Suppose the following data are product weights for the same items produced on two different production lines.Line 113.113.814.013.713.413.213.513.112.714.4Line 213.914.614.214.014.113.314.414.814.314.515.014.7Test for a difference between the product weights for the two lines. Use α = 0.05.State the null and alternative hypotheses.Ho: Median for line 1 - Median for line 2 <0H₂: Median for line 1 - Median for line 2 = 0Ho:Median for line 1 - Median for line 2 ≤ 0H₂: Median for line 1 - Median for line 2 > 0Ho: The two populations of product weights are not identical.The two populations of product weights are identical.Ho: Median for line 1 - Median for line 2 20H₂: Median for line 1 - Median for line 2 <0Ho: The two populations of product weights are identical.H₂: The two populations of product weights are not identical.Find the value of the test statistic.W =Find the p-value. (Round your answer to four decimal places.)p-value =

Given data are product weights for the same items produced on two different production lines.…

Answered: Suppose the following data are product…

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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excel Sample 1 2 3 4 5 6 7 8 9 10 11 2 3 4 12 14 15 16 17 18 19 20 11. As cereal boxes are filled in a factory, they are weighed for their contents by an automatic scale. The target value is to put 10 ounces of cereal in each box. Twenty samples of three boxes each have been weighed for quality control purposes. The fill weight for each box is shown below. 1 10.01 9.87 10.08 10.17 10.21 10.16 10.14 9.86 10.18 9.91 10.08 9.71 10.14 10.16 10.13 10.16 10.20 9.87 9.84 10.06 Observation 2 9.90 10.20 9.89 10.01 10.13 10.02 9.89 9.91 10.04 9.87 10.14 9.87 10.06 10.17 9.94 9.81 10.10 9.93 9.91 10.19 3 10.03 10.15 9.76 9.83 10.04 9.85 9.80 9.99 9.96 10.06 10.03 9.92 9.84 10.19 9.92 9.87 10.03 10.06 9.99 10.01 a. Calculate the center line and control limits for the and R charts from these data. b. Plot each of the 20 samples on the and R control charts and determine which samples are out of control. c. Do you think the process is stable enough to begin to use these data as a basis for…
3. The Save More Rental Car Agency at the Cincinnati airport would like to examine records from last summer in order to plan for the coming summer demand. The data for last year's demand, broken down by type of vehicle requested, is shown in the table below. Vehicle Type Sub-compact Compact Full-size Frequency 545 892 740 360 280 168 2985 * Total is not equal to 1.000 due to rounding error. Relative Frequency 0.183 0.299 0.248 Luxury SUV Van Total 0.121 0.094 0.056 1.001* a) Construct a frequency and relative frequency bar chart for the data. b) Construct a pie chart to display the information. c) This summer's demand is expected to be 20% higher than demand for last summer. Approximately how many luxury cars are expected to be rented this summer?
5 and 6. The following data was collected from 1 bag of Hershey Kisses®. Each Kiss® was weighed in grams with the wrapper and recorded in the table below. Hershey claims that there are 368 grams of chocolate in one bag. Hershey Kiss Weights in Grams 4.76 4.72 4.74 4.55 4.91 4.74 4.78 4.71 4.80 4.78 4.78 4.75 4.79 4.82 4.91 4.83 4.68 4.74 4.70 4.80 4.70 4.76 4.70 4.83 4.93 4.74 4.84 4.82 4.76 4.77 4.72 4.78 4.83 4.75 4.74 4.68 4.84 4.71 4.71 4.76 4.66 4.78 4.73 4.74 4.92 4.77 4.80 4.79 4.86 4.64 4.78 4.70 4.75 4.78 4.76 4.83 4.66 4.77 4.83 4.78 4.69 4.81 4.68 4.78 4.88 4.72 4.85 4.85 4.81 4.74 4.80 4.82 4.84 4.70 4.85 4.70 4.81 4.72 4.79 4.73 4.61 Based on Hershey's® claim for 368 total net grams of chocolate in the bag, approximately how many Kisses® too many or too few are there? Assume that the Kisses® were weighed with the wrapper, each wrapper weighs 0.12 grams, and the net grams listed on the bag are for the chocolate only. Give some possible…
A certain manufacturing company has the following data on quantities shipped and unit costs for each of its four products. Mean Shipping Cost per Unit ($) Base-Period Quantities (Year 1) Products Year 1 Year 5 A 1,500 10.50 15.90 B 5,000 16.25 33.00 6,000 12.20 17.40 D 2,500 20.00 35.50 Use the price data to compute a Paasche index for the shipping cost if year 5 quantities are 4,500, 3,000, 7,000, and 3,000 for products A, B. C. and D, respectively. (Round your answer to the nearest integer.)
9. 18 2 1 3 1 5 1 3 499 40 2 9 5 5 6 1 2 Evaluate the following for the actual data value from the stem and leaf plot. (a) What was the highest weekly income range ? (b) What was the lowest weekly income range? (c) How many families are there in the plot? (d) What range does 4|2 represent?
Why can’t the following mode predict 100% of the variability in the price of a used Corolla?
To test whether the mean time needed to mix a batch of material is the same for machines produced by three manufacturers, the Jacobs Chemical Company obtained the following data on the time (in minutes) needed to mix the material. Manufacturer 1 2 3 25 34 15 31 32 14 29 37 18 27 33 17 a. Use these data to test whether the population mean times for mixing a batch of material differ for the three manufacturers. Use a = 0.05. Sum of squares, treatment = Sum of squares, error = Mean squares, treatment = mean squares, error = Calculate the value of the test statistic (to 2 decimals). The P-value is: Less than 0.01, between 0.01 and 0.025, between 0.025 and 0.05, between 0.05 and 0.10, greater than 0.10 What is your conclusion? b. At the a = 0.05 level of significance, use Fisher's LSD procedure to test for the equality of the means for manufacturers 1 and 3. What conclusion can you draw after carrying out this test?
i. Trying to predict weekly sales with a standard error of estimate of $1,955, we would conclude that 68 percent of the predictions would not be off more than $1,955, 95 percent would not be off by more $3,910, and 99.7 percent would not be off by more than $5,865.ii. The smaller the sample, the smaller the possible error as measured by the standard error of estimate.i. ut of Approximately 95% of the values lie within two standard errors of the regression line. Select one: a. (i), (ii), and (iii) are all correct statements. b. (i) and (ii) are correct statements but not (iii). c. (ii) and (iii) are correct statements but not (i). d. (i), (ii), and (iii) are all false statements. e. (i) and (iii) are correct statements but not (ii). 14
6. Accidents on highways are one of the main causes of death or injury in developing countries and the weather conditions have an impact on the rates of death and injury. In foggy, rainy, and sunny conditions, 1/4, 1/8, and 1/21 of the accidents result in death, respectively. Sunny conditions occur 60% of the time, while rainy and foggy conditions each occur 20% of the time. Given that an accident without deaths occurred, what is the conditional probability that it was foggy at the time?
9. The following data was collected from 1 bag of Hershey Kisses®. Each Kiss® was weighed in grams with the wrapper and recorded in the table below. Hershey claims that there are 368 grams of chocolate in one bag. Hershey Kiss Weights in Grams 4.76 4.72 4.74 4.55 4.91 4.74 4.78 4.71 4.80 4.78 4.78 4.75 4.79 4.82 4.91 4.83 4.68 4.74 4.70 4.80 4.70 4.76 4.70 4.83 4.93 4.74 4.84 4.82 4.76 4.77 4.72 4.78 4.83 4.75 4.74 4.68 4.84 4.71 4.71 4.76 4.66 4.78 4.73 4.74 4.92 4.77 4.80 4.79 4.86 4.64 4.78 4.70 4.75 4.78 4.76 4.83 4.66 4.77 4.83 4.78 4.69 4.81 4.68 4.78 4.88 4.72 4.85 4.85 4.81 4.74 4.80 4.82 4.84 4.70 4.85 4.70 4.81 4.72 4.79 4.73 4.61 Standard Deviation & the Empirical Rule: Variation is a big factor in the analysis of most any data set and it will be very important to have a way of measuring it. Standard Deviation is one such measure. Find the Standard Deviation of the data. You can use the stdev or stdev.s functions in Excel. A bell-shaped…
Only 2.4
1. A study of the relation between the waistline and percent body fat in men yielded the data shown (waistline measurement in inches). waistline (x) 34.7 46.5 32.8 41.3 29.8 39.3 35.7 29.0 percent fat (y) 21.8 33.6 13.4 27.1 13.7 30.2 18.5 11.2 x = 36.1375 y = 21.1875 SSxx = 248.53875 SSay = 332.67375 SSyy = 494.30875 (a) Find the proportion of the variability in percent body fat that is accounted for by the size of the waistline. Explain fully. (b) Find the regression line for predicting y from x. (c) Construct a 95% confidence interval for the average percent body fat of all men whose waistline size is 34.0 inches. (d) test HO: Beta1 is 0 vs. Ha : Beta1 is not 0, compute the appropriate test statistic and p-value, then make your decision at a = 0.05. (e) Construct a 90% confidence interval for beta1. (f) Compute a 90% confidence interval for the average percent fat when waistline is 35.

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