Data from the United Nations on individual countries Obesity rates and vegetable consumption by country were organized into the scatter plot below. Linear Regression Equation for this data: y = -0.8449x + 50.328Correlation Coefficient: 0.5 Question 8 options: Strongly Correlated Weakly Correlated Not correlated Moderately Correlated This scatter plot displays the relationship between the average consumption of vegetable products (in kilograms) and the percentage of obese individuals. Each blue dot on the graph represents data points from different samples.### Key Features:1. **Axes:** - **X-axis:** Represents the average consumption of vegetable products, measured in kilograms, ranging from 20 to 50 kg. - **Y-axis:** Represents the percentage of obese individuals, ranging from 0 to 50%.2. **Trend Line:** - A dotted line indicates a negative correlation between vegetable consumption and the percentage of obesity. 3. **Equation and Correlation:** - **Equation:** $ y = -0.8449x + 50.328 $ - This equation suggests that for every additional kilogram of vegetable products consumed, the percentage of obesity decreases by approximately 0.8449%. - **Correlation Coefficient:** 0.5 - This value indicates a moderate negative correlation, suggesting that as vegetable consumption increases, the percentage of obesity tends to decrease.### Interpretation:The scatter plot suggests a general trend where higher consumption of vegetables is associated with lower levels of obesity. However, individual data points still show variability around the trend line, indicating other factors may also influence obesity levels.

Given that Linear regression equation for this data is Y=-0.8449X+50.328 Correlation…

Data from the United Nations on individual countries Obesity rates and vegetable consumption by country were organized into the scatter plot below. Linear Regression Equation for this data: y = -0.8449x + 50.328 Correlation Coefficient: 0.5 Question 8 options: Strongly Correlated Weakly Correlated Not correlated Moderately Correlated

Data from the United Nations on individual countries Obesity rates and vegetable consumption by country were organized into the scatter plot below. Linear Regression Equation for this data: y = -0.8449x + 50.328 Correlation Coefficient: 0.5 Question 8 options: Strongly Correlated Weakly Correlated Not correlated Moderately Correlated

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A doctor has collected data on cholesterol, blood pressure, and weight. She also collected data on the eating habits of the subjects (eg. how many ounces of red meat, fish, dairy products, and chocolate consumed per week) She wants to investigate the relationship between the three measures of health and eating habits. Which statistical test can be best used for this study? a.Linear Regression b. Correlation Coefficient c. Frequency Distribution Table d. ANOVA
Data from the United Nations on individual countries Obesity rates and vegetable consumption by country were organized into the scatter plot below. Linear Regression Equation for this data: y = -0.8449x + 50.328 Correlation Coefficient: 0.5 --- The data on this table can be simplified into a single statement. Which of the following statements would be the most accurate based on the chart, equation, and correlation coefficient? Question 10 options: As vegetable consumption goes up, obesity rates would tend to decrease. As vegetable consumption goes down, obesity rates would tend to increase. As vegetable consumption goes up, obesity rates would tend to increase. As vegetable consumption goes down, obesity rates would tend to decrease.
= 12.3 The Regression Equation X E Rounding Numbers Calculator X xyzHomework Assessment + → C A A https://www.xyzhomework.com 80% Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in 3- millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit . Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 3 21 4 22 18 6. 23 30 8 33 23 10 31 Ho: p = 0 Ha: p # 0 Find the Linear Correlation Coefficient r= Find the p-value p-value = The p-value is O Less than (or equal to) a O Greater than a The p-value leads to a decișion to Reject Ho O Accept Ho O Do Not Reject Ho signific voters p. The n The conclusion is There is a significant positive linear correlation between advertising expense and profit. O There is a significant negative linear correlation between advertising expense and profit. There ie n cianifiannt linaar corralation…
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I. Show complete solutions. Simple Linear Regression and Correlation: a.) Results of a study on the occurrence of sodium and chloride in surface streams in a coastal area. The following data are chloride concentration (y) in mg/liter and road-way area in the watershed (x) in %. Y 0.24 4.6 1. What is the value of the correlation coefficient? 2. What is the interpretation for this case? 3. What is the estimated chloride concentration if road way 0.3 6.4 0.45 9.8 area in the watershed is 1.30% 0.75 10.7 4. What is the equation of the prediction line? 0.57 10.6 0.75 10.9 0.4 11.7 0.8 12.5 0.6 14 0.78 14.7 0.85 15.1 0.68 18.3 0.69 19.2 1.4 23.1 1.15 27.3 1.05 27.7 1.69 32 1.62 38.4
The mortality rate of a sample of the population as a function of daily tobacco consumption can be seen below: Number of cigarettes "x": 3 5 6 15 20 Mortality rate "y" : 0.2 0.3 0.4 0.5 0.7 With these data, solve the following: a. Determine and interpret the correlation coefficient. b. Find the regression line of y on x. c. What will be the mortality rate for a consumer of 40 cigarettes per day?
Do movies of different types have different rates of return on their budgets? Here's a regression of USGross (SM) on Budget for comedies and action movies with an indicator variable. Complete parts (a) through (d). Dependent variable is: USGross ($M) Coefficient SE(Coeff) - 6.78278 16.95 1.00523 Variable Constant Budget ($M) Comedy 24.0373 0.1613 11.73 t-ratio P-value -0.400 0.6907 6.23 <0.0001 2.05 0.0451 a) Write out the regression model. USGross = + ( Budget + (Comedy R-squared = 32.8% R-squared (adjusted) = 31.0% s = 47.51 55 degrees of freedom
We conduct a regression of size on hhinc, owner, hhsize1, hhsize2, and hhsize3. Wedo not include the constant. The regression output is reported in Table 3. Would youconclude that the home size increases with the household size? Interpret the signand magnitude of the estimated coefficients of hhsize1, hhsize2, and hhsize3
There is a dataset of size n = 51 and is for the 50 states and the District of Columbia in the United States. The dependent variable is year 2002 birth rate per 1000 females 18 to 19 years old and independent variable is the violent crime rate (per 1000 population). A simple linear regression model is run with the results given below. What is the Pearson correlation coefficient between x and y variables? The R squared of the model? What kind of relationship there is?
A researcher is interested in examining the relationship between spousal abuse and child abuse. Specifically, they are interested in determining whether there is a predictive relationship between spousal abuse and child abuse in 5 county social services offices. Calculate the linear regression line for the following data. Note you have already calculated the first step to this analysis (Pearson's Correlation)
a ift = The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, bo + b₁x, for predicting a woman's bone density based on her age. 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. tab ** Answer How to enter your answer (opens in new window) Step 2 of 6: Find the estimated y-Intercept. Round your answer to three decimal places. esc 1945 ! 1 q alt a Z ebook x360 ng from any angle. @ 2 W S →>> X # 3 e d C $ 4 C r f Age Bone Density 359 % 5 V t g 36 51 63 66 70 357 328 314 310 Oll A 6 b y hp h & 7 O n u j * 8 N O i m ( 9 k O 100- 4 Tables ctrl { [ Keypad Keyboard Shortcuts Previous step answers Submit Answer D + = 11 : 90 ; ? Copy Data O SEAR Dec 2 Table ] USE YOUR SMARTPHONE FOR…