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- A weather forecaster examines the weather patterns in a random sample of cities in order to better understand how the number of days of rain a city gets per year is related to the number of hours of sunshine that city gets per year.The regression equation to predict hours of sunshine based on days of rain is as follows:
Predicted hours of sunshine = 2847 – 6.88(days of rain).
From this regression equation, we know that r, or the
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- positive
- negative
- non-linear.
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- For the 2011 season, suppose the average number of passing yards per attempt for a certain NFL team was 6.1. Use the estimated regression equation developed in part (c) to predict the percentage of games won by that NFL team. (Note: For the 2011 season, suppose this NFL team's record was 7 wins and 9 losses. Round your answer to the nearest integer.)Answer the following question based on this scenario: A clinical psychologist identifies the relationship between the number of weeks spent in therapy at a hospital (X = hospital) and the number of strokes per week (Y = stroke), i.e. Y = 14.09 - 91(X). This is based on a sample of 50 patients and is associated with r = -.93. Using this information, answer the following question: How many strokes per week can you predict for a person who has been in hospital for 10 weeks?a) 4,49b) 4,99c) 14,09d) 91e) 13,18A study is conducted to determine if there is a relationship between the two variables, blood haemoglobin (Hb) levels and packed cell volumes (PCV) in the female population. A simple linear regression analysis was performed using SPSS. Based on the SPSS output of the ANOVA table, which of the following statements is the CORRECT interpretation? 1. The regression model statistically significantly predicts the blood haemoglobin level. 2. About 39.98 % of variance in Hb is explained by PCV. 3. The regression model does not fit the data. 4. There is significant contribution of Hb towards PCV.
- A company has a set of data with employee age (X) and the corresponding number of annual on-the-job-accidents (Y). Analysis on the set finds that the regression equation is Y=60-0.5*X. What can be said of the correspondence (relation) between age and accidents? Are younger workers safer or more prone to accident? What is the likely number of accidents for someone aged 25?Bill wants to explore factors affecting work stress. He would like to examine the relationship between age, number of years at the workplace, perceived social support, and work stress. He collects data on the variables from 100 employees (males and females) working in banks. Conduct a multiple regression analysis to answer the following questions: What is the relationship of age, number of years, and social support with work stress? Is the regression significant? If yes, what does it indicate? What is the regression equation for all the predictors? Write a results section based on your analysis that answers the research question. * last person got this wrong*A study of king penguins looked for a relationship between how deep the penguins dive to seek food and how long they stay underwater. For all but the shallowest dives, there is a linear relationship that is different for different penguins. The study report gives a scatterplot for one penguin titled, “The relation of dive duration (DD) to depth (D).” Duration DD is measured in minutes, and depth D is in meters. The report then says, “The regression equation for this bird is, DD=2.69+0.0138D.is, DD=2.69+0.0138D.” According to the regression line, how long does a typical dive to a depth of 200200 meters last? The dives varied from 40 meters40 meters to 300 meters300 meters in depth. Use the regression equation to determine DD for D=40D=40 and D=300D=300 and then plot the regression line from D=40D=40 to D=300.to D=300. Give your answers to two decimal places.
- A study of king penguins looked for a relationship between how deep the penguins dive to seek food and how long they stay underwater. For all but the shallowest dives, there is a linear relationship that is different for different penguins. The study report gives a scatterplot for one penguin titled, “The relation of dive duration (DD) to depth (D).” Duration DD is measured in minutes, and depth D is in meters. The report then says, “The regression equation for this bird is, DD=2.69+0.0138D.is, DD=2.69+0.0138D.” What is the slope of the regression line? Give your answer to four decimal places.What does this slope says about this penguin's dives ?4. An observational study was conducted to investigate the association between age and total serum cholesterol. The study involved 125 participants with an average age of 44.3 and an age range between 35-55 years. The regression equation was calculated and is as follows: y = 124.4 + 1.6x a) Estimate the total serum cholesterol for a 50-year old person. b) Estimate the total serum cholesterol for a 44-year old person. c) Would it be appropriate to utilize the equation to predict a 70-year old individual's cholesterol level? Please explain your answer.Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 92 80 79 O Left Arm 175 169 182 149 148 E Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y =+x. (Round to one decimal place as needed.)
- A random sample of college students was surveyed about how they spend their time each week. The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week. The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows: Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to…The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0 0.5 2 3 4.5 5 6 Overall Grades 99 98 96 92 89 88 80 Table Step 6 of 6 : Find the value of the coefficient of determination. Round your answer to three decimal places.12. A consumer advocacy group recorded several variables on 140 models of cars. The resulting information was used to produce the following regression output that relates the city gas mileage (in mpg) and the engine displacement (in cubic inches). The regression equation is mpg:city= 33.4 0.0624 displacement Predictor Constant SE Coef T P 0.7762 43.00 0.000 displacement -0.0624 0.003810 0.000 S 3.13923 = R-Sq 66.0% R-Sq (adj) = 65.8% a. We have a car that has an engine with 150 cubic inches. Based on this output, what city gas mileage would you predict for this car? Coef 33.4 b. Based on this output what is the correlation between city gas mileage and displacement? C. I value: The test statistics for testing the slope is zero is missing. Calculate this The group o also recorded the power of the engine (in horsepower) for each car. The following regression output was produced. Predictor Constant horsepower The regression equation is mpg:city = 32.2 0.0572 horsepower Coef SE Coef 32.2 T…
