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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&P 500, Walmart,

 

and Sara Lee corporations are given. Please construct a linear

 

sion model for each of the three stocks with the S&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

 







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