1. Given the following semivariogram model: 3h Y(h) = Co+C (323) for h≤a 2a (h) Co+C for ha == where: - C_0 is the nugget effect (0.2) -C is the sill (1.0) - a is the range (15) a. Calculate and plot the theoretical semivariogram for distances up to meters. (15 Marks)
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- Please Explain results. Thank youThe temperatures of Charleston, South Carolina on May 3rd are recorded in the table below. Determine the equation that models this data. Hour 11 am 12 am 1 pm 2 pm 3 pm 4 pm 5 pm 6 pm 7 pm 8 pm 9 pm 10 pm 77 75 73 70 °F 74 76 78 79 80 82 81 79 OA. y = 7.496 sin(0.3243x -0.315) + 73.489 OB. y 7.496 sin(0.3243x+0.315) + 73.489 Oc.y=0.788 sin(1.738 +0.074) + 76.932 OD. y = 0.788 sin(1.738 -0.074) + 76.932Illustration 37. Calculate the (i) two regression co-efficients, (ii). Co-efficient of correlation, and (iii). the two regression equations from the following information: N= 10, ΣΧ= 350, ΣΥ= 310, Σ(Χ- 35)? 162, E(Y- 31)? = 222, E(X – 35) (Y – 31) = 92
- Q3. For 17 observations on Price (X) and Supply (Y), the following data were obtained in appropriate units. Σx= 544 Ex² = 19040 Ey = 244 Ev² = Exy = 8413 a. Calculate the correlation coefficient (r) b. Calculate the intercept of the regression formula c. Calculate the slope of the regression formula d. Obtain the regression equation e. What is supply when price is 35? 377333q3.
- In your Capstone software create a table with the following variables: Position x, m: 1, 2, 3, 4 Coefficient of friction μ: 0.36 Velocity v, m/s: v=V 2. u.9.8.xComplete the following worksheet and then use it to calculate the coefficient of determination. (ŷ; - y)² (Yi - y)² 2.208 196.000 565.203 2,116.000 432.765 1,156.000 79.477 256.000 797.046 2,916.000 178.837 196.000 973.690 676.000 1,856.834 576.000 2,121.800 1,296.000 35.331 256.000 i 1 2 3 4 5 6 7 8 9 Ti Yi y 15 160 175.486 13.5 220 197.774 16.5 140 153.197 14.5 190 182.915 17 120 145.768 16 160 160.627 13 200 205.204 18 150 130.909 12 210 220.063 10 15.5 190 168.056 Total The coefficient of determination (²) is According to the regression model, which of the following is the best estimate together with the 95 percent prediction interval of paint sales (in thousands of gallons) in a sales region where the selling price is $12.50? O 212.620 + 2(3.190) O 409.370 + 2(3.190) O212.620 2(18.017) O 409.370 = 2(18.017) What is the price elasticity of demand at a selling price of $12.50? O -0.56 -0.06 O -0.87The next four questions (29 to 32) refer to the following: To most Canadians, earthquakes are viewed as rare occurrences, but they are actually quite common. In just one month in 2001, Natural Resources Canada recorded 215 earthquakes that affected Canada from B.C. to Nunavut to Newfoundland. We would like to determine whether there is a linear relationship between the location of an earth- quake X (measured in degrees latitude north of the equator) and the magnitude of the earthquake Y (measured on the Richter scale). The explanatory and response variables are measured for a sample of 13 earthquakes. The equation of the least squares regression line is ŷ = -3.05+ 0.10x. The ANOVA table is showm below: ге Source of Variation df Sum of Squares Mean Square F Regression Error Total 1.40 25.10
- The time of arrival of an artificial seismic wave versus the distance from the source is given in the following table. Distance (m.) Time (sec.) 530 0.349 600 0.391 670 0.441 1130 0.739 1200 0.787 1270 0.831 1800 1.160 1870 1.177 1940 1.192 2730 1.377 Choose an appropriate chart and produce a graph of this data.The domain of y = cos x is. range is and the %3DDesign a Dendrogram using Complete linkage agglomerative clustering for the dataset given in Table. Also show cluster in graph form. Where, ‘X’ is 57.