### Regression Analysis of Heart Weight and Body Weight in CatsThe regression output from a model for predicting the heart weight (in grams) of cats from their body weight (in kilograms) is provided below. The coefficients are estimated using a dataset of 144 domestic cats.#### Regression Output Table:| | Estimate | Std. Error | t value | Pr(>|t|) ||-----------------------|----------|------------|---------|----------|| (Intercept) | -0.357 | 0.692 | -0.515 | 0.607 || body wt | 4.034 | 0.250 | 16.119 | 0.000 || | | | | || *s* | | | | 1.452 || R² | | | | 64.66% || R²adj | | | | 64.41% |The symbols and terms used in the table include:- **Estimate**: The estimated coefficient for each predictor.- **Std. Error**: The standard error of the estimated coefficient.- **t value**: The t-statistic value used to test the null hypothesis.- **Pr(>|t|)**: The p-value associated with the t-statistic.- *s*: The standard error of the regression (residual standard deviation).- R²: The coefficient of determination.- R²adj: The adjusted coefficient of determination.#### Hypothesis Testing:##### (a) Determining HypothesesWhat are the hypotheses for evaluating whether body weight is positively associated with heart weight in cats?- **Null Hypothesis \(H_0\)**: \(\beta_1 = 0\)- **Alternative Hypothesis \(H_A\)**: \(\beta_1 > 0\)The chosen hypotheses are correct based on the detailed setup:\( \circ \) \( H_0: \beta_1 = 0 \) (checked)\( \circ \) \( H_A: \beta_1 > 0 \)#### (b) Conclusion of Hypothesis Test:State the conclusion of the hypothesis test from part (a) in the context of the data. (Use a significance level of 0

(a) The null and alternative hypothesis is, The level of significance is, The sample size is, n =…

The regression output from a model for predicting the heart weight (in g) of cats from their body weight (in kg) is provided below. The coefficients are estimated using a dataset of 144 domestic cats. Estimate Std. Error t value Pr(>|t|) (Intercept) -0.357 0.692 -0.515 0,607 body wt 4.034 0.250 16.119 0.000 S = 1.452 R2 = 64.66% R2 adj = 64.41% (a) What are the hypotheses for evaluating whether body weight is positively associated with heart weight in cats? O Hoi B120 HA: B1 < 0 O Ho: B1 = 0 HA: B1 > 0 O Ho: B1 # 0 HAi B1 = 0 O Hoi B1 = 0 HA: B1 * 0 O Hoi B1 = 0 HA: B1 < 0 (b) State the conclusion of the hypothesis test from part (a) in context of the data. (Use a significance level of 0.05. Enter your test statistic to three decimal places.) The test statistic is T = 0 x with degrees of freedom df = .05 X . The p-value is less than 0.05. Therefore, we reject vv Ho. The data provide strong evidence that body weight is positively associated with heart weight in cats.

The regression output from a model for predicting the heart weight (in g) of cats from their body weight (in kg) is provided below. The coefficients are estimated using a dataset of 144 domestic cats. Estimate Std. Error t value Pr(>|t|) (Intercept) -0.357 0.692 -0.515 0,607 body wt 4.034 0.250 16.119 0.000 S = 1.452 R2 = 64.66% R2 adj = 64.41% (a) What are the hypotheses for evaluating whether body weight is positively associated with heart weight in cats? O Hoi B120 HA: B1 < 0 O Ho: B1 = 0 HA: B1 > 0 O Ho: B1 # 0 HAi B1 = 0 O Hoi B1 = 0 HA: B1 * 0 O Hoi B1 = 0 HA: B1 < 0 (b) State the conclusion of the hypothesis test from part (a) in context of the data. (Use a significance level of 0.05. Enter your test statistic to three decimal places.) The test statistic is T = 0 x with degrees of freedom df = .05 X . The p-value is less than 0.05. Therefore, we reject vv Ho. The data provide strong evidence that body weight is positively associated with heart weight in cats.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

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 CRITS
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=-1.271 b=39.101 ²-0.407044 r=-0.638 Assume the correlation is significant, and use this to predict the number of situps a person who watches 14 hours of TV can do (to one decimal place) Aft
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right am 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 100 mm Hg Use a significance level of 0.05. Right Arm 103 102 95 78 Left Arm 175 169 147 146 144 m Click the icon to view the critical values of the Pearson correlation coetticient r The regression oquation is y =+O (Round to one decimal place as needed) Given that the systolic blood pressure in the right arm is 100 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg (Round to one decimal place as needed.)
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=-1.108 b-34.092 r2-0.736164 r=-0.858 Assume the correlation is significant, and use this to predict the number of situps a person who watches 4 区 hours q TV can do (to one decimal place) 72222392 口 D
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 784 838 822 949 1188 813 Temperature (°F) 65.7 67.8 66.2 79.9 92.3 73.5 What is the regression equation? What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000chirps per minute are? What is wrong with this predicted value? Choose the correct answer below. A. It is unrealistically high. The value 3000 is far outside of the range of observed values. B. The first variable should have been the dependent variable. C. It is only an…
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 760 1119 1125 850 965 785 D Temperature (°F) 70.5 90.8 90.8 74.7 77.1 69.9 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. Explain clearly what this slope says about the change in the mean of y for a change in x.
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 CRITS
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 1004 952 1237 1116 1177 1249 Temperature (°F) 83.1 76 95.1 87.5 92.1 88.4 What is the regression equation? (^ over y)=_____+_____x (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per minute? The best-predicted temperature when a bug is chirping at 3000 chirps per minute is ____°F. (Round to one decimal place as needed.) What is wrong with this predicted value? Choose the correct answer below. A. It is…
For half-life demonstration, plot ln(half-lives) in the x-axis vs ln(no. of “tails-up” coins) in the y-axis. Perform linear regression to obtain the value of the slope, the value of the y-intercept, and the correlation coefficient r. What is the significance of these values?
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 90 mm Hg. Use a significance level of 0.05. Right Arm 101 100 92 75 75 O Left Arm 174 167 181 149 147 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.) 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. (Round to one decimal place as needed.)
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 924 1150 840 1166 1087 930 Temperature (°F) 77.5 84.6 74.1 91 79.6 79.7 What is the regression equation? What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? What is wrong with this predicted value? Choose the correct answer below. A. It is unrealistically high. The value 3000 is far outside of the range of observed values. B. The first variable should have been the dependent variable. C. It is only an approximation. An unrounded value would be considered accurate. D. Nothing is wrong with this value. It can be…