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Please help thank you! Selling Information For Real Estate Value Price SqFt Brick (1 if brick,...

Question

Please help thank you! Selling Information For Real Estate Value Price SqFt Brick (1 if brick,...
please help thank you!

Selling Information For Real Estate Value Price SqFt Brick (1 if brick, if othewise) $241,255 3,392 0 $184,518 2,038 1 $176,4
Industrial CEO Salary Experience Service $133,125 $126,200 $195,729 $217,435 $147,113 $158,000 $148,363 $160,165 $132,750 $16
For this question your answer will come from Table 4. Suppose the data were collected relating the selling price of a house t
For this question your answer will come from Table 4. Suppose the data were collected relating the selling price of a house t
Use Table 5 to answer the following question. For the last series of questions, ensure that your data includes a 90% Confiden
Selling Information For Real Estate Value Price SqFt Brick (1 if brick, if othewise) $241,255 3,392 0 $184,518 2,038 1 $176,488 1,906 0 $240,068 3,329 0 $169,760 1,828 0 $185,335 2,081 0 $172,735 1, 9260 $224,281 3,4250 $172,589 1,676 1 $214,635 2,735 1 $199,666 2,373 1 $208,348 2,662 1 $218,360 2, 8341 $230,160 3, 2540 $164,812 1,431 0 $191,560 1,839 1 $203,255 2, 4561 $173,325 1,530 $168,073 1.381 1 $179,620 1,4571 TABLE 4
Industrial CEO Salary Experience Service $133,125 $126,200 $195,729 $217,435 $147,113 $158,000 $148,363 $160,165 $132,750 $160,500 $183,625 $166,975 $144,400 $159,913 $190,813 OHHOOHHooooo TABLE 5
For this question your answer will come from Table 4. Suppose the data were collected relating the selling price of a house to square footage and whether or not the house is made out of brick. Use statistical software to find the regression equation. The dependent variable is the selling price, the first independent variable (B1) is the square footage of the home being sold, and the second independent variable (B2) whether or not the house exterior is brick. Using this data, consider the equation of the regression line for predicting the price of a house using square foot and whether or not the exterior is made of brick. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant Question 35. Whether or not the regression model is significant. What is the predicted value of the house, when SqFt is 2,275 and it is made of brick? (Round to the nearest Dollar) $183,551 O $193,006 $201,755 $198,689
For this question your answer will come from Table 4. Suppose the data were collected relating the selling price of a house to square footage and whether or not the house is made out of brick. Use statistical software to find the regression equation. The dependent variable is the selling price, the first independent variable (B1) is the square footage of the home being sold, and the second independent variable (B2) whether or not the house exterior is brick. Using this data, consider the equation of the regression line for predicting the price of a house using square foot and whether or not the exterior is made of brick. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Question 31. Based upon the coefficient of SqFt (B2), using an alpha level at 0.05, evaluate the P-value for significance. Does the coefficient of SqFt test to be significant? Chose your answer based upon the Regression Data output. Yes No answer text provided. Not enough information to determine outcome Ο Νο
Use Table 5 to answer the following question. For the last series of questions, ensure that your data includes a 90% Confidence Interval. Suppose the following data were collected from a sample of CEOs relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the regression analysis and equation. Using this data, consider the equation of the regression line for predicting the salary of the CEO using years of experience and what sector the CEO belongs to. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Question 39. What is the expected salary of a CEO who is in the Service Sector, with 14 years of experience? (Round your answers UP). $140,206 $177,868 $181,539 $143,400

Answers

Price SqFt Brick Salary Experience Service Industrial
241255 3392 0 133125 6 0 1
184518 2038 1 126200 2 1 0
176488 1906 0 195729 23 0 0
240068 3329 0 217435 28 0 0
169760 1828 0 147113 13 0 1
185335 2081 0 158000 21 0 1
172735 1926 0 148363 15 1 0
224281 3425 0 160165 12 0 0
172589 1676 1 132750 1 0 1
214635 2735 1 160500 23 0 1
199666 2373 1 183625 21 0 0
208348 2662 1 166975 30 1 0
218360 2834 1 144400 12 1 0
230160 3254 0 159913 29 1 0
164812 1431 0 190813 21 0 0
191560 1839 1
203255 2456 1
173325 1530 0
168073 1381 1
179620 1457 1

35)

The regression equation is Price = 111974 + 35.619 SqFt + 5683 Brick Predictor Constant SqFt Brick Coef SE Coef T P 111974 55

The predicted value of the house price when SqFt is 2275 and it is made of brick is

Price = 111974 + 35.619*2275 + 5683 *1 = $ 198689.

Q 31) The p-value for the test of slope corresponding to the independent variable SqFt is 0.000 and less than 0.01. Hence, we can conclude that the coefficient of SqFt is significant at the 0.01 level of significance.

Ans: Yes.

Q. 39.

The regression equation is Salary - 155889 - 34933 Service + 1603.1 Experience - 30111 Industrial Predictor Coef SE Coef IP C

The expected salary for a CEO who is the service sector with 14 years of experience is

Salary = 155889 - 34933*1+ 1603.1*14 - 30111*0=143400

Ans: $ 143400


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