Use two indicator (dummy) variables to code the forest. Let 'indicator2' be 1 for forest 2, and O otherwise, let 'indicator3' be 1 for forest 3 and 0 otherwise. With this coding, the logisitic regression model will use the intercept for forest 1, the intercept plus the coefficient of the first indicator variable for forest 2, and the intercept plus the coefficient of the second indicator variable for forest 3. Include at least 3 decimals of precision (a) Run the multiple logistic regression. Give the coefficient estimates here: log- odds(gambling) = "indicator2 + "indicator3 (b) What are the log-odds for tar spot in Forest 1? (c) What are the log-odds for tar spot in Forest 2? (d) What are the log-odds for tar spot in Forest 3?

This was not the correct quadratic model for the data

A particular article proposed a quadratic regression model to describe the relationship between x = degree of delignification during the processing of wood pulp for paper and y = total chlorine content. Suppose that the actual model is the following. y=245+ 75x-4x² + e (a) Mean chlorine content for x = 6 is ---Select--- mean chlorine content for x = 8. (b) What is the change in mean chlorine content when the degree of delignification increases from 8 to 9? What is the change in mean chlorine content when the degree of delignification increases from 12 to 13?

A study tests the effect of earning a Master's degree on the salaries of professionals. Suppose that the salaries of the professionals (S,) are not dependent on any other variables. Let D, be a variable which takes the value 0 if an individual has not earned a Master's degree, and a value 1 if they have earned a Master's degree. What would be the regression model that the researcher wants to test? A. S,= Po + B,D,+u, i=1, .. , n. O B. S,= Po + B, + u, i= 1, .. , n. OC. 1=6o +B1,S, + u, i= 1, .. , n. O D. 0=Bo +B, S, + u,, i= 1, .. , n. Suppose that a random sample of 160 individuals suggests that professionals without a Master's degree earn an average salary of $59,000 per annum, while those with a Master's degree earn an average salary of $80,000 per annum. The OLS estimate of the coefficient B, will be $ and that of B, will be $ Click to select your answer(s). DELL

Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂  = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…

Data was collected for a regression analysis comparing car weight and fuel consumption. b0 was found to be 32.7, b1 was found to be -7.6, and R2 was found to be 0.86. Interpret the y-intercept of the line. On average, each one unit increase in the weight of a car decreases its ful consumption by 7.6 units. On average, when x=0, a car gets -7.6 miles per gallon. On average, when x=0, a car gets 32.7 miles per gallon. On average, each one unit increase in the weight of a car increases its fuel comsumption by 32.7 units. We should not interpret the y-intercept in this problem.

A particular article used a multiple regression model to relate y = yield of hops to x₁ = mean temperature (°C) between date of coming into hop and date of picking and x₂ = mean percentage of sunshine during the same period. The model equation proposed is the following. y = 415.116.6x₁4.50x2+e (a) Suppose that this equation does indeed describe the true relationship. What mean yield corresponds to a temperature of 20 and a sunshine percentage of 39? (b) What is the mean yield when the mean temperature and percentage of sunshine are 19.1 and 42, respectively? You may need to use the appropriate table in Appendix A to answer this question.

A researcher wishes to examine the relationship between years of schooling completed and the number of pregnancies in young women. Her research discovers a linear relationship, and the least squares line is:    ˆy=5−4xy^=5-4x   where x is the number of years of schooling completed and y is the number of pregnancies.  The slope of the regression line can be interpreted in the following way: When amount of schooling increases by one year, the number of pregnancies tends to decrease by 4.   When amount of schooling increases by one year, the number of pregnancies tends to increase by 4.   When amount of schooling increases by one year, the number of pregnancies tends to increase by 5.   When amount of schooling increases by one year, the number of pregnancies tends to decrease by 5.