The correlation matrix between the variables is given X, Y,Z, U, W, V: 1 0,96 1 0,88 0,91 1 R= 0,46 0,53 0, 56 1 -0, 20 - 0,26 - 0,02 0,28 0, 70 1 0,72 0,89 0,31 - 0, 11 1 Create the appropriate correlation matrices for calculating the multiple correlation coefficient in the model: v= ao+a¡x+a2y+a3z+ɛ
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- Use graphical method to solve the following LPP: Maximise Z = x +4y Subject to the constraints: x + y < 1 2x + 2y 2 4 *2 0, y 2 0All 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…The height (in inches) of a random sample of 28 players in NCAA Division 1 basketball teams is shown to the left. x 75 80 75 87 77 77 67 83 77 80 73 82 73 71 80 76 72 78 68 74 74 72 76 86 77 77 78 5 Sum of squared x minus n times squared mean: ∑x² − nx̅² =_____ a 590.74 b 582.01 c 573.41 d 564.94
- The Results below show the output of the following model: ?=?0+?1?1+?2?2+? Coefficient St. Error t-ratio Intercept 10.492 0.6655 15.77 ?1 0.0154 0.1889 0.08 ?2 0.1353 0.1889 0.72 Observations 100 ?2 0.985 Correlation matrix: X1 X2 X1 1 X2 0.950 1 Instructions: a. The above results show that the model has the problem of multicollinearity, what are the indicators of multicollinearity that can be identified from these results? b. What are the solutions to rectify multicollinearity?Find the OLS estimators for the parameters using the matrices above.Assign the figure with the correct correlation coefficients. Possible coefficients: 0.99, 0.58, -0.10, -0.63 Graph A 14 12- 10- 14 Graph B 12- 8- 10- 8. 0- 4- -2 -4- -6- -8 0- 4 8. 14 Graph C 12- 2 Graph D 10- 8- -2- 6- -4- 4- -6- 24 -8- -10- 8 2. 642
- 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…What is the index formulation used for?18 Given that the sum of the squared deviations of EDUC is 9025.6 The standard error of the slope coefficient is ________. a 0.1524 b 0.1249 c 0.1024 d 0.0839
- exaplin Z-D model with the help of diagramsQuestion 4 - Type Your Answers in Space Provided (a) An accountant wishes to undertake a cost analysis of electricity consumption of office heating for various maximum daily temperatures. He recorded the data over a period of 8 random days, as shown below: 2 3 4 5 6 8 Temp(°C) Max Electricity Consumption 26 31 25 26 14 20 16 34 35 20 37 24 42 41 40 17 You may use the following Excel print-outs or summary statistics provided to answer the questions. SUMMARY OUTPUT SUMMARY STATISTICS Regression Statistics Multiple R R Square Adjusted R Square Standard Error n = 8. 0.895 24 0.801 32 0.768 s2x = 48.29 4.862 s?y = 101.71 %3! Observations 8 Sxy = -62.7143 Coefficient Standard Error t Stat P-value 6.575 Intercept temp(°C) 63.172 9.607 0.000 -1.299 0.264 -4.911 0.003 Find the least squares regression line to predict the electricity consumption from the maximum temperature.The table to the right contains price-demand and total cost data for the production of projectors, where p is the wholesale price (in dollars) of a projector for an annual demand of x projectors and C is the total cost (in dollars) of producing x projectors. Answer the following questions (A) - (D). (A) Find a quadratic regression equation for the price-demand data, using x as the independent variable. X 270 360 520 780 The fixed costs are $. (Round to the nearest dollar as needed.) ITTI y = (Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) Use the linear regression equation found in the previous step to estimate the fixed costs and variable costs per projector. The variable costs are $ per projector. (Round to the nearest dollar as needed.) (C) Find the break even points. The break even points are (Type ordered pairs. Use a comma to separate answers as needed. Round to the nearest integer as…