Chicken diet and weight. In previous chapter, we compared the effects of two typesof feed at a time. A better analysis would first consider all feed types at once:casein, horsebean, linseed, meat meal, soybean, and sunflower. The ANOVA outputbelow can be used to test for differences between the average weights of chicks ondifferent diets.DFSum SqMean Sq F value Pr(>F)feed231129.16 46225.8315.36 0.0000residuals65 195556.02Conduct a hypothesis test to determine if these data provide convincing evidencethat the average weight of chicks varies across some (or all) groups. Make sure tocheck relevant conditions. Figures and summary statistics are shown below.3008.55400350-300250200150 -100caseinhorsebean linseedmeatmeal soybean sunflowerMeanSDcasein323.5864.4312horsebean160.238.6310linseed218.7552.24meatmeal276.9164.911soybean 246.4354.1314sunflower328.9248.8412What aro tho huWeight (in grams)O21T2 What are the hypotheses for this test?O Ho: Hc = Hh = H = Pm = Psoy = HsunHa: At least one of the means is differentO Ho: µc = Hh = HL = Pm = Psoy = HsunHa: Hc = Hh = Hi = Pm * Hsoy = HsunO Ho: µc = Hh = Hi = Pm = Hsoy = PsunHa: At least one pair of means is the same%3D%3D%3DThe test statistic for the hypothesis test is:(please round to twodecimal places)The p-value for the hypothesis test is:(please round to fourdecimal places)Interpret the result of the hypothesis test in the context of the study:O Since p < a, we reject the null hypothesis and accept that the average weight ofchicks is different for each type of dietO Since p < a, we reject the null hypothesis and accept that the average weight ofchicks is not the same across all of the dietsO Since p = 0, there is no chance that the average weight of chicks is the sameacross all of the dietsO Since p < a,chicks is the same across all of the dietsreject the null hypothesis and accept that the average weight of

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Description: Chicken diet and weight. In previous chapter, we compared the effects of two typesof feed at a time. A better analysis would first consider all feed types at once:casein, horsebean, linseed, meat meal, soybean, and sunflower. The ANOVA outputbelow c

Hypothesis for one-way ANOVA H0:μc=μh=μl=μm=μsoy=μsunH1: At least one of the means is different

Math

Statistics

Chicken diet and weight. In previous chapter, we compared the effects of two types of feed at a time. A better analysis would first consider all feed types at once: casein, horsebean, linseed, meat meal, soybean, and sunflower. The ANOVA output below can be used to test for differences between the average weights of chicks on different diets. DF Sum Sq Mean Sq F value Pr(>F) feed 231129.16 46225.83 15.36 0.0000 residuals 65 195556.02 Conduct a hypothesis test to determine if these data provide convincing evidence that the average weight of chicks varies across some (or all) groups. Make sure to check relevant conditions. Figures and summary statistics are shown below. 3008.55 400 350- 300- 250- 200- 150- 100 casein horsebean linseed meatmeal soybean sunflower Mean SD casein 323.58 64.43 12 horsebean 160.2 38.63 10 linseed 218.75 52.24 12 meatmeal 276.91 64.9 11 soybean 246.43 54.13 14 sunflower 328.92 48.84 Weight (in grams) ONE 42

Chicken diet and weight. In previous chapter, we compared the effects of two types of feed at a time. A better analysis would first consider all feed types at once: casein, horsebean, linseed, meat meal, soybean, and sunflower. The ANOVA output below can be used to test for differences between the average weights of chicks on different diets. DF Sum Sq Mean Sq F value Pr(>F) feed 231129.16 46225.83 15.36 0.0000 residuals 65 195556.02 Conduct a hypothesis test to determine if these data provide convincing evidence that the average weight of chicks varies across some (or all) groups. Make sure to check relevant conditions. Figures and summary statistics are shown below. 3008.55 400 350- 300- 250- 200- 150- 100 casein horsebean linseed meatmeal soybean sunflower Mean SD casein 323.58 64.43 12 horsebean 160.2 38.63 10 linseed 218.75 52.24 12 meatmeal 276.91 64.9 11 soybean 246.43 54.13 14 sunflower 328.92 48.84 Weight (in grams) ONE 42

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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Chicken diet and weight: Compare the effects of six types of feed: casein, horsebean, linseed, meat meal, soybean, and sunflower. The ANOVA output below can be used to test for differences between the mean weights of chicks on different diets. df Sum Sq Mean Sq F value Pr(>F) feed 231129.16 46225.83 15.36 0.0000 residuals 65 195556.02 3008.55 If we conducted a hypothesis test to determine if these data provide convincing evidence that the mean weight of chicks varies across some (or all) groups we would find a significant difference between average weight of chicks between at least two of the diets using an overall a = 0.05. Мean SD n casein 323.58 64.43 horsebean 160.2 38.63 10 linseed 218.75 52.24 12 meatmeal 276.91 64.9 11 soybean 246.43 54.13 14 sunflower 328.92 48.84 12 Now use the summary above to test that meatmeal and sunflower are significantly different. Fedf = (Round to three decimal places.) The test statistic for this pairwise comparison is: t = (Round to three decimal…
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We give the total variation, the unexplained variation (SSE), and the least squares point estimate b1 . Total variation = 13.459; SSE = 2.806; b1 = 2.6652 Click here for the Excel Data File Using the information given, find the explained variation, the simple coefficient of determination (r2), and the simple correlation coefficient (r). Interpret r2. (Round your answers to 3 decimal places. Round your percent to 1 decimal place.) Explained variation r2 r % of the variation in demand can be explained by variation in price differential.
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation Ŷ=42.80-0.53x. This line is shown in the scatter plot below. (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.)
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