Answered: Calculate Karl Pearson's coeffcient of…

Calculate Karl Pearson's coeffcient of skewness from the following data: Weekly Sales (Ksh) 10-12 12-14 14-16 16-18 18-20 20-22 22-24 24-26 Number of Companies 12 18 35 42 50 30 8 4

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

Chapter3: Straight Lines And Linear Functions

Section3.3: Modeling Data With Linear Functions

Problem 18SBE

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Olympic 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?
am. 13.
The materials handling manager of a manufacturing company is trying to forecast the cost of maintenance for the company's fleet of over-the-road tractors. The manager believes that the cost of maintaining the tractors increases with their age. The following data was collected: Age (years) 5.5 where Y = Yearly maintenance cost in dollars and X = Age in years. 5.5 5.5 5.0 5.0 5.0 6.0 6.0 6.5 Yearly Maintenance Cost (S) 1,319 1,749 1,733 1,195 1,423 1,381 1,590 2,222 1,687 Age (years) 5.0 1.5 1.5 Y = 7.0 7.0 2.0 2.0 2.0 Yearly Maintenance Cost ($) 1,894 863 882 a. Use POM for Windows' least squares-linear regression module to develop a relationship to forecast the yearly maintenance cost based on the age of a tractor. (Enter your responses rounded to three decimal places and include a minus sign if necessary.) 1,464 2,073 1,678 1,166 1,249
Ex.1. A fuel company wants to state the relationship between the amount of gas it sells for heating and the daily temperature. The following table on sales and temperature is given to show X daily average temperature, Y gas sales amount. Find the regression line of Y on X.
4. The sales (in millions of dollars) for an 18-month period are as follows. Month Sales Month Sales 1 840 10 1085 1225 11 840 3 805 12 805 4 910 13 875 980 14 910 1120 15 840 7 770 16 973 8 1085 17 1050 840 18 1085 a. Compare a three-month moving average forecast with an exponential smoothing forecast. Use a = 0.3. Which provides the better forecasts based on MSE? b. What is the forecast for the next month?
In the area of Russia récords were kept on the relationship between the rain Fall in inches and the yield of wheat (bushels per acre ).the data for a 9 year period is as follow. Rain fall x 13.1,11.4,16.0,15.1,21.4,12.9,9.6,18.2,18.6. yield y : 48.5,44.2,56.8,80.4,47.2,29.9,74.0,74.0,76.8 the equation of the line of least squares is given as ^y= -9.12+4.38x. How many bushels of wheat peen acre can be predicted if it’s is expected that there will be 30 inches of rain?
Which of the following situations could produce data sets or plots that could have a regression line with a negative y-intercept? Select all that apply. Select all that apply: The net profit of a company as a function of the number of months since it was founded, where net income is gross income minus expenses. The number of employees of a company as a function of the number of months since it was founded. The cost of a company's flagship product as a function of the number of months since it was founded. The number of franchises of a company as a function of the number of months since it was founded.
9 students were surveyed to see what their age is and what their income level is. Find the equation of the line using linear regression. We want to predict their age using their income. age income 18 456 24 786 38 835 22 855 19 645 35 244 28 587 19 1400 27 975 Just a side note, the 19 year old is making 1400, would be considered an outlier since they are making way more than everyone else. (y=-.0061x+34,9874 decimals) hp
The following data relate to the sales, in a time of trade depression of a certain article in wide demand. Do the data suggest that the sales are significantly affected by depression ? District where sales are : Districts Not hit by depression Hit Total Satisfactory 140 60 200 Not satisfactory 40 60 100 180 120 300 Total
The following data of regular growth (linear regression) sales for XYZ company during last eleven years (2005 to 2015): Student Name Student ID cW16 Years Sales 2005 5500 2006 6500 2007 7800 2008 9000 2009 11500 2010 10600 2011 10000 2012 8600 2013 10200 2014 10500 2015 10900 2016 2017 2018 2019 2020 eee Required: 1. Create a new worksheet "Q3", and enter the previous data in it. 2. Use the (FORECAST) functions to predict the expected sales for the following five years (2016 to 2020) "predict expected sales based on actual sales). Actual Sales Expected Sales
The data to the right compare the on-time arrival performance of two airlines, X and Y. The table shows the status of 13,423 arrivals for one year. Complete parts (a) through (c) below. Airline X Airline Y On time 6,121 5,090 Delayed 1,138 1,074 Does it appear that a lurking variable might be at work here? How can you tell? Select the correct choice below and, if necessary, fill in the answer boxes to complete your choice. CA. Yes, because the on-time rate for flights to Denver is 85.3 %, whereas the on-time rate for flights to Philadelphia is 84.6 %. This is important since most of airline X's flights go to Denver, whereas most of airline Y's flights go to Philadelphia. (Round to one decimal place as needed.) O B. Yes, because the on-time rate for flights to Dallas is %, whereas the on-time rate for flights to Minneapolis is %. This is important since these two cities have the fewest number of flights. (Round to one decimal place as needed.) O C. Yes, because there appears to be…