A study is conducted in patients with HIV. The primary outcome is CD4 cell count, which is a measure of the stage of the disease. Lower CD4 counts are associated with more advanced disease. The investigators are interested in the association between vitamin and mineral supplements and CD4 count. A multiple regression analysis is performed relating CD4 count to use of supplements (coded as 1 = yes, 0 = no) and to duration of HIV, in years (i.e., the number of years between the diagnosis of HIV and the study date). For the analysis, (Y with a upside down v on top of it)  = CD4 count. (Y with a upside down v on top of it)  = 501.41 + 12.67 Supplements – 30.23 Duration of HIV What is the expected CD4 count for a patient not taking supplements who has had HIV for 5 years?   A) 350.26 B) 501.41 C) 30.23 D) 5

What does the correlation matrix for a multiple regression analysis contain?A. Multiple correlation coefficientsB. Simple correlation coefficientsC. Multiple coefficients of determinationD. Multiple standard errors of estimate

A study was conducted on 64 female college athletes. The researcher collected data on a number of variables including percent body​ fat, total body​ weight, height, and age of athlete. The researcher wondered if​ % body fat​ (%BF), height​ (HGT), and/or age are significant predictors of total body weight. All conditions have been checked and are met and no transformations were needed. The technology output from the multiple regression analysis is given below. Interpret the coefficient of​ % body fat

You plan to fit a regression model that will be used to predict first-year college GPA (FYGPA) from high-school GPA (HSGPA), ACT score (ACT), first-generation status (Yes or No), socioeconomic class (lower class, lower to middle class, middle to upper class, and upper class), and school type (public or private).  What is the total number of estimated regression coefficients? If the sample size is n = 250 students, what are the degrees of freedom for the following sources of variation: Regression Error Total

Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Answer Coefficients 933 167.94 - 102.63 -199.18 Does the sign of the coefficient for the variable credit score make sense? Coefficients Standard Error 95.65 17.99 10.89 19.98 t Stat P-value 9.754 0.0000 9.335 0.0000 -9.424 0.0000 -9.969 0.0000 O Yes, because it is expected that as the credit score increases then the cost should decrease. O No, because it is expected that as the credit score increases then the cost should decrease. O Yes, because it is expected that as the credit score increases then the cost should also increase. O No, because it is expected that as the credit score increases then the cost should also increase. Tables Keypad Keyboard Shortcuts

A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the​employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees.   a. Predict the sales next month for an employee with 2.5 years of experience.   The predicted sales is 8.6 cars.   b. Compute the coefficient of determination and interpret its meaning.   The coefficient of determination is 0.234 ​ ​Therefore, about _________% of the variation in monthly sales is explained by the years of sales experience.   ​(Type an integer or decimal rounded to one decimal place as​ needed.)

A study of elementary school children, ages 6 to 11, finds a high positive correlation between shoe size x and score y on a test of reading comprehension. The observed correlation is most likely due to:     the effect of a lurking variable, such as age or years of reading experience.     a mistake, because the correlation must be negative.     cause and effect (larger shoe size causes higher reading comprehension).     reverse cause and effect (higher reading comprehension causes larger shoe size).

Check My Work (5 remaining) Each year, over 2 million people in the United States become infected with bacteria that are resistant to antibiotics. In particular, the Centers of Disease Control and Prevention has launched studies of drug-resistant gonorrhea (CDC.gov website). Of 142 cases tested in Alabama, 9 were found to be drug-resistant. Of 268 cases tested in Texas, 5 were found to be drug-resistant. Do these data suggest a statistically significant difference between the proportions of drug- resistant cases in the two states? Use a 0.02 level of significance. What is the p-value, and what is your conclusion? Test statistic = (to 2 decimals) p-value = (to 4 decimals) Conclusion: Reject the null hypothesis Choose the correct option. There is a significant difference in drug resistance between the two states. Alabama has the higher drug resistance rate. True

A team of epidemiologists at the Mayo Clinic wanted to find whether there is an association between obesity and cardiovascular disease (CVD). They conducted a prospective cohort study following obese and non-obese individuals who were free of CVD at the beginning of study for five years. The investigators were also interested in assessing age as a potential confounder, effect modifier, or both. Use the data below to answer the accompanying questions.     CVD No CVD Total Obese 10 90 100 Not Obese 35 465 500 Total 45 555 600       CVD No CVD Total Obese 36 164 200 Not Obese 25 175 200 Total 61 339 400   1. Compute the appropriate measure of association for those who were less than age 50.  2. Compute the appropriate measure of association for those who were older or equal to age 50.  3. Compute the crude measure of association?  4. List three attributes that age must satisfy before it could…