Find the Pearson r Correlation Coefficient: r = Blank 1 Find the equation of the regression line: y = Blank 2x + Blank 3 Predict power consumption for an ambient temperature of 65 °F: Blank 4 BTU
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- Find the Pearson r
Correlation Coefficient : r = Blank 1 - Find the equation of the regression line: y = Blank 2x + Blank 3
- Predict power consumption for an ambient temperature of 65 °F: Blank 4 BTU
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- D E FListed 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 signicance level of 0.05. Right Arm 101 100 92 80 81 Left Arm 174 168 178 147 146A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:ŷ=ax+b a=-1.229 b=35.936 r2=0.508369 r=-0.713 Use this to predict the number of situps a person who watches 3.5 hours of TV can do (to one decimal place)
- Study 3: Age & Pain. Using the same data set PillShapeColor - use Pearson Correlation and simple linear Regression to create results and then summarize these in APA-style. Run a Pearson Correlation on age and pain rating. Create a scatterplot with pain rating on Y and age on X axis. Run a simple linear regression model with pain rating as DV and age as IV. Regression → Linear Regression, choose your DV, IV(s) –use defaults settings. 1-What is the equation for the regression line? How much variance in pain is accounted for by age (R2)? Interpret this in context of the study (is this a lot of variance explained or not much).2-Describe the regression results simply and clearly- include the slope and constant and describe what the slope means. Is age a significant predictor of perceived pain? Finally, describe what the model predicts about the pain rating of a person who is 40 years old and someone 70 years old.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 101 100 94 75 Left Arm 174 167 146 144 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is (Round to one decimal place as needed.) 76 144 CRITSThe superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), mean teacher salary in thousands of dollars (Salaries), and instructional spending per pupil in thousands of dollars (Spending) of 47 schools in the state. Following is the multiple regression output with Y= % Passing as the dependent variable, X, = Salaries and X, = Spending. E Click the icon to view the multiple regression output. Determine whether the following statement is true or false: The null hypothesis Hn: B, = B, = 0 implies that percentage of students passing the proficiency test is not related to one of the explanatory variables. Multiple Regression Output True False Regression Statistics Multiple R 0.4276 R Square 0.1828 Adjusted R Square 0.1457 Standard Error 5.7351 Observations 47 ANOVA df SS MS F Significance F Regression 323.8284 161.9142 4.9227 0.0118…
- Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight 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 90 mm Hg. Use a significance level of 0.05. Right Arm 101 100 93 75 Left Arm 177 171 148 146 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.) 5 Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. = T 6 & 1' a pyright © 2022 Pearson Education Inc. All rights reserved. Terms of Use | Privacy Policy | Permissions Contact Us (...) + 8 √₁ 74 146 1) 1₁ () More 110 11 85°F + Next = insert 4) prt sc backspaListed 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 90 mm Hg. Use a significance level of 0.05. Right Arm 103 102 95 79 80 Left Arm 174 168 143 143 144 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=____?+____?x.The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 63 inches. Is the result close to the actual weight of 462 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) 58 50 65 59 59 48 414 312 499 450 456 260 Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y =+x (Round to one decimal place as needed.) What is the best predicted weight of a bear with a chest size of 63 inches? The best predicted weight for a bear with a chest size of 63 inches is pounds. (Round to one decimal place as needed.) Is the result close to the actual weight of 462 pounds? O A. This result is very close to the actual weight of the bear. O B. This result is close to the actual weight of the bear. O C. This result is not very close to the actual weight of the bear. O D.…
- based on data above ( the table ) Determine the regression equation in three different ways (using raw data,deviation data and standard data) by first determining the values of the coefficients b and a. Find the value of the correlation coefficient and write down the interpretation of the correlation coefficient with r2A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=ax+b a=-0.634 b=30.709 r2=0.948676 r=-0.974 Use this to predict the number of situps a person who watches 10.5 hours of TV can do (to one decimal place)A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.244 b=35.185 r=0.974169 r=-0.987 Assume the correlation is significant, and use this to predict the mumber of situps a person who watches 5.5 hours of TV can do (to one decimal place)
