CHALLENGE ACTIVITY3.1.5: Using regression to make predictions.Jump to level 1A boutique wants to determine how the amount of time a customer spends browsing in the store affects theamount the customer spends.50403020101020304050Browsing time (in minutes)For a browsing time of 24 minutes, the amount spent is observed to be 36.6 dollars. 36.6 dollars is abovethe regression line, so the residual for this observation isPicknegativepositive3zeroCheckNextAmount spent (in dollars)

It is given that the A scatter plot between the Amount spent (in dollars) and the Browsing time (in…

Answered: A boutique wants to determine how the…

A boutique wants to determine how the amount of time a customer spends browsing in the store affects the amount the customer spends.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.6: Higher-degree Polynomials And Rational Functions

Problem 1TU: The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t=...

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The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Cable TV The following table shows the number C. in millions, of basic subscribers to cable TV in the indicated year These data are from the Statistical Abstract of the United States. Year 1975 1980 1985 1990 1995 2000 C 9.8 17.5 35.4 50.5 60.6 60.6 a. Use regression to find a logistic model for these data. b. By what annual percentage would you expect the number of cable subscribers to grow in the absence of limiting factors? c. The estimated number of subscribers in 2005 was 65.3million. What light does this shed on the model you found in part a?
Special Rounding Instructions For this exercise set, round all regression parameters to three decimal places, but round all other answers to two decimal places unless otherwise indicated. First Down The following table shows the probability P, as a percentage, of a college football team converting on fourth down when not goal to go versus the distance D, in yards, to the first down maker. 28 D = distance, in yards P = probability 1 70 2 60 3 53 4 46 5 41 6 37 7 34 8 32 a.Use exponential regression to model the data. b.Plot the data along with the exponential model. c.What probability does the model give of gaining a first down for fourth down and 10yards to go? Note: the actual probability is 28.
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? 0.35, which makes sense even though MARRIED? can only be a zero or one 0, because the calculated value is 0.35 so we round down. 0.55, which makes sense even though MARRIED? can only be a zero or one. Calculating a predicted value should not be done here because the dependent variable is a dummy variable. 1, because the calculated value is 0.55 so we round up.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? a 0, because the calculated value is 0.35 so we round down. b 0.55, which makes sense even though MARRIED? can only be a zero or one. c 0.35, which makes sense even though MARRIED? can only be a zero or one d Calculating a predicted value should not be done here because the dependent variable is a dummy variable. e 1, because the calculated value is 0.55 so we round up.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. Complete this sentence: For every additional year of education someone has:... a ...their chance of getting married by 40 increases by 0.03 percentage points. b ...their chance of getting married by 40 increases by 3 percentage points. c ...their chance of getting married by 40 increases by 0.03. d ...their chance of getting married by 40 increases by 3%. e This regression means nothing because the dependent variable is a dummy variable.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25.
Solvent cement is used to join PVC joints. Researchers are interested in predicting the amount of time is needed for the joint to set (measured in hours) as it is related to temperature measured in degrees F for 4 to 8-inch diameter pieces. A random sample of PVC joints was taken and a linear regression model was found. Use the following output and the fact that R2 = 0.965 to answer the following questions. 4. Find the Correlation and interpret in the words of the problem. 5. Find the residual for a temperature of 60 F, if the time to set was 5.6 hours. 6. Find the residual for a temperature of 50 F, if the time to set was 5.4 hours.
Data from the United Nations on individual countries Obesity rates and vegetable consumption by country were organized into the scatter plot below. Linear Regression Equation for this data: y = -0.8449x + 50.328 Correlation Coefficient: 0.5 --- A country has a high rate of consumption of average vegetable producs in kg (45.9 kg). What would be a reasonable estimate of the rate of obesity based on the information from this scatter plot? Question 11 options: 22% 12% 25% 5%
Refer to Exercise 12.12. In the plot of the data, airport 20 had a much larger revenue than any of the other 21 airports. Replot the three scatterplots with the data from airport 20 deleted. Does there appear to be any relationship among revenue and the two explanatory variables in this data set? Fit a first-order regression model relating revenue to distance and population size. Comment on the quality of the fit of the model to the data. Is revenue related to distance from hub and population size once airport 20 is deleted from the data? What conclusions can be inferred from parts (a) and (b) about the importance of plotting the data and not just running models through a software program? 12.12 A regional airline transfers passengers from small airports to a larger regional hub airport. The airline’s data analyst was assigned to estimate the revenue (in thousands of dollars) generated by each of the 22 small airports based on two variables: the distance from each airport (in…