X2 X3 X4 12 2. 13 3. 7. 2. 6. 7. 23.2 13 13 15 2. 12 15 11 17 2. In Table 1. you have data for variables y, xt, x2, K3, x4. You specifled your regression as folowing Y &+B,Xx+B,X+ X1+ u and E(u) 0 and cor(X, u) 0 You also know that Xa-142 X,. Which of the folowing OLS assumption does not hold? %3D O Homoscedestcty O Linear in perameters O Veretion in X3 ORendom samping oZero-conditional Mean O Perlect muticelinearity
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- You estimated a regression with the following output. Source | SS df MS Number of obs = 289 -------------+---------------------------------- F(1, 287) = 41986.64 Model | 664544048 1 664544048 Prob > F = 0.0000 Residual | 4542496.25 287 15827.5131 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 669086544 288 2323217.17 Root MSE = 125.81 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 43.81013 .2138056 204.91 0.000 43.38931 44.23096 _cons | 49.31707 16.96222 2.91 0.004 15.93094 82.70319…Q4. The Omantel firm has estimate the Sales of fibre internet connections in Oman with the related to advertising expenditure made by the company over the past 26 months. Following is the firm estimated results of the regression equation. DEPENDENT VARIABLE: Y R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 26 0.85121212 8.747 0.0187 PARAMETER STANDARD VARIABLE ESTIMATE ERROR T-RATIO P-VALUE INTERCEPT 7.6 6.33232 1.200 0.2643969 3.53 0.52228 ? 0.0001428 a. What is the dependent and independent variables in the above regression equation of Omantel firm? b. Calculate the estimated t-ratio. Test the slope estimates for statistical significance at the 10 percent significance level. d. Interpret the coefficient of determination.As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…
- Consider the following OLS regression results, In(inc)=1.970+.083educ, R2=.186, where inc represents annual income (in $1000s) and educ represents years of education. The R² can be interpreted as .186% of the variation in annual income is explained by years of education. .186% of the variation in log annual income is explained by years of education. O 18.6% of the variation in annual income is explained by years of education. O 18.6% of the variation in log annual income is explained by years of education.OA linear regression model is Units 3,414-0.839xWeek. For week 45, what is the forecast for the number of units? Round your answer to the nearest whole number. OO unitsTime left U:18:05 CLEAR MY CHOICE Finish a Question Consider the following model Y=B1+B2 X2 +e. Assume you change the scale of the y and X variables in such a way that Not yet Y*=aY and X2*=bX2. and you run the regression Y*=B1*+B2* X". What effect this scaling will have on answered the slope Marked out of 1.00 Select one: P Flag O a. B2=a+b (B2) question O b. B2*=a/b^2 (B2) O c. B2*-ab (B2) O d. B2*=a/b (B2) NEXT PAGE PREVIOUS PAGE
- 2. Consider the following estimated regression equation (standard errors in parentheses): Yi-120+ 0.10Ft + 5.33Rt (0.05) (1.00) R² = 0.5 i. ii. iii. where A Yi = the corn yield (bushels/ha) in year t Ft = fertilizer intensity (pounds/ha) in year t Rt = rainfall (inches) in year t Interpret the meaning of the intercept. Suppose you are told that the true value of BF (coefficient on fertilizer intensity) is known to be 0.20. Does this show that the estimate is biased? Why or why not? Suppose you were told that the equation does not meet all the classical assumptions and, therefore, the OLS estimator used is not BLUE. Does this mean that the true BR (coefficient on rainfall) is definitely not equal to 5.33? Why or why not?5. In classical linear regression model, Var (u) = o² refers to the assumption of Zero mean value of disturbance term a. b. Homoscedasticity c. No autocorrelationThe following data relate the sales figures of restaurant, to the number of customers registered that week: Week Customers Sales (SR) First 16 330 Second 12 270 Third 18 380 Fourth 14 300 a) Perform a linear regression that relates bar sales to guests (not to time). b) If the forecast is for 20 guests next week, what are the sales expected to be?
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