Concept explainers
The accompanying summary quantities for x = Particulate pollution (μg/m3) and y = Luminance (0.01 cd/m2) were calculated from a representative sample of data that appeared in the article “Luminance and Polarization of the Sky Light at Seville (Spain) Measured in White Light” (Atmospheric Environment [1988]: 595–599).
- a. Test to see whether there is a
positive correlation between particulate pollution and luminance in the population from which the data were selected. - b. What proportion of observed variation in luminance can be attributed to the approximate linear relationship between luminance and particulate pollution?
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Introduction To Statistics And Data Analysis
- The standard pH of a drinking water of MRWD is pH 7.0, the pH obtained from several water station are as follows: Station 1 7.8 Station 2 6.5 Station 3 5.8 Station 4 8.9 Station 5 6.8 Station 6 5.8 Station 7 9.0 Station 8 6.9 Test the hypothesis that the drinking water of the MRWD is safe for drinking.arrow_forwardThe article refered to in Exercise 1 also considered the effect of gypsum on the electric conductivity (in dS m) of soil. Two types of soil were each treated with three different amounts of gypsum, with two replicates for each soil-gypsum combination. The data are presented in the following table. Soil Type Gypsum (g/kg) Las Animas Madera 0.00 1.52 1.05 1.01 0.92 0.27 1.49 0.91 1.12 0.92 0.46 0.9 0.92 0.88 0.92 Is there convincing evidence of an interaction between the amount of gypsum and soil type? Can you conclude that the conductivity differs among the soil types? Can you conclude that the conductivity differs with the amount of gypsum added? C.arrow_forwardFill the chi square for Cross 1 and Cross 2 from the following data in the attached image: Cross 1: Phenotypes Ratio Observed Expected (O-E)2/E Totals X2 = Cross 2: Phenotypes Ratio Observed Expected (O-E)2/E Totals X2 =arrow_forward
- C webassign.net/web/Student/Assignment-Responses/submit?dep=30747029&tags=autosave#question4028933_1 62°F Cloudy Bighorn sheep are beautiful wild animals found throughout the western United States. Let x be the age of a bighorn sheep (in years), and let y be the mortality rate (percent that die) for this age group. For example, x = 1, y = 14 means that 14% of the bighorn sheep between 1 and 2 years old died. A random sample of Arizona bighorn sheep gave the following information: kuami b= ý = 20.0 Σχ = 15; Σy = 84.1; Σx2 = 55; Σy2 = 1,452.93; Σxy = 268.8 = (a) Find x, y, b, and the equation of the least-squares line. (Round your answers for x and y to two decimal places. Round your least-squares estimates to three decimal places.) X 22 20 (b) Draw a scatter diagram for the data. Plot the least-squares line on your scatter diagram. y 18 16 14 1 12 IOI 8 + Q Search 22 20 18 16 12 20 13 Paused Update MAX 174 11:16 PM 2/26/2023arrow_forwardThe following scatterplot shows the mean annual carbon dioxide (CO,) in parts (CO2) per million (ppm) measured at the top of a mountain and the mean annual air temperature over both land and sea across the globe, in degrees Celsius (C). Complete parts a through h on the right. f) View the accompanying scatterplot of the residuals vs. CO2. Does the scatterplot of the residuals vs. CO, show evidence of the violation of any assumptions behind the regression? 16.800 A. Yes, the outlier condition is violated. 16.725 O B. Yes, the linearity and equal variance assumptions are violated. 16.650 C. Yes, the equal variance assumption is violated. 16.575 O D. No, all assumptioris are okay. 16.500 O E. Yes, all the assumptions are violated. 325.0 337.5 350.0 362.5 CO2 (ppm) OF Yes, the linearity assumption is violated. his vear, What mean temperature doesarrow_forwardA pilot study was performed to investigate the effect of temperature, x (in degree Fahrenheit) on the electrical power consumed, y (in Watt) by an automotive factory. Other factors were kept constant and the data were collected from the study. The summary of the data are given as follows: n = 8, 2x = 401, 2301, x? = 22495 2y? = 666509, Σ > xy = 118652. %3D Compute the equation of the least squares regression line of y on x. Interpret the equation obtained in part (i). Predict the power consumption for a temperature of 65°F. (i) (ii) (iii) (iv) Compute the coefficient of determination and explain.arrow_forward
- I. Solve for Z and find its area 1. Mean = 81, score: x = 71.5, standard deviation: o = 9. 2. Mean = 47, score: x = 55.6, standard deviation: o = 4. 3. Mean = 68, score: x = 78.8, standard deviation: o = 6. 4. Mean = 51, score: x = 69, standard deviation: o =7. 5. Mean = 85, score: x = 93, standard deviation: o =5. 6. The mean expenses of Dela Cruz Family is PhP12,600 every 15 days and the standard deviation is PhP 1, 570., If the expenses are normally distributed, what is the probability of consuming less than PhP 11,000 in 15 days? 7. The mean production of factory workers is 10 finished products per hour, with a standard deviation of 1.5 products/hour. What is the probability of finishing: a. less than 12.8 products/hour? b. more than 12 products/hour? 8. The mean weight of insects for laboratory experiments is 13.2 g, with the standard deviation of 0.8 g. What is the probability of randomly selecting insects with weight of: a. 14 g? b. 13.1 to 13.6 g? 9. The lifetime of CP…arrow_forwardIn an experiment to determine factors related to weld toughness, the Charpy V-notch impact toughness in ft - 1b (v) was measured for 22 welds at 0°Č, along with the lateral expansion at the notch in % (x,), and the brittle fracture surface in % (x2). The data are presented in the following table. X1 X2 y 32 20.0 28 39 23.0 28 20 12.8 32 21 16.0 29 25 10.2 31 20 11.6 28 32 17.6 25 29 17.8 28 27 16.0 29 43 26.2 27 22 9.6 32 22 15.2 32 18 8.8 43 32 20.4 24 22 12.2 36 25 14.6 36 25 10.4 29 20 11.6 30 20 12.6 31 24 16.2 36 18 9.2 34 28 16.8 30 a. Fit the model y = Bo + B1 X1 + ɛ. For each coefficient, test the null hypothesis that it is equal to 0. b. Fit the model y = Bo + B, xz + ɛ. For each coefficient, test the null hypothesis that it is equal to 0. c. Fit the model y = Bo + B1 X1 + Bzx2 + ɛ. For each coefficient, test the null hypothesis that it is equal to 0. d. Which of the models in parts (a) through (c) is the best of the three? Why do you think %3D + E. so?arrow_forwardShow the ouput from Excel. Use Excel .arrow_forward
- The amount of oxygen consumption (ml/min) was measured in 6 individuals over two 10- minute periods while sitting with their eyes closed. During the first period, they listen to an exciting adventure story and then again, an hour later while they heard restful music. Based on the results shown, is oxygen consumption different depending on whether it is a story or music one is listening to? The data is in Table 2 and test at an alpha of 0.05. Table 2. Oxygen consumption (ml/min)arrow_forwardThe amount of oxygen consumption (ml/min) was measured in 6 individuals over two 10- minute periods while sitting with their eyes closed. During the first period, they listen to an exciting adventure story and then again, an hour later while they heard restful music. Based on the results shown, is oxygen consumption different depending on whether it is a story or music one is listening to? The data is in Table 2 and test at an alpha of 0.05. Table 2. Oxygen consumption (ml/min)arrow_forwardStaff members of an oil refinery believe that the octane reading of a particular petrol depends on one of its raw materials. A sample of the related raw material (x) and octane number (y) was collected and the measurements below were found. E-1 X = 401.90 E-1 x;? = 23,126,35 El-1 yi = 644.00 El-1y? = 59,254.08 El-1 Xi Yi = 36,966.83 i) Estimate and interpret a fitted linear regression model for the data collected. ii) Test the hypotheses Ho : B1 = 0 versus H : B1 # 0 at a 10% level of significance. Include a p-value in your test. ii) Construct a 90% confidence interval for the estimated intercept of the linear regression model. iv) Conduct an F test at a = 0.10, to determine whether the regression model obtained in (i) is significant. v) Calculate and interpret the coefficient of determination, R². Using this value, determine whether the regression model adequate. vi) Suppose that another regression model was constructed from a similar data set where its adjusted R2 was found to be R2…arrow_forward
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