Question: Based on the above results, the researcher tested the hypotheses: Ho: B1=0 versus B1 not equal to 0, versus using T test. What do we know about the test statistic of the test? Based on the approximate p-value, what's the conclusion?
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Please help me understand this problem more in depth. A researcher is investigating possible explanations for deaths in traffic accidents. He examined data from 2000 for each of the 52 cities randomly selected in the US. The data included information on the following variables: Deaths: The number of deaths in traffic accidents per city Income: The median income per city As part of his study, he ran the following simple linear regression model attached in photo.
Question: Based on the above results, the researcher tested the hypotheses: Ho: B1=0 versus B1 not equal to 0, versus using T test. What do we know about the test statistic of the test? Based on the approximate p-value, what's the conclusion?
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- A random sample of 65 high school seniors was selected from all high school seniors at a certain high school. The following scatterplot shows the height, in centimeters (cm), and the foot length, in cm, for each high school senior from the sample. The least-squares regression line is shown. The computer output from the least-squares regression analysis is also shown. Term Coef (SE) Coef T-Value P-Value Constant 105.08 6.00 17.51 0.000 Foot length 2.599 0.238 10.92 0.000 S=5.90181 R–sq=65.42% (a) Calculate and interpret the residual for the high school senior with a foot length of 20cm and a height of 160cm. (b) The standard deviation of the residuals is s=5.9. Interpret the value in context. (c) The following histogram summarizes the 65 residuals. Assume that the distribution of residuals is approximately normal with mean 0cm and standard deviation 5.9cm. What percent of the residuals are greater than 8cm? Justify your answer.…A random sample of 65 high school seniors was selected from all high school seniors at a certain high school. The following scatterplot shows the height, in centimeters (cm), and the foot length, in cm, for each high school senior from the sample. The least-squares regression line is shown. The computer output from the least-squares regression analysis is also shown. Term Coef (SE) Coef T-Value P-Value Constant 105.08 6.00 17.51 0.000 Foot length 2.599 0.238 10.92 0.000 S=5.90181 R–sq=65.42% (a) The following histogram summarizes the 65 residuals. Assume that the distribution of residuals is approximately normal with mean 0cm and standard deviation 5.9cm. What percent of the residuals are greater than 8cm? Justify your answer. (b) Based on your answer to part (a), would it be surprising to randomly select a high school senior from the high school with a foot length of 20cm and a height greater than 165cm? Justify your answer.A random sample of 65 high school seniors was selected from all high school seniors at a certain high school. The following scatterplot shows the height, in centimeters (cm), and the foot length, in cm, for each high school senior from the sample. The least-squares regression line is shown. The computer output from the least-squares regression analysis is also shown. Term Coef (SE)Coef T-Value P-Value Constant 105.08 6.00 17.51 0.000 Foot length 2.599 0.238 10.92 0.000 S=5.90181 R–sq=65.42% (a) Calculate and interpret the residual for the high school senior with a foot length of 20cm and a height of 160cm.
- A random sample of 65 high school seniors was selected from all high school seniors at a certain high school. The following scatterplot shows the height, in centimeters (cm), and the foot length, in cm, for each high school senior from the sample. The least-squares regression line is shown. The computer output from the least-squares regression analysis is also shown. Term Coef (SE) Coef T-value P-value Constant 105.08 6.00 17.51 0.000 Foot Length 2.599 0.238 10.92 0.000 S=5.90181 R-sq= 65.42% (a) Calculate and interpret the residual for the high school senior with a foot length of 20cm and a height of 160cm. (b) The standard deviation of the residuals is s=5.9. Interpret the value in context. (c) The following histogram summarizes the 65 residuals. Assume that the distribution of residuals is approximately normal with mean 0cm and standard deviation5.9cm. What percent of the residuals are greater than 8cm? Justify your answer. (d) Based on your answer to part (c), would…A random sample of 65 high school seniors was selected from all high school seniors at a certain high school. The following scatterplot shows the height, in centimeters (cm), and the foot length, in cm, for each high school senior from the sample. The least-squares regression line is shown. The computer output from the least-squares regression analysis is also shown. Term Coef(SE) CoefT-ValueP-Value Constant 105.086.0017.510.000 Foot length 2.5990.23810.920.000 S=5.90181R–sq=65.42% (a) Calculate and interpret the residual for the high school senior with a foot length of 20cm and a height of 160cm. BoldItalicUnderlineSuperscriptSubscriptUndoRedoΩBullet listNumbered listImage (12 image limit) Edit imageView imageDelete image Question 2 (b) The standard deviation of the residuals is s=5.9. Interpret the value in context. BoldItalicUnderlineSuperscriptSubscriptUndoRedoΩBullet listNumbered listImage (12 image limit) Edit imageView imageDelete…Riboflavin (Vitamin B2) is determined in a cereal sample by measuring its fluorescence intensity(형광세기) in 5% acetic acid solution. A calibration curve was prepared by measuring the fluorescence intensities of a series of standards of increasing concentrations. The following data were obtained. Riboflavin (μg/mL) 0.000 0.100 0.200 0.400 0.800 Unknown sample Fluorescence intensity 0.0 5.8 12.2 22.3 43.3 15.4 (a) Use the method of least squares to obtain the best straight line through these five points (n=5). (b) Make a graph showing the experimental data and the calculated straight line. (c) An unknown sample gave an observed fluorescence intensity of 15.4. Calculate the concentration of Riboflavin (Vitamin B2) in the unknown sample (μg/mL). (d) Calculate the coefficient of determination (R2).
- An electric utility wishes to examine the relationship between temperature and electricity use in its service region during the summer months. The bivariate data below give the maximum temperature (denoted by x, in degrees Fahrenheit) and the electricity use (denoted by y, in thousands of kilowatt hours) for a random sample of fifteen summer days. The data are shown in the Figure 1 scatter plot. The least-squares regression line for these data has a slope of approximately 2.67. Answer the following. Carry your intermediate computations to at least four decimal places, and round your answers as specified below. (If necessary, consult a list of formulas.) What is the value of the y-intercept of the least-squares regression line for these data? Round your answer to at least two decimal places. What is the value of the sample correlation coefficient for these data? Round your answer to at least three decimal places.For a sample of 15 rural counties in one state, a researcher has collected information about the rate of deaths due to traffic accidents per 1,000,000 population and the percentage of the population which consists of males, age 16 to 25. For these variables, find the least squares regression line and compute r and r squared. Write a sentence or two summarizing and explaining your results. RATE OF % OF POPULATION County Traffic Deaths Young Males County Traffic Deaths Young Males A 15 4 B 16 5 C 22 10 D 23 9 E 20 8 F 25 11 G 18 2 H 15 6 I 19 12 J 10 3 K 9 2 L 18 15 M 15 9 N 24 16 O 9 3Two specimens of cold rolled steel sheet, which have differentcopper contents and annealing temperature are measured in hardness with the following results: First column = HardnessSecond column = Copper contentThird column = Annealing temperature a) Create a scatter plot to verify that it is reasonable to assume that the regression of Y on x is linear. b) Fit a straight line using the method of least squares. c) Fit an equation of the form (image 2), where x1 represents the copper content, x2 represents the annealing temperature, and y represents the hardness.
- Advertising share x and market share y fir a particular brand of cigarettes were sampled at ten randomly selected year. Summary information is: n = 10, Ex = .688, Ex2 = .050072, Ey =.835, Ey2 = 0.079491, Exy = .060861 a) Compute the least squares regression line b) Find the coefficient of determination r2 and explain what it means in the context of this problem. c) Compute a 90% prediction interval for market share when advertising share is .07(7%)An agent for a residential real estate company in a large city would like to be able to predict the monthly rental cost for apartments, based on the size of an apartment, as defined by square footage. The agent selects a sample of 25 apartments in a particular residential neighborhood and gathers the following data a. Construct a scatter plot. b. Use the least-squares method to determine the regression coefficients b0 and b1 c. Interpret the meaning of and in this problem. d. Predict the monthly rent for an apartment that has 1,000 square feet
