Quantitative noninvasive techniques are needed for routinely assessing symptoms of peripheral neuropathies, such as carpal tunnel syndrome (CTS). An article reported on a test that involved sensing a tinygap in an otherwise smooth surface by probing with a finger; this functionally resembles many work-related tactile activities, such as detecting scratches or surface defects. When finger probing was notallowed, the sample average gap detection threshold for m = 5 normal subjects was 1.62 mm, and the sample standard deviation was 0.53; for n = 10 CTS subjects, the sample mean and sample standarddeviation were 2.36 and 0.85, respectively. Does this data suggest that the true average gap detection threshold for CTS subjects exceeds that for normal subjects?USE SALTState and test the relevant hypotheses using a significance level of 0.01. (Use μ₁ for normal subjects and 2 for CTS subjects.)0 Но М1-М2=0Hải My My 20| Ho: M₁ M₂ = 0на: H1 - H2#0о но: 41-H2=0Hải My M2 > 0ⒸH₁ H₁ H₂ = 0H₂: M₁ - H₂ <0Calculate the test statistic and determine the P-value. (Round your test statistic to one decimal place and your P-value to three decimal places.)t =P-value=

The random sample of normal subjects, The sample mean, The sample standard deviation, The random…

Answered: Quantitative noninvasive techniques are…

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 processing of raw coal involves “washing,” in which coal ash (nonorganic, incombustible material) is removed. The article “Quantifying Sampling Precision for Coal Ash Using Gy’s Discrete Model of the Fundamental Error” (Journal of Coal Quality, 1989:33–39) provides data relating the percentage of ash to the volume of a coal particle. The average percentage of ash for six volumes of coal particles was measured. The data are as follows: Volume (cm3) 0.01 0.06 0.58 2.24 15.55 276.02 Percent ash 3.32 4.05 5.69 7.06 8.17 9.36 Using the most appropriate model, predict the percent ash for particles with a volume of 48 cm3. Round answer to three decimal places.
When trying to determine how many samples are needed to perform an experiment, the desired accuracy of the results are often a determining factor. Dr. Smith has designed a new scanner capable of determining blood bilirubin levels using a cell phone. Because this is a diagnostic device, Dr. Smith is concerned not just about having small standard errors, but also having good estimates for population percentiles. If he needs to determine the 95th percentile to an accuracy of 5%, what is the number of samples needed to ensure that the percentile as determined from his data (using a t distribution) is within 5% of the percentile as calculated as if the standard deviation was taken from the entire population (using a z distribution)?
An agent from a property rental company would like to predict the monthly rental for condominium based on the size as defined in square metres. A sample of 25 condominiums was selected. Data is generated and the summary of outputs are shown in Table 1 and Figure 1. ANOVA df SS MS F 59.914 Significance F 7.52E-08 Regression Residual 1 2268776.545 2268777 23 870949.4547 37867.37 Total 24 3139726 Standard Error 161.00427 t Stat P-value Coefficients 177.1208 1.0651 1.100 Intercept Size 0.282 0.13760 7.740 7.52E-08 Table 1 Size Residual Plot 500 400 300 200 100 0 -100 -200 ◆ 1000 -300 -400 -500 Residuals 500 Size Figure 1 +500 2000 2500
Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y , in dollars) and the maximum temperature (denoted by x , in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x(in degrees Fahrenheit) Coffee sales, y(in dollars) 53.1 2215.5 76.6 1982.7 70.1 1937.3 39.7 2251.1 72.5 1603.9 48.4 2024.2 46.7 2135.3 83.9 1536.5 58.4 1965.6 38.5 1944.3 68.2 1746.1 75.4 1472.1 65.6 1819.5 53.9 1627.4 45.1 1782.3 Send data to calculator Send data to Excel Coffee sales(in dollars) y 1200 1400 1600 1800 2000 2200 2400 x 40 50 60 70 80 90…
Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here) SUMMARY OUTPUT Regression Statistics Multiple R 0.998041188 R Square 0.996086212 Adjusted R Square 0.994781616 Standard Error 917.1714273 Observations 5 ANOVA df SS MS F Significance F Regression 1 642276389.7 642276389.7 763.5208906 0.000104039 Residual 3 2523610.281 841203 427 Total 4 644800000 Coefficients Standard Errort Stat P-value Intercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639 X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876 What is variable cost per machine hour (rounded to the nearest cent)? OA. $6,679.62 OB. $917.17 OC. $9.80 OD. $.99 Lower 95% Upper 95.0% 9645.198517 10.92509577
B. Would the Floor Area be a significant predictor of Assessed Value?
Can you please help me answer this question
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95% confidence interval around the sample slope and interpret its meaning. The confidence interval is (__________,_________). (Type an integer or decimal rounded to three decimal places as needed.)
For Finasteride study, photographs are taken of each head. They are assessed using a standard methodology. This results in a score between 1 and 7: 1 Indicating greatly decreased hair growth and 7 gently increased. Simulated data, presented as pairs, is in Table 1. Table 1 - Assessment for hair loss on 1 -7 scale for twin study Group Finasteride Placebo Define the hypothesis. Provide the Critical Value and Region Calculate the test statistic. Provide a conclusion. Score 5, 3, 5, 7, 4, 4, 7, 4, 3 2, 3, 2, 4, 2, 2, 3, 4, 2
Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of randomly selected days during the past year are given below. These data are plotted in the scatter plot below. (a)For these data, temperature values that are less than the mean of the temperature values tend to be paired with coffee sales values that are ▼(Choose one) the mean of the coffee sales values. (b)According to the regression equation, for an increase of one degree in temperature, there is a corresponding ▼(Choose one) of 9.94 dollars in coffee sales. (c)From the regression equation, what is the predicted coffee sales value (in dollars) when the temperature is 74.6 degrees Fahrenheit? (Round your…
Question 3 You wanted to improve the model from question two and have decided to include a newvariable, percentage of English learners (EL PCT). SUMMARYOUTPUTRegression StatisticsMultiple R 0.653017122R Square 0.426431361Adjusted R Square 0.423680433Standard Error 14.46448496Observations 420 ANOVA df SS MS FRegression 2 64864.3011 32432.15055 155.0135989Residual 417 87245.29254 209.221325Total 419 152109.5936 Coefficients Standard Error t Stat P-valueIntercept 686.0322487 7.411312484str -1.101295889 0.380278316el_pct -0.649776777 0.039342549 (a) Is this second model a better or worse model than the one in question 2? Why?(b) Are all the signs of the variables as expected? State…
(c) Calculate the test statistic.

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