Price (in K)	Sqft	Age	Features	CornerCODE	Corner_Label
310.0	2650	13	7	0	NO
313.0	2600	9	4	0	NO
320.0	2664	6	5	0	NO
320.0	2921	3	6	0	NO
304.9	2580	4	4	0	NO
295.0	2580	4	4	0	NO
285.0	2774	2	4	0	NO
261.0	1920	1	5	0	NO
250.0	2150	2	4	0	NO
249.9	1710	1	3	0	NO
242.5	1837	4	5	0	NO
232.0	1880	8	6	0	NO
230.0	2150	15	3	0	NO
228.5	1894	14	5	0	NO
222.0	1928	18	8	0	NO
223.0	1830	16	3	0	NO
220.5	1767	16	4	0	NO
216.0	1630	15	3	1	YES
218.9	1680	17	4	1	YES
204.5	1725	13	3	0	NO
204.5	1500	15	4	0	NO
202.5	1430	10	3	0	NO
202.5	1360	12	4	0	NO
195.0	1400	16	2	1	YES
201.0	1573	17	6	0	NO
191.0	1385	22	2	0	NO
274.5	2931	28	3	1	YES
260.3	2200	28	4	0	NO
230.0	2277	30	4	0	NO
235.0	2000	37	3	0	NO
207.0	1478	53	3	1	YES
207.0	1713	30	4	1	YES
197.2	1326	25	4	0	NO
197.5	1050	22	2	1	YES
194.9	1464	34	2	0	NO
190.0	1190	41	1	0	NO
192.6	1156	37	1	0	NO
194.0	1746	30	2	0	NO
192.0	1280	28	1	0	NO
175.0	1215	43	3	0	NO
177.0	1121	46	4	0	NO
177.0	1050	48	1	0	NO
179.9	1733	43	6	0	NO
178.1	1299	40	6	0	NO
177.5	1140	36	3	1	YES
172.0	1181	37	4	0	NO
320.0	2848	4	6	0	NO
264.9	2440	11	5	0	NO
240.0	2253	23	4	0	NO
234.9	2743	25	5	1	YES
230.0	2180	17	4	1	YES
228.9	1706	14	4	0	NO
225.0	1948	10	4	0	NO
217.5	1710	16	4	0	NO
215.0	1657	15	4	0	NO
213.0	2200	26	4	0	NO
210.0	1680	13	4	0	NO
209.9	1900	34	3	0	NO
200.5	1565	19	3	0	NO
198.4	1543	20	3	0	NO
192.5	1173	6	4	0	NO
193.9	1549	5	4	0	NO
190.5	1900	3	3	0	NO
188.5	1560	8	5	1	YES
186.0	1365	10	2	0	NO
185.5	1258	7	4	1	YES
184.9	1314	5	2	0	NO
180.0	1338	2	3	1	YES
180.9	997	4	4	0	NO
180.5	1275	8	5	0	NO
180.0	1030	4	1	0	NO
178.0	1027	5	3	0	NO
177.9	1007	19	6	0	NO
176.0	1083	22	4	0	NO
182.3	1320	18	5	0	NO
174.0	1348	15	2	0	NO
172.0	1350	12	2	0	NO
166.9	837	13	2	0	NO
234.5	3750	10	4	1	YES
202.5	1500	7	3	1	YES
198.9	1428	40	2	0	NO
187.0	1375	28	1	0	NO
183.0	1080	20	3	0	NO
182.0	900	23	3	0	NO
175.0	1505	16	2	1	YES
167.0	1480	19	4	0	NO
159.0	1142	10	0	0	NO
212.0	1464	7	2	0	NO
315.0	2116	25	3	0	NO
177.5	1280	14	3	0	NO
171.0	1159	23	0	0	NO
165.0	1198	10	4	0	NO
163.0	1051	15	2	0	NO
289.4	2250	40	6	0	NO
263.0	2563	17	2	0	NO
174.9	1400	45	1	1	YES
238.0	1850	5	5	1	YES
221.0	1720	5	4	0	NO
215.9	1740	4	3	0	NO
217.9	1700	6	4	0	NO
210.0	1620	6	4	0	NO
209.5	1630	6	4	0	NO
210.0	1920	8	4	0	NO
207.0	1606	5	4	0	NO
205.0	1535	7	5	1	YES
208.0	1540	6	2	1	YES
202.5	1739	13	3	0	NO
200.0	1715	8	3	0	NO
199.0	1305	5	3	0	NO
197.0	1415	7	4	0	NO
199.5	1580	9	3	0	NO
192.4	1236	3	4	0	NO
192.2	1229	6	3	0	NO
192.0	1273	4	4	0	NO
191.9	1165	7	4	0	NO
181.6	1200	7	4	1	YES
178.9	970	4	4	1	YES

Multiple Regression Modeling Steps

1. Open the Excel worksheet containing your Team Project Data.
2. As you learned in Modules 3 and 4, you will be using the set of potentially meaningful numerical independent variables and the one selected “two-category” dummy variable in your study to develop a “best” multiple regression model for predicting your numerical response variable Y. Follow the step by step modeling process described in the PowerPoints at the end of Module 4.
   1. Start with a visual assessment of the possible relationships of your numerical dependent variable Y with each potential predictor variable by developing the scatterplot matrix (use JMP) and paste this into your report.
   2. Then fit a preliminary multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable.
   3. Then assess collinearity with VIF until you are satisfied that you have a final set of possible predictors that are “independent,” i.e., not unduly correlated with each other.
   4. Use stepwise regression approaches to fit a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable).
      1. (1) Based on the forward modeling criterion determine which independent variables should be included in your regression model.
      2. (2) Based on the backward selection modeling criterion determine which independent variables should be included in your regression model.
      3. (3) Based on the mixed selection modeling criterion determine which independent variables should be included in your regression model.
      4. (4) Based on the Adjusted r² criterion determine which independent variables should be included in your regression model.
   5. Comment on the consistency of your findings in Step 2D (1)-(4).
   6. Paste screenshots of (1), (2), and (3) outputs from Step 2D above into your report.
   7. Based on Step 2D (along with the principle of parsimony if necessary) select a “best”multiple regression model.
   8. Using the predictor variables from your selected “best” multiple regression model, rerun the multiple regression model in order to assess its assumptions. You may use Excel or JMP for this step.
   9. Look at the set of residual plots, cut and paste them into the report, and briefly comment on the appropriateness of your fitted model.
      1. (1) If the assumptions are met and the fitted model is appropriate, continue to Step 2J.
      2. (2) If the normality assumption is problematic, state this but continue to Step 2J with caution because your sample size is large enough for the central limit theorem to enable the use of classical inferential methods. Note: You do not need to check the assumption of independence in your project. That assumption is met because your project is not time-dependent.
      3. (3) If either the linearity or equality of variance assumption is violated in one or two scatter plots of Y with individual predictors then transform the particular independent variables involved following Tukey’s “ladder of powers” and rerun the multiple regression model as in Step 2H.
   10. Assess the significance of the overall fitted model.
   11. Assess the significance of each predictor variable.
3. Write the sample multiple regression equation for the “final best” model you have developed.
   1. Interpret the meaning of the Y intercept and interpret the meaning of all the slopes for your fitted model (but do this in whatever units you used for Y to build this model).
   2. Interpret the meaning of the coefficient of multiple determination r ² .
   3. Interpret the meaning of the standard error of the estimate SYX (in the units you used to build this model).
   4. Determine the 95% confidence interval estimate of the average value of Y for all occasions when the independent variables have the values you selected.
   5. Select one value for each of your independent variables in their respective relevant ranges:
   6. Predict ŷ

Assume that X has a Poisson distribution with mean of 3.7. Calculate the followings.

1. P(X=1)
2. P(1<X<4)
3. P(1<X<2)
4. P(2<X<4)
5. P(X=1.5)
6. P(X=0)
7. P(X<-2)
8. Standard Deviation of X
9. Mean of X

Problem 1

1.1 If A is an n x n matrix, prove that if A has n linearly independent eigenvalues, then A^T is diagonalizable.

1.2 Diagonalize the matrix below with eigenvalues equal to -1 and 5.

0	1	1
2	1	2
3	3	2

1.3 Assume that A is 4 x 4 and has three different eigenvalues, if one of the eigenspaces is dimension 1 while the other is dimension 2, can A be undiagonalizable? Explain.

Answer for all 3 questions required.

The National Marine Fisheries Services (NMFS) is part of the National Oceanic and Atmospheric Administration (NOAA). NMFS's programs support the conservation and management of living marine resources. In a study by Hays and Marsh reported in the Canadian Journal of Zoology, 71 loggerhead sea turtles were captured and measured off the coast of Britain. The shell lengths of the turtles are shown in the stem-and-leaf plot below.

1|5 5 6 6 6 7 7 7 8 8 8 8 8 8 8 9 9 9 9 9
|
2|0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 3 4 4
2|5 5 5 6 6 7
|
3|0 0 0 3 4 8
|
4|0 5 9
|
5|1 4 5
|
6|0 1 1 4
|
7|5 8
|
8|8 8
|
9|0 0 4 6

1) A loggerhead sea turtle is classified as a juvenile if its shell length is less than 40 centimeters. How many of the turtles in the sample were juveniles?

2) Use the sample to make a point estimate of the mean shell length of all juvenile loggerhead sea turtles that drift from their hatching site (in Florida) to the coast of Britain.

3) Find the standard deviation of the sample of juveniles.

4) Use the sample to make an interval estimate of the mean shell length of juvenile loggerhead sea turtles that drift from their hatching site to the coast of Britain.

a) Use a 90% confidence level

b) Use a 95% confidence level

c) Use a 99% confidence level

How do I create a histogram for the following set of data?
Legend:
Result (1-100)
Age and gender are self explanatory
Relationship (are you in a romantic relationship?)
Medu (Mothers highest lvl of education. 1= year 10; 2= year 12; 3=bachelor; 4= post grad
Lectures (how many lectures missed)
Tutorials (How many tutorials missed)

RESULT	Gender	Age	Medu	Relationship	Lectures	Tutorials
55	F	20	4	NO	4	3
55	F	19	1	NO	2	3
65	M	18	4	NO	6	4
65	M	18	2	NO	0	0
65	F	18	3	NO	0	1

Scenario 10.5:

A firm produces garden hoses in California and in Ohio. The marginal cost of producing garden hoses in the two states and the marginal revenue from producing garden hoses are given in the following table:

      California             Ohio

    Qc        MCc      Qo     MCo    Qc + o   MR   

     1            2           1         3            1        24

     2            3           2         4            2        20

     3            5           3         6            3        16

     4            9           4         8            4        12

     5          16           5       12            5          8     

     6          24           6       17            6          4

21) Refer to Scenario 10.5. From the perspective of the firm, what is the marginal cost of the 5th garden hose?

A) 4

B) 5

C) 16

D) 12

why the answer is B

Question 1) Charlie's utility is U(x1, x2) = 4x^1/2+x2. If the price of nuts (good 1) is $1, the price of berries (good 2) is $4, and his income is $132, how many units of nuts will Charlie choose?

A. 128

B. 32

C. 67

D. 64

E. 17

Question 2) Kyle's utility function is U(A, B) = AB, where A and B are the numbers of apples and bananas, respectively, that he consumes. If Kyle is consuming 15 apples and 30 bananas, then if we put apples on the horizontal axis and bananas on the vertical axis, the slope of his indifference curve at his current consumption is

A. -2

B. -16

C. -1/2

D. -4

E. -1/4

A company produces six products in the following manner. Each unit of raw material purchased yields 4 units of product 1, 2 units of product 2, and 1 unit of product 3. Up to 1200 units of product 1 can be sold and up to 300 units of product 2 can be sold. Demand for products 3 and 4 is unlimited. Each unit of product 1 produced from raw material can be sold or processed further. Each unit of product 1 that is processed further yields 1 unit of product 4. Each unit of product 2 can be sold or processed further. Each unit of product 2 that is processed further yields 0.8 unit of product 5 and 0.3 unit of product 6.

For products 3 through 6, the production cost is additional to the costs already incurred.

Up to 1000 units of product 5 can be sold, and up to 800 units of product 6 can be sold. Up to 3000 units of raw material can be purchased at $6 per unit. Leftover units of products 5 and 6 must be destroyed. It costs $4 to destroy each leftover unit of product 5 and $3 to destroy each leftover unit of product 6. The selling price and production cost per unit of each product is provided in the table. The cost of raw material is irrelevant to solving this problem and is ignored in the costs provided.

Determine a profit-maximizing production schedule.

MICROSOFT EXCEL SOLVER SOLUTION PLEASE!!!!!!! The other solutions listed for this problem are incorrect.

Product	Units produced per unit of raw material used	Units produced per unit of Product 1 processed further	Units produced per unit of Product 2 processed further	Max Dem	Selling price	Production cost
1	4			1200	7	4
2	2			300	6	4
3	1			No limit	4	2
4		1		No limit	3	1
5			0.8	1000	20	5
6			0.3	800	35	5
3000	Units of raw material available to purchase
$6	Cost per unit of raw material
$4	Cost to destroy excess Product 5
$3	Cost to destroy excess Product 6

Krishna Kulkarni has not kept proper books of accounts prepare the statement of profit or loss for the year ending December 31, 2005 from the following information.

The following adjustments were made
(a) Krishna withdrew cash Rs 5,000 per month for private use.
(b) Depreciation @ 5% on car and furniture @ 10%.
(c) Outstanding rent Rs6,000.
(d) Fresh capital introduced during the year Rs 30,000.