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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

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