Suppose that we are working with some doctors on heart attack patients. The dependent variable is whether the patient has had a second heart attack within 1 year (yes=1). We have two independent variables, one is age of the patient and the other is a score on anxiety scale (a higher score means more anxious). After applying logistic regression model, we have the following output: Deviance Residuals: Min 10 Median |-1.064 30 Маx 0.000 0.000 0.000 1.446 Coefficients: Estimate Std. Error z value Pr (>|z|) (Intercept) Age Anxiety -471.441 223186.509 -0.002 0.998 6.394 3057.349 0.002 0.998 1.347 611.470 0.002 0.998 (Dispersion parameter for binomial family taken to be l1) Null deviance: 27.7259 on 19 degrees of freedom Residual deviance: 3.7087 on 17 degrees of freedom AIC: 9.7087 Number of Fisher Scoring iterations: 23 a. Determine the estimated logistic regression equation. b. Calculate the odds ratio and interpret.

Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x = percent of women using HRT and y = breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in the paper "Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model to describe the relationship between HRT use and breast cancer incidence. HRT Use Breast Cancer Incidence 46.30 103.30 40.60 105.00 39.50 100.00 36.60 93.80 30.00 83.50 n USE SALT (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) ý = 45.5727 + (1.3354 )x (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) 1.3354 cases per 100,000 women (c) What breast cancer incidence…

If two girl differ in height by 4pm by how much would you expect their FEV1 Predict the FEV1 for a boy who is 1.2 m tall, if the measurement was taken at a pressure of 700 mm and last years measurement was 2.113 L (round to four decimals places)

What is the mathematical representation for the multiple logistic regression model in photo?

The coefficient of correlation between the ages of husbands and wives in a community was found to be +0.8, the average of husbands age was 25 years and that of wives age 22 years. Their standard deviations were 4 and 5 years respectively. Find with the help of regression equations: (a) the expected age of husband when wife's age is 16 years, and (b) the expected age of wife when husband's age is 33 years.

A clothing manufacturer wants to estimate the amount of scrap cloth generated each day by its fabric cutting machines. Eight potential independent variables have been identified. These include the following. = amount of cloth run through cutting machines (in square feet) X2 = machine cutting speed (in feet per minute) age of machine (in years) The manufacturer selects 6 of the candidate independent variables to use in a multiple regression model for estimating y, the amount of scrap cloth (in square feet). Using data collected from 24 different cutting machines operating on different days, the model y = Bo+B1*1 +B,x2+ + Bax, is fit to the data. Fill in the blanks in the analysis of variance (ANOVA) table associated with this model. Do all ... calculations to at least three decimal places.

A researcher is hired to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance in miles between home and work for the employees. A sample of 10 employees was chosen and the results are listed in the chart below. Use the proper Data Analysis function in Excel to computethe regression equation needed to predict the number of missed days that could be expected foremployees who live certain distances away from the workplace.  Distance to Work (miles): 1, 3, 4, 6, 9, 10, 12, 14, 14, 18                                    Number of Days Absent: 9, 6, 7, 7, 6, 4, 5, 3, 4, 2

Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the date and the least-squares regression line. The equation for this line is y=82.84-0.49x. Birthrate, x (number of births per 1000 people) 24.6 50.1 40,6 20.0 50.7 28.1 143 49.0 31.9 14.8 33.6 46.5 Send data to calculator Send data to Excel Female life expectancy, y (in years) 74.3 54.4 66.2 73.6 58.9 72.7 76.1 61.5 62.4 71.4 68.0 57:5 Based on the sample data and the regression line, complete the following. Female life expectancy (in years) Birthrate (number of births per 1000 people)

An investigator modeled the log-odds of getting stomach cancer as a function of number of servings of vegetables per week and the number of servings of red mean per week using logistic regression. The odds that a person gets stomach cancer if they have 4 servings of red meat a week are 1.2 times the odds of cancer for someone who has 3 servings of red meat a week.  Suppose the fitted regression model is given as log-odds = -6 + 0.182 M – 0.105 V, where M = #servings of red meat per week and V = #servings of vegetables per week. What is the odds ratio representing the change in odds for an increase in one serving of vegetables? Give the answer to three decimal places.

What does this data mean if the values are logistic regression analysis of presence of sarcopenia as the dependent variable   OR (95% CI) p-value 3.069 (1.42-6.62) 0.004