An Internet retailer would like to investigate the relationship between the amount of time in minutes a purchaser spends on its Website and the amount of money he or she spends on an order. The table to the right shows the data from a random sample of 12customers. Construct a 99% confidence interval for the regression slope.Construct a 99% confidence interval for the slope.LCL = and UCL =(Round to three decimal places as needed.)CTime20122822113Order Size$71$23$93$198$54$35Time537837258Order Size$57$360$122$161$72$248

The data of time and order size is given here.The sample size is The confidence level is The…

Answered: An Internet retailer would like to…

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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The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance.Which of the given independent variables is/are significant? * avil Years of Starting salary GPA service experience ratings 79.5 15000 80.1 15000 81.2 1 1 78.0 15500 81.3 16000 82.4 2 3 79.0 80.0 85.0 16200 83.4 3 17500 87.9 89.9 89.1 84.1 89.0 89.2 4 18000 90.3 5 16,300 84.2 3 17000 87.0 4 17900 88.1 GPA and years of experience GPA, years of experience and civil service ratings intercept, GPA, years of experience and civil service ratings O years of experience and civil service ratings
Define the association between variables selected? What is the probability a car on the list will be less $29,000 and what is the probability the car will be more than $29,000? Develop a 95% confidence interval estimate that the higher the price of the car, the higher the road test scores. (For assistance, see the Excel steps on page 415 of your textbook). What are the upper and lower limits for car cost and road test score? Perform a regression analysis for predicated reliability and price to develop an estimated regression equation that could be used to predict the reliability and price.
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + bjx, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 47 49 50 51 58 Bone Density 360 353 336 333 310 Table Copy Data Step 3 of 6: Find the estimated value of y when x = 58. Round your answer to three decimal places.
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, ŷ = bo + bx, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 2.5 3 4 6 Overall Grades 100 98 97 87 86 74 62 Table Copy Data Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.
The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, y = bo + bjx, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 21 23 29 43 50 Number of Bids 1 2 4 7 Table Copy Data Step 5 of 6: Determine if the statement "All points predicted by the linear model fall on the same line" is true or false.
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, y = bo + bx, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 1 1.5 3 4 4.5 5.5 Overall Grades 87 86 79 78 76 67 65 Table Copy Data Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places. Prev AnswerHow to enter your answer (opens in new window) E Tables E Keypad Keyboard Shortcuts © 2021 Hawkes Learning UIS A
Togetherness Are good grades in high school associ-ated with family togetherness? A simple random sample of 142 high-school students was asked how many mealsper week their families ate together. Their responses produced a mean of 3.78 meals per week, with a stand-ard deviation of 2.2. Researchers then matched these responses against the students’ grade point averages. Thescatterplot appeared to be reasonably linear, so they went ahead with the regression analysis, seen below. No appar-ent pattern emerged in the residuals plot. Dependent variable: GPAR-squared = 11.0%s = 0.6682 with 142 - 2 = 140 dfVariable Coefficient SE(Coeff)Intercept 2.7288 0.1148Meals/wk 0.1093 0.0263 a) Is there evidence of an association? Test an appropri-ate hypothesis and state your conclusion. b) Do you think this association would be useful inpredicting a student’s grade point average? Explain.c) Are your answers to parts a and b contradictory?Explain.
Provide answer.
Select the statistic test that best fits each description. Study to investigate whether a significant difference exists between male and female students on a math test. A. T-test independent samples B. T-test dependent samples C. One-way ANOVA D. Correlation E. Chi square F. Simple Regression
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 47 48 56 60 67 Bone Density 359 350 334 314 313 Find the estimated slope. Round your answer to three decimal places.
Sir Francis Galton, in the late 1800s, was the first to introduce the statistical concepts of regression and correlation. He studied the relationships between pairs of varlables such as the size of parents and the size of their affspring. Data similar to that which he studied are given below, with the variable x denoting the height (in centimeters) of a human father and the variable y denoting the height at maturity (in centimeters) of the father's oldest son. The data are given in tabular form and also displayed in the Figure 1 scatter plot. Also given is the product of the father's height and the son's helght for each of the fifteen pairs. (These products, written in the column labelled "xy", may aid in calculations.) Height of Height of son, father, x y (in centimeters) centimeters) xy (in 192.7 190.6 36,728.62 174.8 179.2 31,324.16 210 158.5 175.3 27,785.05 200 160.9 167.7 26,982.93 180- 171.6 182.0 31,231.2 172.2 171.1 29,463.42 170 179.9 187.9 33,803.21 181.5 177.5 32,216.25 130_…
Explain whether each scenario below is a regression, classification, or unsupervised learn- ing problem. If it is a supervised learning scenario, indicate whether we are more interested in inference or prediction. Finally, provide in each case the number of observations, n, and the number of predictors, p. (1) An online retailer must decide whether to display advertisement A or advertisement B to each customer on the basis of collected customer demographics (age, income, zip code, and gender). A set of 150 of its customers have already expressed a preference for one advertisement or the other. (2) A policy analyst is interested in discovering factors that are associated with the crime rate across different U.S. cities. For each of 500 cities, the policy analyst gathers the following data: the crime rate, unemployment rate, population, median income, median home price, and state. (3) The the channel owner to see where the subscribers are located, their age and gender, the times and days…

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