Use MS Excel to make the scatterplots and to verify the determined least-squares line using its Trendlinefunction.The article "Characterization of Highway Run-off in Austin, Texas, Area" (Journal of Environmental En-gineering [1998]: 131–137) gave a scatterplot, along withthe least-squares line for x = rainfall volume (in cubicmeters) and y = runoff volume (in cubic meters), for aparticular location. The following data were read from theplot in the paper:5 12 14 17 233040 47 5567 72 81 96 112127y4 10 13 15 152527 46 38 46 53 70 82 99100a. Does a scatterplot of the data suggest a linear relation-ship between x and y?b. Calculate the slope and intercept of the least-squaresline.c. Compute an estimate of the average runoff volumewhen rainfall volume is 80.

Scatter plot are used to visualize the data set to understand the correlation between the two…

Answered: Use MS Excel to make the scatterplots…

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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3 of 14 > A study of nutrition in developing countries collected data from the Egyptian village of Nahya. Researchers recorded the mean weight (in kilograms) for 170 infants in Nahya each month during their first year of life. A hasty user of statistics enters the data into software and computes the least-squares line without looking at the scatterplot first. The result is weight = 4.88 +0.267age. 0.50 - Use the residual plot to determine if this linear model is appropriate. O No. There is an unequal variability in the residual plot so a linear model is not appropriate for these 0.25 data. -0.25 O Yes. There is an obvious negative-positive-negative pattern in the residual plot so a linecar model is appropriate for these data. O Yes. There is equal variability in the residual plot so a linear model is appropriate for these data. O No. There is an obvious negative-positive-negative pattern in the residual plot so a linear model is not appropriate for these data. O Yes. The residuals vary…
A traffic safety company publishes reports about motorcycle fatalities and helmet use. In the first accompanying data table, the distribution shows the proportion of fatalities by location of injury for motorcycle accidents. The second data table shows the location of injury and fatalities for 2073 riders not wearing a helmet. Complete parts (a) and (b) below. (a) Does the distribution of fatal injuries for riders not wearing a helmet follow the distribution for all riders? Use α = 0.10 level of significance. What are the null and alternative hypotheses? A. Ho: The distribution of fatal injuries for riders not wearing a helmet follows the same distribution for all other riders. H₁: The distribution of fatal injuries for riders not wearing a helmet does not follow the same distribution for all other riders. B. Ho: The distribution of fatal injuries for riders not wearing a helmet does not follow the same distribution for all other riders. H₁: The distribution of fatal injuries for…
An article on the cost of housing in California that appeared in the San Luis Obispo Tribunet included the following statement: "In Northern California, people from the San Francisco Bay area pushed into the Central Valley, benefiting from home prices that dropped on average $4000 for every mile traveled east of the Bay area." If this statement is correct, what is the slope of the least-squares regression line, ý = a + bx, where y = house price (in dollars) and x = distance east of the Bay (in miles)? Your answer cannot be understood or graded. More Information
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…
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 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.
Explain these tables, especially those in green, and what they are used for
Identify which of the following scatterplots contain possible outliers or observations influential for the least squares regression line. Select all that apply 100- 90- 80- 을 70어 3 60- 50- 40- 25 20- 20 15 - 10- 5 10 15 20 25 30 35 40 45 50 20,000 40,000 60,000 80,000 100,000 120,000 Number of incorrect answers Distance driven (mi) Age (years) Outlier Influential observation Outlier 70 24– 50- 65 60 - 22- 40- 55 - 20-. 8 30- 50 – 18- 20- 45 65 70 75 80 5.0 5.5 6.0 6.5 7.0 7.5 8.0 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 Daily high temperature (°F) Price of gasoline ($/gal) Hours slept Influential observation No outliers or influential observations Outlier Answer Bank Influential observation Outlier No outliers or influential observations Daily low temperature (°F) Duration of commute (min) Years of full-time employment Number of oil changes (zo) pəwnsuoɔ əəyoɔ
A financial analyst is examinıng the Pela each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with x denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least-squares regression line to be y = -0.147+0.043x. This line, along with a scatter plot of her data, is shown below. Earnings per Current stock price, y (in dollars) share, x (in dollars) 36.55 1.64 14.18 0.57 41.79 1.37 39.16 1.10 2.5+ 57.70 2.71 26.95 0.90 32.65 1.70 41.94 1.17 52.79 2.56 42.72 2.01 16.89 0.76 22.46 0.58 Earnings per share, x (in dollars) 58.88 2.19 30.13 1.48 50.08 1.73 28.92 0.81 Submit Assi Continue D 2021 McGraw-H Education. All Rights Reserved. Terms of Use Privacy e to search 近 Current stock price, y (in dollars)
The article "Approach to Confidence Interval Estimation for Curve Numbers" (R. McCuen, Journal of Hydrologic Engineering, 2002:43-48) discusses the relationship between rainfall depth and runoff depth at several locations. At one particular location, rainfall depth and runoff depth were recorded for 13 rainstorms. Following is MFNITAB output for a fit of the least-squares line to predict runoff depth from rainfall depth (both measured in inches). The regression equation is Runoff =-0.23 + 0.73 Rainfall Predictor Coef SE Coef тР Constant -0.23429 0.23996 -0.98 0.350 Rainfall 0.72868 0.06353 11.47 0.000 S= 0.40229 R-Sq = 92.3% R-Sq(adj) = 91.6% Analysis of Variance Source Regression 121.290 21.290131.550.000 Residual Епог SS MS F P 11 1.7800.16184 Total 1223.070 a Predict the runoff for a storm with 2.5 in. of rainfall. b. Someone claims that if two storms differ in their rainfall by 1 in, then their runoffs will differ, on the average, by 1 in. as well. Is this a plausible claim?…
From the article “Association of cognitive functioning with retinal nerve fiber layer thickness” by van Koolwijk et al., in Investigative Ophthalmology & Visual Science, October 2009, Vol. 50, No. 10, below is table 2 showing the results of fitting several multiple linear regression models for different response variables. Write down the fitted model corresponding to the last row of the table. You can leave the intercept as hatB0. Interpret the coefficient values corresponding to the RNFL Thickness and Male variables.
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 randomly selected days during the past year are given below. These data are plotted in the scatter plot below. (a)For these data, temperature values that are less than the mean of the temperature values tend to be paired with coffee sales values that are ▼(Choose one) the mean of the coffee sales values. (b)According to the regression equation, for an increase of one degree in temperature, there is a corresponding ▼(Choose one) of 9.94 dollars in coffee sales. (c)From the regression equation, what is the predicted coffee sales value (in dollars) when the temperature is 74.6 degrees Fahrenheit? (Round your…
The following data represent the speed at which a ball was hit (in miles per hour) and the distance it traveled (in feet) for a random sample of home runs in a Major League baseball game in 2018. Complete parts (a) through (f). Click here to view the data. Click here to view the critical values of the correlation coefficient. (a) Find the least-squares regression line treating speed at which the ball was hit as the explanatory variable and distance the ball traveled as the response variable. y = 5x+ (5 (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Begin by interpreting the slope. A. The slope of this least-squares regression line says that the distance the ball travels increases by the slope with every 1 mile per hour increase in the speed that the ball was hit. O B. The slope of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit.…

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