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I have no idea how to fill in the blank in this regression output... please help

Why is the equation of the regression line for this scatter plot ?

The regression equation is Health Index= y + a Age + ß Blood sugar + 8 Blood Pressure SE 20986 339.28 Age Blood sugar 209.2 Blood pressure 207.2 S = 962.233 R-Sq = 86.6% R-Sq (adj) = 76.5% Coef Constant Analysis of Variance Source DF Regression 3 Residual Error. 4 Total 7 SS Coef 2912 71.95 179.3 225.4 23863180 3703570 27566750 T 7.21 4.72 * 0.92 MS 7954393 925892 F *** P 0.002 0.009 0.308 ** P 0.032 a) What is dependent and independent variables? b) Fully write out the regression equation. c) Fill in the missing values **, ****, and *****. d) Hence test whether & is significant. Give reasons for your answer. e) Perform the F Test making sure to state the null and alternative hypothesis.

В. A model estimated using a dataset with 125 observations generates the following results. SS df MS Regression 919587.543 4 229896.9 Error 2590390.62 121 534.2113 Std. Variable B Error t P>lt| X2 -0.0126355 0.005519 -2.28937 0.022 X3 0.5957923 0.014482 41.13934 0.000 Х4 1.124589 0.877192 1.282032 0.200 X5 0.3237421 0.060709 5.332661 0.000 constant 8.86016 1.766116 5.016749 0.000 What is the R2 for this sample? What information does the R² provide?

Is the y- intercept meaningful or meaningless?

Please help me with question d, e, f, g, h, and I. Thank you.

5) Can i get help with this question please

A researcher’s results are shown below using Femlab (labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 U.S. states.   Regression Statistics Multiple R 0.313422848   R Square 0.098233882   Adjusted R Square 0.079447088   Standard Error 32.07003698   Observations 50       Variable Coefficients Standard Error t Stat Intercept   343.619889     61.0823514     5.62552   Femlab   –2.2833659     0.99855319     –2.28667     Which statement is valid regarding the relationship between Femlab and Cancer? Multiple Choice   At the .05 level of significance, there isn’t enough evidence to say the two variables are related.   This model explains about 10 percent of the variation in state cancer rates.   If your sister starts working, the cancer rate in your state will decline.   A rise in female labor participation rate will cause the cancer rate to decrease within a state.

Q1 Data displayed in this graph were standardized. True or false? Q2 Based on this graph, one can accurately estimate shoe size heritability to be 0.8676. True or false?

A student collected concentration versus absorbance data for a series of standards and produced a standard curve. Which value of 2 would reflect the linear regression curve the student produced? Absorbance 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 ² = 0.10 ² = 0.76 ² = 0.98 2 = 0.45 1 Absorbance vs. Concentration 2 3 Concentration (ppm) 4 5 6

To investigate the relationship between the milage and sales price for a popular car model the pictured scatterplot was used. a) Based on the excel output that's pictured, what is the estimated regression equation that could be used to predict the price given the miles? b) Does the model fit the data? (ie whether the regression relationship is statistically significant) Did the estimated regression equation provide a good fit? (ie use the coefficient of determination to explain variability independent variable) c) Suppose you are considering purchasing a car of this model with 60000 miles. Using the estimated regression equation, predict the price.

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Multiple regression analysis was used to study the relationship between a dependent variable, y, and four independent variables; x1, x2, x3, and x4. The following is a partial result of the regression analysis involving 31 observations.       Coefficients Standard Error     Intercept 18.00 6.00     x1 12.00 8.00     x2 24.00 48.00     x3 -36.00 36.00     x4 16.00 2.00     ANOVA   df SS MS F Regression     125   Error         Total   760       Compute the multiple coefficient of determination. Perform a t test and determine whether or not β1 is significantly different from zero (α = .05). Perform a t test and determine whether or not β4 is significantly different from zero (α = .05). At α = .05, perform an F test and determine whether or not the regression model is significant.

What variables do Teczar find have the most significant controlled associations with women in national parliaments?

Report the correlations between the three independent variables (age, educ and Protestant) and your dependent variable (childs). Which category had the correlation that was the weakest?

/was/ui/v2/assessment-player/index.html?launchid=831489a9-d255-4984-8d47-074c80bc17d6#/question Problems Question 2 of 8 Two variables are defined, a regression equation is given, and one data point is given. Weight Training = Weight = = maximum weight capable of bench pressing (pounds) number of hours spent lifting weights a week The data point is an individual who trains 5 hours a week and can bench 150 pounds. 99 + 11.7(Training) (a) Find the predicted value for the data point and compute the residual. Enter the exact answers. Residual = i II Predicted value = eTextbook and Media 3' " lbs lbs C D 6 O W

What does the slope and the intercept mean in this setting?

please do all parts!   The estimated regression equation for this data set is y=4.4878+1.9549x. Part A: (in image) Part B:  is the linear function is the appropriate regression function for this data set? Part C: do the residuals have a constant variance? Part D: are the residuals independent? Part E: are the error terms are normally distributed? y    x22    821    818    846    2241    2254    2276    3258    3268    32

sample of trucks and their "static weight" and "weight in motion" weights are in thousands of pounds. What percent of the variability in static weight can be explained by the linear model rounded to the nearest 10th?

3. You are working on a regression when accidentally you dump coffee all over yourregression output. Using the remaining values, find the missing values in each blank numbered 1-9 SUMMARY OUTPUT Regression Statistics Multiple R 0.860644193 R Square # 4 Adjusted R Square 0.72282625 Standard Error #5 Observations 32 ANOVA df MS F Significance F Regression 2 834.0726419 417.0363209 41.4216016 3.16178E-09 Residual #2 #1 # 3 Total 31 1126.047188 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 36.90833048 2.190798638 16.84697527 1.62066E-16 32.42764417 41.38901679 cyl -2.264693597 0.575889243 #7 0.000480375 -3.442519346 -1.086867847 hp #6 0.01500073 -1.274717745 0.212528465 #8 #9 Screenshot

The service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year.  The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage   Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782     Total 2628.025 49       a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints   Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357   4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…

The equation that represents the average least squared error and the regression line on the scatterplot is called a:     regression equation     multiple regression line     partial correlation equation     slope and intercept line

What variables do Teczar find to have the most significant controlled association with women in ministries?

3. The regression model below estimated the impact of tax on income using 41 observations. a. Is the slope parameters of variable of tax significant at 5 percent level? Explain. b. What the R-square in this regression indicates. const tax Model 1: High-Precision OLS, using observations 1-41 Dependent variable: income Coefficient 1.78191 7.00074 Mean dependent var S.D. dependent var Sum squared resid S.E. of regression R-squared Adjusted R-squared F(1,49) P-value(F) Std. Error 0.880324 0.0575306 T-ratioPvalue 2.024 121.7 0.0484 <0.0001 7.055 8.30 1.14 4.82 9.97 9.96 1.48 1.79 **

I need help with this question   1. Are the main effects significant? Explain

I would like to get help to know how to make a report of the multiple regression analysis I performed about 30 employees salaries. it should include a description of the predictice model, pre-analysis, regression analysis, interpretation of the relevant findings and a conclusion with recommendations. thank you