Mann-whitney

Data on 12 randomly selected athletes was obtained concerning their cardiovascular fitness (measured by time to exhaustion running on a treadmill) and performance in a 20-km ski race. Both variables were measured in minutes and a regression analysis was performed. ski 85 2.5. treadmill = Coefficients Estimate (Intercept) Treadmill 85 - 2.5 Std. Error What is the test statistic? -2.5 0.45 1 Is there sufficient evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance? Round your answers to three decimal places. Using your answer from the previous question, find the p-value. Part 2 o

Based on these data, multiple regression model equations can be obtained to predict per capita consumption which is influenced by paper consumption, fish consumption and fuel oil consumption. From Microsoft Excel processing, the following data are obtained:   Question : a. Make a multiple regression equation model!b. How is the hypothesis testing based on each independent variable!c. Based on the answer to b how to model the enhanced multiple regression equation

A student used multiple regression analysis to study how family spending (y) is influenced by income (x) family size (x2), and addition to savings(x3). The variables y, x1, and x3. The variables y, x1, and x3 are measured in thousands of 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   Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is significantly related to the independent variables. Use a 5% level of significant. Carry out a test to see if X3 and y are significantly related. Use a 5% level of significance

When testing for bl in a regression model, the null hypothesis (2-tailed) is b1=0. Why? O Then the alternative hypothesis can be b0=1 This indicates that bl=b2 This indicates that bl=b0 A change in x does not result in a change in y

Bivariate data obtained for the paired variables x and y are shown below, in the table labeled "Sample data." These data are plotted in the scatter plot in Figure 1, which also displays the least-squares regression line for the data. The equation for this line is y = 14.87+0.88x. In the "Calculations" table are calculations involving the observed y-values, the mean y of these values, and the values y predicted from the regression equation. Sample data Calculations 160+ x y (x-1)² (-5)² (v-^^)² 150+ 107.2 110.7 396.0100 457.7032 2.2320 122.0 130.3 140- 0.0900 70.0569 65.1249 131.5 122.1 130- 72.2500 0.0001 72.0801 142.5 129.9 120. 0.4900 93.5089 107.5369 152.5 160.0 110- 864.3600 341.1409 119.4649 Send data to Excel LL 130 130 140 150 160 Column sum: 1333.2000 Column sum: 962.4100 Column sum: 366.4388 Figure 1 Answer the following. (a) The least-squares regression line given above is said to be a line that "best fits" the sample data. The term "best fits" is used because the line has an…

Consider the accompanying data set of dependent and independent variables a. Perform a general stopwise regression using a 0.05 for the p-value to enter and to remove independent variables from the regression model b. Perform a residual analysis for the model developed in part a to verify that the regression conditions are met Click the icon to view the data a. Use technology to perform the general stepwise regression What is the resulting regression equation? Note that the coefficient is 0 for any variable that was removed or not significant -0.69 (050), (050)+(018) - X (Round to two decimal places as needed) • Data Table: y 63 43 51 49 40 42 23 37 30 27 20 31 FR 74 63 78 3534 52 44 47 35 17 15 20 17 Print X₂ 21 259. 15 9 38 18 17 5 40 27 30 33 x₂ 22 aadosa 2NNG 29 20 17 13 17 8 15 10 10 Done 1

A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings(x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained.   ANOVA                                   df                  SS Regression                3                   45.9634 Residual                  11                   2.6218 Total                                 Coefficients     Standard Error Intercept                0.0136 x1                          0.7992                   0.074 x2                          0.2280                   0.190 x3                         -0.5796                   0.920   Write out the estimated regression equation for the relationship between the variables.  Compute coefficient of determination. What can you say about the strength of this relationship?  Carry out a test to determine whether y is…