A paper gives data on xchange in Body Mass Index (BMI, in kilograms/meter2) and y change in a measure of depression for patients suffering from depression who participated in a pulmonary rehabilitationprogram. The table below contains a subset of the data given in the paper and are approximate values read from a scatterplot in the paper.BMI Change (kg/m²) 0.5 -0.500.1 0.7 0.81 1.5 1.2 1 0.4 0.4Depression Score Change -194 46$131416 19 12 14The accompanying computer output is from Minitab.Fitted Line PlotDepression score change = 6.598 +5.327 BMI change20-S5.1025427.32%R-SqR-Sq (adj) 20.06%15-10-5-0--0.50.00.5 1.0 1.5BMI changeOS5.10254 23.324CoefficientsVIRTerm Cont ST CoatConstant 6.598AMT change5.327-Valle P-Value3.01 0.01320.0012 1.002.5Regression Equationrepression acora changa= 6.598 +5.387 5MT changa(a) What percentage of observed variation in depression score change can be explained by the simple linear regression model? (Round your answer to two decimal places.)96(b) Give a point estimate of a. (Round your answer to five decimal places.)Depression score change BMI changeSR-sq5.10254 27.32%Coefficients.TermVIFConstant 6.598Coef SE Coef2.19P-Value3.01 0.0132BMI change 5.3272.751.940.0812 1.00Regression Equation.Depression score change = 6.598 +5.327 BMI change(a) What percentage of observed variation in depression score change can be explained by the simple linear regression model? (Round your answer to two decimal places.)%(b) Give a point estimate of a. (Round your answer to five decimal places.)S =Interpret this estimate..s is the typical amount by which the ---Select---value-Select-✓what is predicted using the least squares regression line.(c) Give an estimate of the average change in depression score change associated with a 1 kg/m2 increase in BMI change. (Round your answer to three decimal places.)(d) Calculate a point estimate of the mean depression score change for a patient whose BMI change was 1.2 kg/m2. (Round your answer to three decimal places.)ý =T-Value

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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 data found below measure the amounts of greenhouse gas emissions from three types of vehicles. The measurements are in tons per year, expressed as CO2 equivalents. Use a 0.025 significance level to test the claim that the different types of vehicle have the same mean amount of greenhouse gas emissions. Based on the results, does the type of vehicle appear to affect the amount of greenhouse gas emissions? : Click the icon to view the data. What are the hypotheses for this test? Data Table O A. Ho: H1 = H2 =H3 H: At least one of the means is different from the others. Туре с O B. Ho: H1 # H2 * H3 Туре А 6.2 Type B 8.2 7.7 Hyi Hy = H2 =H3 5.8 7.3 7.6 6.2 8.4 8.8 OC. Ho: H1 = H2 = H3 H: H1 # H2 #H3 5.7 7.8 8.8 6.9 8.8 9.3 6.1 7.7 8.1 O D. Ho: At least one of the means is different from the others. 7.9 5.7 7.3 5.9 7.3 7.8 H: H1 =H2 = H3 5.6 8.1 5.6 Determine the test statistic. F = (Round to two decimal places as needed.) Print Done Identify the P-value. Click to select your answer(s).…
Use the given data values (a sample of female arm circumferences in centimeters) to identify the corresponding z scores that are used for a normal quantile plot, then identify the coordinates of each point in the normal quantile plot. Construct the normal quantile plot, then determine whether the data appear to be from a population with a normal distribution. a. List the z scores for the normal quantile plot. (Round to two decimal places as needed. Use ascending order.) b. identify the coordinates of each point in the normal quantile plot. Use ordered pairs for the form (x,y), where x is the sorted arm circumferences in ascending order, and y is the corresponding z score. (Round to two decimal places as needed. Use ascending order.)
Find the following for this data on the length of an eruption of a geyser at Yellowstone to the amount of time until the next eruption. Geyser Eruption Data Length of Eruption (in minutes) Time until next Eruption (in minutes) 3 4.54 2.53.55 2 68 89 83 62 75 9257 Step 1 Plot the data points. Be sure to: Title your graph Label each axis including units Use appropriate scales Step 2 Identify the correlation. Step 3 Sketch the linc of best fit. Step 4 Identify two points on the line. Step 6 Write an equation using slope and the y-intercept. Show all work. Step 7 Use your line and its equation to predict how long until the next eruption if the geyser only erupted for 1 minute. Show all work. Step 5 is: "find slope of line of best fit. Show all work."Please show all work!
"Durable press" cotton fabrics are treated to improve their recovery from wrinkles after washing. "Wrinkle recovery angle" measures how well a fabric recovers from wrinkles. Higher is better. Here are data on the wrinkle recovery angle (in degrees) for two types of treated fabrics: Permafresh Hylite 15 17 12 14 13 16 16 15 14 18 A manufacturer wants to know how large is the difference in mean wrinkle recovery angle. Give a 98% confidence interval for the difference in mean wrinkle recovery angle: [three decimal accuracy] [three decimal accuracy]
Construct a normal quantile-quantile plot of the values provided in the accompanying data set, which represent the diameters of 36 rivet heads in 1/100 of an inch. Click the icon to view the diameter data. .... . Choose the correct graph below. OA. В. Normal Quantile-Quantile Plot Normal Quantile-Quantile Plot 5.95- 5.85- 5.75- 5.65- 0.2 0.4 0.6 0.8 1 5.65 5.75 5.85 5.95 Theoretical Quantities Theoretical Quantities C. D. Normal Quantile-Quantile Plot Normal Quantile-Quantile Plot 5.95- 11 5.85- 0.5- 5.75- • 000 5.65- -2 어 5.65 -1 1 5.75 5.85 5.95 Theoretical Quantities Theoretical Quantities Diameter data 5.83 5.77 5.81 5.73 5.73 5.75 5.85 5.84 5.83 5.89 5.71 5.83 5.71 5.81 5.87 5.81 5.84 5.74 5.83 5.84 5.78 5.76 5.71 5.87 5.82 5.78 5.75 5.82 5.89 5.79 Help me solve this View an example Get n 5.71 5.85 5.85 5.78 5.76 5.71 Sample Quantities Sample Quantities Sample Quantities Sample Quantities
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.6 8.5 7.1 3.4 2.6 2.8 2.6 0.7 y 13.8 11.3 9.9 6.8 6.4 6.7 6.1 4.4 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.85x +4.02. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. Use the equation with the values rounded to two decimal places to make your predictions. A) How many murders per 100,000 residents can be expected in a state with 6.7 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 8 thousand automatic weapons? Answer= Answer = Round to 3 decimal places.
Use a calculator or computer software to generate a normal quantile plot for the data in the accompanying table. Then determine whether the data come from a normally distributed population. Click the icon to view the data set. Generate a normal quantile plot for the data. Choose the correct graph below. O A. 3.00- 2.00- 1.00- 0.00- -1.00- -2.00- -3.00- 0 100 200 300 400 500 X Value B. 3.00- 2.00- 1.00- 0.00- -1.00- -2.00- -3.00+ 0 100 200 300 400 500 X Value Determine whether the data come from a normally distributed population. Choose the correct answer below. Data table A. The distribution is normal. The points are reasonably close to a straight line and donot show a systematic pattern that is not a straight-line pattern. B. The distribution is normal. The points show a systematic pattern that is not a straight-line pattern. C. The distribution is not normal. The points show a systematic pattern that is not a straight-line pattern. D. The distribution is not normal. The points are…
The data show the distance (in miles) from an airport of a sample of 22 inbound and outbound airplanes. Use technology to answer parts (a) and (b). a. Find the data set's first, second, and third quartiles. b. Draw a box-and-whisker plot that represents the data set. 3.3 4.7 3.3 2.5 5.3 5.6 3.6 5.3 3.5 4.4 4.5 4.7 5.7 3.9 4.2 4.8 3.4 5.4 3.2 3.2 5.8 4.8 a. Find the three quartiles. Q1 Q₂ Q3 (Type integers or decimals. Do not round.) b. Choose the correct box-and-whisker plot below. OA. 0 6 Q B. 3 S Q 5 OC. 3 6 9 Q N
The data show the distance (in miles) from an airport of a sample of 22 inbound and outbound airplanes. Use technology to answer parts (a) and (b). a. Find the data set's first, second, and third quartiles. b. Draw a box-and-whisker plot that represents the data set. 2.4 5.8 4.3 3.3 4.5 2.2 5.6 3.5 5.9 4.8 1.9 4.4 5.5 4.9 4.7 2.5 3.8 4.1 4.3 3.4 5.3 58 a. Find the three quartiles. Q, = Q2 = Q2 = (Type integers or decimals. Do not round.) b. Choose the correct box-and-whisker plot below. OA. O B. Oc. 6. Click to select your answer(s).
Use the given data values (a sample of female arm circumferences in centimeters) to identify the corresponding z scores that are used for a normal quantile plot, then identify the coordinates of each point in the normal quantile plot. Construct the normal quantile plot, then determine whether the data appear to be from a population with a normal distribution. 44.2, 34.5, 38.2, 32.7, 40.9 Part A- List the z scores for the normal quantile plot. Part B- Identify the coordinates of each point in the normal quantile plot. Use ordered pairs for the form (x,y), where x is the sorted arm circumferences in ascending order, and y is the corresponding z score. Part C- Do the data come from a normally distributed population?
of A uncw.instructure.com Bob's Burgers is a chain of hamburger fast food restaurants located all over the state of Alaska. Bob, the OM, has collected sales data from their famous Jalapeño Burger over the past 5 weeks, and wants to generate a linear regression equation to estimate potential sales for upcoming weeks. Given the data below, what is the regression equation? Jalapeño Burger Week Sales 354 345 367 3. 380 4. 413 5. Use the Summary Output below to write the equation for the model that explains the linear relationship between weeks and Jalapeño Burger sales? SUMMARY OUTPUT Regression Statistics Multiple R 0.91065 R Square 0.829283 Adjusted R 0.772377 Square Standard 12.67412 Error Observations 5 ANOVA Significance SS MS F. Regression 1. 2340.9 2340.9 14.57294 0.031628 Residual 481.9 160.6333 3.
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