A forecaster used the regression equation Q=a+b+q+c202 + c303 and quarterly sales data for 1998/2016/V(-1,72) for an appliance manufacturer to obtain the results shown below. Qis quarterly sales, and D, D and D3 are dummy variables for quarters I, II and III. DEPENDENT VARIABLE QT R-SQUARE 08798 OBSERVATIONS 72 VARIABLE INTERCEPT T 01 02 PARAMETER ESTIMATE 25 12.0 300 450 F-RATIO 122 547 STANDARD ERROR 12.54 0860 326 10 68 P-VALUE ON F 00001 T-RATIO 287 291 244 P-VALUE 00055 00049 0.0005 00066 00174
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- A forecaster used the regression equation Qt= a + bt+q₁D₁ + C2D2 + c3D3 and quarterly sales data for 2004/-2021/V (t = 1, ..., 64) for an appliance manufacturer to obtain the results shown below. Q is quarterly sales, and D1, D₂ and D3 are dummy variables for quarters I, II, and III. DEPENDENT VARIABLE: QT R-SQUARE OBSERVATIONS: 64 0.8768 VARIABLE INTERCEPT T D1 D2 D3 F-RATIO P-VALUE ON F 107.982 0.0001 PARAMETER STANDARD ESTIMATE 30.0 1.5 10.0 25.0 40.0 ERROR T-RATIO P-VALUE 2.34 0.0224 2.14 0.0362 3.33 0.0015 3.47 0.0010 2.53 0.0140 12.80 0.70 3.00 7.20 15.80 In any given year, quarterly sales tend to vary as follows: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…4. The estimation of the model with quarterly car sales in the U.S. from 1975 to 1990 gives: Source | df MS Number of obs = 64 F( 2, Prob > F 61) = 12.21 Model .32720224 2 .16360112 0.0000 Residual | .817286587 61 .013398141 R-squared Adj R-squared = 0.2625 Root MSE 0.2859 Total | 1.14448883 63 .018166489 .11575 lqne | cCoef. t P>|t| std. Err. [95% Conf. Interval] 1price lincome -.4604611 3.37186 6.89398 -.8280926 .1838504 -4.50 0.000 -1.195724 2.399991 . 4860261 4.94 0.000 1.428121 _cons 5.92543 .4843662 12.23 0.000 4.95688 Based on the parameter estimates, what is the predicted effect of a 10% increase in price on the number of cars sold? What would be the effect of that price increase on the value of car sales?
- The regression equation Netincome = 2,049 +.0478 Revenue was estimated from a sample of 100 leading world companies (variables are in millions of dollars). (0-1) If Revenue = 1, then Netincome = (a-2) Choose the correct statement. (b) Choose the right option. (c) O Increasing the revenue raises the net income. Decreasing the revenue raises the net income. O Increasing the revenue lowers the net income. million. (Round your answer to 2 decimal places.) O The intercept is not meaningful because a firm cannot have net income when revenue is zero. The intercept is meaningful because a firm can have net income when revenue is zero. If Revenue = 24,574, then NetIncome = million. (Round your answer to the nearest whole number.)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.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?
- Sales in a company are $188 million in 2009 and increase $208 million in 2010. Compute the percentage increase in sales using the usual formula Compare this value to the approximation The approximation performs (1) percentage change increases. 100 x (1) O better (2) O deteriorates O worst O improves 100 x 100 x (Sales 2010 - Sales 2009) Sales 2009 100 x [In (Sales2010) -In (Sales2009)] (Sales 2010 - Sales2009) Sales 2009 100 x [In (Sales 2010) -In (Sales2009)] = [ (Express your response as a percentage and round to three places) Now, assume that sales in a company are $188 million in 2009 and increase $264 million in 2010. = (Sales2010 - Sales 2009) Sales 2009 % % % 100 x [In (Sales 2010) -In (Sales 2009)] = [ (Express your response as a percentage and round to three places) % when the change is small. The quality of the approximation (2). as theConsider the output here from a regression in R. What is 3₂? Coefficients: Estimate (Intercept) 1.708 5.404 -1.478 9.531 X1 X2 X3 Std. Error 0.555 2.792 0.6 2.758As 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…
- All questions utilize the multivariate demand function for Smooth Sailing sailboats in C6 on text page 83. Compute to three decimal places. Initial values are: PX = $9500 PY = $10000 I = $15000 A = $170000 W = 160 This function is: Qs = 89830 -40PS +20PX +15PY +2I +.001A +10W 1.(a). Use the above to calculate the arc price elasticity of demand between PS = $9000 decreasing to PS = $8000. The arc elasticity formula is: 1.(b). Judging from the computation in (a), do you expect the revenue resulting from the decrease in Ps to $8000 to increase, remain the same, or decrease relative to the revenue at Ps = $9000. (Hint: see the table on page 65 of Truett). Explain your choice. 1.(c). Calculate the point elasticity of demand for Smooth Sailing sailboats at PS = $9000 (which should make Qs = 101600). The formula is: 1.(d). Does this elasticity value indicate that Smooth Sailing demand is relatively responsive to changes in the price of these sailboats? Explain…Plastic cups are made by forming of raw plastic. The price of plastic is 4.500 OMR per kg and the price of plastic cups is 13 OMR per 1000 cups. Plant requires differ- ent machine which cost about 50,000 OMR, but the machines has to be imported, after paying 25% as customs duty. On a graph paper, draw a freehand graph and represent all important parameters. Due to increase in plastic price, the break-even quantity has increased by 10%. Find out the new plastic price?regression result for your product is shown in the table below: Variable Parameter Estimates Standard error Constant -164.0 20.24 Price of good X (P,) Price of good Y (P,) -3.50 1.55 2.50 0.28 Per capita Income (/) 0.45 0.52 R-squared Adjusted R-squared 0.8672 0.8132 F-statistic 15.6893 a) Suppose the average price of 3 units of good X is GH¢12, price of 2 units of goodY is GH¢60, the per capita income of Ghana is GH¢420. i. Write down the estimated demand equation for your firm's product and interpret the parameter estimates. Determine the quantity of good X sold. Estimate the own price elasticity of demand and state the type of demand curve 11. 111. your firm has? What would be the effect of a price increase on the firm's total revenue? iv. Assess the probable impact on your firm if the firm producing geod Y decreases their price by 10%. Explain the adjusted R-squared Is the good X a normal or inferior good? What is the relationship between good X and good Y? V. vi. vii. viii..