7. The yield (Y) of the product according to the product production conditions temperature (T) and pressure (P) was measured as follows. temperature 30 40 50 60 10 78% 84% 73% 63% 20 82% 92% 97% pressure 85% (Mpa) 30 83% 89% 90% 79% 40 75% 79% 85% 75% (1) Using the experimental data, find the coefficients of the regression model expressed as y= Bo+B1T+B2P. (2) Find the coefficient of determination of the regression model.
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- Solids (grams) obtained from a material as y, with respect to drying time (Hours) as x. Ten experiments were carried out to obtain the following observations: Table on the picture (a) Create a scatter diagram for the data. (b) Estimated regression model according to the data conditions. (c) Calculate Model Accuracy (R²) and Relationship Between Variables (r²)The shear resistance of soil, y kPa, is determined by measurements as a function of the normal stress, x kPa. The data are as shown below. Find the regression model, and the coefficient of correlationY llustration 23. The following tests give the aptitude test scores and productivity indices of 10 workers selected at random Aptitude scores (X) Productivity index (Y) Calculate the two regression equations and estimate the productivity index of a worker whose test score is 92. 60 62 65 70 72 48 53 73 65 82 68 60 62 80 85 40 52 62 60 81
- The scatter plot below shows the average cost of a designer jacket in a sample of years between 2000 and 2015. The least squares regression line modeling this data is given by yˆ=−4815+3.765x. A scatterplot has a horizontal axis labeled Year from 2005 to 2015 in increments of 5 and a vertical axis labeled Price ($) from 2660 to 2780 in increments of 20. The following points are plotted: (2003, 2736); (2004, 2715); (2007, 2675); (2009, 2719); (2013, 270). All coordinates are approximate. Interpret the slope of the least squares regression line. Select the correct answer below: 1.The average cost of a designer jacket decreased by $3.765 each year between 2000 and 2015. 2.The average cost of a designer jacket increased by $3.765 each year between 2000 and 2015. 3.The average cost of a designer jacket decreased by $4815 each year between 2000 and 2015. 4. The average cost of a designer jacket increased by $4815 each year between 2000 and…2. Using the following regression summery output for the estimation of demand for a product. Regression Statistics R Square 0.969 df SS F Significance F Regression 3 3656.960437 318.144172 0.00000000 Residual 31 118.7781973 Total 34 3775.738635 Coefficients t Stat P-value Intercept 87.30 24.15250613 0.00000000 Price (Px) -0.80 -10.1936142 0.00000000 Price Other (Py) -0.60 3.263427775 0.01580374 Income (I) 1.00 6.097885873 0.00000985 a. Comment on the significance of the regression model as a whole: b. Comment on the significance of the specific coefficients: c. Interpret the R2 value: c. State the estimated demand function (Qx is a linear function of Px, Py, and I): d. Determine the change…sample consists of 500 houses sold in Karachi between January 2020 and December 2020. The multiple linear regression analysis is carried out to predict the house prices for investment in residential properties in Karachi, Pakistan. The output below is produced using SPSS. (300 words) Table: Coefficients Model Unstandardized Coefficients t VIF Constant 14.208 5.736 Age of house -0.299 -2.322 1.58 Square footage of the house 0.364 2.931 1.71 Income of families in the area 0.004 0.392 1.01 Transportation time to major markets -0.337 -2.619 1.90 R2 = 0.67; DW = 2.08 Dependent Variable: House price (Pakistani rupees in Million) You are required to write the multiple regression How would you interpret the above ‘Output’ of a regression analysis performed in SPSS? Is there multicollinearity in regression? How do you know?
- Q1: A sample consists of 500 houses sold in Karachi between January 2020 and December 2020. The multiple linear regression analysis is carried out to predict the house prices for investment in residential properties in Karachi, Pakistan. The output below is produced using SPSS. (300 words) Table: Coefficients Model Unstandardized Coefficients t VIF Constant 14.208 5.736 Age of house -0.299 -2.322 1.58 Square footage of the house 0.364 2.931 1.71 Income of families in the area 0.004 0.392 1.01 Transportation time to major markets -0.337 -2.619 1.90 R2 = 0.67; DW = 2.08 Dependent Variable: House price (Pakistani rupees in Million) You are required to write the multiple regression How would you interpret the above ‘Output’ of a regression analysis performed in SPSS? From the above results, what can you say about the nature of autocorrelation? Is there multicollinearity in regression? How do you know?…The scatter plot below shows the average cost of a designer jacket in a sample of years between 2000 and 2015. The least squares regression line modeling this data is given by yˆ=−4815+3.765x. A scatterplot has a horizontal axis labeled Year from 2005 to 2015 in increments of 5 and a vertical axis labeled Price ($) from 2660 to 2780 in increments of 20. The following points are plotted: (2003, 2736); (2004, 2715); (2007, 2675); (2009, 2719); (2013, 270). All coordinates are approximate. Interpret the y-intercept of the least squares regression line. Is it feasible? Select the correct answer below: The y-intercept is −4815, which is not feasible because a product cannot have a negative cost. The y-intercept is 3.765, which is not feasible because an expensive product such as a designer jacket cannot have such a low cost. The y-intercept is −4815, which is feasible because it is the value from the regression equation. The y-intercept is…The prelim grades (x) and midterm grades (y) of a sample of 10 MMW students is modeled by the regression line y = 12.0623 + 0.7771x. Predict the midterm grade if the prelim grade is 80. A 80 74 78 86
- student used multiple regression analysis to study how family spending (y) is influenced by income(x1), family size (x2), and additionsto savings(x3). The variables y, x1, and x3 are measured in thousandsof dollars. The following results were obtained. anova df ss regression 3 45.9634 residual 11 2.6218 total coefficient standard error intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 d. Carry out a test to see if x3 and y are significantly related. Use a 5% level of significance.Consider the bivariate dataset for variable X and variable Y given by the table. Group Y A 9 8 B 4 D 8 6 Solve the following questions. a. Find the sum of X^2: b. Find the Correlation coefficient: c Estimate the slope of the regression line: 4. 3.J 1 Use technology to identify the SST, SSR, and SSE. Calculate the multiple coefficient of determination. Test the significance of the overall regression model using α=0.10. Identify the value of the F-test statistic Interpret the p-value for the overall regression model. Calculate the adjusted multiple coefficient of determination.