I. The relationship between impression Y and advertising expenditure X rom the annual observation from 2000 to 2020 in USA, the following egression results were obtain: Y₁-14.4773+0.0022X₁ se=6.1523 t = ( ) 6.8750
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- Q7. Do quadretic regression to find the forcast value of quauter 9 and 10 Quarters (x) 1 2 3 4 5 6 7 8 9 10 Demand (y) 5.10 11.40 22.70 35.00 64.22 81.77 115.50 149.90does the simple regression of log (price) on log (nox) produce an upward or a downward biased estimator of β1?Based on some careful market analysis you have hypothesised that the demand for breakfast cereal can be modeled as where Y is the monthly consumption of cereal for a family of four, X1 is the price of cereal per Kg.X2 is the family income and X3 is the price of eggs per dozen. You have arandom sample of 200 families which contains measurements of the variable for each family. Explain how you wuld determine the effect of the price of eggs on the sales of breakfast cereal given the effects of family income and the price of breakfast cereal given the effects of family income and the price of breakfast cereal.
- With Panel Data, if we assume that the individual effects vi are not correlated with the regressors Xit (i.e. E(vi|Xit) = 0), which one of the following statements is correct: The Fixed Effects estimator is not consistent. Both the OLS and the Random Effects estimators are not consistent. The OLS estimator is not consistent, but the Random Effects estimator is consistent. The OLS and the Random Effects estimator are consistent. All of the above. None of the aboveConsider a linear demand model to explain the quantity demanded for a product: where Q = quantity sold, Price = price of the product, Income = purchaser’s income, Advert = advertising. The following data was collected in year 2018. The company spends millions of money in advertisements. The company wants to know how advertisement as well how other factors affect the quantity of units sold. The results are as follows: Model Summary R 0.986 R2 0.973 Standard error of estimate 6.9872 Variables Coefficient Std error Sig Constant 205.862 19.354 0.000 Price -12.242 1.407 0.000 Income 1.414 0.422 0.015 Advert -3,344 1.798 0.112 Interpret and write a report based on the results obtained above.Southwest Airlines is able to keep fares low, in part because of relatively low maintenance costs on its airplanes. One of the main reasons for the low maintenance costs is that Southwest flies only one type of aircraft, the Boeing 737. However, Southwest flies three different versions of the 737. Suppose Southwest decides to conduct a study to determine whether there is a significant difference in the average annual maintenance costs for the three types of 737s used. a. State an independent variable for such a study. b. What are some of the levels or classifications that might be studied under this variable? c. Give a dependent variable for this study.
- et R2unrestricted and R2restricted be 0.54 and 0.30 respectively. The difference between the unrestricted and the restricted model is that you have imposed two restrictions. The number of regressors in the unrestricted model is 5. There are 500 observations. The F-statistic (under the assumption of homoskedasticity) in this case is 1. 4.61 2. 8.01 3. 98.5 4. 128.9In the Managerial Solution, we estimated a focus group's demand curve for iTunes downloads. The estimated coefficient on price was-413, and the 1-statistic was -12.8. nage d coe ndard Using these values, what is the standard error of this estimated coefficient? est The standard error of the price coefficient is (Enter your response rounded to two decimal places) Suppose we had another focus group sample, ran a regression on that sample, and obtained the same coefficient on price but with a standard error five times as large What can you say about the statistical significance of the price coefficient in this second sample? rt A The price coefficient would be statistically significantly different than zero at the 0.05 confidence level Sulag 50,00 t-Val Stan would be would not beYou are an analyst. Your friend started a new business of selling masks. She knows that you have adequate knowledge of economics and business, and hence, asked for your help in making some important business decisions. From the data obtained from her, you computed the Price Elasticity of Demand (PED) of Masks, and found PED in February was 1.8 (absolute value), and PED of Masks in April was 0.7 (absolute value). (a) What type of good mask is in the month of April in terms of elasticity? (b) The market price of the mask in June was 10 taka per unit and revenue earned in June was 150 taka. How many masks were sold in June? (c) In 2(b), you found the quantity demanded masks in June. You received the data for August and observed that the price in August was 25 per unit and the quantity demanded of masks in August is 7. Calculate the Price Elasticity of Demand (PED) for masks from June to August.
- "In the regression model InY=b0+b1*InX+u, the coefficient b1 is interpreted as" O the intercept O A covariance O A regressor O An elasticityQUESTION 1 Which one of the following assumptions is known as the spherical disturbance assumption? E(uu') = 0 E(uu') = XXxy1 E(uu') = =o²xy E(uu') = a2 All of the above. None of the above. QUESTION 2 If the error term covariance matrix is not spherical, but it is diagonal, then you have: O Autocorrelation, but no heteroskedasticity. O Heterskedasticity and autocorrelation. Heteroskedasticity, but no autocorrelation. Both heteroskedasticity and autocorrelation. Neither hetroskedasticity or autocorrelation. QUESTION 3 Which one of the following statements is true for a linear regression model with non-spherical disturbances (i.e. E(uu') = Q): The GLS estimator is inefficient. The OLS estimator is inefficient. The OLS estimator covariance matrix is diagonal. The GLS estimator covariance matrix is block-diagonal. All of the above. None of the above.Consider the following formula: y i - ( β 0 ^ + β 1 ^ x i ) . What does this formula describe? OLS slope estimator Error term Causal effect of x on y residual