Can someone look at these and assist

Question 1.

sing the accompanying Home Market Value data and associated regressionline,

MarketValue=$27,540+$37.795Square

Feet, compute the errors associated with each observation using the formula

ei=Yi<="" path="">Yi

and construct a frequency distribution and histogram.

Square Feet	Market Value
1810	104800
1916	104300
1843	93400
1813	90900
1836	101900
2028	108400
1731	87700
1848	96000
1793	89200
1666	88400
1853	100900
1622	96600
1694	87500
2373	113900
2373	113300
1665	87500
2123	116000
1620	94600
1730	86300
1666	87000
1519	83400
1483	79900
1586	81500
1600	87200
1483	82500
1484	78700
1519	87700
1700	94200
1485	82000
1469	88200
1522	88200
1522	88500
1483	76700
1521	84300
1669	90800
1590	80900
1782	91400
1485	81400
1522	100600
1521	87200
1682	96800
1581	87400

Question 2.

The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c.

Overhead Costs	Billable Hours
335000	4000
375000	5000
400000	6000
452000	7000
520000	8000
535000	9000

a. Develop a simple linear regression model between billable hours and overhead costs.

Overhead

Costs=enter your response here+enter your response hereBillable

Hours

(Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the$ symbol in youranswers.)

Question 3.

The accompanying data are from a football league for one season.

a. Construct a scatter diagram forpoints/game andyards/game. Does there appear to be a linearrelationship?

b. Use the Regression tool to develop a model for predictingpoints/game as a function ofyards/game. Explain the statistical significance of the model and the

R2

value.

Points/Game	Yards/Game
27	346.5
16.5	302.4
19.1	305.6
17.6	276.2
15.9	291.5
19.3	300.3
24.7	352.4
24	345.9
27.3	374.2
19.4	337.4
21.2	315.5
28.9	368.6
24	327.3
28.9	362
25.9	366.9
14.6	280.7
16	294
23	339.6
36.9	417.9
22.4	358.8
24.4	326.3
18.2	287.2
18.8	303.6
20.7	349.8
24.3	334.8
26.1	315.1
14.6	246.8
24.3	352.6
17.3	300.1
20.5	329.3
20.4	313
20.7	336.7

Question 4.

Using the accompanying Concert Sales data on sales dollars and the number ofradio, TV, and newspaper ads promoting the concerts for a group of cities. Develop simple linear regression models for predicting sales as a function of the number of each type of ad. Compare these results to a multiple linear regression model using both independent variables. State the model and explainR-square, SignificanceF, andp-values, with an alpha of

0.05.

Sales ($1000)	Thousands of radio and TV ads	Thousands of newspaper ads
1167	0	41
985	0	38
909	24	26
637	25	25
872	31	32
956	29	30
955	35	33
1223	37	36
888	38	24
1011	38	24
1574	44	46
1503	45	43
1083	53	51
948	48	51
1383	56	19
1305	55	21
1363	63	31
1452	58	32
1572	67	33
1672	64	33
1807	69	42
1690	73	39

State the hypotheses for the simple linear regression tests.

H0:

H1:

Question 5.

The accompanying Cereal Data provide a variety of nutritional information about 67 cereals in a supermarket. Use regression analysis to find the best model that explains the relationship between calories and the other variables. Keep in mind the principle of parsimony. Use a level of significance of 0.05.

Calories	Sodium	Fiber	Carbs	Sugars
70	130	20	5	6
70	260	9	7	5
50	140	14	8	0
110	200	1	14	8
110	180	1.5	10.5	10
110	125	1	11	16
130	210	2	19	8
90	200	4	15	6
90	210	5	13	5
120	220	0	12	12
110	290	2	17	1
120	210	0	13	9
110	140	2	13	7
110	180	0	12	12
110	280	0	22	3
100	290	1	21	2
110	90	1	13	12
110	180	0	12	13
110	140	4	10	7
100	80	1	21	0
110	220	1	23	3
100	140	2	11	10
100	190	1	18	5
110	125	1	11	13
110	200	1	14	11
100	0	3	14	7
120	160	5	12	10
120	240	5	14	12
110	135	0	13	12
110	280	0	15	9
100	140	3	15	5
110	170	3	17	3
120	75	3	13	4
110	180	0	14	11
120	220	1	12	11
110	250	1.5	11.5	10
110	170	1	17	6
140	170	2	19	9
110	260	0	21	3
100	150	2	12	6
110	180	0	12	12
160	150	3	17	11
100	220	2	15	6
120	190	0	15	9
130	170	1.5	13.5	10
120	200	6	11	16
100	320	1	20	3
50	0	0	13	0
50	0	1	10	0
100	135	2	14	6
120	210	5	14	12
90	0	2	15	6
110	240	0	22	2
110	290	0	20	3
90	0	3	21	0
80	0	3	16	0
90	0	4	19	0
110	70	1	9	16
110	230	1	16	3
90	15	3	15	5
110	200	0	21	3
140	190	4	15	14
100	200	3	16	3
110	140	0	13	12
100	230	3	17	3
100	200	3	17	3
110	200	1	18	8

Select the best model below and fill in the corresponding answer boxes to complete your choice.

(Type integers or decimals rounded to three decimal places asneeded.)

A.

Calories=enter your response here+enter your response hereSodium+enter your response hereCarbs+enter your response hereSugars

B.

Calories=enter your response here+enter your response hereSodium+enter your response hereFiber+enter your response hereSugars

C.

Calories=enter your response here+enter your response hereSodium+enter your response hereFiber+enter your response hereCarbs+enter your response hereSugars

D.

Calories=enter your response here+enter your response hereCarbs+enter your response hereSugars

E.

Calories=enter your response here+enter your response hereFiber+enter your response hereCarbs+enter your response hereSugars

F.

Calories=enter your response here+enter your response hereSodium+enter your response hereFiber

G.

Calories=enter your response here+enter your response hereSodium+enter your response hereFiber+enter your response hereCarbs

Question 6.

The accompanying Credit Approval Decisions data provide information on credit history for a sample of banking customers. Use regression analysis to identify the best model for predicting the credit score as a function of the other numericalvariables, using both thep-value andt-statistic criteria. How do the modelscompare? Which would youchoose? Use a level of significance of

0.05.

Credit Score	Years of Credit History	Revolving Balance ($)	Revolving Utilization (%)
725	20	11320	0.25
573	9	7200	0.7
677	11	20000	0.55
625	15	12800	0.65
527	12	5700	0.75
795	22	9000	0.12
733	7	35200	0.2
620	5	22800	0.62
591	17	16500	0.5
660	24	9200	0.35
700	19	22000	0.18
500	16	12500	0.83
565	6	7700	0.7
620	3	37400	0.87
774	13	6100	0.07
802	10	10500	0.05
640	7	17300	0.59
523	14	27000	0.79
811	20	13400	0.03
763	2	11200	0.7
555	4	2500	1
617	9	8400	0.34
642	13	16000	0.25
688	3	3300	0.11
649	12	7500	0.05
695	15	20300	0.22
701	9	11700	0.15
635	7	29100	0.85
507	2	2000	1
677	12	7600	0.09
485	5	1000	0.8
582	3	8500	0.65
699	17	12800	0.27
703	22	10000	0.2
585	18	31000	0.78
620	8	16200	0.55
695	16	9700	0.11
774	13	6100	0.07
802	10	10500	0.05
640	7	17300	0.59
536	14	27000	0.79
801	20	13400	0.03
760	2	11200	0.7
567	4	2200	0.95
600	10	12050	0.81
702	11	11700	0.15
636	8	29100	0.85
509	3	2000	1
595	18	29000	0.78
733	15	13000	0.24

What is the best model using thep-value criteria? Select the best answer below and fill in the answer boxes to complete your choice.

(Type integers or decimals rounded to three decimal places asneeded.)

A.

Credit

Score=enter your response here+enter your response hereUtilization

B.

Credit

Score=enter your response here+enter your response hereYears+enter your response hereBalance+enter your response hereUtilization

C.

Credit

Score=enter your response here+enter your response hereYears

D.

Credit

Score=enter your response here+enter your response hereBalance+enter your response hereUtilization

E.

Credit

Score=enter your response here+enter your response hereYears+enter your response hereBalance

F.

Credit

Score=enter your response here+enter your response hereYears+enter your response hereUtilization

G.

Credit

Score=enter your response here+enter your response hereBalance

Question 7.

The accompanying Major League Baseball data provide data for one season. Use the data to build a multiple regression model that predicts the number of wins. Complete parts a through c.

Won	Runs	Hits	Earned Run Average	Strike Outs	Walks
65	713	1366	4.81	1527	589
91	738	1411	3.56	1140	634
66	613	1440	4.59	1056	424
89	820	1511	4.19	1140	587
75	685	1414	4.18	1234	479
88	752	1467	4.09	922	467
91	795	1515	4.01	1218	522
69	646	1362	4.3	1184	545
83	770	1452	4.14	1274	585
81	751	1515	4.3	1147	546
80	719	1403	4.08	1375	514
76	611	1348	4.09	1025	415
67	676	1534	4.97	905	471
80	681	1363	4.04	1070	466
80	667	1368	4.01	1184	533
77	750	1471	4.58	1214	546
94	780	1521	3.95	967	559
79	656	1361	3.7	1095	502
95	855	1485	4.06	1136	662
81	663	1396	3.56	1061	527
97	772	1451	3.67	1064	560
57	587	1303	5	1207	463
90	665	1338	3.39	1183	538
92	697	1411	3.36	1099	487
61	513	1274	3.93	1184	459
86	736	1456	3.57	1027	541
96	801	1343	3.78	1293	672
90	787	1556	3.93	986	511
85	755	1364	4.22	1164	471
69	655	1355	4.13	1221	503

a. Construct and examine the correlation matrix. Is multicollinearity a potentialproblem?

	Won	Runs	Hits	Earned Run Average	Strike Outs	Walks
Won	enter your response here
Runs	enter your response here	enter your response here
Hits	enter your response here	enter your response here	enter your response here
Earned Run Average	enter your response here	enter your response here	enter your response here	enter your response here
Strike Outs	enter your response here	enter your response here	enter your response here	enter your response here	enter your response here
Walks	enter your response here	enter your response here	enter your response here	enter your response here	enter your response here	enter your response here

(Type integers or decimals rounded to three decimal places asneeded.)

Question 8.

Use thep-value criterion to find the best model for predicting the number of points scored per game by football teams using the accompanying National Football League Data. Does the model make logicalsense?

Points/Game	Rushing Yards/Game	Passing Yards/Game	Penalties	Interceptions	Fumbles
25.1	90.1	251.2	139	18	5
16.6	95.2	202.5	107	16	6
17.9	112.3	183.8	109	17	0
15.2	112.7	164.4	80	18	6
16.2	114.5	175.5	97	14	10
20.4	83.2	216.9	113	16	11
23.6	97.5	257.1	92	19	10
25.1	118.1	238.2	116	17	4
28.9	109.9	259.5	106	19	4
20.4	122.6	229.8	92	14	10
21.2	80.4	248.7	102	17	12
27.7	99.2	277.5	115	19	3
23.5	99.8	236.7	84	11	8
28.2	106.2	255.2	69	22	9
25.9	149.6	204.3	78	20	4
14.7	78.7	193.4	103	14	2
16.4	98.8	182.6	93	14	2
22.3	164.9	171.2	88	15	10
36.2	115.4	291.1	80	19	6
23.1	91.3	262.2	70	13	4
23.6	134.2	193.8	79	15	4
16.2	106.1	184.7	65	15	0
17.1	130.5	165.8	122	18	2
21.6	123.8	236.4	85	11	2
24.5	135.1	197.2	82	11	8
25.3	127.2	188.3	96	30	12
13.2	92.3	149.6	89	12	4
24.8	101.6	249.5	61	20	8
16.9	95.7	208.8	96	18	3
20.3	117.2	207.3	83	16	13
18.2	131.4	176.2	103	22	6
20.3	116.5	215.1	92	14	4

Determine the best multiple regression model. Let

X1

represent RushingYards, let

X2

represent PassingYards, let

X3

representPenalties, let

X4

representInterceptions, and let

X5

represent Fumbles. Enter the terms of the equation so that the

Xk-values

are in ascending numeral order by base. Select the correct choice below and fill in the answer boxes within your choice.

(Type an integer or decimal rounded to three decimal places asneeded.)

A.

Points/Game=enter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here

B.

Points/Game=enter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here

C.

Points/Game=enter your response here+enter your response hereXenter your response here

D.

Points/Game=enter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here

E.

Points/Game=enter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response here

Question 9.

A national homebuilder buildssingle-family homes andcondominium-style townhouses. The accompanying dataset provides information on the sellingprice, lotcost, and type of home for closings during one month. Complete parts a through c.

Type	Sales_Price	Lot_Cost
Townhouse	112740	20700
Single Family	136530	25500
Townhouse	147905	24650
Single Family	170000	25200
Townhouse	181916	45025
Townhouse	187390	27000
Single Family	189120	35000
Townhouse	196898	45025
Townhouse	203076	45025
Single Family	205821	39299
Single Family	214205	36500
Townhouse	250800	73400
Single Family	255000	43198
Single Family	268000	43344
Single Family	268500	41099
Single Family	271105	45000
Single Family	277720	44650
Single Family	294990	57000
Single Family	301500	59000
Single Family	307387	45850
Single Family	312898	40768
Single Family	319602	82250
Single Family	324412	62523
Single Family	337374	70399
Single Family	337380	49150
Single Family	338065	54850
Single Family	354117	56219
Single Family	359949	50591
Single Family	432426	57422
Single Family	492820	84122

a. Develop a multiple regression model for sales price as a function of lot cost and type of home without any interaction term.

Create a dummy variable named"Townhouse", where it is equal to 1 when

Type="Townhouse"

and 0 otherwise. Determine the coefficients of the regression equation.

Sales

Price=enter your response here+(enter your response here)Lot

Cost+(enter your response here)Townhouse

(Round the constant and coefficient of Townhouse to the nearest integer as needed. Round all other values to two decimal places asneeded.)

Question 10.

For the provideddata, develop a regression model for overall satisfaction as a function of years of service and department that has the largest appropriate

R2

value. Note that the categorical variable department has multiple levels and will require the use of multiple dummy variables. Whichdepartment, ifany, has the highest impact onsatisfaction?

Department	Years	Ideas	Communication	Recognition	Training	Work Conditions	Tools and Information	Work/Life Balance	Satisfaction
Management	1	5	4	4	3	5	3	5	9
Administrative	16	2	3	2	2	4	5	2	3
Production	16	2	3	2	4	4	4	2	5
Production	15	2	3	1	4	4	4	2	4
Quality Control	1	5	5	5	5	5	5	5	10
Maintenance	17	5	4	3	5	5	5	3	8
Maintenance	15	5	5	5	5	5	5	5	9
Production	13	3	3	3	4	4	4	3	8
Quality Control	11	3	4	4	4	5	5	2	7
Production	3	5	5	5	5	5	5	5	10
Production	6	2	2	1	3	3	4	2	4
Production	1	5	4	4	3	4	5	5	9
Production	3	3	4	3	4	5	5	4	7
Production	2	4	4	4	4	5	5	5	9
Production	3	3	4	3	3	2	4	4	6
Shipping/ Receiving	21	3	2	2	3	2	4	3	6
Production	2	4	3	4	3	3	4	4	6
Production	2	4	5	4	4	4	4	4	7
Production	15	5	4	3	4	3	5	3	7
Management	3	3	4	3	3	4	5	5	8
Production	5	4	5	3	2	3	5	4	7
Shipping/ Receiving	8	3	2	2	2	2	4	2	4
Management	3	2	2	2	2	3	5	3	3
Administrative	2	4	4	3	4	4	5	3	9
Production	8	5	5	3	5	3	5	3	8
Shipping/ Receiving	32	2	3	2	4	2	5	3	5
Production	17	4	3	4	3	3	5	2	6
Shipping/ Receiving	2	5	5	5	5	5	5	5	9
Production	15	5	3	4	5	5	5	5	9
Production	5	2	4	2	2	2	5	3	3
Administrative	14	4	3	2	2	5	5	5	6

Determine the regression model for overall satisfaction as a function of years of service and department that has the largest

R2.

Let"Administrative" be the baselinedepartment, let

X1

representMaintenance, let

X2

representManagement, let

X3

representProduction, let

X4

represent QualityControl, and let

X5

represent Shipping/ Receiving, coding each department variable with a 1 if the person is in that department and 0 otherwise. Inaddition, let

X6

represent Years. Enter the terms of the equation so that the

Xk-values

are in ascending numerical order by base. Select the correct choice below and fill in any answer boxes within your choice.

(Type integers or decimals rounded to three decimal places asneeded.)

A.

enter your response here+(enter your response here)Xenter your response here

B.

enter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here

C.

enter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here

D.

enter your response here+(enter your response here)X1+(enter your response here)X2+(enter your response here)X3+(enter your response here)X4+(enter your response here)X5+(enter your response here)X6

E.

enter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here

F.

enter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here+(enter your response here)Xenter your response here

Question 11.

The Helicopter Division of Aerospatiale is studying assembly costs at its Marseilles plant. Past data indicates the accompanying data of number of labor hours per helicopter. Reduction in labor hours over time is often called a"learning curve" phenomenon. Using thesedata, apply simple linear regression and examine the residual plot. What do youconclude? Construct a scatter chart and use the Excel Trendline feature to identify the best type of curvilinear trendline(but not going beyond asecond-order polynomial) that maximizes

R2.

Helicopter Number	Labor Hours
1	2000
2	1350
3	1235
4	1143
5	1071
6	1029
7	987
8	953

The residuals plot has

no apparent pattern.

clusters of points.

points randomly scattered about zero.

a nonlinear shape.

Therefore, this data

cannot

can

be modeled with a linear model.