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- Please provide the answer using minitabManagers 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…a) Compute the value of the sample correlation coefficient, r. (Round your answer to four decimal places.) r = b) Calculate 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 = c) If a regression analysis were to be carried out to predict endurance from lactate level, what proportion of observed variation in endurance could be attributed to the approximate linear relationship? Answer the question without doing any regression calculations. (Round your answer to four decimal places.) ____________ d) If a regression analysis were to be carried out to predict lactate level from endurance, what proportion of observed variation in endurance could be attributed to the approximate linear relationship? Answer the question without doing any regression calculations. (Round your answer to four decimal places.) _____________
- 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. Also given is the product of the temperature and the coffee sales for each of the fifteen days. (These products, written in the column labelled "xy", may aid in calculations.) Temperature, X 10 Coffee sales, y (in dollars) (in degrees Fahrenheit) 37.2 51.3 59.7 53.7 63.1 44.5 47.5 75.9 82.2 40.0 74.8 70.6 69.0 48.3 74.7 Send data to calculator V 2000.4 2205.9 1944.8 1579.3 1846.3 1797.3 2016.5 1563.3 1556.5 2249.4 1653.9 1923.0 1747.9 2154.6 1979.7 74,414.88 113,162.67 116,104.56 84,808.41…A study is being conducted to compare vitamin C and zinc to determine which is better at fighting colds. Customers believe vitamin C is better at fighting colds. What are the appropriate hypotheses for this testing scenario? Let μc equal the mean of the effectiveness of vitamin C and μz equal the mean of the effectiveness of zinc. O Ho: Hc - Hz= 0 Ha Hc - Hz > 0 O Ho: Hc - Hz= 0 Ha Mc-Hz 0 o Ho: Xe – xz = 0 Hai Xi — Xz #0Managers 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 following information represents chlorine residuals in swimming pool (y) and the testing time (y) in different hours: n = 6, Ey = 8.8, Ey = 18.68, Ex = 42, Ex2 = 364, and Exy = 48.6 The sample correlation coefficient is: -0.6467 - 0.9899 - 0.9088 0.6467Devise a hypotheses for the research question: Is there singificant correlation between students height and typical amount of sleep a student gets per night? Identify the appropriate statistical test using alpha = 0.05. Perform the statistical test. Use descriptive statistics to describe the variables both numerically and graphically. Height (whole inches) Sleep (minutes) 60 360 62 400 66 420 68 440 68 540 70 480 72 320 75 440 66 360 63 420 69 420 75 390 69 360 67 480 69 360 73 360 69 480 70 420 68 360 63 480 69 270 68 360 71 420 71 360 62 420Data for fuel economy ratings in mi/gal for different cars are listed below. Each pair of data represents the rating using the Old test and the New test. Old (x) 31 22 23 33 27 21 22 19 31 16 New (y) 25 17 26 26 23 18 19 16 28 19 Find the best predicted value for the New test method given that the Old test method rating was 26. Use a significance level of 0.05.Round your answer to 1 decimal place, if needed.
- 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.)A data set includes weights of garbage discarded in one week from 62 different households. The paired weights of paper and glass were used to obtain the results shown to the right. Is there sufficient evidence to support the claim that there is a linear correlation between weights of discarded paper and glass? Use a significance level of α = 0.05. Click here to view a table of critical values for the correlation coefficient. Determine the null and alternative hypotheses. Ho: P H₁: p (Type # V 11 > cimals. Do not round.) ... Correlation matrix: Variables Paper Glass 10.1768 Paper Glass 0.1768 1A data set includes weights of garbage discarded in one week from 62 different households. The paired weights of paper and glass were used to obtain the results shown to the right. Is there sufficient evidence to support the claim that there is a linear correlation between weights of discarded paper and glass? Use a significance level of α = 0.05. Click here to view a table of critical values for the correlation coefficient. Determine the null and alternative hypotheses. Ho: P H₁: P (Type integers or decimals. Do not round.) Identify the test statistic, r. r= (Round to three decimal places as needed.) Identify the critical value(s). (Round to three decimal places as needed.) A. There are two critical values at r = ± B. There is one critical value at r = State the conclusion. Because the absolute value of the test statistic is the positive critical value, there there is a linear correlation between the weights of discarded paper and glass for a significance level of α = 0.05.…
