Compute the value of X in Table 2. ii) Estimate mass that dissolved in the water if the temperature is set at 100 Celcius.
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i) Compute the value of X in Table 2.
ii) Estimate mass that dissolved in the water if the temperature is set at 100 Celcius.
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- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?Here is a dataset containing plant growth measurements of plants grown in solutions of commonly-found chemicals in roadway runoff.Phragmites australis, a fast-growing non-native grass common to roadsides and disturbed wetlands of Tidewater Virginia, was grown in a greenhouse and watered with either: Distilled water (control); A weak petroleum solution (representing standard roadway runoff); Sodium chloride solution; Magnesium chloride solution; De-icing brine (50% sodium chloride and 50% magnesium chloride).Twenty grass preparations were used for each solution, and total growth (in cm) was recorded after watering every other day for 40 days.-Perform the correct statistical test to determine the p-value.-Report your answer rounded to four decimal places.-You should use formulas, functions, and the Data Analysis ToolPak in MS Excel to avoid additive rounding errors. Here are some useful functions: =t.test(array1,array2,tails,type) Produces a p-value for any…Determine the critical values of r and if it represents a significant linear correlation.
- We have estimated the impact of gross domestic product (GDP), energy consumption (ENERGY) and population (POP) on CO2 emiisions (CO2) in Cyprus. The results are as follows; Dependent Variable: CO2 Method: Least Squares Date: 04/20/17 Time: 09:46 Sample: 1990 2013 Included observations: 24 Variable Coefficient Std. Error t-Statistic Prob. C 2.002813 6.458672 0.310097 0.7597 GDP 0.022114 0.011872 1.862670 0.0773 ENERGY -0.734352 0.328388 -2.236233 0.0369 POP 0.203927 0.293686 0.694371 0.4954 R-squared 0.825079 Mean dependent var 3.625982 Adjusted R-squared 0.798841 S.D. dependent var 0.108170 S.E. of regression 0.048515 Akaike info criterion -3.062883 Sum squared resid 0.047074 Schwarz criterion -2.866541 Log likelihood 40.75460 Hannan-Quinn criter. -3.010793 F-statistic 31.44583 Durbin-Watson stat 1.410912…A multiple regression model has the form y^=b0+b1x1+b2x2 The coefficient b1 is interpreted as the:A. change in y per unit change in x1, holding x2 constantB. change in the average value of y per unit change in x1, holding x2 constantC. change in y per unit change in x1, when x1 and x2 values are correlatedD. change in y per unit change in x1 If multicollinearity exists among the independent variables included in a multiple regression model, the:A. multiple coefficient of determination will assume a value close to zeroB. standard errors of the regression coefficients for the correlated independent variables will increaseC. regression coefficients will be difficult to interpretD. regression coefficients will be difficult to interpret and the standard errors of the regression coefficients for the correlated independent variables will increase please explain which answer is correct and whyBelow is the SPSS output for the relationship between height at age 3 (X) and at age 20 (Y). Correlations heightAt3 heightAt20 heightAt3 Pearson Correlation 1 .840** Sig. (2-tailed) .000 N 16 16 heightAt20 Pearson Correlation .840** 1 Sig. (2-tailed) .000 N 16 16 **. Correlation is significant at the 0.01 level (2-tailed). ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 236.838 1 236.838 33.628 .000b Residual 98.599 14 7.043 Total 335.438 15 Dependent Variable: heightAt20 Predictors: (Constant), heightAt3 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 41.679 4.470 9.325 .000 heightAt3 .664 .114 .840 5.799 .000 Dependent Variable: heightAt20 State hypothesis for the…