Question

In: Math

3. Load the dataset called ec122a.csv and decide the appropriate regression to run. Write down what...

3. Load the dataset called ec122a.csv and decide the appropriate regression to run. Write down what transformations, corrections, etc... you make and why.

y1

x1

5.3478787576716

-0.930542577578737

-69.4411002445282

-14.3360876802962

17.6647698924475

1.81741420842464

98.6511466667161

16.8769469917607

14.7965900933862

1.44147861051093

-34.5302655286703

-8.00737844994315

93.0899709372717

15.9601981407006

9.21693205816442

0.677367144474178

82.6007511115692

13.940352476942

115.798113882096

21.2544523041556

210.387049747658

38.2407928740359

25.53810654411

2.87106608048978

103.832140647001

18.1287219709914

69.9887102526973

11.9894172917371

115.53192498448

20.8016798770016

121.344292025264

22.0189019228638

92.7341812552436

15.9508245127554

141.336831165046

25.3838968113616

43.9676084746945

6.62783843142594

170.312498248916

30.6056891002234

100.141722965535

18.0744156617512

135.127526516403

25.1557427275658

35.4910615569294

5.34067840867235

49.0886162426323

7.66630180243485

183.23305880313

33.6747888141339

133.899669788226

31.0484776835843

119.472386558899

19.6774321421239

158.382012262513

38.7948124929967

158.265751170527

32.3449530783571

143.893438668698

31.318399069747

209.554152576129

39.0470592670422

269.696741210151

47.0260908373683

214.277835307116

39.4621037661542

137.448728114245

32.1951502465506

207.142331867495

30.4353133960267

195.530279391204

37.8895119687649

260.613365801387

47.5777648932458

193.358564414283

32.544387671943

214.355032319599

35.3968738633248

236.246295426679

46.8573949752216

179.510295035057

40.1659721878024

212.202997184581

42.9660084481672

207.263001917022

38.3563234239438

189.537080855405

36.9994190965688

293.77520107103

52.1838828310905

275.816868619373

46.9729303988204

213.777730095761

51.623410535034

234.515710668196

54.7713564277226

305.755164538293

58.2281799071355

247.574028943277

48.2612135595578

216.487201880791

44.4805702248886

298.939398951728

59.5098240982159

294.087515977881

59.7323992058337

242.47071086964

56.2423387201774

314.216664214321

52.099463290858

198.64183568504

45.5524703388986

451.501739075897

71.7371279051027

334.639764748968

56.3003387632005

325.539711644784

61.3753202076771

334.360999254428

65.6509595487347

375.501692057963

69.0188962996762

279.92394271145

50.820033316856

391.747159079897

84.4387655124175

256.755426083081

61.8425207887276

335.348364682454

78.2972291401232

326.862654481865

67.2701863797509

409.199061682728

67.0226394402898

315.278602307445

62.6960929012151

389.115799067651

67.3988546408951

324.558498258645

74.4613819502999

277.860262868103

51.1152020598209

348.220952805656

73.4318499899927

394.101591698092

69.6828387504708

378.574744964529

70.3390300051774

345.129291309579

65.1443486887627

431.388383747861

86.5385881418601

461.246340384882

80.7778216315798

393.128587286873

79.4875434916298

457.413617158369

93.3535591485397

490.030080973679

86.1469790728216

445.013611790392

88.2858459293727

502.433226880918

90.1840214865989

531.919402102633

84.5845337879384

459.430685958911

101.584476353668

524.534588061157

93.7218122436017

384.831820262549

88.3997031202485

369.255646051443

64.059370789963

460.011550161416

94.6172825629485

581.849448405881

100.291462036955

487.238487436963

86.277129080676

554.389543790077

106.054358170647

476.138213629779

77.2218509347784

360.434234419891

84.2953663204438

497.064285198229

98.5692172324988

559.620017287958

104.394884303588

570.274724422607

113.867023345632

526.006391282654

110.550311578395

668.85329391523

118.103935026271

567.23894595309

105.330310697067

551.525236136496

104.258750461435

Solutions

Expert Solution

Let's use excel:

Simple linear regression using Excel.

Step 1) First enter the given data set in excel columns.

Step 2) Then click on Data >>> Data Analysis >>>Regression >>>>OK

Step 3) Input Y Range: Select the data of column "A"

Input X Range: Select the data of column "B"

Click on Lable

then Click on Ouput Range

Select the box "Residual plot"

Look the following Image

Then Click on OK, so we get following result.

From the above output the Correlation = R = 0979733

Even though the correlation between y1 and x1 is very hight the ordinary least square estimators are misleading. Because the assumption of constant variance of the residual is violated.

From the residual plot, we conclude that as the level of x1 increases the variance of the residuals also increases.

So we need to used transformation.

The transformation here such that the weight of the small values of x is greater than the weight of the larger vaues of X.


Related Solutions

Install and load the dataset named Carseats (in the ISLR package) into R. Run a multiple...
Install and load the dataset named Carseats (in the ISLR package) into R. Run a multiple linear regression with all the variables. Using the coefficients, write down the model. ( be careful with the qualitative variable ShelveLoc. ) obtain the interaction plot of ShelveLoc and price.
b. Write down potential questions that you could answer using regression analysis for the Happiness_2011.xls dataset...
b. Write down potential questions that you could answer using regression analysis for the Happiness_2011.xls dataset c. Perform one simple regression using any two reasonable variables from the Happiness_2011.xls file (two quantitative variables) and show the analysis result d. Interpret the findings from the simple regression analysis e. Add one or more quantitative variable (including dummy variable that have values of 0 and 1) to the analysis in #b, perform one multiple regression analysis f. Interpret your findings from the...
Load the regression data in the file called wagedata.csv and answer the following questions: (a) Create...
Load the regression data in the file called wagedata.csv and answer the following questions: (a) Create an interaction between Ability and PhD (b) Run a regression with the interaction a constant Ability and PhD. Write down you estimators and the t-statistics (c) Compute the difference-in-difference estimate and write down you answer. (d) Test if the difference is significant by showing relevant steps, and write down the conclusion to the test. Wage Ability Phd 3.52942833628898 2.57892317214096 1 11.5241044105103 0.217444617867018 1 6.43708200805673...
1. Load the regression data in the le called wagedata.csv and answer the following questions: (a)...
1. Load the regression data in the le called wagedata.csv and answer the following questions: (a) Create an interaction between Ability and PhD (b) Run a regression with the interaction a constant Ability and PhD. Write down you estimators and the t-statistics (c) Compute the di erence-in-di erence estimate and write down you answer. (d) Test if the di erence is signi cant by showing relevant steps, and write down the conclusion to the test. 2. Which of these photos...
3. Long-run exchange rate model (based on PPP) (a) Write down the fundamental equation of the...
3. Long-run exchange rate model (based on PPP) (a) Write down the fundamental equation of the monetary approach to the exchange rate. (b) Using the above equation explain how a change in domestic interest rate affects the long-run level of exchange rate. Compare the prediction of this model with prediction of the interest rate parity. (c) Explain what is the Fisher effect. Write down the mathematical formula on which this effect is based. (d) Explain how Fisher effect allows us...
How does a firm decide whether to shut down production in the short run and in...
How does a firm decide whether to shut down production in the short run and in the long run? Explain both scenarios Can a competitive firm earn positive profits in the long run? Explain.
Write down a brief report of the results from this regression analysis explaining: (1) what is...
Write down a brief report of the results from this regression analysis explaining: (1) what is the impact of each variable over the demand? (2) How strong are the results from this analysis to support a forecast? (3) What are the limitations you foresee by using this analysis to forecast production for the following five years? SUMMARY OUTPUT Regression Statistics Multiple R 0.72916937 R Square 0.53168797 Adjusted R Square 0.51496254 Standard Error 72.98925047 Observations 30 ANOVA df SS MS F...
Write the regression equation. Discuss the statistical significance of the model using the appropriate regression statistic...
Write the regression equation. Discuss the statistical significance of the model using the appropriate regression statistic at a 95% level of confidence. Discuss the statistical significance of the coefficient for the independent variable using the appropriate regression statistic at a 95% level of confidence. Interpret the coefficient for the independent variable. What percentage of the observed variation in income is explained by the model? Predict the value of a person’s income with 3 children, using this regression model. SUMMARY OUTPUT...
Physical Chemistry Questions: 3. Write down the simplest appropriate LCAO (linear combinations of atomic orbitals) variational...
Physical Chemistry Questions: 3. Write down the simplest appropriate LCAO (linear combinations of atomic orbitals) variational wavefunction and give the explicit expressions for the atomic orbitals used, i.e., how they depend on the coordinates. 4. Write down the variational formula for the energy and give explicit expressions for the integrals that have to be computed. Don’t compute all the integrals, as it is too time consuming, but point out which are easy to evaluate and which are equivalent. Give an...
a) Briefly write down the basic heat gains that make up the cooling load in items....
a) Briefly write down the basic heat gains that make up the cooling load in items. b) A desired feature from refrigerants is that the evaporation pressure is high and the condensing pressure is low. Explain the main reason for this by drawing the T-s diagram of a single-stage cooling cycle.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT