**Question 32** The equation which has the form of E(y) = ŷ = b0 + b1x1 + b2x2 + ... + bpxp is a(n) a. estimated multiple nonlinear regression equation. b. multiple nonlinear regression model. c. estimated multiple regression equation. d. multiple regression equation. **Question 33** The mathematical equation that explains how the dependent variable y is related to several independent variables is a(n) a. simple nonlinear regression model. b. multiple regression model. c. estimated multiple regression equation. d. multiple regression equation. --- The responses indicate a focus on understanding types of regression equations and models, specifically regarding multiple regression.

A 10-year study conducted by the American Heart Association provided data on how age, blood pressure, and smoking related to the risk of strokes. The data file “Stroke.xslx” includes a portion of the data from the study. The variable “Risk of Stroke” is measured as the percentage of risk (proportion times 100) that a person will have a stroke over the next 10-year period.   Regression Analysis As Image:  1) Based on the simple regression analysis output, write the estimated regression equation.   2) What is the correlation coefficient between Risk of Stroke and Age? How do you find i

A family would like to build a linear regression equation to predict the amount of grain harvested per acre of landon their farm. They subdivide their land into several smaller plots of land for testing and would like to select anexplanatory variable they can control. Which of the following is an appropriate explanatory variable that the familycould use to create a linear regression equation? (A) (B) (C) (D) (E) The total amount of rainfall recorded at their farmThe type of crop planted in the plot the previous yearThe average daily temperature at their farmThe variety of grain planted in the plotThe amount of fertilizer applied to each plot of land Why is it that the correct answer is letter E? Why not D? Please explain to me the reason for other choices to be wrong. Thanks!

4) Use computer software to find the multiple regression equation. Can the equation be used for prediction? A wildlife analyst gathered the data in the table to develop an equation to predict the weights of bears. He used WEIGHT as the dependent variable and CHEST, LENGTH, 4)_ and SEX as the independent variables. For SEX, he used male-1 and female=2. WEIGHT CHEST LENGTH SEX 344 45.0 67.5 1 416 54.0 72.0 1 220 41.0 70.0 360 49.0 68.5 332 44.0 73.0 1 140 32.0 63.0 436 48.0 72.0 1 132 33.0 61.0 356 48.0 64.0 150 35.0 59.0 1 202 40.0 63.0 365 50.0 70.5 1 A) WEIGHT = 196 + 2.35CHEST + 3.40LENGTH + 25SEX; Yes, because the R2 is high. B) WEIGHT =-320+10.6CHEST + 7.3LENGTH-10.7SEX; Yes, because the P-value is high. C) WEIGHT =-442.6 + 12.1CHEST + 3.6LENGTH- 23.8SEX; Yes, because the adjusted R² is high. D) WEIGHT = 442.6+ 12.1CHEST + 4.2LENGTH– 21SEX; Yes, because the P-value is low. %3D |D %3D

researcher collects some socioeconomic variables of Turkey and wants to model the natural gas consumption arough years by multiple linear regression. Below is the table summarizing the coefficients of variables and their statistical parameters, and the figure showing the change of natural gas consumption in this time period • Actual constant term X₁: population (million people) X2: GDP per capita (thousand $) X3: Inflation percentage X4: Mean winter temperature coefficients -36.5 0.578 2.133 -0.0733 -0.0375 standard error 6.73 0.133 0.221 0.0126 0,201 t value -5.43 4.34 p-value 1.42* 10.5 2.20*10- 9.67 9.47*10-10 -0.19 -5.81 5.46* 10.6 0.853 Natural gas consumption (billon m²) 50 40 30 20 10 R¹ = 0.992 standard error 1.51 1986 1990 1994 1998 -Estimated 2002 Star 2006 2010 a) Write the multiple regression equation in its standard form: y =B₁ + B₁x₁ + ß₂x₂ + ... b) Which variables significantly affect the natural gas consumption? Why? c) Which variables positively, which variables…

In a simple linear regression prediction equation, the slope is defined as which of the following? The slope is the value of X when Y = 0. For a one-unit change in Y (the criterion), there will be a slope-unit change in X (the predictor) For a one-unit change in X (the predictor), there will be a slope-unit change in Y (the criterion) The slope is the value of Y when X = 0.

Finally, the researcher considers using regression analysis to establish a linear relationship between the two variables – hours worked per week and yearly income. (a) Estimate a simple linear regression model and present the estimated linear equation. Display the regression summary table and interpret the intercept and slope coefficient estimates of the linear model. (b) Display and interpret the value of the coefficient of determination, R-squared (R2).   Data Hours Per Week Yearly Income ('000's) Class  18 43.8   13 44.5   18 44.8   25.5 46.0   11.5 41.2   18 43.3   16 43.6   27 46.2   27.5 46.8   30.5 48.2   24.5 49.3   32.5 53.8   25 53.9   23.5 54.2   30.5 50.5   27.5 51.2   28 51.5   26 52.6   25.5 52.8   26.5 52.9   33 49.5   15 49.8   27.5 50.3   36 54.3   27 55.1   34.5 55.3   39 61.7   37 62.3   31.5 63.4   37 63.7   24.5 55.5   28 55.6   19 55.7   38.5 58.2   37.5 58.3   18.5 58.4   32 59.2   35…

· Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable.

The equation used to predict college GPA (range 0-4.0) is y = 0.17 +0.51x, +0.002x,, where x, is high school GPA (range 0-4.0) and x, is college board score (range 200-800). Use the multiple regression equation to predict college GPA for a high school GPA of 3.8 and a college board score of 500. The predicted college GPA for a high school GPA of 3.8 and a college board score of 500 is (Round to the nearest tenth as needed.)

How to write the model specifications of a multiple regression analysis with dummy variables that are more than two categories? Provide your solutions using the example, y=spending,x1=education level (1 if primary,2 if secondary,3 if tertiary,4 if above),x2=type of job(1 if full times,2 if part timed,3 if retired) Write the regression equation of the example.