The weight and systolic blood pressure of 30 randomly selected males in the age group 25-30 areshown below. Assuming that weight and blood pressure (BP) are normally distributed.a) Find a regression line relating systolic blood pressure wo weightb) Estimate the coefficient of determination and interpret your findingsc) Test the hypothesis that p=0d) Test the hypothesis that p=0.6.e) Find the 95% CI for p) Consider the weight and blood pressure and fit a non-intercept model to the data andcompare it to the model obtain in part (a). which model would you consider superior?SubjectWeightSystolic BP Subject130WeightSystolic BP153128116516716172|213317159318015018168132415512819174149212151201831586.175146212151501901502219516321014023180156920014824143124101252524017016516014911158133262351216913527192131701502818715914156185131127292001371530220140

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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 number of hours 10 students spent studying for a test and their scores on that test are shown in the table. Is there enough evidence to conclude that there is a significant linear correlation between the data? Use a 0.05. Hours, x Test score, y 4 4 7. 41 44 53 57 64 68 75 76 85 96 Click here to view a table of critical values for Student's t-distribution. Setup the hypothesis for the test, Hg: p = 0 H p 0 Identify the critical value(s). Select the correct choice below and fill in any answer boxes within your choice. (Round to three decimal places as needed.) A The critical values are tn = -2.306 and to = 2.306 OB. The critical value is Calculate the test statistic. t=(Round to three decimal places as needed.)
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…
Shown below is a portion of a computer output for regression analysis relating y (dependent variable) and x (independent variable). ANOVA df SS Regression 1 24.061 Residual 10 67.979 Coefficients Standard Error Intercept 11.064 2.049 x −0.566 0.301 (a) What has been the sample size for the above regression analysis? (b) Perform a t-test and determine whether or not x and y are related. Let ? = 0.05. State the null and alternative hypotheses. (Enter != for ≠ as needed.) H0: Ha: Find the value of the test statistic. (Round your answer to three decimal places.) Find the p-value. (Round your answer to four decimal places.) p-value = What is your conclusion? .
a) Are the body mass leanness and heat-loss ratecorrelated? Explain your reasoning. b) Calculate the standard error of the correlation coefficient; what does this value represent?Using the standard error how do you report (write) your results in a scientific paper?
Does Job stress influence overall life satisfaction? A sample of 7,814 people were surveyed, and this is the result of the regression analysis: Descriptive Statistics Mean Std. Deviation N Life satisfaction 7.97 1.194 7817 Job stress 3.16 1.667 7817 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .228a .052 .052 1.162 .052 427.062 1 7815 .000 a. Predictors: (Constant), Job stress b. Dependent Variable: Life satisfaction ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 577.130 1 577.130 427.062 .000b Residual 10561.161 7815 1.351 Total 11138.291 7816 a. Dependent Variable: Life satisfaction b. Predictors: (Constant), Job stress Coefficientsa Model Unstandardized…
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 sixteen 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) 71.5 1970.9 58.7 1953.9 53.7 1791.1 2400+ 83.0 1570.3 2200+ 62.8 1852.7 74.6 1633.6 2000- ** 40.4 1973.9 1800- 51.5 2250.9 44.6 1808.5 1600- 45.5 1977.3 1400- 45.6 2190.5 1200 69.4 1789.1 55.0 1598.2 50 70 80 90 40.6 2272.0 74.0 1937.0 Figure 1 76.6 1547.1 Send data to Excel Continue Submit Assignment O 2021 McGraw-Hill Education. All Rights Reserved. Terms of Use Privacy Accessibility here to search 99+ 5 4.
The number of hours 10 students spent studying for a test and their scores on that test are shown in the table. Is there enough evidence to conclude that there is a significant linear correlation between the data? Use a=0.01. Hours, x Test score, y Click here to view a table of critical values for Student's t-distribution. 0 43 1 2 4 4 5 5 41 53 48 60 66 72 Setup the hypothesis for the test. Ho: P 0 HP YO Calculate the test statistic. There Identify the critical value(s). Select the correct choice below and fill in any answer boxes within your choice. (Round to three decimal places as needed.) OA. The critical values are -to = and to = OB. The critical value is t= (Round to three decimal places as needed.) What is your conclusion? 6 82 7 8 80 95 enough evidence at the 1% level of significance to conclude that there a significant linear correlation between hours spent studying and test score.
Our environment is very sensitive to the amount of ozone in the upper atmosphere. The level of ozone normally found is 4.6 parts/million (ppm). A researcher believes that the current ozone level is at an excess level. The mean of 14 samples is 4.9 ppm with a variance of 1.2 Does the data support the claim at the 0.01 level? Assume the population distribution is approximately normal. Step 2 of 5 : Find the value of the test statistic. Round your answer to three decimal places.
The number of hours 10 students spent studying for a test and their scores on that test are shown in the table. Is there enough evidence to conclude that there is a significant linear correlation between the data? Use a = 0.05. Hours, x Test score, y 2 50 55 E Click here to view a table of critical values for Student's t-distribution. 1 4 4 5 7 8 39 47 65 67 76 78 83 95 Setup the hypothesis for the test. Họ: p 0 Ha: p Identify the critical value(s). Select the correct choice below and fill in any answer boxes within your choice. (Round to three decimal places as needed.) O A. The critical value is O B. The critical values are - to = and to = Calculate the test statistic. t= (Round to three decimal places as needed.) What is your conclusion? There enough evidence at the 5% level of significance to conclude that there a significant linear correlation between hours spent studying and test score. is or is not isNot
Refer to the below table. Using an alpha = 0.05, test the claim that IQ scores are the same for children in three different blood lead level groups: low lead level, medium lead level, and high lead level). One-Way Analysis of Variance Summary Table for IQ Measurements for Children among Three Blood Lead Level Groups: Low Lead Level, Medium Lead Level, and High Lead Level. Source df SS MS F p Between-group (treatment) 2 469.1827 2677.864 2.30 0.104 Within-group (error) 118 203.6918 11745.05 Total 120
The total expenses of a hospital (dependent variable) are related to many factors. One of these factors is the number of admissions to the hospital (independent variable). Using the summary output below to answer, what is the meaning (analysis) of the coefficient of determination? SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Statistics Regression Residual Total df 0.98706 0.974288 0.972145 8.106794 Coefficients 14 1 12 13 SS 29883.07 788.6412 30671.71 Standard Error Intercept 1.518053 Admissions 0.668591 OA) is the value of Expenses with one unit increase in Admissions 3.248929 0.031354 MS 29883.07 65.7201 t Stat 0.467247 21.32375 F 454.7022 P-value 0.648693 6.59E-11 Significance F 6.59E-11 Lower 95% -5.56075 0.600276 Upper 95% 8.59686 0.736907
determine if the frequency of deficiencies of variable (influenza/pneumococcal shots) are statistically significantly different between for-profit and non-profit nursing homes, interpret the results for the chi-square?

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