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=5−3xy^=5-3x   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 increase by 5.   When amount of schooling increases by one year, the number of pregnancies tends to decrease by 3.   When amount of schooling increases by one year, the number of pregnancies tends to increase by 3.   When amount of schooling increases by one year, the number of pregnancies tends to decrease by 5.

A scientist collected data on the mating call frequency of frogs and temperature. They found the least squares line to be: y = 3 + 5x where x is the temperature (in Celsius) and y is mating call frequency. The slope of the regression line can be interpreted in the following way: OWhen temperature decreases by one degree, the mating call frequency increases by 5. When temperature increases by one degree, the mating call frequency increases by 3. O When temperature increases by one degree, the mating call frequency increases by 5. O When temperature decreases by one degree, the mating call frequency increases by 3. OWhen temperature increases by one degree, the mating call frequency decreases by 5. Submit Question

In a linear regression model, the dependent variable is "Final exam score (%) for WPC 300" and the independent variable is "Hours studied". A coefficient of 4 could be interpreted as for every one % increase in the final exam score, the expected hours of study is 4. four hours of additional study, the expected increase in the final exam score is 1%. four hours of additional study, the final exam score is expected to increase by 4%. hour of additional study, the expected final exam score increases by 4%.

A wildlife biologist has spent the past 5 years studying the relationship between the length (inches) and the average weight (lb) of the striped bass. His results are summarized in the table below. Length (inches) 14 18 22 26 Weight (lb) 3 |11 14 Find the least squares regression line y = ax + b for the data in the table, where x represents the length of the fish in inches and y represents the weight in pounds. Use this regression line to estimate the average weight of a striped bass that is 21 inches in length. Oy=0.75(21) - 9.5 = 6.25 lb O y = 0.65(21) - 10.5 = 3.15 lb y = 0.95(21) - 10.5 = 9.45 lb y = 0.95(21)-8.5 = 11.45 lb y = 0.85(21) 12.5 = 5.35 lb

The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) mported by a country, for the last seven years. Construct and interpret a 95% prediction interval for the amount of crude oil imported by the this country when the amount of crude oil produced by the country is 5,508 thousand barrels per day. The equation of the regression line is y = - 1.120x + 15,839.271. Oil produced, x 5,684 5,654 5,452 5,157 5,061 5,030 5,826 9,680 10,041 10,154 10,121 10,060 Oil imported, y 9,304 9,105 Construct and interpret a 95% prediction interval for the amount of crude oil imported when the amount of crude oil produced by the country is 5,508 thousand barrels per day. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) and O A. We can be 95% confident that when the amount of oil produced is 5,508 thousand barrels, the…

Please help me understand question and how to solve. A researcher at a large company has collected data on the beginning salary and current salary of 48 randomly selected employees. The least-squares regression equation for predicting their current salary from their beginning salary is Y=-2500 + 2.1X. Where Y is the current salary and X is the beginning salary.    Question: Jay started working for the company earning $ 30,000. He currently earns $60,000. What is the residual for Jay (assuming no extrapolation error)?

Data on tuition and mid-career salary are collected from a number of universities and colleges. The result of the data collection is the linear regression model ŷ where x = annual tuition and y = average mid-career salary of graduates, both in thousands of dollars. = 0.84x + 161 1. Which quantity is the independent variable? annual tuition average mid-career salary of graduates 2. According to this model, what is the average salary for a graduate of a college or university where the annual tuition is $30,000? S 3. What is the slope of this regression model?

Biologist Theodore Garland, Jr. studied the relationship between running speeds and morphology of 49 species of cursorial mammals (mammals adapted to or specialized for running). One of the relationships he investigated was maximal sprint speed in kilometers per hour and the ratio of metatarsal-to-femur length. A least-squares regression on the data he collected produces the equation ŷ = 37.67 + 33.18x where x is metatarsal-to-femur ratio and y is predicted maximal sprint speed in kilometers per hour. The standard error of the intercept is 5.69 and the standard error of the slope is 7.94. Construct a 96% confidence interval for the slope of the population regression line. Give your answers precise to at least two decimal places. contact us help 6:42 PM povecy polcy terms of use careers A E O 4») 18 -క90.4 58 12/14/2020 a 17 |耳 即 delets prt sc insert 112 19 18 + 16 backspace f5 fA

An assumption of regression analysis is homoscedasticity, which states that the      residuals exhibit no patterns across values for the dependent variable.     relationship between the independent and dependent variables is linear.     residuals exhibit no patterns across values for the independent variable.     variation of the dependent variable is the same across all values for the independent variable.

Using a sample of recent university graduates, you estimate a simple linear regression using initial annual salary as the dependent variable and the graduate's weighted average mark (WAM) as the explanatory variable. If the regression model has an estimated intercept of 3200 and an estimated slope coefficient of 550, what is the predicted starting salary of a student with a WAM of 64?