Shown below is output for regression analysis relating Y (dependent variable) and X (independent variable). 1. What is the estimated regression equation that relates Y to X? 2.  Is there a linear association between dependent and independent variable? Test the hypothesis at α = 0.05 3. What is the estimated value of Y if the value of X is 4.5?

If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x. True or false?

13/

Select the appropriate interpretation for the slope of the linear regression equation below. Y (Dependent Variable) = Grade Point Average X (Independent Variable) = Average number of hours spent using electronic devices for entertainment purposes   yhat = 4 - 0.125*X A. For every 1 hour more spent using electronic devices for entertainment per week then a person's GPA will increase on average by 0.125 points B. For every 1 GPA gained obtained by a student then on average that person will have watched 0.125 hours fewer of entertainment on electronic devices per week C. For every 1 GPA point lost by a student then on average that person will have watched 0.125 hours more of entertainment on electronic devices per week D. For every 1 hour more spent using electronic devices for entertainment per week then a person's GPA will decrease on average by 0.125 points

You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ý = 0.406 + 1.3385X, + 2X2 The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ý = 1.9 – 3X + 12X2 + 4Xg + 8x, This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = B₂= B3 =B4 = 0 O Ho: One or more of the parameters is not equal to zero. H₂: B3 =B4 = 0 O Ho: B3 = P4 = 0 H₂: None of the parameters are equal to zero. ⒸH₁: B3 =B₁ = 0 H: One or more of the parameters is not equal to zero. O Ho: B₁ = P₂ = B3 =B4 = 0 H: One or more of the parameters is not equal to zero. ✔ Find…

A particular article used a multiple regression model to relate y = yield of hops to x, = average temperature (°C) between date of coming into hop and date of picking and x, = average percentage of sunshine during the same period. The model equation proposed is the following. y = 415.11 – 6.6x1 – 4.50x2 +e (a) Suppose that this equation describes the actual relationship. What mean yield corresponds to a temperature of 20 and a sunshine percentage of 40? (b) What is the mean yield when the average temperature and average percentage of sunshine are 19 and 44, respectively?

The following data shows memory scores collected from adults of different ages. Age (X) Memory Score (Y) 25 10 32           10 39 9 48 9 56 7 Use the data to find the regression equation for predicting memory scores from age. The regression equation is: Ŷ = 4.33X + 0.11 Ŷ = -0.11X + 4.33 Ŷ = -0.11X + 13.26 Ŷ = -0.09X + 5.4 Ŷ = -0.09X + 12.6   Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 28 For the calculations, leave two places after the decimal point and do not round:   Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 43 For the calculations, leave two places after the decimal point and do not round:   Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 50 For the calculations, leave two places after the decimal point and do not round:

In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67  the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?

A linear regression analysis reveals a strong, negative linear relationship between x and y. Which of the following could possibly be the results from this analysis? (A) ŷ 13.1 27.4x, r = 0.85 (B) == 27.4+ 13.1x, r = -0.95 (D) == (C) ŷ 13.1+ 27.4x, r = 0.95 542 385x, r = -0.15 (E) 0.85 0.25x, r = -0.85

This is my question! All parts please.