-06

30-Jan-06

23-Jan-06

17-Jan-06

9-Jan-06

3-Jan-06

27-Dec-05

19-Dec-05

12-Dec-05

5-Dec-05

28-Nov-05

21-Nov-05

14-Nov-05

7-Nov-05

31-Oct-05

24-Oct-05

17-Oct-05

10-Oct-05

3-Oct-05

26-Sep-05

19-Sep-05

12-Sep-05

6-Sep-05

29-Aug-05

22-Aug-05

15-Aug-05

8-Aug-05

1-Aug-05

25-Jul-05

18-Jul-05

11-Jul-05 SP500 Walmart

-0.39

0.06

-1.87

2.03

-2.60

-1.49

1.16

4.93

-0.05

-1.73

1.72

0.11

-0.49

-0.55

0.05

-2.57

-0.62

-1.98

-0.33

3.20

2.02

3.01

-0.45

0.00

-0.17

-0.26

0.17

-1.41

1.60

0.77

0.23

0.57

-1.53

1.76

-2.03

0.17

2.98

-1.61

0.11

0.63

-0.45

-0.25

1.60

1.10

1.19

1.81

1.60

-0.59

-0.78

-2.68

1.11

-1.83

-0.29

1.93

1.07

-1.20

-0.87

0.32

-0.63

0.04

0.47

1.33 -0.77

1.87

-0.88

-1.05

-1.97

-3.18

-1.89

2.78

0.23

-4.98

1.99

1.03

2.74

4.82

-0.48

1.50

2.31

0.48

1.42

-1.51

-4.41

3.00

-2.51

-1.90

-4.04

-1.25

-0.06

-0.39

-1.43

0.71 Target Sara Lee

-0.06

-0.87

-5.64

-2.26

-4.28

-3.49

2.72

3.85

4.41

0.00

-1.23

1.96

-0.91

-2.26

-0.10

0.28

-3.17

-0.95

-0.11

-0.72

0.86

2.10

-0.15

1.97

-1.22

-1.43

-0.48

0.23

-0.37

-0.23

-0.64

2.04

1.46

1.11

-1.48

0.09

-1.17

-0.98

3.44

-0.24

-0.11

-2.49

0.02

-5.35

1.82

5.31

-0.24

2.14

1.37

1.61

-1.24

-1.34

-2.02

2.33

-4.52

0.60

-2.01

2.00

-5.57

-0.60

0.69

3.10 -5.61

0.39

-0.39

-1.62

0.00

-0.54

0.54

5.30

-0.96

1.20

0.81

-0.97

2.33

0.23

-2.56

-1.90

-1.43

-0.93

1.16

-1.36

-0.86

1.20

-0.44

-3.77

0.47

-2.46

2.20

2.47

-2.92

3.29 One hundred weeks of data

Target, and Sara Lee corpor

regression model for each o

and compare the values of

Assuming the risk-free rate 5-Jul-05

27-Jun-05

20-Jun-05

13-Jun-05

6-Jun-05

31-May-05

23-May-05

16-May-05

9-May-05

2-May-05

25-Apr-05

18-Apr-05

11-Apr-05

4-Apr-05

28-Mar-05

21-Mar-05

14-Mar-05

7-Mar-05 1.46

0.24

-2.09

1.57

0.17

-0.23

0.80

3.05

-1.48

1.25

0.41

0.83

-3.27

0.71

0.13

-1.53

-0.87

-1.80 3.37

1.92

-3.20

1.98

1.34

0.17

0.19

0.41

-3.72

3.85

0.71

-1.87

-1.79

-0.85

-3.30

-1.54

0.00

-2.83 4.36

0.89

-0.63

1.62

-0.69

0.79

3.11

7.18

3.44

1.00

-0.48

-2.86

-4.09

1.33

-1.39

-1.31

-1.91

-1.23 -1.33

1.79

-2.74

-0.52

-2.35

-0.81

-1.79

3.13

-4.70

0.74

-1.55

0.05

-1.48

0.19

1.26

2.03

-2.75

-2.35 28-Feb-05

22-Feb-05

14-Feb-05

7-Feb-05

31-Jan-05

24-Jan-05

18-Jan-05

10-Jan-05

3-Jan-05

27-Dec-04

20-Dec-04

13-Dec-04

6-Dec-04

29-Nov-04

22-Nov-04

15-Nov-04

8-Nov-04

1-Nov-04

25-Oct-04

18-Oct-04

11-Oct-04

4-Oct-04

27-Sep-04

20-Sep-04

13-Sep-04

7-Sep-04

30-Aug-04

23-Aug-04

16-Aug-04

9-Aug-04 0.89

0.81

-0.31

0.19

2.70

0.30

-1.41

-0.14

-2.12

0.15

1.33

0.52

-0.27

0.72

1.05

-1.17

1.54

3.18

3.14

-1.12

-1.24

-0.83

1.93

-1.63

0.41

0.92

0.53

0.86

3.15

0.08 3.14

-2.33

1.19

-2.55

1.96

-1.09

-1.81

0.00

2.21

0.52

1.02

-1.08

-0.40

-4.33

0.13

-2.81

0.67

4.74

3.70

-1.03

-0.60

-0.54

0.62

0.62

-1.81

0.36

-0.57

-1.99

2.58

4.04 1.98

1.50

2.98

-4.43

4.11

0.78

-1.30

1.48

-5.61

2.84

-0.06

-2.27

0.00

-0.99

1.79

-1.25

0.37

3.62

4.84

1.75

-0.92

4.12

-1.66

1.99

-1.08

-0.24

2.21

2.56

2.22

5.22 -0.65

-0.83

-2.57

1.19

0.00

-5.77

3.66

0.13

-1.32

0.49

-0.66

0.66

0.85

0.49

-0.54

-1.19

4.33

0.14

4.53

-0.91

-0.85

-2.27

3.89

-2.39

1.86

-1.65

2.46

2.17

5.12

-1.18 2-Aug-04

26-Jul-04

19-Jul-04

12-Jul-04

6-Jul-04

28-Jun-04 -3.43

1.43

-1.38

-1.03

-1.12

-0.80 -3.18

-0.27

0.97

1.72

-0.33

-1.11 -6.40

-0.53

3.37

2.75

-1.17

-6.07 -3.60

-1.41

-2.09

-1.82

0.00

2.10 undred weeks of data for log-returns in the S&amp;P 500, Walmart,

and Sara Lee corporations are given. Please construct a linear

sion model for each of the three stocks with the S&amp;P500. Interpret

mpare the values of Beta by writing a brief summary.

ing the risk-free rate is 0.2%. Suppose that an economist has been able to gather data on the

relationship between demand and price for a particular product. After

analyzing scatterplots and using economic theory, the economist decid

to estimate an equation, Q=aPb, where Q is quantity demanded and P

price. An appropriate regression analysis is then performed, and the

estimated parameters turn out to be a=1,000 and b=-1.3. Now consid

two scenarios: (1) the price increases from \$10 to \$12.50 and (2) the p

increases from \$20 to \$25. a. Write out the log-log regression model.

b. Do you expect the percentage decrease in demand to be the same i

Scenario 1 as in Scenario 2? Why or why not?

c. What is the expected percentage decrease in demand in Scenario 1;

Scenario 2. a on the

r product. After

economist decides

manded and P is

med, and the

.3. Now consider

50 and (2) the price o be the same in d in Scenario 1; in Prices of new and used Taurus sedans

Age Resale value Resale Price New Price

10

14%

1700

11790

9

17%

2125

12688

8

19%

2525

13280

7

26%

3475

13544

6

30%

4450

14722

5

37%

5525

14990

4

47%

7125

15290

3

51%

8575

16656

2

61%

10450

17220

1

67%

12600

18680 The data contains the price of new and use

For example, a new Taurus bought in 1985

1995 was \$1,700. A new Taurus bought in

1995 for \$12,600. a. Use a visual check to see if there is any r b. You want to predict the resale value (as

function of the vehicle's age. Find an equa

choose the one with the best fit). Interpret price of new and used Taurus sedans. All prices for used cars are from 1995.

aurus bought in 1985 cost \$11,790 and the wholesale used price of that car in

ew Taurus bought in 1994 cost \$18,680 and it could have been sold as used in o see if there is any relationship between vehicle age and resale values. the resale value (as a percentage of the original price of the vehicle) as a

e's age. Find an equation to do this. (You should try at least two equations and

he best fit). Interpret the results. 995.

car in

sed in a

ns and

STATUS
QUALITY
Approved

This question was answered on: Feb 21, 2020

Solution~00065880102.zip (18.37 KB)