**Title: Analysis of Lemon Imports and Crash Fatality Rates**A researcher collects data on both lemon imports (x) and the crash fatality rate (y) in the USA. The seven observations are randomly selected cities across the country.### Graph Description:The scatter plot titled "Crash Fatality Rate" displays data points representing the relationship between lemon imports and crash fatality rates in various cities.### Summary Output:- **Multiple R:** 0.939894457- **R Square:** 0.88340159- **Adjusted R Square:** 0.860810197- **Standard Error:** 0.125395887- **Observations:** 7### ANOVA Table:| Source | df | SS | MS | F | Significance F ||--------------|----|-----------|----------|---------|-----------------|| Regression | 1 | 0.595665072 | 0.595665 | 37.88223 | 0.001646686 || Residual | 5 | 0.07826042 | 0.015724 | | || Total | 6 | 0.674285714 | | | |### Coefficients Table:| | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% ||----------------|---------------|----------------|---------|----------|-------------|-------------|| Intercept | 16.517343501 | 0.1831502 | 90.18528 | 3.18E-09 | 16.04663289 | 16.9882371 || Lemon Imports | -0.002918808 | 0.000471112 | -6.15485| 0.001647 | -0.00413683 | -0.00169935 |### Questions:**1. What is the regression equation?**- \( \hat{y} = 16.517 - 0.003x \)**2. The best description of the correlation is to describe it as:**- Strong negative correlation. **Interpret the slope of the line.**- For a 1 unit increase in the crash fatality rate, lemon imports will, on average, increase by 16.517 units.- For a 1 unit increase in lemon imports, the crash fatality rate will, on average, increase by 0.003 units.- For a 1 unit increase in lemon imports, the crash fatality rate will, on average, increase by 16.517 units.- For a 1 unit increase in the crash fatality rate, lemon imports will, on average, increase by 0.003 units.- None of the above**Give a practical interpretation of the coefficient of determination.**- 88.34% of the sample variation in crash fatality rates can be explained by the least-squares regression line.- 88.34% of the differences in crash fatality rates are caused by differences in lemon imports.- We can predict the crash fatality rate correctly 88.34% of the time using lemon imports in a least-squares regression line.- 93.99% of the differences in crash fatality rates are caused by differences in lemon imports.- We can predict the crash fatality rate correctly 93.99% of the time using lemon imports in a least-squares regression line.- 93.99% of the sample variation in crash fatality rates can be explained by the least-squares regression line.**Is it reasonable to use the regression equation to make a prediction for lemon imports of 200? Justify your answer.**- No, \( r^2 \) does not indicate that there is a reasonable amount of correlation.- No, this prediction is far outside the scope of available data.- Yes, all of the criteria are met.- No, the regression line does not fit the points reasonably well.**What can we say about the relationship between lemon imports and crash fatality rate?**- There is no relationship between lemon imports and crash fatality rate.- While lemon imports and crash fatality rate and strongly related, this relationship is likely due to some third variable that affects them both.- Because of the strong relationship, lemon imports are a leading cause of crash fatality rate.

What is the regression equation? Oy = 16.517 – 0.003z Oŷ = 16.517 – 0.003z y = 16.517z – 0.003 Oŷ = 16.517z – 0.003 The best description of the correlation is to describe it as strong linear correlation. O strong negative correlation. O strong positive correlation. O None of the above

What is the regression equation? Oy = 16.517 – 0.003z Oŷ = 16.517 – 0.003z y = 16.517z – 0.003 Oŷ = 16.517z – 0.003 The best description of the correlation is to describe it as strong linear correlation. O strong negative correlation. O strong positive correlation. O None of the above

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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Which one of the following statements is true? When predicting one quantitative variable based on another, the variable you are trying to predict should always go on the x-axis of the scatterplot. The intercept in a regression equation can never negative. The correlation coefficient has no units. A very strong relationship between variables can result in a value of r that is larger than 1. A correlation coefficient can only range in value from 0 to 1.
Which of the following correlation (r) show a negative association between 2 variables? 0.2 -1.4 0.4 -0.8
11. The National Oceanic and Atmospheric Administration tracks the amount of oysters harvested from the Chesapeake Bay each year. Years since 1900 10 20 43 52 84 89 Oysters (metric tons) 36 20.6 9.24 8.22 2.73 2.25 a) Is there a correlation? If so, describe the shape and strength. If no correlation, go on to #12, if there is a correlation, complete the parts below. b) The regression equation that describes this: c) Based on this, how much was harvested in 1931? d) What does x = 60 represent? e) Find the value of f(x) when x = 60 What does this mean in context?
M Wed Feb 15 Measles and Mumps The data show the number of cases of measles and mumps for a recent 5-year period. Measles Cases Mumps Cases 48 150 1797 475 70 60 203 1954 2575 370 Send data to Excel www-awu.connectmath.com The correlation coefficient for the data is r=-0.911 and a= 0.05. Should regression analysis be done? O The regression analysis should not be done. O The regression analysis should be done. Find the equation of the regression line. Round the coefficients to at least three decimal places. y' = a + bx a = b = Given a year with 185 cases of measles, predict the expected number of cases of mumps for that year. Round your answer to at least one decimal place. The number of cases of mumps for that year is 47% Submit Assignment 2023 McGraw Hill LLC. All Rights Reserved. Terms of Use | Privacy Cent 0
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Prev The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting the number of bids an Item will receive based on the list price. 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. Price in Dollars 28 33 36 42 45 Number of Bids 1 7 8 9 10 Step 3 of 6: Find the estimated value of y when x = 33. Round your answer to three decimal places. Table Copy Data Next
linear regression. I already answered some question
5. Explain what the correlation coefficient tells you about the relationship between your variables. 7. Interpret the slope of the regression equation in terms of the variables in your data set.
K Fit a regression line to the data shown in the chart, and find the coefficient of correlation for the line. Use the regression line to predict life expectancy in the year 2000, where x is the number of decades after 1900. year, x 0 (1900) life expectancy, y 49.1 years 51.4 years 2 (1920) 4 (1940) 53.0 years 6 (1960) 54.2 years 55.2 years 8 (1980) Choose the regression line. OA. y = 0.750x - 49.58 O C. y = 0.750x + 49.58 OB. y=49.58x + 0.750 = 49.58 ○ D. y = The life expectancy in the year 2000, rounded to one decimal place is years.
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y: 30 -10 50 10 1 25 -15-60 15 8 35 20-5 7. 10 9. -40 -5 X: 9. 3 4 6. 0.05. Test for significant positive (linear) correlation at a =