Mind on Statistics
5th Edition
ISBN: 9781285463186
Author: Jessica M. Utts, Robert F. Heckard
Publisher: Brooks Cole
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Chapter 3, Problem 3.62E
To determine
(a)
To find estimated value of average august temperature at the equator for latitude 0.
To determine
(b)
To explain why regression equation should not be used to estimate value at latitude 0.
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Chapter 3 Solutions
Mind on Statistics
Ch. 3 - For each of the following pairs of variables, is...Ch. 3 - For each of the following pairs of variables, is...Ch. 3 - The figure for this exercise is a scatter plot of...Ch. 3 - Prob. 3.4ECh. 3 - Prob. 3.5ECh. 3 - Prob. 3.6ECh. 3 - Prob. 3.7ECh. 3 - Prob. 3.8ECh. 3 - The data in the following table are the geographic...Ch. 3 - Refer to the latitude and temperature data in the...
Ch. 3 - Prob. 3.11ECh. 3 - The following table shows sex, height (inches),...Ch. 3 - Prob. 3.13ECh. 3 - Refer to Exercise 3.13 in which a regression...Ch. 3 - Prob. 3.15ECh. 3 - Prob. 3.16ECh. 3 - The equation for converting a temperature from x =...Ch. 3 - The average August temperatures (y) and geographic...Ch. 3 - A regression equation for y = handspan (cm) and x...Ch. 3 - Imagine a regression line that relates y average...Ch. 3 - Prob. 3.21ECh. 3 - The figure for Exercise 3.8 is a scatterplot of...Ch. 3 - Refer to Exercise 3.22. Predict the pulse rate...Ch. 3 - The average January temperatures (y) and...Ch. 3 - Prob. 3.25ECh. 3 - Prob. 3.26ECh. 3 - Prob. 3.27ECh. 3 - Remember that r2 can be expressed as a proportion...Ch. 3 - Prob. 3.29ECh. 3 - Prob. 3.30ECh. 3 - Prob. 3.31ECh. 3 - Prob. 3.32ECh. 3 - Prob. 3.33ECh. 3 - Explain how two variables can have a perfect...Ch. 3 - Prob. 3.35ECh. 3 - Prob. 3.36ECh. 3 - The figure for this exercise (below) shows four...Ch. 3 - Refer to the figure for the previous exercises. In...Ch. 3 - Prob. 3.39ECh. 3 - Prob. 3.40ECh. 3 - Prob. 3.41ECh. 3 - Prob. 3.42ECh. 3 - Prob. 3.43ECh. 3 - The correlation between latitude and average...Ch. 3 - Prob. 3.45ECh. 3 - Prob. 3.46ECh. 3 - In a regression analysis, the total sum of squares...Ch. 3 - Prob. 3.48ECh. 3 - Suppose you know that the slope of a regression...Ch. 3 - Prob. 3.50ECh. 3 - Prob. 3.51ECh. 3 - Prob. 3.53ECh. 3 - Prob. 3.54ECh. 3 - Refer back to Exercise 3.7 about stopping distance...Ch. 3 - Prob. 3.56ECh. 3 - Prob. 3.57ECh. 3 - Prob. 3.58ECh. 3 - Prob. 3.59ECh. 3 - Prob. 3.60ECh. 3 - Prob. 3.61ECh. 3 - Prob. 3.62ECh. 3 - Prob. 3.63ECh. 3 - Prob. 3.64ECh. 3 - Prob. 3.65ECh. 3 - Prob. 3.66ECh. 3 - Prob. 3.67ECh. 3 - Prob. 3.68ECh. 3 - Prob. 3.69ECh. 3 - Prob. 3.70ECh. 3 - Prob. 3.71ECh. 3 - Given tickets for traffic violations than drivers...Ch. 3 - Prob. 3.73ECh. 3 - Prob. 3.74ECh. 3 - Prob. 3.75ECh. 3 - Prob. 3.76ECh. 3 - Prob. 3.77ECh. 3 - Prob. 3.78ECh. 3 - Prob. 3.79ECh. 3 - The heights (inches) and foot lengths (cm) of 33...Ch. 3 - Prob. 3.81ECh. 3 - The winning time in the Olympic men’s 500-meter...Ch. 3 - Prob. 3.83ECh. 3 - Prob. 3.84ECh. 3 - Prob. 3.86ECh. 3 - Prob. 3.87ECh. 3 - Prob. 3.88ECh. 3 - Prob. 3.89ECh. 3 - Use the dataset ceodata0t on the companion website...Ch. 3 - Prob. 3.91ECh. 3 - Prob. 3.92ECh. 3 - Prob. 3.93ECh. 3 - Prob. 3.94ECh. 3 - Prob. 3.95ECh. 3 - Prob. 3.96ECh. 3 - Prob. 3.97ECh. 3 - Prob. 3.98ECh. 3 - Prob. 3.99ECh. 3 - Prob. 3.100E
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Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
- For the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city of people 25 years or older who are college graduates is given below, by year. 38. Determine whether the trend appears to be linear.If so, and assuming the trend continues, find alinear regression model to predict the percent of unemployed in a given year to three decimal places.arrow_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardFor the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.arrow_forward
- 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.arrow_forwardFor the following exercises, consider this scenario: The profit of a company decreased steadily overa ten-year spam.The following ordered pairs shows dollars and the number of units sold in hundreds and the profit in thousands ofover the ten-year span, (number of units sold, profit) for specific recorded years: (46,600),(48,550),(50,505),(52,540),(54,495). Use linear regression to determine a function Pwhere the profit in thousands of dollars depends onthe number of units sold in hundreds.arrow_forwardFor the following exercises, consider this scenario: The population of a city increased steadily over a ten-year span.The following ordered pairs show the population and the year over the ten-year span (population, year) for specific recorded years: (3,600,2000);(4,000,2001);(4,700,2003);(6,000,2006) 42. Use linear regression to determine a function y,where the year depends on the population, to threedecimal places of accuracy.arrow_forward
- For the following exercises, draw a scatter plot for the data provided. Does the data appear to be linearly related?arrow_forwardFor the following exercise, consider this scenario: In 2004, a school population was 1,700. By 2012 the population had grown to 2,500. Assume the population is changing linearly. a. How much did the population grow between the year 2004 and 2012? b. What is the average population growth per year? c. Find an equation for the population, P, of the school t years after 2004.arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forward
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