ANSWER ALL AND SHOW COMPLETE SOLUTIONS FOR UPVOTE 1. The data below shows the weight and chest size of infants at birth.Weight, (kg)Chest size (cm)(Y)3.204.04ΣΕ3.41Weight (X)3.204.923.172.472.414.045.153.233.413.284.923.172.472.415.153.233.28Chest Size (Y)r=26.728.532.126.824.926.832.136.827.728.928.5III. Test Statistic:32.1I. Statement of Hypothesis:Ho: (in words)Ho: (in symbols) p = 0Ha: (in words)Ha: (in symbols) p #0Two-tailed testΣΕIV. Computation:XYV. Decision and Conclusion:Pearson rnΣxy-ExΣy√[n([x²)-([x)³][n(Σy²)−(Σy)³]pearsonR² =r26.7spearman32.126.824.926.836.827.728.9II. Level of Significance and Critical Valuea = 0.05df = n-2 =CV =Decision Rule: Reject Ho if t-computed is...Σ=X²Compute the Pearson correlation coefficient.Test the hypothesis that there exist no significantrelationships between the weight and chest size ofthe infants. Use 0.05 level of significance.Determine the coefficient of multiple determinationand interpret the result.Compute the Spearman correlation coefficient.Σ=Y²Spearman's Rank6(Σd²)n(n² - 1)7 = 1-.Rank (X)Formula: t=Rank (Y)Statistical Test: t-test for the significance of rr√√n-2dΣ=d²

Data given on weight (X) and chest size (Y) of infants of birth.n=10According to our Q&A…

Answered: 1. The data below shows the weight and…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

1. Use geometric similarity to estimate a relationship between the masses and lengths of a specific fish species 2. Test graphically if the given data below supports the proportionality y ∝ x3 .
You give a final attempt to bring the effect of Size back into the equation by forcing the assumption of homoskedasticity onto your estimation. The results are as follows: cost^= = 7,311.17 + 3,985.20 × Reputation – 0.20 × Size (1,985.17) (593.65) (0.07)+ 8,406.79 × Dpriv – 416.38 × Dlibart – 2,376.51 × Dreligion (1,423.59) (1,096.49) (989.23) R 2=0.72, SER = 3,682.02Calculate the t-statistic on the Size coefficient and perform the hypothesis test that its coefficient is zero. Is this test reliable? Explain.
The data given below indicate the existence of a linear relationship between the x and y variables. Suppose an analyst who prepared the solutions and carried out the RI measurements was not skilled and as a result of poor technique, allowed intermediate errors to appear. The results are the following:Concentration of solution in percent (x) 10 26 33 50 61Refractive indices (y) 1.497 1.493 1.485 1.478 1.477Step 1. Carefully plot the given x and y values (from the table) on a regular graphing paper. Label then connect the points to observe a zigzag plot due to the scattered points. Step 2: Copy and fill the table given below: x (x - x̄) (x - x̄) 2 y (y - ȳ) (y - ȳ) 2 (x - x̄) (y - ȳ) 10 1.497 26 1.49333 1.48550 1.47861 1.477∑ = ∑ = ∑ = ∑ = ∑ = ∑ = ∑ =x̄= ∑xi ÷ Nx̄= ȳ = ∑yi ÷ Nȳ = Step 3. After completing the table, present following computations and the interpretation.a. Calculate the correlation coefficient (r), using the working formula: r =Σ (x − x ) (y − ȳ)√(Σ(x − x )2)(Σ(y −…
In a given community, a survey was conducted to determine whether there is any relationship between the size of one’s yearly income x (in hundred thousands of pesos) and the size of one’s home y (in hundreds of square meters). The data were collected for 10 samples. x. Income 41.2 68.3 22.4 56.7 42.2 86.1 50.3 35.7 44.4 47.5 y, Home size 2.9 3.5 2.5 3.1 3.3 4 3.7 2.9 3 3.1 a.) Find the correlation coefficient r between income and home size b.) Determine the equation of the regression line of home size on c.) Determine the home size of a person with an annual income of Php 4,000,000.00
We want to examine the relationship between body temperature Y and heart rate X. Further, we would like to use heart rate to predict the body temperature. ID Gender Age HeartRate Temperature 1 M 33 69 97 2 M 32 72 98.8 3 M 42 68 96.2 4 F 33 75 97.8 5 F 26 68 98.8 6 M 37 79 101.3 7 F 32 71 97.8 8 F 45 73 97.4 9 F 31 77 99.2 10 M 49 81 99.2 11 M 40 69 97.5 12 F 45 70 97.7 13 F 49 71 98.3 14 F 37 74 98.8 15 F 47 79 98.5 16 M 34 73 97.3 17 F 41 71 98.9 18 M 33 77 98.6 19 F 38 70 99.2 20 F 31 69 97.9 21 F 41 67 97.1 22 F 45 67 97 23 F 36 74 98.9 24 M 35 77 99 25 F 28 76 97.9 26 M 25 79 98 27 M 30 76 97 28 M 41 61 97.6 29 M 37 84 99 30 F 37 67 97.8 31 F 34 73 97.9 32 F 34 81 100 33 M 50 68 97.1 34 M 48 76 98.9 35 M 34 80 98.6 36 F 36 68 97.7 37 F 21 70 98.3 38 M 23 79 98.9 39 F 42 76 99.4 40 F 33 75 98.6 41 M 37 73 97.2 42 M 33 72 98 43 F 36 77 99.3 44 M 43 81 99.4 45 M 36 73 98.8 46 M 29 64 98.5…
We want to examine the relationship between body temperature Y and heart rate X. Further, we would like to use heart rate to predict the body temperature. ID Gender Age HeartRate Temperature 1 M 33 69 97 2 M 32 72 98.8 3 M 42 68 96.2 4 F 33 75 97.8 5 F 26 68 98.8 6 M 37 79 101.3 7 F 32 71 97.8 8 F 45 73 97.4 9 F 31 77 99.2 10 M 49 81 99.2 11 M 40 69 97.5 12 F 45 70 97.7 13 F 49 71 98.3 14 F 37 74 98.8 15 F 47 79 98.5 16 M 34 73 97.3 17 F 41 71 98.9 18 M 33 77 98.6 19 F 38 70 99.2 20 F 31 69 97.9 21 F 41 67 97.1 22 F 45 67 97 23 F 36 74 98.9 24 M 35 77 99 25 F 28 76 97.9 26 M 25 79 98 27 M 30 76 97 28 M 41 61 97.6 29 M 37 84 99 30 F 37 67 97.8 31 F 34 73 97.9 32 F 34 81 100 33 M 50 68 97.1 34 M 48 76 98.9 35 M 34 80 98.6 36 F 36 68 97.7 37 F 21 70 98.3 38 M 23 79 98.9 39 F 42 76 99.4 40 F 33 75 98.6 41 M 37 73 97.2 42 M 33 72 98 43 F 36 77 99.3 44 M 43 81 99.4 45 M 36 73 98.8 46 M 29 64 98.5…
We want to examine the relationship between body temperature Y and heart rate X. Further, we would like to use heart rate to predict the body temperature. ID Gender Age HeartRate Temperature 1 M 33 69 97 2 M 32 72 98.8 3 M 42 68 96.2 4 F 33 75 97.8 5 F 26 68 98.8 6 M 37 79 101.3 7 F 32 71 97.8 8 F 45 73 97.4 9 F 31 77 99.2 10 M 49 81 99.2 11 M 40 69 97.5 12 F 45 70 97.7 13 F 49 71 98.3 14 F 37 74 98.8 15 F 47 79 98.5 16 M 34 73 97.3 17 F 41 71 98.9 18 M 33 77 98.6 19 F 38 70 99.2 20 F 31 69 97.9 21 F 41 67 97.1 22 F 45 67 97 23 F 36 74 98.9 24 M 35 77 99 25 F 28 76 97.9 26 M 25 79 98 27 M 30 76 97 28 M 41 61 97.6 29 M 37 84 99 30 F 37 67 97.8 31 F 34 73 97.9 32 F 34 81 100 33 M 50 68 97.1 34 M 48 76 98.9 35 M 34 80 98.6 36 F 36 68 97.7 37 F 21 70 98.3 38 M 23 79 98.9 39 F 42 76 99.4 40 F 33 75 98.6 41 M 37 73 97.2 42 M 33 72 98 43 F 36 77 99.3 44 M 43 81 99.4 45 M 36 73 98.8 46 M 29 64 98.5…
We want to estimate the causal effect of D through regression, and S allows a perfect stratification. There are N stratums in S and we use Si to represent stratum i. If the estimated coefficient of Si*D is A and is significant, then the treatment effect of D for stratum i is A.
In recent years, a substantial amount of research has focused on a possible relationship between chemical contamination of various sorts and mental impairment in children. An article reported data on hair-lead concentration for a sample of mentally impaired children for which the cause of impairment was unknown. Sample statistics were n = 41 X Bar = 16 ppm s = 5.36 ppm The paper states that 14.27 ppm is considered the acceptable upper limit of hair-lead concentration. Let μ= the average hair-lead concentration for all mentally impaired children with the cause of impairment unknown. H0: μ = 14.27 versus H1: μ > 14.27 at 0.01 significance level Does the given sample data support the research hypothesis that true average hair concentration for all such mentally impaired children exceeds the acceptable upper limit? Assume normality. 1. reject null hypothesis 2. fail to reject null hypothesis
The forecast for year 6 using a weighted 2-year moving average with weights of 0.350.35 and 0.650.65 (the weight of 0.650.65 is for the most recent period) is what?
When water flows across farmland, some soil is washed away, resulting in erosion. An experiment was conducted to investigate the effect of the rate of water flow (liters per second) on the amount of soil (kilograms) washed away. The data are given in the following table: Flow rate 0.31 0.85 1.26 2.47 3.75 Eroded soil 0.82 1.95 2.18 3.01 6.07 Let xx represent the flow rate variable and yy represent the variable for soil eroded. Then \bar x = 1.73, s_x = 1.38, \bar y = 2.81, s_y = 1.99xˉ=1.73,sx=1.38,yˉ=2.81,sy=1.99 Use this to complete the following calculation of the correlation coefficient for these data. r = \frac{1}{n-1}\left[\left(\frac{x_1 - \bar x}{s_x}\right)\left(\frac{y_1 - \bar y}{s_y}\right) + \left(\frac{x_2 - \bar x}{s_x}\right)\left(\frac{y_2 - \bar y}{s_y}\right) + \cdots + \left(\frac{x_n - \bar x}{s_x}\right)\left(\frac{y_n - \bar y}{s_y}\right)\right]r=n−11[(sxx1−xˉ)(syy1−yˉ)+(sxx2−xˉ)(syy2−yˉ)+⋯+(sxxn−xˉ)(syyn−yˉ)]…
I need help with part (c) only, I need t statistic and p value. Mist (airborne droplets or aerosols) is generated when metal-removing fluids are used in machining operations to cool and lubricate the tool and workpiece. Mist generation is a concern to OSHA, which has recently lowered substantially the workplace standard. An article gave the accompanying data on x = fluid-flow velocity for a 5% soluble oil (cm/sec) and y = the extent of mist droplets having diameters smaller than 10 µm (mg/m3): x 88 177 182 354 369 442 970 y 0.39 0.60 0.50 0.66 0.61 0.69 0.92 (a) The investigators performed a simple linear regression analysis to relate the two variables. Does a scatter plot of the data support this strategy? Yes, a scatter plot shows a reasonable linear relationship.No, a scatter plot does not show a reasonable linear relationship. (b) What proportion of observed variation in mist can be attributed to the simple linear regression relationship between velocity and…

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS