KAn institution reports many demographic statistics about countries of the world. Data were collected about the Fertility rate (births per woman)and the female Life Expectancy at birth (in years) for 200 countries of the world. The regression output and a scatterplot of the data are shownbelow. Complete parts a through d below.Response variable is: Life expectancy219 total cases of which 19 are missingR squared 71.9%$= 4.303VariableInterceptFertilityCoefficient83.3753-4.54984OA. Yes, an outler occurs at approximately x(Round to one decimal place as needed.)ICCOLife ExpectancyOB. Yes, an outlier occurs at approximately x=(Round to one decimal place as needed.)OC. No, there is no outlier.c) Interpret the value of R². Choose the correct answer below.90-80-70-60-50+40-2FersityThis outier has an unusually high life expectancy given its fertility rate.b) is there an outlier? If so, identify it. Which data value is unusual? Select the correct choice below and, if necessary, fill in the answer box tocomplete your choice.0This outlier has an unusually low life expectancy given its fertility rate.QQG

R-squared value is 71.9%Standard deviation s=4.303.Intercept is 83.3753.Slope is -4.54984.

Answered: An institution reports many demographic…

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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The ages (in years) and heights (in inches) of all pitchers for a baseball team are listed. Find the coefficient of variation for each of the two data sets. Then compare the results. E Click the icon to view the data sets. CVneights % (Round to one decimal place as needed.) Data Table CVages (Round to one decimal place as needed.) Compare the results. What can you conclude? O A. Ages are more variable than heights for all pitchers on this team. Heights Ages 76 25 O B. Ages and heights for all pitchers on this team have about the same amount of variability. 71 28 OC. Heights are more variable than ages for all pitchers on this team. 75 28 70 25 74 25 74 25 75 27 74 27 74 26 79 34 78 34 79 37 Print Done Click to select your answer(s).
Mrs. Pearce conducted a survey in her class on the ages of famous people. The table below shows the results of her survey. Guess Actual Age 18 25 28 39 50 36 72 Which equations could best represent the line-of-best-fit for the given data set? F. G. 15 28 24 42 56 45 80 y = 1.19x - 4.3 H. J. y = 0.81x + 4.56 y = -1.19x +4.3 y = -0.81x - 4.56 DRAN
Step 4 The following table summarizes the given data and the forecast values. There will not be a forecast error for the first month. Complete the columns for the forecast error and squared forecast error. Month Value Forecast Value 1 2 3 4 5 6 7 19 27 23 28 24 23 28 19 23 23 24.25 24.2 24 Forecast Error Actual - Forecast Submit Skip (you cannot come back) 27 19 = 8 0 5 -0.25 4 Total Squared Forecast Error (Actual - Forecast)² (8)² = 64 0 1.44 The MSE will be the average of the squared forecast error column. Find the average of this column, rounding the result to two decimal places. MSE =
3. Correlation and linear regression
The trend of thinner beauty pageant winners has generated charges that the contest encourages unhealthy diet habits among young women. Listed below are body mass indexes (BMI) for beauty pageant winners from two different time periods. Find the coefficient of variation for each of the two sets of data, then compare the variation. The coefficient of variation for the BMI's of beauty pageant winners from the 1920s and 1930s is %
A Hospital record for a patient indicates the patient's assigned department, room location and number of days the patient was treated. Classify each variable as qualitative or quantitative. A sample data table is listed below. Department Room Location Days of Treatment Maternity A102 2 Cardiac G210 5 Orthopedic C309 3
Is the variable(foot print) below normally distributed or skewed? If skewed, indicate the direction. male shoe print #1 32.2 #2 30.1 #3 29.0 #4 31.8 #5 29.4
ea surface temperature26.726.726.626.626.626.626.526.526.326.326.126.1Growth0.850.850.850.850.790.790.860.860.890.890.920.92 Make a scatterplot. Which is the explanatory variable? The plot shows a negative linear pattern. Explanatory Variable:
Instructions: Use the technology of your choice (calculator, Excel, GeoGebra, Google Sheets) to find the regression line, the correlation coefficient of the following data sets, and interpolate or extrapolate the requested data. (Round all numbers to the nearest hundredth, when necessary.) Data Set: {(0,7),(1,5),(1.5,3),(2,5),(2.3,3.2),(3,2),(3.5,4)}{(0,7),(1,5),(1.5,3),(2,5),(2.3,3.2),(3,2),(3.5,4)} The regression line is: y=y= Answer −− Answer xx Based on the regression line, we would expect the value of response variable to be Answer when the explanatory variable is 0. (NOTE: This is the yy-intercept.) For each increase of 1 in of the explanatory variable, we can expect a(n) Answerdecreaseincrease of Answer in the response variable. (NOTE: This is the slope.) If x=2.5x=2.5, the yˆ=y^= Answer. This is an example of Answerextrapolationinterpolation. The correlation coefficient is r=r= Answer.
Use the example of temperature and the amount of ice cream sold in a city each day, we are interested in creating an equation that will allow us to predict the amount of ice cream that are sold in the future simply by knowing temperature. (Please fill out the red blanks with appropriate numbers randomly.) a. Construct a scatter plot of the data. What should the independent variable be? Why? b. Find the values and discuss what the following mean in context.i. The slope of the regression equationii. The y-intercept of the regression equationiii. The correlation riv. The coefficient of determination r2. c. Do the data provide convincing evidence that there is a linear relationship between temperature and the amount of ice cream Carry out an appropriate test at a significance level of 0.05 to help answer this question.
Given the scatter plot below, which of the following describes the strength and direction of the data? 10 Weak negative correlation Strong negative correlation Strong positive correlation Weak positive correlation

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